How-To Tutorials

Dive deeper into the world of AI innovation and stay ahead of the AI curve! Subscribe to our AI_Distilled newsletter for the latest insights. Don't miss out – sign up today!👋 Hello ,Welcome to another scintillating edition of AI_Distilled, featuring recent advancements in training and fine-tuning LLMs, GPT and AI models for enhanced business outcomes. Let’s get started with this week’s news and analysis with an industry expert’s opinion. “For me, the biggest opportunity we have is AI. Just like the cloud transformed every software category, we think AI is one such transformational shift. Whether it's in search or our Office software.” - Satya Nadella, CEO, Microsoft.  AI is indeed the biggest opportunity for mankind, a paradigm shift that can fundamentally redefine everything we know across industries. Recent reports suggest Apple’s deployment of cloud-based and on-device edge AI in iPhones and iPads in 2024. Qualcomm’s newly unveiled Snapdragon Elite X chips will find use in Microsoft Windows “AI PCs” for AI acceleration of tasks ranging from email summarization to image creation. It’s remarkable how AI has disrupted even PC environments for everyday users.  This week, we’ve brought you industry developments including DALL·E 3 unveiling for ChatGPT Plus and Enterprise users, Universal Music Group suing Anthropic over copyrighted lyrics distribution, OpenAI in talks for $86 billion valuation, surpassing leading tech firms, and Mojo SDK’s availability for Macs, unleashing AI power on Apple Silicon.  Look out for our curated collection of AI secret knowledge and tutorials on PyTorch Edge unveiling ExecuTorch for on-device inference, scaling reinforcement learning with cloud TPUs, building an IoT sensor network with AWS IoT Core and Amazon DocumentDB, and deploying embedding models with Hugging Face inference endpoints. 📥 Feedback on the Weekly EditionWhat do you think of this issue and our newsletter?Please consider taking the short survey below to share your thoughts and you will get a free PDF of the “The Applied Artificial Intelligence Workshop” eBook upon completion. Complete the Survey. Get a Packt eBook for Free!Writer’s Credit: Special shout-out to Vidhu Jain for their valuable contribution to this week’s newsletter content!  Cheers,  Merlyn Shelley  Editor-in-Chief, Packt     SignUp | Advertise | Archives⚡ TechWave: AI/GPT News & Analysis👉 Apple Aims to Introduce Generative AI to iPhone and iPad in Late 2024: Tech analyst Jeff Pu suggests that Apple is planning to integrate generative AI into its devices, beginning as early as late 2024. Apple is expected to deploy a combination of cloud-based and on-device edge AI. This move is aimed at letting users automate complex tasks and enhance Siri's capabilities, possibly starting with iOS 18. Apple remains cautious about privacy and responsible use of AI, acknowledging potential biases and hallucinations. 👉 DALL·E 3 Unveiled for ChatGPT Plus and Enterprise Users: OpenAI has introduced DALL·E 3 in ChatGPT, offering advanced image generation capabilities for Plus and Enterprise users. This feature allows users to describe their desired images, and DALL·E 3 creates a selection of visuals for them to refine and iterate upon within the chat. OpenAI has incorporated safety measures to prevent the generation of harmful content. Moreover, they are researching a provenance classifier to identify AI-generated images.  👉 Universal Music Group Sues AI Company Anthropic Over Copyrighted Lyrics Distribution: Universal Music Group and music publishers have filed a lawsuit against Anthropic for distributing copyrighted lyrics through its AI model Claude 2. The complaint alleges that Claude 2 can generate lyrics closely resembling copyrighted songs without proper licensing, even when not explicitly prompted to do so. The music publishers claim that while other lyric distribution platforms pay to license lyrics, Anthropic omits essential copyright management information.  👉 Nvidia's Eureka AI Agent, Powered by GPT-4, Teaches Robots Complex Skills: Nvidia Research has introduced Eureka, an AI agent driven by GPT-4 from OpenAI, capable of autonomously training robots in intricate tasks. Eureka can independently craft reward algorithms and has successfully instructed robots in various activities, including pen-spinning tricks and opening drawers. It also published the Eureka library of AI algorithms, allowing experimentation with Nvidia Isaac Gym. This innovative work leverages the potential of LLMs and Nvidia's GPU-accelerated simulation technologies, marking a significant step in advancing reinforcement learning methods.   👉 OpenAI in Talks for $86 Billion Valuation, Surpassing Leading Tech Firms: OpenAI, the company responsible for ChatGPT, is reportedly in discussions to offer its employees' shares at an astounding $86 billion valuation, surpassing tech giants like Stripe and Shein. This tender offer is in negotiation with potential investors, although final terms remain unconfirmed. With Microsoft holding a 49% stake, OpenAI is on its way to achieving an annual revenue of $1 billion. If this valuation holds, it would place OpenAI among the ranks of SpaceX and ByteDance, becoming one of the most valuable privately held firms globally.  👉 Mojo SDK Now Available for Mac: Unleashing AI Power on Apple Silicon: The Mojo SDK, which has seen considerable success on Linux systems, is now accessible for Mac users, specifically Apple Silicon devices. This development comes in response to user feedback and demand. The blog post outlines the steps for Mac users to get started with the Mojo SDK. Additionally, there's a Visual Studio Code extension for Mojo, offering a seamless development experience. The Mojo SDK's remarkable speed and performance on Mac, taking full advantage of hardware capabilities, is highlighted. 👉 Qualcomm Reveals Snapdragon Elite X Chip for AI-Enhanced Laptops: Qualcomm introduced the Snapdragon Elite X chip for Windows laptops, optimized for AI tasks like email summarization and text generation. Google, Meta, and Microsoft plan to use these features in their devices, envisioning a new era of "AI PCs." Qualcomm aims to rival Apple's chips, claiming superior performance and energy efficiency. With the ability to handle AI models with 13 billion parameters, this chip appeals to creators and businesses seeking AI capabilities.  🔮 Expert Insights from Packt Community  Deep Learning with TensorFlow and Keras - Third Edition - By Amita Kapoor, Antonio Gulli, Sujit Pal Prediction using linear regression Linear regression is one of the most widely known modeling techniques. Existing for more than 200 years, it has been explored from almost all possible angles. Linear regression assumes a linear relationship between the input variable (X) and the output variable (Y). If we consider only one independent variable and one dependent variable, what we get is a simple linear regression. Consider the case of house price prediction, defined in the preceding section; the area of the house (A) is the independent variable, and the price (Y) of the house is the dependent variable.  We import the necessary modules. It is a simple example, so we’ll be using only NumPy, pandas, and Matplotlib: import tensorflow as tf import numpy as np import matplotlib.pyplot as plt import pandas as pd  Next, we generate random data with a linear relationship. To make it more realistic, we also add a random noise element. You can see the two variables (the cause, area, and the effect, price) follow a positive linear dependence: #Generate a random data np.random.seed(0) area = 2.5 * np.random.randn(100) + 25 price = 25 * area + 5 + np.random.randint(20,50, size = len(area)) data = np.array([area, price]) data = pd.DataFrame(data = data.T, columns=['area','price']) plt.scatter(data['area'], data['price']) plt.show() Now, we calculate the two regression coefficients using the equations we defined. You can see the result is very much near the linear relationship we have simulated: W = sum(price*(area-np.mean(area))) / sum((area-np.mean(area))**2) b = np.mean(price) - W*np.mean(area) print("The regression coefficients are", W,b)  ----------------------------------------------- The regression coefficients are 24.815544052284988 43.4989785533412 Let us now try predicting the new prices using the obtained weight and bias values: y_pred = W * area + b  Next, we plot the predicted prices along with the actual price. You can see that predicted prices follow a linear relationship with the area: plt.plot(area, y_pred, color='red',label="Predicted Price") plt.scatter(data['area'], data['price'], label="Training Data") plt.xlabel("Area") plt.ylabel("Price") plt.legend() This content is from the book “Deep Learning with TensorFlow and Keras - Third Edition” by Amita Kapoor, Antonio Gulli, Sujit Pal (Oct 2022). Start reading a free chapter or access the entire Packt digital library free for 7 days by signing up now. To learn more, click on the button below.Read through the Chapter 1 unlocked here...  🌟 Secret Knowledge: AI/LLM Resources📀 The Advantages of Small LLMs: Smaller LLMs are easier to debug and don't require specialized hardware, which is crucial in today's chip-demanding market. They are cost-effective to run, expanding their applicability. Additionally, they exhibit lower latency, making them suitable for low-latency environments and edge computing. Deploying small LLMs is more straightforward, and they can even be ensembled for improved performance. 📀 PyTorch Edge Unveils ExecuTorch for On-Device Inference: The PyTorch Edge team has introduced ExecuTorch, a solution that empowers on-device inference on mobile and edge devices with the support of industry leaders like Arm, Apple, and Qualcomm Innovation Center. ExecuTorch aims to address the fragmentation in the on-device AI ecosystem by offering extension points for third-party integration to accelerate ML models on specialized hardware.  📀 AI-Boosted Software Development Journey: AI assistance simplifies design, code generation, debugging, and impact analysis, streamlining workflows and enhancing productivity. From idea to production, this post takes you through various stages of development, starting with collaborative design sessions aided by AI tools like Gmail's help me write and Google Lens. Duet AI for Google Cloud assists in code generation, error handling, and even test case creation. This AI assistance extends to operations, service health monitoring, and security.  📀 Scaling Reinforcement Learning with Cloud TPUs: Learn how Cloud TPUs are revolutionizing Reinforcement Learning by enhancing the training process for AI agents. This article explores the significant impact of TPUs on RL workloads, using the DeepPCB case as an example. Thanks to TPUs, DeepPCB achieved a remarkable 235x boost in throughput and a 90% reduction in training costs, significantly improving the quality of PCB routings. The Sebulba architecture, optimized for TPUs, is presented as a scalable solution for RL systems, offering reduced communication overhead, high parallelization, and improved scalability.   💡 Masterclass: AI/LLM Tutorials🎯 Building an IoT Sensor Network with AWS IoT Core and Amazon DocumentDB: Learn how to create an IoT sensor network solution for processing IoT sensor data via AWS IoT Core and storing it using Amazon DocumentDB (with MongoDB compatibility). This guide explores the dynamic nature of IoT data, making Amazon DocumentDB an ideal choice due to its support for flexible schemas and scalability for JSON workloads.  🎯 Building Conversational AI with Generative AI for Enhanced Employee Productivity: Learn how to develop a lifelike conversational AI agent using Google Cloud's generative AI capabilities. This AI agent can significantly improve employee productivity by helping them quickly find relevant information from internal and external sources. Leveraging Dialogflow and Google enterprise search, you can create a conversational AI experience that understands employee queries and provides them with precise answers.  🎯 A Step-by-Step Guide to Utilizing Feast for Enhanced Product Recommendations: In this comprehensive guide, you will learn how to leverage Feast, a powerful ML feature store, to build effective product recommendation systems. Feast simplifies the storage, management, and serving of features for machine learning models, making it a valuable tool for organizations. This step-by-step tutorial will walk you through configuring Feast with BigQuery and Cloud Bigtable, generating features, ingesting data, and retrieving both offline and online features.  🎯 Constructing a Mini GPT-Style Model from Scratch: In this tutorial, you’ll explore model architecture, demonstrating training and inference processes. Know the essential components, such as data processing, vocabulary construction, and data transformation functions. Key concepts covered include tokens, vocabulary, text sequences, and vocabulary indices. The article also introduces the Self-Attention module, a crucial component of transformer-based models.  🎯 Deploy Embedding Models with Hugging Face Inference Endpoints: In contrast to LLMs, embedding models are smaller and faster for inference, which is valuable for updating models or improving fine-tuning. The post guides you through deploying open-source embedding models on Hugging Face Inference Endpoints. It also covers running large-scale batch requests. Learn about the benefits of Inference Endpoints, Text Embeddings Inference, and how to deploy models efficiently.  🚀 HackHub: Trending AI Tools🔨 xlang-ai/OpenAgents: Open platform with Data, Plugins, and Web Agents for data analysis, versatile tool integration, and web browsing, featuring a user-friendly chat interface. 🔨 AI-Citizen/SolidGPT: Technology business boosting framework allowing developers to interact with their code repository, ask code-related questions, and discuss requirements. 🔨 SkalskiP/SoM: Unofficial implementation of Set-of-Mark (SoM) tools. Developers can use it by running Google Colab to work with this implementation, load images, and label objects of interest.🔨 zjunlp/factchd: Code for detecting fact-conflicting hallucinations in text for developers to evaluate factuality within text produced by LLMs, aiding in the detection of factual errors and enhancing credibility in text generation. 

Dive deeper into the world of AI innovation and stay ahead of the AI curve! Subscribe to our AI_Distilled newsletter for the latest insights. Don't miss out – sign up today!IntroductionLLM is the Large Language Model or the advanced artificial intelligence algorithms usually trained with vast amounts of text data. Such language models help to generate human-like languages. These models can also perform language-related tasks, including translation, text, competition, answering specific questions, and more.In this technological advancement era, several large language models are on the rise. Despite this, no standardized or fixed measures are used to compare or evaluate the quality of large language models.Here, let us dive into the existing evaluation and compare the framework for large language models. Also, we will analyze the factors on which these large language models should be evaluated.Evaluating Large Language ModelsNeed for a comprehensive evaluation framework Identifying different areas of improvement during the early developmental stages is relatively easy. However, with the advancement of technology and the availability of new alternatives, determining the best becomes increasingly tricky. Therefore, it is essential to have a reliable evaluation framework, helping to judge the quality of large language models accurately. Besides, the need for an immediate, authentic evaluation framework becomes imperative. One can use such a framework in the following ways.Only a proper framework will help the authorities and agencies to assess the accuracy, safety, usability issues, and reliability of the model.The blind race among the big technical companies to release large language models is on the rise. Hence, with the development of a comprehensive evaluation framework, one can help stakeholders to remove the model more responsibly.The comprehensive evaluation framework would help the user of large language models determine how and where to fine-tune the model to enable practical deployment.Issues with the existing framework  Every large language model has its advantages. However, certain factors are an issue and make the frameworks insufficient. Some of these issues includeSafety: Some of the framework does not consider protection a factor for evaluation. Although the open AI moderation API addresses safety to some extent, it is insufficient.Self-sufficiency: Regarding factors, one can evaluate the models; the frameworks are scattered. All of these frameworks need to be more comprehensive to be self-sufficient.Factors to be considered while evaluating large language modelsOnly after reviewing the existing evaluation framework can one determine the factors that must be considered while assessing the quality of large language models.Here are the key factors:Model Size and ComplexityThe primary factors to evaluate in LLMs are their size and complexity. It often gets indicated by the number of parameters. Generally, larger models have a greater capacity to understand context and generate nuanced responses. With the advent of huge models, one might require substantial computational resources, making them impractical for specific applications. Evaluators must balance model size and computational efficiency based on the use case.Training Data Quality and DiversityThe training data's quality and diversity significantly influence LLMs' performance. As users, we know that models get trained on diverse and representative datasets from various sources and tend to have a broader understanding of language nuances. However, evaluators should scrutinize the sources and types of data used for training to ensure the model's reliability across different contexts and domains.Bias and FairnessBias in LLMs is a critical concern, as it can generate discriminatory or unfair content. Evaluators must assess the model's bias, both in the training data and the generated output, and implement strategies to mitigate biases. Besides, ethical considerations demand continuous efforts to improve fairness, ensuring that the models do not reinforce societal biases.Ethical Considerations and Responsible UseEvaluating LLMs extends beyond technical aspects to ethical considerations. Responsible deployment of these models requires a thorough assessment of potential misuse scenarios. In every case, evaluators must devise guidelines and ethical frameworks to prevent generating harmful or malicious content, emphasizing the responsible use of LLMs in applications such as content moderation and chatbots.Fine-Tuning and Transfer Learning LLMs are often fine-tuned on specific datasets to adapt them to particular tasks or domains. One should scrutinize the fine-tuning process to ensure the model maintains its integrity and performance while being customized. Additionally, assessing the effectiveness of transfer learning, where models trained on one task are applied to related tasks, is crucial for understanding their adaptability and generalizability.Explainability and InterpretabilityUnderstanding how LLMs arrive at specific conclusions is essential, especially in applications like legal document analysis and decision-making processes. Being an evaluator, one must assess the model's explainability and interpretability. Transparent models enable users to trust the generated output and comprehend the reasoning behind the responses, fostering accountability and reliability.Robustness and Adversarial Attacks Evaluating the robustness of LLMs involves assessing their performance under various conditions, including noisy input, ambiguous queries, or adversarial attacks. Rigorous testing against potential negative inputs helps identify vulnerabilities and weaknesses in the model, guiding the implementation of robustness-enhancing techniques.Continuous Monitoring and ImprovementThe landscape of language understanding is ever-evolving. Continuous monitoring and improvement are vital aspects of evaluating LLMs. Regular updates, addressing emerging challenges, and incorporating user feedback contribute to the model's ongoing enhancement, ensuring its relevance and reliability over time.Step-by-Step Guide: Comparing LLMs Using Perplexity1. Load Language Model: Load the pre-trained LLM using a library like Hugging Face Transformers.2. Prepare Dataset: Tokenize and preprocess your dataset for the language model.3. Train/Test Split: Split the dataset into training and testing sets.4. Train LLM: Fine-tune the LLM on the training dataset.5. Calculate Perplexity: Use the testing dataset to calculate perplexity.Code example: # Calculate Perplexityfrom math import exp from transformers import GPT2LMHeadModel, GPT2Tokenizer tokenizer = GPT2Tokenizer.from_pretrained("gpt2") model = GPT2LMHeadModel.from_pretrained("gpt2") input_text = "Example input text for perplexity calculation." input_ids = tokenizer.encode(input_text, return_tensors="pt") with torch.no_grad():    output = model(input_ids)    loss = output.loss perplexity = exp(loss) print("Perplexity:", perplexity)Methods of evaluation Quantitative Performance Metrics and Benchmarking Evaluating LLMs requires rigorous quantitative assessment using industry-standard metrics. BLEU, METEOR, and ROUGE scores are pivotal in assessing text generation quality by comparing generated text with human references. For translation tasks, BLEU (Bilingual Evaluation Understudy) calculates the overlap of n-grams between the machine-generated text and human reference translations. METEOR evaluates precision, recall, and synonymy, providing a nuanced understanding of translation quality. ROUGE (Recall-Oriented Understudy for Gisting Evaluation) emphasizes summary evaluation, emphasizing memory. These metrics offer quantitative benchmarks, enabling direct comparison between different LLMs. Additionally, perplexity, a measure of how well a language model predicts a sample text, provides insights into language model efficiency. Lower perplexity values indicate better prediction accuracy, highlighting the model's coherence and understanding of the input text. Often applied to large-scale datasets like WMT (Workshop on Machine Translation) or COCO (Common Objects in Context), these quantitative metrics, off LLM, are a robust foundation for comparing LLMs' performance.Diversity Analysis and Bias Detection Diversity and bias analysis are paramount in evaluating LLMs, ensuring equitable and inclusive performance across diverse demographics and contexts. One critical approach involves employing word embedding techniques, such as Word Embedding Association Test (WEAT), to quantify biases. WEAT assesses associations between word embeddings and predefined categories, unveiling tendencies present in LLMs. By evaluating gender, race, or cultural preferences, organizations can ensure fair and unbiased responses, aligning with ethical considerations.Furthermore, demographic diversity analysis measures the model's performance across different demographic groups. Assessing demographic parity ensures that LLMs provide consistent, unbiased results across various user segments. This comprehensive evaluation approach, deeply rooted in fairness and inclusivity, is pivotal in selecting socially responsible LLMs.Real-World User Studies and Interaction AnalysisIncorporating real-world user studies and interaction analysis is indispensable for evaluating LLMs in practical scenarios. Conducting user tests and surveys provides qualitative insights into user satisfaction, comprehension, and trust. These studies consider how well LLM-generated content aligns with users' expectations and domain-specific contexts.Additionally, analyzing user interactions with LLM-generated content through techniques like eye-tracking studies and click-through rates provides valuable behavioral data. Heatmap analysis, capturing user attention patterns, offers insights into the effectiveness of LLM-generated text elements. User feedback and interaction analysis inform iterative improvements, ensuring that LLMs are technically robust, user-centric, and aligned with real-world application requirements.ConclusionWith the development of large language models, natural language processing experienced a revolution. However, the need for a standardized and comprehensive evaluation framework remains a necessity. It helps in assessing the quality of these LLM models. Though the existing framework offers valuable insights, it needs more standardization and comprehensiveness. At the same time, it does not consider safety as an evaluation factor. Moreover, collaborating with relevant experience becomes imperative to build a comprehensive and authentic evaluation framework for the large language models.Author BioVivekanandan, a seasoned Data Specialist with over a decade of expertise in Data Science and Big Data, excels in intricate projects spanning diverse domains. Proficient in cloud analytics and data warehouses, he holds degrees in Industrial Engineering, Big Data Analytics from IIM Bangalore, and Data Science from Eastern University.As a Certified SAFe Product Manager and Practitioner, Vivekanandan ranks in the top 1 percentile on Kaggle globally. Beyond corporate excellence, he shares his knowledge as a Data Science guest faculty and advisor for educational institutes.

Dive deeper into the world of AI innovation and stay ahead of the AI curve! Subscribe to our AI_Distilled newsletter for the latest insights. Don't miss out – sign up today!IntroductionAzure machine learning prompt flow is one of Microsoft's cutting-edge solutions. It has paved the path for handling streamlined data. Thus enabling all the data scientists to focus on driving valuable insights from data.At the heart of this innovation lies the vector data store. It is one of the powerful tools that ensures seamless data manipulation and integration.Let us delve deeply into the depths of vector data storage while exploring its functionality and significance in Azure machine learning prompt flow.Understanding Vector datastoreA vector datastore can handle large-scale vectorized data efficiently within the Azure Machine Learning ecosystem. It acts as one of the centralized repositories that houses diverse data formats, from texts to images and numerical data. The real power of the vector data store lies in the ability to unify such disparate data types into a cohesive format that helps other data scientists work seamlessly.Some of the key benefits and features of vector data storing in the ecosystem of Azure ML include:Data integrationWith the help of a vector datastore, data scientists can integrate a variety of data types without going through the hassles of format conversion. Due to this hassle, the system accelerates the data preprocessing phase, which is one of the crucial steps in any Machine Learning project.Efficient data manipulationVector datastore makes complex operations like filtering, feature extraction, quality confirmation and transformation the most straightforward process. This efficient data manipulation technique is crucial to derive meaningful patterns from the raw data. Such a process leads to more accurate machine-learning models.Scalability The vector datastore of Azure Machine learning prompt flow helps to scale effortlessly. As a result, it helps in accommodating the growing datasets. Whether the user deals with petabytes or gigabytes of data, a vector datastore ensures smooth operations without compromising the accuracy and speed of the whole process.Version controlVector database makes data versioning simplified. It allows the data scientists to keep track of the changes, reproduce experiments with precision, and collaborate effectively.Let's consider a scenario where we want to preprocess a dataset containing images of handwritten digits for a digit recognition task. First, we'll initialize a Vector Datastore to store our image data.import numpy as np import cv2 # Define a function to load and preprocess images def load_images(file_paths, target_size=(64, 64)):    images = []    for file_path in file_paths:        # Read the image using OpenCV        image = cv2.imread(file_path)        # Resize the image to the target size        image = cv2.resize(image, target_size)        # Normalize pixel values to be between 0 and 1        image = image.astype('float32') / 255.0        images.append(image)    return np.array(images) # Example usage file_paths = ['image1.jpg', 'image2.jpg', 'image3.jpg']  # List of file paths to your images image_data = load_images(file_paths) # Now image_data contains the preprocessed image data ready to be stored in your vector datastoreIn this example, the code snippet demonstrates how to initialize a Vector Datastore, upload the image dataset, and create a dataset from the stored images.Output:Upon successful execution, the image dataset is seamlessly integrated into the Vector Datastore, ready for preprocessing and model training. This integration ensures that data scientists can focus on building robust machine learning models without worrying about data compatibility issues.Creating Vector indexVector index lookup is a tailored tool that helps to make queries within Azure Machine Learning Vector datastore index. It helps to empower the user to extract relevant information related to the context from a domain knowledge base.Here is how one can prepare one's own Data QnA by placing a vector index as an input. However, on the basis of the place where you put the vector index, the identity used by Azure ML promptflow gets granted certain roles.Inputs:After installing Annoy library, you can createa vector index using the below code.from annoy import AnnoyIndex # Assuming image_data is your preprocessed image data (NumPy array) # Each row in image_data represents a flattened image vector # Define the number of dimensions in your vectors (usually the length of each flattened image vector) num_dimensions = len(image_data[0]) # Initialize Annoy index with the number of dimensions in your vectors annoy_index = AnnoyIndex(num_dimensions) # Add vectors to the index for i, vector in enumerate(image_data):    annoy_index.add_item(i, vector) # Build the index for efficient nearest neighbor searches annoy_index.build(n_trees=10)  # You can adjust the number of trees for optimization # Now, the annoy_index is ready for efficient nearest neighbor searches # You can perform queries using annoy_index.get_nns_by_vector(vector, num_neighbors, search_k) # For example: # nearest_neighbors = annoy_index.get_nns_by_vector(query_vector, num_neighbors=5, search_k=-1)Outputs:The index will be initialized with various dimensions in the image vectors.Choosing a vector storeOne must use a vector index to perform Retrieval Augmented Generation in Azure Machine learning. It helps store the embeddings, which can later be converted to number sequences. Such an elaborate process helps the large language models to understand any complex relation between those concepts. Only if the user can create vector stores will it help them to hook up the data with a large language model, including GPT4. At the same time, one can also retrieve the data efficiently.Azure Machine Learning prompt flow usually supports two kinds of vector stores in the RAG workflow.FaissIt is one of the open-source libraries that provides the user with a local file-based store. One can find the vector index stored in the storage account of the Azure machine learning workspace. Since the storage system is locally stored, the costs are also minimal. Hence, the whole process of testing and development is budget-friendly.Azure Cognitive SearchIt is one of the Azure resources that supports information retrieval over the textual data and vector stored in search retrieval. With the help of prompt flow, one can populate, create, and query the vector data stored in Azure cognitive search.Though you can choose any of the vectors mentioned above stores, here is an overview of which should be used.Faiss, an open-source library, emerges as a robust solution, particularly when dealing with vector-only data. It stands as an essential component that can be seamlessly integrated into your solution. Let's explore the key aspects of Faiss, coupled with the capabilities of Azure Cognitive Search, to understand how these tools can be harnessed effectively.Faiss: Optimizing Vector Data ManagementFaiss offers several compelling advantages when it comes to working with vector data:1. Cost-Effective Local Storage: Faiss allows local storage without incurring additional costs for creating an index, offering a budget-friendly option for businesses aiming to optimize their expenses while managing extensive datasets.2. In-Memory Indexing and Querying: One of Faiss' standout features is its ability to build and query indexes entirely in memory. This approach significantly enhances the speed of operations, making it an efficient choice for real-time applications.3. Flexibility in Sharing: Faiss enables the sharing of index copies for individual use, providing flexibility in data access. However, additional setup is necessary for applications requiring index hosting to ensure tailored solutions for diverse use cases.4. Scalability Aligned with Computational Resources: Faiss scales seamlessly with the underlying compute resources, enabling businesses to manage varying workloads effectively. Its ability to adapt to the computational load ensures consistent performance despite fluctuating demands.Example:Consider an e-commerce platform dealing with millions of product vectors. By utilizing Faiss, the platform can create an in-memory index, enabling lightning-fast similarity searches for product recommendations, enhancing user experience, and increasing sales.Azure Cognitive Search: Elevating Vector Data Management to Enterprise Level Azure Cognitive Search, a dedicated Platform as a Service (PaaS) resource, offers a comprehensive solution for businesses seeking robust vector data management:1. Enterprise-Grade Scalability and Security: Cognitive Search supports enterprise-level business requirements, offering scalability, security, and availability. It ensures seamless scaling to accommodate growing data volumes, such an attribute makes it an ideal choice for businesses of all sizes.2. Hybrid Information Retrieval: A unique feature of Cognitive Search is its ability to support hybrid information retrieval. It means that vector data can coexist harmoniously with non-vector data. Businesses can leverage all the features of Azure Cognitive Search, including hybrid search and semantic reranking, ensuring comprehensive data analysis.3. Vector Support in Public Preview: Cognitive Search's vector support is currently in public preview. Although vectors must be generated externally, Cognitive Search handles the indexing and query encoding seamlessly within the prompt flow, simplifying the integration process.Example:Consider a financial institution needing to process massive amounts of transaction data, including structured and vector data, for fraud detection. Azure Cognitive Search allows seamless integration of vector data, enabling the institution to identify patterns effectively and enhance security protocols.Integration for Seamless Vector Data ManagementTo utilize Cognitive Search as a vector store for Azure Machine Learning, you must establish a search service within your Azure subscription. Once the service is in place, developers can access it. Azure Cognitive Search can be chosen as a vector index within the prompt flow. The prompt flow facilitates the entire process, from index creation to vector generation, ensuring a streamlined experience.The synergy between Faiss and Azure Cognitive Search presents a formidable solution for businesses aiming to manage vector data effectively. Faiss' efficiency in local storage and real-time querying, coupled with Cognitive Search's enterprise-grade scalability and hybrid data support, creates a powerful ecosystem. This integration empowers businesses to leverage their vector data fully, facilitating data-driven decision-making and driving innovation in diverse industries.By harnessing the capabilities of Faiss and Azure Cognitive Search, companies can truly unlock the potential of their data, paving the way for a future where data management is as intelligent as the insights derived from it.ConclusionVector datastore accelerates the machine learning pipelines, leading to faster innovations and more accurate models. As organizations continue to grapple with massive data sets, the only solution that can enhance accuracy and efficiency becomes indispensable. Hence, vector datastore in Azure machine learning promptflow is not a choice but a necessity. It unifies the diverse data types, coupled with scalability and efficient manipulation, enabling the data scientist to extract valuable insights, especially from complex and large data sets.Author BioKarthik Narayanan Venkatesh (aka Kaptain), founder of WisdomSchema, has multifaceted experience in the data analytics arena. He has been associated with the data analytics domain since the early 2000s, with a ringside view of transformations in this industry. He has led teams that architected and built scalable data platform solutions across the technology spectrum.As a niche consulting provider, he bridged the gap between business and technology and drove BI adoption through innovative approaches in an agnostic manner. He is a sought-after speaker who has presented many lectures on SAP, Analytics, Snowflake, AWS, and GCP technologies.

Dive deeper into the world of AI innovation and stay ahead of the AI curve! Subscribe to our AI_Distilled newsletter for the latest insights. Don't miss out – sign up today!IntroductionThe ChatGPT chatbot from OpenAI is a large language model that can be used for text writing, translation, content creation, and answers to your questions in an informative way. It's still developing, but has learned to perform many tasks, such as helping with Excel. Using ChatGPT with Excel can be done in several ways. Access to ChatGPT on the OpenAI website is one way of doing this. Another way to do this would be by using a third-party add-in such as the ListenData ChatGPT for Excel Addin. Access to ChatGPT from Excel via this add-in will allow you to do that at any time.What can ChatGPT do for Excel users?ChatGPT can be used to help Excel users with a variety of tasks, including:Learning Excel concepts: It is possible to describe Excel concepts clearly and succinctly by using ChatGPT. It may be of use to newcomers as well as users with a lot of experience.Writing formulas and functions: In Excel, you can use the ChatGPT program to write sophisticated formulas and functions. It's also capable of explaining how the formulas and functions work.Analyzing data: Excel data analysis can be helped by ChatGPT. It's been able to identify trends, patterns, and outliers. Reports and charts may also be generated.Automating tasks: In Excel, you can use the ChatGPT program to perform tasks automatically. There's a lot of time and effort that can be saved.Best Practices for Using ChatGPT for ExcelBe clear and concise in your prompts: ChatGPT is very good at understanding natural language, but it is important to be as specific as possible in your requests. For example, instead of saying "Can you help me with this Excel spreadsheet?", you could say "Can you help me to write a formula to calculate the average sales for each product category?".Provide context: If you are asking ChatGPT to help you with a specific task, it is helpful to provide some context. For example, if you are asking ChatGPT to write a formula to calculate the average sales for each product category, you could provide a sample of your spreadsheet data.Break down complex tasks into smaller steps: If you have a complex task that you need help with, it is often helpful to break it down into smaller, more manageable steps.Be patient: ChatGPT is still under development, and it may not always be able to provide the perfect answer. If you are not satisfied with the response that you receive, try rephrasing your prompt or providing more context.Generating Formulas and FunctionsTo generate Excel formulas and functions, you can use ChatGPT. It may be useful when you have no idea how to create a particular formula or function, or if you need any assistance with the way formulas and functions work.You can create a function or formula with ChatGPT by simply typing the description of what you want it to do. For example, you have a spreadsheet with the following data:You want to generate a formula that will calculate the average daily sales growth rate for the five days, but excluding the weekend days (Saturday and Sunday).Steps:1. Go to ChatGPT and enter the following prompt:Write an Excel formula to calculate the average daily sales growth rate for the following data, but excluding the weekend days (Saturday and Sunday):2. ChatGPT will generate the following formula and steps:=IF(WEEKDAY(A4,2)=7,"",IF(WEEKDAY(A4,2)=1,"",(B4-B3)/B3*100))3. Copy and paste the formula into cell D3 of your Excel spreadsheet.4. Press Enter.5. The formula will calculate the average daily sales growth rate for the five days, excluding the weekend days, which is 20%.Explanation:The formula works by first checking the day of the week for the date in cell A3. If the day of the week is Saturday or Sunday, the formula returns a blank value. Otherwise, the formula calculates the difference in sales between the second and third days, divides it by the sales value in cell B2, and multiplies it by 100 to express the result as a percentage.Data Standardization, Conditional Formatting, and Dynamic Filtering in Excel with ChatGPT1. Data StandardizationWhile analyzing data during data analysis, data standardization plays an important role as the raw data that we might extract from resources may not be in a uniform way. So, we need to ask ChatGPT perfectly to make out data in a standardized manner.For Example:Question: “I have a dataset with names in highly varied formats (e.g., 'John Smith,' 'Smith, John,' 'Dr. Jane Doe'). How can I standardize them to 'First Name Last Name' in Excel while preserving titles and suffixes?"ChatGPT Response: The above image shows that once you apply the formula given by ChatGPT for your query, you will get the result in standardized form.2. Conditional FormattingA feature that enables Excel to automatically format cells according to their value or content is conditional formatting. You can look at any cells that contain a value and color code them according to the range in which they are valued, e.g. You can use any of the options available to make your data more attractive and comprehensible.For Example:Question: "I have a list of sales data in Excel, and I want to highlight cells where the sales are above $1,000 in green and below $500 in red. How can I set up conditional formatting for this?"ChatGPT Response: As you can see that once we perform the stepwise procedure given by ChatGPT, we will be successfully able to get the correct results.3. Data Sorting and FilteringData sorting and filtering are two powerful features in Excel that can help you organize and analyze your data more efficiently. Sorting allows you to arrange your data in a specific order, such as alphabetically, numerically, or by date. This can be useful for finding specific information or for identifying trends in your data. Filtering allows you to display only the data that meets certain criteria. For example, you could filter your data to show only the rows that contain a certain value in a certain column. This can be useful for focusing on the data that is most important to you or for identifying outliers.Question: "I have a large dataset in Excel, and I want to sort it by a specific column in ascending order and then apply a filter to show only rows where the value in column B is greater than 50. What's the code to do this?"ChatGPT Response: The code will display only rows where the value in column B is greater than 50, by sorting data with ascending values and filtering them.ConclusionIn conclusion, the integration of ChatGPT with Excel provides a valuable service to all users whether they are simply starting out and trying to learn Microsoft's concepts or experienced users that need assistance for specific tasks. The ChatGPT is able to help you with a variety of aspects of the use of Excel, such as making complex formulas, analyzing data, standardizing data for consistency, using configurable formatting, and automated tasks.In addition, a practical example of what ChatGPT can do for users to achieve Excel-related goals is given in the report on Data Standardization, Conditional Formatting, Data Sorting, and Filtering with ChatGPT. Overall, ChatGPT has proved to be an invaluable tool for Excel users that enables them to free up time, improve data analysis, and streamline their tasks in a more rapid and engaging way.Author BioChaitanya Yadav is a data analyst, machine learning, and cloud computing expert with a passion for technology and education. He has a proven track record of success in using technology to solve real-world problems and help others to learn and grow. He is skilled in a wide range of technologies, including SQL, Python, data visualization tools like Power BI, and cloud computing platforms like Google Cloud Platform. He is also 22x Multicloud Certified.In addition to his technical skills, he is also a brilliant content creator, blog writer, and book reviewer. He is the Co-founder of a tech community called "CS Infostics" which is dedicated to sharing opportunities to learn and grow in the field of IT.

Dive deeper into the world of AI innovation and stay ahead of the AI curve! Subscribe to our AI_Distilled newsletter for the latest insights. Don't miss out – sign up today!IntroductionIn the ever-evolving landscape of artificial intelligence, Large Language Models (LLMs) have taken center stage, transforming the way we interact with and understand vast amounts of textual data. With the proliferation of these advanced models, the need for efficient data management and retrieval has become paramount. Enter Vector Datastore, a game-changer in the realm of data caching for LLMs. This article explores how Vector Datastore's innovative approach, based on vector representations and similarity search, empowers LLMs to swiftly access and process data, revolutionizing their performance and capabilities.How does Vector datastore enable data cache for LLMs?With every online source you scan, you will come across terms like chatbots, LLMs, or GPT. Most people are speaking about the large language models, and we can see every new language model gets released every week.Before seeing how vector databases enable data caches for large language models, one must learn about them and their importance to the language models.Vector databases: What are they? Getting an idea of vector embeddings is essential to know about vector databases. It is a data representation that consists of semantic information, helping the artificial intelligence system better understand all the datasets. At the same time, it helps to maintain long-term memory. The most critical element is understanding and remembering, especially if you want to learn anything new.AI models usually generate embeddings. Every large language model consists of a variety of features. Due to this reason, it becomes difficult to manage their representations. With the help of embeddings, one could represent the various dimensions of the data. Therefore, the artificial intelligence models would understand the patterns, relationships, and hidden structures.In this scenario, the vector embeddings that use the traditional scalar-based databases could be challenging. It cannot keep up or handle the scale and complexity of various data. The complexities that often come with vector embeddings would require a specialized database. It is the reason why one would need vector databases. With the help of vector databases, one could get optimized storage and query capabilities of any unique structure presented by vector embeddings. As a result, the user would get high performance along with easy search capabilities, data retrieval, and scalability only by comparing the similarities and values of findings between one another.Though vector databases are difficult to implement, until now, various tech giants companies are not only developing them but also making them manageable. Since they are expensive to implement, one must ensure proper calibration to receive high performance. How it works?Taking the simple example of a link with a large language model like chat GPT, we know it consists of a large volume of data and content. At the same time, the user can only proceed with the chat GPT application. Being the user, one has to improve your queries in the application. Once you complete this step, the query gets inserted into the embedding model. It initiates the process of vector embeddings based on the content that requires indexing. After completing this process, the vector embeddings move into the vector databases. It usually occurs regarding the content that wants to be used for embedding.As a result, you will receive the outcome produced by vector databases. Therefore, the system sends it back to the user as a result.As a user continues making different queries, it goes through the same embedding model that helps create embeddings. It helps in processing the database query for similar—vector embeddings.Let us know the whole process in detail.A vector database incorporates diverse algorithms dedicated to facilitating Approximate Nearest Neighbor (ANN) searches. These algorithms encompass techniques such as hashing, graph-based search, and quantization, which are intricately combined into a structured pipeline for retrieving neighboring vectors concerning a queried input.The outcomes of this search operation are contingent upon the proximity or approximation of the retrieved vectors to the original query. Hence, the pivotal factors under consideration are accuracy and speed. A trade-off exists between the query output's speed and the results' precision; a slower output corresponds to a more accurate outcome.The process of querying a vector database unfolds in three fundamental stages: 1. IndexingA diverse array of algorithms comes into play upon the ingress of the vector embedding into the vector database. These algorithms serve the purpose of mapping the vector embedding onto specific data structures, thus optimizing the search process. This preparatory step significantly enhances the speed and efficiency of subsequent searches. 2. QueryingThe vector database systematically compares the queried vector and the previously indexed vectors. This comparison entails the application of a similarity metric, a crucial determinant in identifying the nearest neighbor amidst the indexed vectors. The precision and efficacy of this phase are paramount to the overall accuracy of the search results. 3. Post ProcessingUpon pinpointing the nearest neighbor, the vector database initiates a post-processing stage. The specifics of this stage may vary based on the particular vector database in use. Post-processing activities may involve refining the final output of the query, ensuring it aligns seamlessly with the user's requirements.Additionally, the vector database might undertake the task of re-ranking the nearest neighbors, a strategic move to enhance the database's future search capabilities. This meticulous post-processing step guarantees that the delivered results are accurate and optimized for subsequent reference, thereby elevating the overall utility of the vector database in complex search scenarios.Implementing vector data stores in LLM Let us consider an example to understand how a vector data store can be installed or implemented in a large language model. Before we can start with the implementation, one has to install the vector datastore library. pip install vectordatastore Assuming you have the data set containing the text snippets, you will get the following in code format. # Sample dataset dataset = {    "1": "Text snippet 1",    "2": "Text snippet 2",    # ... more data points ... } # Initialize Vector Datastore from vectordatastore import VectorDatastore vector_datastore = VectorDatastore() # Index data into Vector Datastore for key, text in dataset.items():    vector_datastore.index(key, text) # Query Vector Datastore from LLM query = "Query text snippet" similar_texts = vector_datastore.query(query) # Process similar_texts in the LLM # ...In this example, one can see that the vector data store efficiently indexes the datasets while utilizing vector representations. When the large language model requires retrieving data similar to the query text, it often uses a vector datastore to obtain the relevant snippets quickly.Process of enabling data caches in LLMsVector Datastore enables efficient data caching for Large Language Models (LLMs) through its unique approach to handling data. Traditional caching mechanisms store data based on keys, and retrieving data involves matching these keys. However, LLMs often work with complex and high-dimensional data, such as text embeddings, which are not easily indexed or retrieved using traditional key-value pairs. Vector Datastore addresses this challenge by leveraging vector representations of data points.Process of how Vector Datastore enables data cache for LLMs 1. Vector Representation:Vector Datastore stores data points in vectorized form. Each data point, whether a text snippet or any other type of information, is transformed into a high-dimensional numerical vector. This vectorization process captures the semantic meaning and relationships between data points. 2. Similarity Search: Instead of relying on exact matches of keys, Vector Datastore performs similarity searches based on vector representations. When an LLM needs specific data, it translates its query into a vector representation using the same method employed during data storage. This query vector is then compared against the stored vectors using similarity metrics like cosine similarity or Euclidean distance. 3. Efficient Retrieval: By organizing data as vectors and employing similarity searches, Vector Datastore can quickly identify the most similar vectors to the query vector. This efficient retrieval mechanism allows LLMs to access relevant data points without scanning the entire dataset, significantly reducing the time required for data retrieval. 4. Adaptive Indexing: Vector Datastore dynamically adjusts its indexing strategy based on the data and queries it receives. As the dataset grows or the query patterns change, Vector Datastore adapts its indexing structures to maintain optimal search efficiency. This adaptability ensures the cache remains efficient even as the data and query patterns evolve. 5. Scalability: Vector Datastore is designed to handle large-scale datasets commonly encountered in LLM applications. Its architecture allows horizontal scaling, efficiently distributing the workload across multiple nodes or servers. This scalability ensures that Vector Datastore can accommodate the vast amount of data processed by LLMs without compromising performance.Vector Datastore's ability to work with vectorized data and perform similarity searches based on vector representations enables it to serve as an efficient data cache for Large Language Models. By avoiding the limitations of traditional key-based caching mechanisms, Vector Datastore significantly enhances the speed and responsiveness of LLMs, making it a valuable tool in natural language processing.ConclusionThe development of LLM is one of the crucial technological advancements of our time. Not only does it have the potential to revolutionize various aspects of our lives, but at the same time, it is imperative on our part to utilize them ethically and responsibly to retrieve all its benefits.Author BioKarthik Narayanan Venkatesh (aka Kaptain), founder of WisdomSchema, has multifaceted experience in the data analytics arena. He has been associated with the data analytics domain since the early 2000s, with a ringside view of transformations in this industry. He has led teams that architected and built scalable data platform solutions across the technology spectrum.As a niche consulting provider, he bridged the gap between business and technology and drove BI adoption through innovative approaches in an agnostic manner. He is a sought-after speaker who has presented many lectures on SAP, Analytics, Snowflake, AWS, and GCP technologies.

Dive deeper into the world of AI innovation and stay ahead of the AI curve! Subscribe to our AI_Distilled newsletter for the latest insights. Don't miss out – sign up today!IntroductionIn large language models, the term "hallucination" describes a behavior where an AI model produces results that are not entirely accurate or might sound nonsensical. It's important to understand that large language models are not search engines or databases. They do not search for information from external sources or perform complex computations. Instead, large language models (LLM) belong to a category of generative artificial intelligence.Recap How Generative AI WorksGenerative AI is a technology trained on large volumes of data and, as a result, can " generate" text, images, and even audio. This makes it fundamentally different from search engines and other software tools you might be familiar with. This foundational difference presents challenges, most notably that generative AI can’t cite sources for its responses. Large language models are also not designed to solve computational problems like math. However, generative AI can quickly generate code that might solve complex mathematical challenges. A large language model responds to inputs, most notably the text instruction called a "prompt." As the large language model generates text, it uses its training data as a foundation to extrapolate information.Understanding HallucinationsThe simplest way to understand a hallucination is the old game of telephone. In the same way, a message gets distorted in the game of telephone; information can get "distorted" or "hallucinated" as a language model tries to generate outputs based on patterns it observed in its training data. The model might "misremember" or "misinterpret" certain information, leading to inaccuracies.Let's use another model example to understand the concept of generating unique combinations of words in the context of food recipes. Imagine you want to create new recipes by observing existing ones. If you were to build a Markov model for food ingredients, you would:1.  Compile a comprehensive dataset of recipes and extract individual ingredients.2.   Create pairs of neighboring ingredients, like "tomato-basil" and "chicken-rice," and record how often each pair occurs.For example, if you start with the ingredient "chicken," you might notice it's frequently paired with "broccoli" and "garlic" but less so with "pineapple." If you then choose "broccoli" as the next ingredient, it might be equally likely to be paired with "cheese" or "lemon." By following these ingredient pairings, at some point, the model might suggest creative combinations like "chicken-pineapple-lemon," offering new culinary ideas based on observed patterns.This approach allows the Markov model to generate novel recipe ideas based on the statistical likelihood of ingredient pairings.Hallucinations as a FeatureWhen researching or computing factual information, a hallucination is a bad thing. However, the same concept that gets a bad rap for accurate information or research is what makes large language models demonstrate another human condition of creativity. As a developer, if you want to make your language model creative, OpenAI, for example, has a "temperature" input, a hyperparameter that makes the model's outputs more random. A high temperature of 1 or above will result in hallucinations and randomness. For example, a lower temperature of .2 will make the modern outputs more deterministic to match patterns it was trained on.  As an experiment, try inputting a prompt to any large language model chatbot, including ChatGPT, to provide a plot of any romantic story without copying existing accounts on the internet, a new storyline, and new characters. The LLM will offer a fictitious story with characters, a plot, multiple acts, an arc, and an ending.In specific scenarios, end users or developers might intentionally coax their large language models into a state of "hallucination. When seeking out-of-the-box ideas to think beyond its training, you can get abstract ideas. In this scenario, the model's ability to "hallucinate" isn't a bug but rather a feature. To continue the experiment, you can return to ChatGPT and ask it to pretend you have changed the temperature hyperparameter to 1.1 and re-write the story. Your results will be very “creative.”In creative pursuits, like crafting tales or penning poems, these so-called "hallucinations" aren't just tolerated; they're celebrated. They can add depth, surprise, and innovation layers to the generated content.Types of hallucination One can categorize hallucinations into different forms.Intrinsic hallucination happens to contradict the source material directly. It also offers logical inconsistency and factual inaccuracies.Extrinsic hallucination does not contradict. However, at the same time, it cannot be verified against any source. Hence, it adds elements that are considered to be unconfirmable and speculative. Detecting hallucinations Detecting hallucinations in the large language models is a tricky task. LLMs will deliver information with the same tone and certainty even if the answer is unknown. It puts the responsibility of users and developers to be careful about how information from LLMs is used.The following techniques can be utilized to uncover or measure hallucinations in large language models.Identify the grounding dataGrounding data is the standard against which the Large Language Model (LLM) output is measured. The selection of grounding data depends on the specific application. For example, real job resumes could be grounding data for generating resume-related content. Conversely, search engine outcomes could be employed for web-based inquiries. Especially in language translation, the choice of grounding data is pivotal for accurate translation. For example, official legal documents could serve as grounding data for legal translations, ensuring precision in the translated content.Create a measurement test setA measurement test data set comprises input/output pairs incorporating human interactions and the Large Language Model (LLM). These datasets often include various input conditions and their corresponding program outputs. These sets may involve simulated interactions between users and software systems, depending on the scenario.Ideally, there should be a minimum of two kinds of test sets:1. A standard or randomly generated test set that would be conventional but cater to diverse scenarios.2. An adversarial test set is created through performance in edge cases, high-risk situations, or when presented with deliberately misleading or tricky inputs, even security threats.Extract any claimsFollowing the preparation of test data sets, the subsequent stage involves extracting assertions from the Large Language Model (LLM). This extraction can occur manually, through rule-based methodologies, or even by employing machine learning models.In data analysis, the next step is to extract specific patterns from the data after gathering the datasets. This extraction can be done manually or through predefined rules, basic descriptive analytics, or, for large-scale projects, machine learning algorithms. Each method has its merits and drawbacks, which we will thoroughly investigate.Use validations against any grounding dataValidation guarantees that the content generated by the Large Language Model (LLM) corresponds to the grounding data. Frequently, this stage replicates the techniques employed for data extraction.To support the above, here is the code snippet of the same.# Define grounding data (acceptable sentences) grounding_data = [    "The sky is blue.",    "Python is a popular programming language.",    "ChatGPT provides intelligent responses." ] # List of generated sentences to be validated generated_sentences = [    "The sky is blue.",    "ChatGPT is a popular programming language.",    "Python provides intelligent responses." ] # Validate generated sentences against grounding data valid_sentences = [sentence for sentence in generated_sentences if sentence in grounding_data] # Output valid sentences print("Valid Sentences:") for sentence in valid_sentences:    print("- " + sentence) # Output invalid sentences invalid_sentences = list(set(generated_sentences) - set(valid_sentences)) print("\nInvalid Sentences:") for sentence in invalid_sentences:    print("- " + sentence)Output: Valid Sentences: - The sky is blue. Invalid Sentences: - ChatGPT is a popular programming language. - Python provides intelligent responses. Furthermore, in verifying research findings, validation ensures that the conclusions drawn from the research align with the collected data. This process often mirrors the research methods employed earlier.Metrics reportingThe "Grounding Defect Rate" is a crucial metric that measures the proportion of responses lacking context to the total generated outputs. Further metrics will be explored later for a more detailed assessment. For instance, the "Error Rate" is a vital metric indicating the percentage of mistranslated phrases from the translated text. Additional metrics will be introduced later for a comprehensive evaluation.A Multifaceted Approach to Mitigate Hallucination in the Large Language Model Leveraging product designThe developer needs to employ large language models so that it does not create material issues, even when it hallucinates. For example, you would not design an application that writes your annual report or news articles. Instead, opinion pieces or content summarization within a prompt can immediately lower the risk of problematic hallucination.If an app allows AI-generated outputs to be distributed, end users should be able to review and revise the content. It adds a protective layer of scrutiny and puts the responsibility into the hands of the user.Continuous improvement and loggingPersisting prompts and LLM output are essential for auditing purposes. As models evolve, you cannot count on prompting an LLM and getting the same result. However, regression testing and reviewing user input are critical as long as it adheres to data, security, and privacy practice.Prompt engineeringIt is essential to get the best possible output to use the concept of meta prompts effectively. A meta prompt is a high-level instruction given to a language model to guide its output in a specific direction. Rather than asking a direct question, provide context, structure, and guidance to refine the output.For example, instead of asking, "What is photosynthesis?", you can ask, "Explain photosynthesis in simple terms suitable for a 5th-grade student." This will adjust the complexity and style of the answer you get.Multi-Shot PromptsMulti-shot prompts refer to a series of prompts given to a language model, often in succession. The goal is to guide the model step-by-step toward a desired output instead of asking for a large chunk of information in a single prompt.  This approach is extremely useful when the required information is complex or extensive. Typically, these prompts are best delivered as a chat user experience, allowing the user and model to break down the requests into multiple, manageable parts.ConclusionThe issue of hallucination in Large Language Models (LLMs) presents a significant hurdle for consumers, users, and developers. While overhauling the foundational architecture of these models isn't a feasible solution for most, the good news is that there are strategies to navigate these challenges. But beyond these technical solutions, there's an ethical dimension to consider. As developers and innovators harness the power of LLMs, it's imperative to prioritize disclosure and transparency. Only through openness can we ensure that LLMs integrate seamlessly into our daily lives and gain the trust and acceptance they require to revolutionize our digital interactions truly.Author BioRyan Goodman has dedicated 20 years to the business of data and analytics, working as a practitioner, executive, and entrepreneur. He recently founded DataTools Pro after 4 years at Reliant Funding, where he served as the VP of Analytics and BI. There, he implemented a modern data stack, utilized data sciences, integrated cloud analytics, and established a governance structure. Drawing from his experiences as a customer, Ryan is now collaborating with his team to develop rapid deployment industry solutions. These solutions utilize machine learning, LLMs, and modern data platforms to significantly reduce the time to value for data and analytics teams.

Dive deeper into the world of AI innovation and stay ahead of the AI curve! Subscribe to our AI_Distilled newsletter for the latest insights. Don't miss out – sign up today!Introduction  AI is transforming the way businesses operate, enabling them to improve efficiency, reduce costs, and enhance customer satisfaction. However, building and deploying AI solutions can be challenging, even at times for pro developers due to the inherent complexity of traditional tools. That’s where Microsoft AI Builder comes in. AI Builder is a low-code AI platform that empowers pro developers to infuse AI into business workflows without writing a single line of code. AI Builder is integrated with Microsoft Power Platform, a suite of tools that allows users to build apps, automate processes, and analyze data. With AI Builder, users can leverage pre-built or custom AI models to enhance their Power Apps and Power Automate solutions. One of the most powerful features of AI Builder is the prediction model, which allows users to create AI models that can predict outcomes based on historical data. The prediction model can be used to predict the following outcomes,  Binary outcome, choice between one value. An example would be booking status, canceled/redeemed.  Multiple outcomes, a choice between multiple yet fixed outcomes. An example would be the Stage of Delivery, early/on-time/delayed/escalated.  Numeric outcomes, a number value. An example would be revenue per customer. In this blog post, we will show you how to create and use a prediction model with AI Builder using our business data. We will focus on Numeric outcomes and use the example mentioned above, we will attempt to predict the possible revenue we can generate from customers in a lifetime. Let’s get started! Getting Data Ready The process of building a model begins with data. We will not cover the AI builder prerequisites in the chapter but you can easily find them at Microsoft learn. The data in focus is sample data of customer profiles from the retailer system. The data include basic profile details such as (education, marital status, customer since, kids at home, teens at home), interaction data (participation in the campaign), and transaction summary (purchases both online and offline, product categories)  The data needs to be either imported in Dataverse or already existing. In this case, we will import the file “Customer_profile_sample.xls”. To import the data, the user should perform the following actions.  1. Open http://make.powerapps.com  and log in to your power platform environment.  2. Select the right environment, and recommend performing these actions in a development environment.  3. From the left menu pan select table . 4. Now select the option upload from excel. This will start a data import process.  Figure 1 Upload data in dataverse from excel file. 5. Upload the Excel file mentioned above “Customer_profile_sample.xls.” The system will read the file content and give a summary of the data in the file. Note if your environment has the copilot feature on, you will see a GPT in action where it will not only get the details of the file but also choose the table name and add descriptions to columns as well.   Figure 2 Copilot in action with file Summary 6. Verify the details, make sure the table is selected as “Customer Profile” and the Primary column is “ID.” Once verified, click Create and let the system upload the data into this new table.   The system will move you to the table view screen.   Figure 3 Table View Screen  7. In this screen, lets click on Columns under the Schema section. This will take us to the column list. here we need to scroll down and find a column called “Revenue.” Right-click the column and select edit.   Figure 4 Updating column information.  8. Let's check the feature searchable and save the changes.   9. We will move back all the way to the table list, by clicking on Table in the left navigation. Here we will select our “Customer Profile” table and choose Publish from the top menu. This will apply to the change made in step 8. We will wait till we see a green bar with the message “Publish completed.”   This concludes our first part of getting the sample data imported. Creating a Model Now that we have our data ready and available in dataverse, let's start building our model. We will follow the next set of actions to deliver the model with this low code / no code tool. 1. The first step is to open AI Builder. To open AI Builder Studio, let go to http://make.powerapps.com. 2. From the left navigation, click on AI Models . This will open the AI model studio.  3. Select from the top navigation bar. There are many OOB models for various business use cases that developers can choose but this time we will select a prediction model from the options.   Figure 5 Prediction Model Icon 4. The next pop-up screen will provide details about the prediction model feature and how it can used. Select to begin the model creation process. The model creation process is a step journey that we will explain one by one.  5. The first action is to select the historical outcome. Here we need to select the table we created in the above section “Customer Profile” and the column (Label) we want the model to predict, in this case, “revenue.”  Figure 6 Step one - Historical Outcome Selection 6. The next step is the critical step in any classification model. it is called the feature selection. In this step, we will select the columns to make sure we provide enough information to our AI model so it can assess the impact and influence of these features and train itself. The table has now 33 columns (27 we imported from the sample file and 5 added as part of the dataverse process). We will select 27 columns again as the most important feature for this model. The ones we will not select are.  Created On: it is a date column created by dataverse to track the record creation date. Not relevant in predicting revenue.  ID: it is a numerical sequential number, again we can decide with confidence that it is not going to be relevant in predicting our label “revenue.” Record Created On: Dataverse added column.  Revenue (base): a base currency value.  UTC Conversion Time zone: Dataverse added column. Before moving to next step make sure that you can see 27 columns selected.  Figure 7 Selecting Features / Columns 7. The next step is to choose the training data with business logic. If you would have noticed, our original imported data contains some rows where the revenue field is empty. Such data would not be helpful to train the model. Hence, we would like a model to train on rows that have revenue information available. We can do so by selecting “Filter the Data” and then adding the condition row as shown in the below figure.  Figure 8 Selecting the right dataset. 8. Finally, we are our last step of verification, here will perform one last action before training the model, that is to give this model proper name. let's click on an icon to change the name of the model. We shall name the model “Prediction – Revenue.”  Figure 9 Renaming the Model 9. Let’s click on and begin model training.  Evaluation of model The ultimate step of any model creation is the assessment of the model. Once our model is ready and trained, the system will generate model performance details. These details can be accessed by clicking on the model from AI Studio. Let's evaluate and read into our model.   Figure 10 Model Performance Summary PerformanceAI builder grade models based on model R-squared (goodness of fit). An R-squared value of 88% for a model means that 88% of the variation in revenue can be explained by the model’s inputs. The remaining 12% could be due to other factors not included in the model. For a set of information provided, it is a good start and, in some cases, an acceptable outcome as well.  Most Influential data The model also explains the most influential feature to our outcome “revenue.” In this case, Monthly Wine purchase (MntWines) is the highest weighted and suggests the highest association with revenue an organization can make from a customer. These weights can trigger a lot of business ideation and improve business KPIs further.  WarningsIn the detail section, you can also view the warnings the system has generated. In this case, it has identified a few columns that we intentionally selected in our earlier steps as having no association with revenue. This information can be used to further fine-tune and remove unnecessary features from our training and feature selection that were explained earlier.   Figure 11 Warning Tab in Details ConclusionThis marks the completion of our model preparation. Once we are satisfied with the model performance, we can choose to Publish this model. The model then can be used either through Power Apps or Power Automate to predict the revenue and reflect in dataverse. This feature of AI Builder opens the door to so many possibilities and the ability to deliver it in a short duration of time makes it extremely useful. Keep experimenting and keep learning.  Author BioMohammad Adeel Khan is a Senior Technical Specialist at Microsoft. A seasoned professional with over 19 years of experience with various technologies and digital transformation projects. At work , he engages with enterprise customers across geographies and helps them accelerate digital transformation using Microsoft Business Applications , Data, and AI solutions. In his spare time, he collaborates with like-minded and helps solve business problems for Nonprofit organizations using technology.  Adeel is also known for his unique approach to learning and development. During the COVID-19 lockdown, he introduced his 10-year-old twins to Microsoft Learn. The twins not only developed their first Microsoft Power Platform app—an expense tracker—but also became one of the youngest twins to earn the Microsoft Power Platform certification. 

 Dive deeper into the world of AI innovation and stay ahead of the AI curve! Subscribe to our AI_Distilled newsletter for the latest insights. Don't miss out – sign up today!IntroductionStarting a project requires good tools to keep things running smoothly. That's where our guide, combining ChatGPT with PMBOK, comes in handy. We'll walk you through each step, from beginning your project to making detailed plans. With easy-to-use templates and clear examples, we aim to make things simpler for you. In short, our guide brings together the best of ChatGPT and PMBOK to help you manage projects better. Let's get started!First, let’s have a look at the steps defined under PMBOK for project management planningStep 1: Initiating the Project1. Objective: Set the foundation for your project.2. Actions:   - 1.1 Identify the need or problem the project aims to address.   - 1.2 Develop the Project Charter:      - Define the project's purpose, objectives, and scope.      - Identify primary stakeholders.      - Outline initial budget estimates.   - 1.3 Identify all stakeholders, including those who can influence or are impacted by the project.3. Outcome: A Project Charter that provides a high-level overview and project stakeholders are identified.Step 2: Planning the Project1. Objective: Develop a comprehensive roadmap for your project.2. Actions:   - 2.1 Define success criteria.   - 2.2 Detail the project's scope and boundaries.   - 2.3 List out deliverables.   - 2.4 Break down the project into tasks and set timelines.   - 2.5 Create a budget, detailing estimated costs for tasks.   - 2.6 Develop sub-plans such as:      - Human Resource Plan      - Quality Management Plan      - Risk Management Plan      - Procurement Management Plan   - 2.7 Document change management procedures.Now let’s have a look at a generic template and an example for each step defined aboveStep 1.1: Initiating the ProjectGeneric Prompt: “As a project manager, I'm looking to address an underlying need or problem within [specific domain/area, e.g., 'our software development lifecycle']. Based on recent data, stakeholder feedback, market trends, and any other relevant information available in this domain, can you help identify the primary challenges or gaps that our project should target? The ultimate goal is to [desired outcome, e.g., 'improve efficiency and reduce bug counts']. Please provide a comprehensive analysis of potential problems and their implications."Prompt Example: “In our organization, managing vendors has become an increasingly complex task, with multiple touchpoints and communication channels. Given the crucial role vendors play in our supply chain and service delivery, there's an urgent need to streamline our vendor management processes. As a software solution is desired, can you help identify the primary requirements, challenges, and functionalities that our vendor management software should address? The primary objective is to enhance vendor communication, monitor performance metrics, ensure contract compliance, and facilitate swift issue resolution. Please provide a detailed analysis that can serve as a starting point for our software development."Response:Step 1.2: Develop the Project CharterGeneric Prompt: “For our objective of [specific domain or objective, e.g., 'customer relationship management'], draft a concise project charter. Address the phases of [list main stages/phases, e.g., 'identifying customer needs and feedback collection'], aiming to [primary goal, e.g., 'enhance customer satisfaction']. Given the importance of [contextual emphasis, e.g., 'customer relationships'], and involving stakeholders like [stakeholders involved, e.g., 'sales teams and customer support'], define a methodology that captures the essence of our goal."Prompt Example: "For our vendor management objective, draft a succinct project charter for a System Development Life Cycle (SDLC). The SDLC should cover phases from identifying vendor needs to termination or renewal processes, with an aim to enhance cost-efficiency and service reliability. Given our organization's growing dependency on vendors and the involvement of stakeholders like procurement and legal teams, outline a process that ensures optimal vendor relationship management."Response:2.1 Define success criteriaGeneric Prompt: "In light of the complexities in project management, having lucid success criteria is paramount. Can you delineate general success criteria pivotal for any project management initiative? This will gauge the project's triumph throughout its lifecycle, aligning with stakeholder aspirations and company objectives.Prompt Example: "Considering the intricacies of crafting vendor management software, establishing precise success criteria is crucial. To align the software with our goals and stakeholder demands, can you list and elaborate on success criteria tailored for this task? These standards will evaluate the software's efficiency throughout its phases, from design to updates. Supply a list specific to vendor management software, adaptable for future refinementsOutput:2.2 Detail the project's scope and boundariesGeneric Prompt: "Given the intricacies of today's projects, clear scope and boundaries are vital. Can you elucidate our project's scope, pinpointing its main objectives, deliverables, and focal areas? Additionally, specify what it won't encompass to avoid scope creep. Offer a clear outline demarcating the project's inclusions and exclusions, ensuring stakeholder clarity on its scope and constraintsPrompt Example: "In light of the complexities in vendor management software development, clear scope and boundaries are essential. Can you describe the scope of our software project, highlighting its main objectives, deliverables, and key features? Also, specify any functionalities it won't include to avert scope creep. Furnish a list that distinctly differentiates the software's capabilities from its exclusions, granting stakeholders a clear perspective."Output: 2.3 & 2.4:  List out deliverables & Break down the project into tasks and set timelinesGeneric Prompt: “For our upcoming project, draft a clear roadmap. List the key deliverables encompassing objectives, functionalities, and related documentation. Then, dissect each deliverable into specific tasks and suggest timelines for each. Based on this, provide a structured breakdown suitable for a Gantt chart representation."Prompt Example: "For our vendor management software project, provide a succinct roadmap. Enumerate the key deliverables, encompassing software functionalities and associated documentation. Subsequently, dissect these deliverables into specific tasks, suggesting potential timelines. This breakdown should be structured to facilitate the creation of a Gantt chart for visual timeline representation."Output:2.5 Create a budget, detailing estimated costs for tasksGeneric Prompt: Can you draft a budgetary outline detailing the estimated costs associated with each major task and deliverable identified? This should consider potential costs for [list some generic cost categories, e.g., personnel, equipment, licenses, operational costs], and any other relevant expenditures. A clear financial breakdown will aid in the effective management of funds and ensure the project remains within its financial boundaries. Please provide a comprehensive budget plan suitable for [intended audience, e.g., stakeholders, team members, upper management]."Prompt Example: "Can you draft a budgetary outline detailing the estimated costs associated with each major task and deliverable identified in the project? This should include anticipated costs for personnel, software and hardware resources, licenses, testing, and any other potential expenditures. Remember, a clear financial breakdown will help in managing funds and ensuring the project remains within the set financial parameters. Please provide a comprehensive budget plan that can be presented to stakeholders for approval."Output:2.6 Develop sub-plans such asHuman Resource PlanQuality Management PlanRisk Management PlanProcurement Management PlanGeneric prompt: "In light of the requirements for comprehensive project management, it's crucial to have detailed sub-plans addressing specific areas. Could you assist in formulating a [specific sub-plan, e.g., 'Human Resource'] plan? This plan should outline the primary objectives, strategies, and actionable steps relevant to [specific domain, e.g., 'staffing and team development']. Additionally, consider potential challenges and mitigation strategies within this domain. Please provide a structured outline that can be adapted and refined based on the unique nuances of our project and stakeholder expectations."By replacing the placeholders (e.g., [specific sub-plan]) with the desired domain (Human Resource, Quality Management, etc.), this prompt can be tailored for various sub-plans.By filling in the "[specific project or objective]" placeholder with details pertaining to your specific project, this prompt layout can be tailored to various projects or initiatives.Have a glimpse at the output generated for various sub-plans in the context of the Vendor Management Software projectHuman Resource PlanQuality Management PlanRisk Management PlanProcurement Management Plan2.7 Document change management proceduresGeneric Prompt: “As a project manager, outline a Document Change Management procedure for a project. Ensure you cover change initiation, review, approval, implementation, communication, version control, auditing, and feedback."Prompt Example: "As the project manager of a Vendor Management Software deployment, design a Document Change Management procedure. Keeping in mind the dynamic nature of vendor integrations and software updates, outline the process for initiating, reviewing, approving, and implementing changes in documentation. Also, address communication with stakeholders, version control mechanisms, auditing frequency, and feedback integration from both team members and vendors. Aim for consistency and adaptability in your procedure."Output:ConclusionWrapping things up, effective project planning is foundational for success. Our guide has combined the best of ChatGPT and PMBOK to simplify this process for you. We've delved into creating a clear project roadmap, from setting success markers to managing changes effectively. By detailing scope, listing deliverables, breaking tasks down, budgeting, and designing crucial sub-plans, we've covered the essentials of project planning. Using our straightforward templates and examples, you're equipped to navigate project management with clarity and confidence. As we conclude, remember: proper planning today paves the way for smoother project execution tomorrow. Let's put these tools to work and achieve those project goals!Author BioDr. Anshul Saxena is an author, corporate consultant, inventor, and educator who assists clients in finding financial solutions using quantum computing and generative AI. He has filed over three Indian patents and has been granted an Australian Innovation Patent. Anshul is the author of two best-selling books in the realm of HR Analytics and Quantum Computing (Packt Publications). He has been instrumental in setting up new-age specializations like decision sciences and business analytics in multiple business schools across India. Currently, he is working as Assistant Professor and Coordinator – Center for Emerging Business Technologies at CHRIST (Deemed to be University), Pune Lavasa Campus. Dr. Anshul has also worked with reputed companies like IBM as a curriculum designer and trainer and has been instrumental in training 1000+ academicians and working professionals from universities and corporate houses like UPES, CRMIT, and NITTE Mangalore, Vishwakarma University, Pune & Kaziranga University, and KPMG, IBM, Altran, TCS, Metro CASH & Carry, HPCL & IOC. With a work experience of 5 years in the domain of financial risk analytics with TCS and Northern Trust, Dr. Anshul has guided master's students in creating projects on emerging business technologies, which have resulted in 8+ Scopus-indexed papers. Dr. Anshul holds a PhD in Applied AI (Management), an MBA in Finance, and a BSc in Chemistry. He possesses multiple certificates in the field of Generative AI and Quantum Computing from organizations like SAS, IBM, IISC, Harvard, and BIMTECH.Author of the book: Financial Modeling Using Quantum Computing

Dive deeper into the world of AI innovation and stay ahead of the AI curve! Subscribe to our AI_Distilled newsletter for the latest insights. Don't miss out – sign up today!IntroductionLarge language models are a type of AI that can create and understand human language. The article deals with the potential of large language models in education and how they can be transformed. The ability to create and understand the language of man, by drawing on a vast database of textual data, is possessed by LLMs powered by artificial intelligence.It shows how LLMs could, by means of practical examples, put in place individual learning pathways, providing Advanced Learning Analytics and developing participatory simulations that would lead to the creation of more effective educational strategies.Benefits of LLMs in Education                                       Personalized learningThe capacity of LLMs in education to customize learning experiences for each student is one of their greatest advantages. Lesson-plan customization, individualized feedback, and real-time monitoring of student progress are all possible with LLMsAutomated tasksAdditionally, LLMs can be utilized to automate processes like grading and lesson planning. By doing this, instructors may have more time to give to other important responsibilities like teaching and connecting with students.New and innovative educational tools and resourcesLLMs can be applied to the development of innovative and cutting-edge learning resources and technology. LLMs can be used to create interactive simulations, games, and other educational activities.Real-time feedback and supportLLMs can also be utilized for providing quick help and feedback to students. For example, LLMs can be used to create chatbots that can assist students with their academic work and respond to their queries. Potential Challenges of LLMs in EducationIncorrect or misleading informationThe fact that LLMs might provide inaccurate or misleading information is one of the main problems with their use in education. This is due to the fact that LLMs are taught using vast volumes of data, some of which could be old or erroneous.Lack of understandingAnother issue with utilizing LLMs in teaching is that they might not be able to fully understand the material they produce in its entirety. This is so that they may better understand the complexity of human communication as LLMs receive instruction on statistical patterns in language.Ethical concernsThere are also some ethical concerns associated with the use of LLMs in education. LLMs should be used carefully, and their usage might have ethical consequences, which should be considered.How LLM can be used for Transforming Education with Advanced Learning StrategiesLet's look at a few examples that show the possibilities of Large Language Models (LLM) in Education.1. Advanced Personalized Learning PathwayIn this example, in order to reflect a student's individual objectives, teaching style, and progress, we are going to form an even more detailed personalized education path. Follow the steps perfectly given in the input code to create a personalized learning pathway.Input Code:    # Step 1: First we will define the generate_learning_pathway function def generate_learning_pathway(prompt, user_profile):    # Step 2: Once the function is defined we will create a template for the learning pathway    learning_pathway_template = f"Dear {user_profile['student_name']},\n\nI'm excited to help you create a personalized learning pathway to achieve your goal of {user_profile['goals']}. As a {user_profile['learning_style']} learner with {user_profile['current_progress']}, here's your pathway:\n\n"    # Step 3: Now let’s define the specific steps in the learning pathway    steps = [        "Step 1: Introduction to Data Science",        "Step 2: Data Visualization Techniques for Visual Learners",        "Step 3: Intermediate Statistics for Data Analysis",        "Step 4: Machine Learning Fundamentals",        "Step 5: Real-world Data Science Projects",    ]    # Step 4: Combine the template and the specific steps    learning_pathway = learning_pathway_template + "\n".join(steps)    return learning_pathway # Step 5: Define a main function to test the code def main():    user_profile = {        "student_name": "Alice",        "goals": "Become a data scientist",       "learning_style": "Visual learner",        "current_progress": "Completed basic statistics"    }    prompt = "Create a personalized learning pathway."    # Step 6: Generate the learning pathway    learning_pathway = generate_learning_pathway(prompt, user_profile)    # Step 7: Print the learning pathway    print(learning_pathway) if __name__ == "__main__":    main() Output:This example gives the LLM a highly customized approach to teaching taking into account students' names, objectives, methods of education, and how they are progressing.2. AI-Enhanced Learning AnalyticsThe use of LLMs in Learning Analytics may provide teachers with more detailed information on the student's performance and help them to make appropriate recommendations.Input code:# Define the generate_learning_analytics function def generate_learning_analytics(prompt, student_data): # Analyze the performance based on quiz scores average_quiz_score = sum(student_data["quiz_scores"]) / len(student_data["quiz_scores"]) # Calculate homework completion rate total_homeworks = len(student_data["homework_completion"]) completed_homeworks = sum(student_data["homework_completion"]) homework_completion_rate = (completed_homeworks / total_homeworks) * 100 # Generate the learning analytics report analytics_report = f"Learning Analytics Report for Student {student_data['student_id']}:\n" analytics_report += f"- Average Quiz Score: {average_quiz_score:.2f}\n" analytics_report += f"- Homework Completion Rate: {homework_completion_rate:.2f}%\n" if homework_completion_rate < 70: analytics_report += "Based on their performance, it's recommended to provide additional support for completing homework assignments." return analytics_reportThis code defines a Python function, ‘generates_learning_analytics’, which takes prompt and student data as input, calculates average quiz scores and homework completion rates, and generates a report that includes these metrics, together with possible recommendations for additional support based on homework performance. Now let’s provide student performance data.Input code:student_data = {    "student_id": "99678",    "quiz_scores": [89, 92, 78, 95, 89],    "homework_completion": [True, True, False, True, True] } prompt = f"Analyze the performance of student {student_data['student_id']} based on their recent quiz scores and homework completion." analytics_report = generate_learning_analytics(prompt, student_data) print(analytics_report)Output:The student's test scores and the homework completion data included in the ‘student_data’ dictionary are used to generate this report.3. Advanced Interactive Simulations for LearningThe potential for LLMs to provide an engaging learning resource will be demonstrated through the creation of a comprehensive computerised training simulation on complicated topics, such as physics.Input code:# Define the generate_advanced_simulation function def generate_advanced_simulation(prompt): # Create the interactive simulation    interactive_simulation = f"Interactive {prompt} Simulation" # Provide a link to the interactive simulation (replace with an actual link)    interactive_simulation_link = "https://your-interactive-simulation-link.com"    return interactive_simulation, interactive_simulation_link # Define a main function to test the code def main():    topic = "Quantum Mechanics"    prompt = f"Develop an interactive simulation for teaching {topic} to advanced high school students." # Generate the interactive simulation    interactive_simulation, interactive_simulation_link = generate_advanced_simulation(prompt) # Print the interactive simulation and link    print(f"Explore the {topic} interactive simulation: {interactive_simulation_link}") if __name__ == "__main__":    main()Output:In this example, for a complex topic like quantum physics, the LLM is asked to create an advanced interactive simulation that will make learning more interesting and visual. Also, make sure to replace and provide your link to the interactive simulation.Such advanced examples demonstrate the adaptability of LLMs to create highly customized learning pathways, Advanced Learning Analytics Reports, and sophisticated interactive simulations with in-depth educational experiences.ConclusionIn conclusion, by providing advanced learning strategies and tools, large language models represent a tremendous potential for revolutionizing education. These models provide a range of benefits, including personalized learning experiences, timely feedback and support, automated tasks, and the development of useful tools for innovation in education.The article considers the practical use of LLMs in education, which includes developing more sophisticated personalized school paths that take into account students' specific educational objectives and how they learn. Moreover, by giving details of the student's performance and recommendations for improvement, LLMs can improve Learning Analytics. In addition, how LLMs can enhance learning by enabling interactivity and engagement has been demonstrated through the development of real-time simulations on complicated topics.The future of education appears promising by taking into account the LLMs' ability to offer a more diverse, creative learning environment with limitless opportunities for learners around the world.Author BioChaitanya Yadav is a data analyst, machine learning, and cloud computing expert with a passion for technology and education. He has a proven track record of success in using technology to solve real-world problems and help others to learn and grow. He is skilled in a wide range of technologies, including SQL, Python, data visualization tools like Power BI, and cloud computing platforms like Google Cloud Platform. He is also 22x Multicloud Certified.In addition to his technical skills, he is also a brilliant content creator, blog writer, and book reviewer. He is the Co-founder of a tech community called "CS Infostics" which is dedicated to sharing opportunities to learn and grow in the field of IT.

Dive deeper into the world of AI innovation and stay ahead of the AI curve! Subscribe to our AI_Distilled newsletter for the latest insights. Don't miss out – sign up today!Machine learning has become ubiquitous, with models powering everything from search engines and recommendation systems to chatbots and autonomous vehicles. As these models grow more complex, testing them thoroughly is crucial to ensure they behave as expected. This is especially true for large language models like GPT-4 that generate human-like text and engage in natural conversations.In this article, we will explore strategies for testing machine learning models, with a focus on evaluating the performance of LLMs.IntroductionMachine learning models are notoriously challenging to test due to their black-box nature. Unlike traditional code, we cannot simply verify the logic line-by-line. ML models learn from data and make probabilistic predictions, so their decision-making process is opaque.While testing methods like unit testing and integration testing are common for traditional software, they do not directly apply to ML models. We need more specialized techniques to validate model performance and uncover unexpected or undesirable behavior.Testing is particularly crucial for large language models. Since LLMs can generate free-form text, it's hard to anticipate their exact responses. Flaws in the training data or model architecture can lead to Hallucinations, biases, and errors that only surface during real-world usage. Rigorous testing provides confidence that the model works as intended.In this article, we will cover testing strategies to evaluate LLMs. The key techniques we will explore are:Similarity testingColumn coverage testingExact match testingVisual output testingLLM-based evaluationBy combining these methods, we can thoroughly test LLMs along multiple dimensions and ensure they provide coherent, accurate, and appropriate responses.Testing Text Output with Similarity SearchA common output from LLMs is text. This could be anything from chatbot responses to summaries generated from documents. A robust way to test quality of text output is similarity testing.The idea is simple - we define an expected response and compare the model's actual response to determine how similar they are. The higher the similarity score, the better.Let's walk through an example using our favorite LLM. Suppose we give it the prompt:Prompt: What is the capital of Italy?The expected response would be:Expected: The capital of Italy is Rome.Now we can pass this prompt to the LLM and get the actual response:prompt = "What is the capital of Italy?" actual = llm.ask(prompt) Let's say actual contains:Actual: Rome is the capital of Italy.While the wording is different, the meaning is the same. To quantify this similarity, we can use semantic search libraries like SentenceTransformers. It represents sentences as numeric vectors and computes similarity using cosine distance.from sentence_transformers import SentenceTransformer model = SentenceTransformer('all-MiniLM-L6-v2') expected_embedding = model.encode(expected) actual_embedding = model.encode(actual) similarity = cosine_similarity([expected_embedding], [actual_embedding])[0][0] This yields a similarity score of 0.85, indicating the responses are highly similar in meaning.We can establish a threshold for the minimum acceptable similarity, like 0.8. Responses below this threshold fail the test. By running similarity testing over many prompt-response pairs, we can holistically assess the textual coherence of an LLM.Testing Tabular Outputs with Column CoverageIn addition to text, LLMs can output tables or data frames. For testing these, we need different techniques that account for structure.A good validation is column coverage - checking what percentage of columns in the expected output are present in the actual output.Consider the LLM answering questions about movies:Prompt: What are the top 3 highest grossing movies of all time?Expected:MovieWorldwide GrossRelease YearAvatar$2,789,679,7942009Titanic$2,187,463,9441997Star Wars Ep. VII$2,068,223,6242015Now we can test the LLM’s actual output:prompt = "What are the top 3 highest grossing movies of all time?" actual = llm.ask(prompt) Actual:MovieGlobal RevenueYearAvatar$2.789 billion2009Titanic$2.187 billion1997Star Wars: The Force Awakens$2.068 billion2015Here, actual contains the same 3 columns as expected - Movie, Gross, Release Year. So even though the headers and cell values differ slightly, we can pair them with cosine similarity and we will have 100% column coverage.We can formalize this in code:expected_cols = set(expected.columns) actual_cols = set(actual.columns) column_coverage = len(expected_cols & actual_cols) / len(expected_cols) # column_coverage = 1.0 For tables with many columns, we may only need say 90% coverage to pass the test. This validation ensures the critical output columns are present while allowing variability in column names or ancillary data.Exact Match for Numeric OutputsWhen LLMs output a single number or statistic, we can use simple exact match testing.Consider this prompt:Prompt: What was Apple's total revenue in 2021?Expected: $365.82 billionWe get the LLM’s response:prompt = "What was Apple's total revenue in 2021?" actual = llm.ask(prompt) Actual: $365.82 billionIn this case, we expect an exact string match:is_match = (actual == expected) # is_match = True For numerical outputs, precision is important. Exact match testing provides a straightforward way to validate this.Screenshot Testing for Visual OutputsBuilding PandasAI, we sometimes need to test generated charts. Testing these outputs requires verifying the visualized data is correct.One method is screenshot testing - comparing screenshots of the expected and actual visuals. For example:Prompt: Generate a bar chart comparing the revenue of FAANG companies.Expected: [Expected_Chart.png]Actual: [Actual_Chart.png]We can then test if the images match:from PIL import Image, ImageChops expected_img = Image.open("./Expected_Chart.png") actual_img = Image.open("./Actual_Chart.png") diff = ImageChops.difference(expected_img, actual_img) is_match = diff.getbbox() is None // is_match = True if images matchFor more robust validation, we could use computer vision techniques like template matching to identify and compare key elements: axes, bars, labels, etc.Screenshot testing provides quick validation of visual output without needing to interpret the raw chart data.LLM-Based EvaluationAn intriguing idea for testing LLMs is to use another LLM!The concept is to pass the expected and actual outputs to a separate "evaluator" LLM and ask if they match.For example:Expected: Rome is the capital of Italy.Actual: The capital of Italy is Rome.We can feed this to the evaluator model:Prompt: Do these two sentences convey the same information? Answer YES or NOSentence 1: Rome is the capital of Italy.Sentence 2: The capital of Italy is Rome.Evaluator: YESThe evaluator LLM acts like a semantic similarity scorer. This takes advantage of the natural language capabilities of LLMs.The downside is it evaluates one black box model using another black box model. Errors or biases in the evaluator could lead to incorrect assessments. So LLM-based evaluation should complement other testing approaches, not act as the sole method.ConclusionTesting machine learning models thoroughly is critical as they grow more ubiquitous and impactful. Large language models pose unique testing challenges due to their free-form textual outputs.Using a combination of similarity testing, column coverage validation, exact match, visual output screening, and even LLM-based evaluation, we can rigorously assess LLMs along multiple dimensions.A comprehensive test suite combining these techniques will catch more flaws and flaws than any single method alone. This builds essential confidence that LLMs behave as expected in the real world.Testing takes time but prevents much larger problems down the road. The strategies covered in this article will add rigor to the development and deployment of LLMs, helping ensure these powerful models benefit humanity as intended.Author BioGabriele Venturi is a software engineer and entrepreneur who started coding at the young age of 12. Since then, he has launched several projects across gaming, travel, finance, and other spaces - contributing his technical skills to various startups across Europe over the past decade.Gabriele's true passion lies in leveraging AI advancements to simplify data analysis. This mission led him to create PandasAI, released open source in April 2023. PandasAI integrates large language models into the popular Python data analysis library Pandas. This enables an intuitive conversational interface for exploring data through natural language queries.By open-sourcing PandasAI, Gabriele aims to share the power of AI with the community and push boundaries in conversational data analytics. He actively contributes as an open-source developer dedicated to advancing what's possible with generative AI.

Dive deeper into the world of AI innovation and stay ahead of the AI curve! Subscribe to our AI_Distilled newsletter for the latest insights. Don't miss out – sign up today!IntroductionChatGPT is an efficient language that may be used in a range of tasks, including the creation of SQL queries. In this article, you will get to know how effectively you will be able to use SQL queries by using ChatGPT to optimize and craft them correctly to get perfect results.It is necessary to have sufficient SQL knowledge before you can use ChatGPT for the creation of SQL queries. The language that the databases are communicating with is SQL. This is meant to be used for the production, reading, updating, and deletion of data from databases. SQL is the most specialized language in this domain. It's one of the main components in a lot of existing applications because it deals with structured data that can be retrieved from tables.There are a number of different SQL queries, but some more common ones include the following:SELECT: It will select data from a database.INSERT: It will insert new data into a database.UPDATE: This query will update the existing data in a database.DELETE: This query is used to delete data from a database.Using ChatGPT to write SQL queriesOnce you have a basic understanding of SQL, you can start using ChatGPT to write SQL queries. To do this, you need to provide ChatGPT with a description of the query that you want to write. After that, ChatGPT will generate the SQL code for you.For example, you could just give ChatGPT the query below to write an SQL query to select all of the customers in your database.Select all of the customers in my databaseFollowing that, ChatGPT will provide the SQL code shown below:SELECT * FROM customers;The customer table's entire set of columns will be selected by this query. Additionally, ChatGPT can be used to create more complex SQL statements.How to Use ChatGPT to Describe Your IntentionsNow let’s have a look at some examples where we will ask ChatGPT to generate SQL code by asking it queries from our side.For Example:We'll be creating a sample database for ChatGPT, so we can ask them to set up restaurant databases and two tables.ChatGPT prompt:Create a sample database with two tables: GuestInfo and OrderRecords. The GuestInfo table should have the following columns: guest_id, first_name, last_name, email_address, and contact_number. The OrderRecords table should have the following columns: order_id, guest_id, product_id, quantity_ordered, and order_date.ChatGPT SQL Query Output:We requested that ChatGPT create a database and two tables in this example. After it generated a SQL query. The following SQL code is to be executed on the Management Studio software for SQL Server. As we are able to see the code which we got from ChatGPT successfully got executed in the SSMS Database software.How ChatGPT Can Be Used for Optimizing, Crafting, and Debugging Your QueriesSQL is an efficient tool to manipulate and interrogate data in the database. However, in particular, for very complex datasets it may be difficult to write efficient SQL queries. The ChatGPT Language Model is a robust model to help you with many tasks, such as optimizing SQL queries.Generating SQL queriesThe creation of SQL queries from Natural Language Statements is one of the most common ways that ChatGPT can be used for SQL optimization. Users who don't know SQL may find this helpful, as well as users who want to quickly create the query for a specific task.For example, you could ask for ChatGPT in the following way:Generate an SQL query to select all customers who have placed an order in the last month.ChatGPT would then generate the following query:SELECT * FROM customers WHERE order_date >= CURRENT_DATE - INTERVAL 1 MONTH;Optimizing existing queriesThe optimization of current SQL queries can also be achieved with ChatGPT. You can do this by giving ChatGPT the query that you want improved performance of and it will then suggest improvements to your query.For example, you could ask for ChatGPT in the following way:SELECT * FROM products WHERE product_name LIKE '%shirt%';ChatGPT might suggest the following optimizations:Add an index to the products table on the product_name column.Use a full-text search index on the product_name column.Use a more specific LIKE clause, such as WHERE product_name = 'shirt' if you know that the product name will be an exact match.Crafting queriesBy providing an interface between SQL and Natural Language, ChatGPT will be able to help with the drafting of complicated SQL queries. For users who are not familiar with SQL and need to create a quick query for a specific task, it can be helpful.For Example:Let's say we want to know which customers have placed an order within the last month, and spent more than $100 on it, then write a SQL query. The following query could be generated by using ChatGPT:SELECT * FROM customers WHERE order_date >= CURRENT_DATE - INTERVAL 1 MONTH AND order_total > 100;This query is relatively easy to perform, but ChatGPT can also be used for the creation of more complicated queries. For example, to select all customers who have placed an order in the last month and who have purchased a specific product, we could use ChatGPT to generate a query.SELECT * FROM customers WHERE order_date >= CURRENT_DATE - INTERVAL 1 MONTH AND order_items LIKE '%product_name%';Generating queries for which more than one table is involved can also be done with ChatGPT. For example, to select all customers who have placed an order in the last month and have also purchased a specific product from a specific category, we could use ChatGPT to generate a query.SELECT customers.*FROM customersINNER JOIN orders ON customers.id = orders.customer_idINNER JOIN order_items ON orders.id = order_items.order_idWHERE order_date >= CURRENT_DATE - INTERVAL 1 MONTHAND order_items_product_id = (SELECT id FROM products WHERE product_name = 'product_name')AND product_category_id = (SELECT id FROM product_categories WHERE category_name = 'category_name');The ChatGPT tool is capable of providing assistance with the creation of complex SQL queries. The ChatGPT feature facilitates users' writing efficient and accurate queries by providing an interface to SQL in a natural language.Debugging SQL queriesFor debugging SQL queries, the ChatGPT can also be used. To get started, you can ask ChatGPT to deliver a query that does not return the anticipated results. It will try to figure out why this is happening.For example, you could ask for ChatGPT in the following way:SELECT * FROM customers WHERE country = 'United States';Let's say that more results than expected are returned by this query. If there are multiple rows in a customer table, or the country column isn't being populated correctly for all clients, ChatGPT may suggest that something is wrong.How ChatGPT can help diagnose SQL query errors and suggest potential fixesYou may find that ChatGPT is useful for diagnosing and identifying problems, as well as suggesting possible remedies when you encounter errors or unexpected results in your SQL queries.To illustrate how ChatGPT could help you diagnose and correct SQL queries, we'll go over a hands-on example.Scenario: You'll be working with a database for Internet store transactions. The 'Products' table is where you would like to see the total revenue from a specific product named "Laptop". But you'll get unexpected results while running a SQL query.Your SQL Query:SELECT SUM(price) AS total_revenue FROM Products WHERE product_name = 'Laptop'; Issue: The query is not providing the expected results. You're not sure what went wrong.ChatGPT Assistance:Diagnosing the Issue:You can ask ChatGPT something like, "What could be the issue with my SQL query to calculate the total revenue of 'Laptop' from the Products table?"ChatGPT’s Response:The ChatGPT believes that the problem may arise from a WHERE clause. It suggests that because the names of products may not be distinctive, and there might be a lot of entries called 'Laptops', it is suggested to use ProductID rather than the product name. This query could be modified as follows:SELECT SUM(price) AS total_revenue FROM Products WHERE product_id = (SELECT product_id FROM Products WHERE product_name = 'Laptop');Explanation and Hands-on Practice:The reasoning behind this adjustment is explained by ChatGPT. In order to check if the revised query is likely to lead to an expected overall profit for a 'Laptop' product, you can then try running it.SELECT SUM(price) AS total_revenue FROM Products WHERE product_id = (SELECT product_id FROM Products WHERE product_name = 'Laptop');We have obtained the correct overall revenue from a 'Laptop' product with this query, which has resolved your unanticipated results issue.This hands-on example demonstrates how ChatGPT can help you diagnose and resolve your SQL problems, provide tailored suggestions, explain the solutions to fix them, and guide you through the process of strengthening your SQL skills by using practical applications.ConclusionIn conclusion, this article provides insight into the important role that ChatGPT plays when it comes to generating efficient SQL queries. In view of the key role played by SQL in database management for structured data, which is essential to modern applications, it stressed that there should be a solid knowledge base on SQL so as to effectively use ChatGPT when creating queries. We explored how ChatGPT could help you generate, optimize, and analyze SQL queries by presenting practical examples and use cases.It explains to users how ChatGPT is able to diagnose SQL errors and propose a solution, which in the end can help them solve unforeseen results and improve their ability to use SQL. In today's data-driven world where effective data manipulation is a necessity, ChatGPT becomes an essential ally for those who seek to speed up the SQL query development process, enhance accuracy, and increase productivity. It will open up new possibilities for data professionals and developers, allowing them to interact more effectively with databases.Author BioChaitanya Yadav is a data analyst, machine learning, and cloud computing expert with a passion for technology and education. He has a proven track record of success in using technology to solve real-world problems and help others to learn and grow. He is skilled in a wide range of technologies, including SQL, Python, data visualization tools like Power BI, and cloud computing platforms like Google Cloud Platform. He is also 22x Multicloud Certified.In addition to his technical skills, he is also a brilliant content creator, blog writer, and book reviewer. He is the Co-founder of a tech community called "CS Infostics" which is dedicated to sharing opportunities to learn and grow in the field of IT.

Dive deeper into the world of AI innovation and stay ahead of the AI curve! Subscribe to our AI_Distilled newsletter for the latest insights. Don't miss out – sign up today!IntroductionLarge language models, also known as LLM and generative Artificial Intelligence (AI), are revolutionizing various enterprises and businesses' productivity. One can expect the benefits of automation of the fast generation of insights and repetitive tasks from a large data pool.Pursuing insights has developed cutting-edge data storage solutions, including the Snowflake data cloud. This has the capabilities of artificial intelligence in visualizing data.Let us explore the synergy between Snowflake and AI, which facilitates data exploration while empowering businesses to acquire profound insights.Snowflake Data Cloud: the foundation for modern data warehousingEven before we start with our exploration, it is imperative to understand how Snowflake plays a significant role in modern data warehousing. It is a cloud-based data warehousing platform known for performance, ease of use, and scalability. As it provides a flexible and secure environment for analyzing and storing data, it is an ideal choice for every enterprise that deals with diverse and large data sets.Key featuresSome of the critical elements of the snowflake data cloud are mentioned below.●  Separates computing and storage.Snowflake's unique architecture helps scale the organization's computing resources independently from the storage. It helps to result in performance optimization and cost efficiency.●  Data SharingWith the help of seamless data sharing, Snowflake helps enterprises to share data between organizations that can foster data monetization opportunities and collaboration.●  Multi-cloud supportOne must know that Snowflake is compatible with most of the cloud providers. Therefore, it allows businesses to leverage their preferred cloud infrastructure.Unleash the potential of AI-Powered Data VisualizationWhen you have understood the concept of Snowflake, it is time that you get introduced to a game changer: AI-powered data visualization. The AI algorithm has undoubtedly evolved. They help to assist in the analyses and exploration of complex data sets while revealing insights and patterns that can be challenging to discover through traditional methods.Role of AI in data visualisationAI in data visualization plays a significant role. Some of these are:●  Predictive analyticsThe machine learning models help forecast anomalies and trends, thus enabling businesses and enterprises to make proactive decisions.●  Automated InsightsArtificial intelligence can analyze data sets quickly. It helps reduce the time required for manual analyses and extracts meaningful insights.●  Natural Language ProcessingNatural Language Processing or NLP algorithms can help to turn the textual data into visual representation. This process makes the unstructured data readily accessible.Harness the power of AI and SnowflakeLet us explore how one can collaborate with snowflakes and artificial intelligence to empower their business in gaining deeper insights.●  Data integrationThe ease of integration presented by Snowflake allows the organization to centralize the data. It does not matter whether the businesses consolidate their data from IOT devices, external partners, or internal sources. The unified data repository eventually becomes the foundation for AI-powered exploration.Example:Creating a Snowflake database and warehouse-- -- Create a new Snowflake database CREATE DATABASE my_database; -- Create a virtual warehouse for query processing CREATE WAREHOUSE my_warehouse  WITH WAREHOUSE_SIZE = 'X-SMALL'  AUTO_SUSPEND = 600  AUTO_RESUME = TRUE; 2. Loading data into Snowflake-- Create an external stage for data loading CREATE OR REPLACE STAGE my_stage URL = 's3://my-bucket/data/' CREDENTIALS = (AWS_KEY_ID = 'your_key_id' AWS_SECRET_KEY = 'your_secret_key'); -- Copy data from the stage into a Snowflake table COPY INTO my_table FROM @my_stage FILE_FORMAT = (TYPE = CSV) ON_ERROR = 'CONTINUE';● AI-driven code generationOne of the exciting aspects of collaborating AI and Snowflake happens to be the ability of artificial intelligence to generate code for data visualization. Here is how the process works.● Data preprocessingAI algorithms can prepare data for visualization while reducing the burden of the data engineers. At the same time, it has the capability of cleaning and transforming the data for visualization● Visualization suggestions Artificial intelligence helps to analyze data while suggesting appropriate visualization types, including scatter plots, charts, bars, and more. It analyses based on the characteristics presented by the data set● Automated code generationAfter choosing the visualization type, artificial intelligence helps generate the code needed to create interactive visualization. Hence, the process becomes accessible to every non-technical user.Let us know this with the help of the below example.from sklearn.cluster import KMeans from yellowbrick.cluster import KElbowVisualizer # Using AI to determine the optimal number of clusters (K) in K-means model = KMeans() visualizer = KElbowVisualizer(model, k=(2, 10)) visualizer.fit(scaled_data) visualizer.show() ● Interactive data explorationWith the help of AI-generated visualization, one can interact with the data effortlessly. The business can drill down, explore, and filter its data dynamically while gaining deeper insights into the real-time scenario. Such a level of interactivity empowers every business user to make informed data-driven decisions without heavily relying on IT teams or data analysts.Examples: import dash import dash_core_components as dcc import dash_html_components as html from dash.dependencies import Input, Output import plotly.express as px app = dash.Dash(__name__) # Define the layout of the web app app.layout = html.Div([    dcc.Graph(id='scatter-plot'),    dcc.Dropdown(        id='x-axis',        options=[            {'label': 'Feature 1', 'value': 'feature1'},            {'label': 'Feature 2', 'value': 'feature2'}        ],        value='feature1'    ) ]) # Define callback to update the scatter plot @app.callback(    Output('scatter-plot', 'figure'),    [Input('x-axis', 'value')] ) def update_scatter_plot(selected_feature):    fig = px.scatter(data_frame=scaled_data, x=selected_feature, y='target', title='Scatter Plot')    fig.update_traces(marker=dict(size=5))    return fig if __name__ == '__main__':    app.run_server(debug=True) From this web application, the users can interactively explore data.Benefits of AI and Snowflake for Enterprises●  Faster decision makingWith the help of code, generation, and data preprocessing automation, the business can enable faster decision-making. Also, the real-time interactive exploration helps in reducing the time it takes to derive specific insights from data.●  Democratize the data access.The AI-generated visualization helps every non-technical user explore data while democratizing access to insights. It reduces the bottleneck faced by the data science team and data analyst.●  Enhance predictive capabilitiesThe AI-powered predictive analytics present within Snowflake helps uncover hidden patterns and trends. It enables every enterprise and business to stay ahead of the competition and make proactive decisions.●  Cost efficiency and scalability The AI-driven automation and Snowflake's scalability ensures that business can handle large data sets without breaking the bank.Conclusion In summary, the combination of Snowflake Data Cloud and data visualization powered by AI is the game changer for enterprises and businesses looking to gain insights from their data. Aiding with automating code creation, simplifying data integration, and facilitating exploration, such collaboration empowers companies to make informed decisions based on data. As we progress in the field of data analytics, it will be crucial for organizations to embrace these technologies to remain competitive and unlock the potential of their data.With Snowflake and AI working together, exploring data evolves from being complicated and time-consuming to becoming interactive, enlightening, and accessible for everyone. Ultimately, this transformation revolutionizes how enterprises utilize the power of their data.Is this code a prompt or does the reader have to manually type? If the reader has to type, please share the text code so they can copy and paste it for convenience.Author BioShankar Narayanan (aka Shanky) has worked on numerous different cloud and emerging technologies like Azure, AWS, Google Cloud, IoT, Industry 4.0, and DevOps to name a few. He has led the architecture design and implementation for many Enterprise customers and helped enable them to break the barrier and take the first step towards a long and successful cloud journey. He was one of the early adopters of Microsoft Azure and Snowflake Data Cloud. Shanky likes to contribute back to the community. He contributes to open source is a frequently sought-after speaker and has delivered numerous talks on Microsoft Technologies and Snowflake. He is recognized as a Data Superhero by Snowflake and SAP Community Topic leader by SAP.

Dive deeper into the world of AI innovation and stay ahead of the AI curve! Subscribe to our AI_Distilled newsletter for the latest insights. Don't miss out – sign up today!This article is an excerpt from the book, ChatGPT for Cybersecurity Cookbook, by Clint Bodungen. Master ChatGPT and the OpenAI API, and harness the power of cutting-edge generative AI and large language models to revolutionize the way you perform penetration testing, threat detection, and risk assessment.IntroductionIn this article, we will explore the basics of ChatGPT prompting using the ChatGPT interface, which is different from the OpenAI Playground we used in the previous recipe. The advantage of using the ChatGPT interface is that it does not consume account credits and is better suited for generating formatted output, such as writing code or creating tables. Getting ready To use the ChatGPT interface, you will need to have an active OpenAI account. If you haven't already, please set up your ChatGPT account. How to do it… In this recipe, we'll guide you through using the ChatGPT interface to generate a Python script that retrieves a user's public IP address. By following these steps, you'll learn how to interact with ChatGPT in a conversation-like manner and receive context-aware responses, including code snippets. Now, let's proceed with the steps in this recipe: 1. In your browser, go to https://chat.openai.com and click “Log in” 2. Log in using your OpenAI credentials. 3. Once you are logged in, you will be taken to the ChatGPT interface. The interface is similar to a chat application, with a text box at the bottom where you can enter your prompts.  Figure – The ChatGPT interface 4. ChatGPT uses a conversation-based approach, so you can simply type your prompt as a message and press "Enter" or click the       button to receive a response from the model. For example, you can ask ChatGPT to generate a piece of Python code to find the public IP address of a user:  Figure – Entering a prompt ChatGPT will generate a response containing the requested Python code, along with a thorough explanation.  Figure – ChatGPT response with code 5. Continue the conversation by asking follow-up questions or providing additional information, and ChatGPT will respond accordingly.  Figure – ChatGPT contextual follow-up response 6. Run the ChatGPT generated code by clicking on “Copy code”, paste it into your code editor of choice (I personally use Visual Studio Code), save it as a “.py” Python script, and run from a terminal. PS D:\GPT\ChatGPT for Cybersecurity Cookbook> python .\my_ip.py Your public IP address is:  Your local network IP address is: 192.168.1.105 Figure – Running the ChatGPT generated script  How it works… By using the ChatGPT interface to enter prompts, you can generate context-aware responses and content that continues over the course of an entire conversation like a chatbot. The conversation-based approach allows for more natural interactions and the ability to ask follow-up questions or provide additional context. The responses can even include complex formatting such as code snippets or tables (more on tables later). There’s more… As you become more familiar with ChatGPT, you can experiment with different prompt styles, instructions, and contexts to obtain the desired output for your cybersecurity tasks. You can also compare the results generated through the ChatGPT interface and the OpenAI Playground to determine which approach best fits your needs. Tip:You can further refine the generated output by providing very clear and specific instructions or using roles. It also helps to divide complex prompts into several smaller prompts, giving ChatGPT one instruction per prompt, building on the previous prompts as you go. In the upcoming recipes, we will delve into more advanced prompting techniques that utilize these techniques to help you get the most accurate and detailed responses from ChatGPT. As you interact with ChatGPT, your conversation history is automatically saved in the left panel of the ChatGPT interface. This feature allows you to easily access and review your previous prompts and responses. By leveraging the conversation history feature, you can keep track of your interactions with ChatGPT and quickly reference previous responses for your cybersecurity tasks or other projects.  Figure – Conversation history in the ChatGPT interface To view a saved conversation, simply click on the desired conversation in the left panel. You can also create new conversations by clicking on the "+ New chat" button located at the top of the conversation list. This enables you to separate and organize your prompts and responses based on specific tasks or topics. Caution Keep in mind that when you start a new conversation, the model loses the context of the previous conversation. If you want to reference any information from a previous conversation, you will need to include that context in your new prompt. ConclusionIn conclusion, this article has unveiled the power of ChatGPT and its conversation-driven approach, making complex tasks like retrieving your public IP address a breeze. With step-by-step guidance, you've learned to harness ChatGPT's capabilities and enjoy context-aware responses, all while keeping your account credits intact. As you dive deeper into the world of ChatGPT, you'll discover its versatility in various applications and the potential to optimize your cybersecurity endeavors. By mastering ChatGPT's conversational prowess, you're on the path to seamless, productive interactions and a future filled with AI-driven possibilities.Author BioClint Bodungen is a cybersecurity professional with 25+ years of experience and the author of Hacking Exposed: Industrial Control Systems. He began his career in the United States Air Force and has since many of the world's largest energy companies and organizations, working for notable cybersecurity companies such as Symantec, Kaspersky Lab, and Booz Allen Hamilton. He has published multiple articles, technical papers, and training courses on cybersecurity and aims to revolutionize cybersecurity education using computer gaming (“gamification”) and AI technology. His flagship product, ThreatGEN® Red vs. Blue, is the world’s first online multiplayer cybersecurity simulation game, designed to teach real-world cybersecurity.

Dive deeper into the world of AI innovation and stay ahead of the AI curve! Subscribe to our AI_Distilled newsletter for the latest insights. Don't miss out – sign up today!IntroductionChatGPT has earned its reputation as a versatile and capable assistant. From helping you craft the perfect piece of writing, planning your next adventure, aiding your coding endeavors, or simply engaging in light-hearted conversations, ChatGPT can do it all. It's like having a digital Swiss Army knife at your fingertips. But have you ever wondered what it would be like if ChatGPT could communicate with you not just through text, but also through speech? Imagine the convenience of issuing voice commands and receiving spoken responses, just like your own personal Siri. Well, the good news is, that this is now possible thanks to the remarkable combination of OpenAI Whisper and Bark.Bringing the power of voice interaction to ChatGPT is a game-changer. Instead of typing out your queries and waiting for text-based responses, you can seamlessly converse with ChatGPT, making your interactions more natural and efficient. Whether you're a multitasking enthusiast, a visually impaired individual, or someone who prefers spoken communication, this development holds incredible potential.So, how is this magic achieved? The answer lies in the fusion of two crucial components: Speech-to-Text (STT) and Text-to-Speech (TTS) modules.STT, as the name suggests, is the technology responsible for converting spoken words into text. OpenAI's Whisper is a groundbreaking pre-trained model for Automatic Speech Recognition (ASR) and speech translation. The model has been trained on an astonishing 680,000 hours of labeled data, giving it an impressive ability to adapt to a variety of datasets and domains without the need for fine-tuning.Whisper comes in two flavors: English-only and multilingual models. The English-only models are trained for the specific task of speech recognition, where they accurately predict transcriptions in the same language as the spoken audio. The multilingual models, on the other hand, are trained to handle both speech recognition and speech translation. In this case, the model predicts transcriptions in a language different from the source audio, adding an extra layer of versatility. Imagine speaking in one language and having ChatGPT instantly respond in another - Whisper makes it possible.On the other side of the conversation, we have Text-to-Speech (TTS) technology. This essential component converts ChatGPT's textual responses into lifelike speech. Bark, an open-source model developed by Suno AI, is a transformer-based text-to-speech marvel. It's what makes ChatGPT's spoken responses sound as engaging and dynamic as Siri's.Just like with Whisper, Bark is a reliable choice for its remarkable ability to turn text into speech, creating a human-like conversational experience. ChatGPT now not only thinks like a human but speaks like one too, thanks to Bark.The beauty of this integration is that it doesn't require you to be a tech genius. HuggingFace, a leading platform for natural language processing, supports both the TTS and STT pipeline. In simpler terms, it streamlines the entire process, making it accessible to anyone.You don't need to be a master coder or AI specialist to make it work. All you have to do is select the model you prefer for STT (Whisper) and another for TTS (Bark). Input your commands and queries, and let HuggingFace take care of the rest. The result? An intelligent, voice-activated ChatGPT can assist you with whatever you need.Without wasting any more time, let’s take a deep breath, make yourselves comfortable, and be ready to learn how to utilize both Whisper and Bark along with OpenAI GPT-3.5-Turbo to create your own Siri!Building the STTOpenAI Whisper is a powerful ASR/STT model that can be seamlessly integrated into your projects. It has been pre-trained on an extensive dataset, making it highly capable of recognizing and transcribing spoken language.Here's how you can use OpenAI Whisper for STT with HuggingFace pipeline. Note that the `sample_audio` here will be the user’s command to the ChatGPT.from transformers import pipeline stt = pipeline( "automatic-speech-recognition", model="openai/whisper-medium", chunk_length_s=30, device=device, ) text = stt(sample_audio, return_timestamps=True)["text"]The foundation of any AI model's prowess lies in the data it's exposed to during its training. Whisper is no exception. This ASR model has been trained on a staggering 680,000 hours of audio data and the corresponding transcripts, all carefully gathered from the vast landscape of the internet.Here's how that massive amount of data is divided:● English Dominance (65%): A substantial 65% of the training data, which equates to a whopping 438,000 hours, is dedicated to English-language audio and matched English transcripts. This abundance of English data ensures that Whisper excels in transcribing English speech accurately.● Multilingual Versatility (18%): Whisper doesn't stop at English. About 18% of its training data, roughly 126,000 hours, focuses on non-English audio paired with English transcripts. This diversity makes Whisper a versatile ASR model capable of handling different languages while still providing English transcriptions.● Global Reach (17%): The remaining 17%, which translates to 117,000 hours, is dedicated to non-English audio and the corresponding transcripts. This extensive collection represents a stunning 98 different languages. Whisper's proficiency in transcribing non-English languages is a testament to its global reach.Getting the LLM ResponseWith the user's speech command now transcribed into text, the next step is to harness the power of ChatGPT or GPT-3.5-Turbo. This is where the real magic happens. These advanced language models have achieved fame for their diverse capabilities, whether you need help with writing, travel planning, coding, or simply engaging in a friendly conversation.There are several ways to integrate ChatGPT into your system:LangChain: LangChain offers a seamless and efficient way to connect with ChatGPT. It enables you to interact with the model programmatically, making it a preferred choice for developers.OpenAI Python Client: The OpenAI Python client provides a user-friendly interface for accessing ChatGPT. It simplifies the integration process and is a go-to choice for Python developers.cURL Request: For those who prefer more direct control, cURL requests to the OpenAI endpoint allow you to interact with ChatGPT through a RESTful API. This method is versatile and can be integrated into various programming languages.No matter which method you choose, ChatGPT will take your transcribed speech command and generate a thoughtful, context-aware text-based response, ready to assist you in any way you desire. We’ll not deep dive into this in this article since there are numerous articles explaining this already.Building the TTSThe final piece of the puzzle is Bark, an open-source TTS model. Bark works its magic by converting ChatGPT's textual responses into lifelike speech, much like Siri talks to you. It adds that crucial human touch to the conversation, making your interactions with ChatGPT feel more natural and engaging.Again, we can build the TTS pipeline very easily with the help of HuggingFace pipeline. Here's how you can use Bark for TTS with HuggingFace pipeline. Note that the `text` here will be the ChatGPT response to the user’s command.from transformers import pipeline tts = pipeline("text-to-speech", model="suno/bark-small") response = tts(text) from IPython.display import Audio Audio(response["audio"], rate=response["sampling_rate"])You can see the example quality of the Bark model in this Google Colab notebook.ConclusionCongratulations on keeping up to this point! Throughout this article, you have learned how to build your own Siri with the help of OpenAI Whisper, ChatGPT, and Bark. Hope the best for your experiment in creating your own Siri and see you in the next article!Author BioLouis Owen is a data scientist/AI engineer from Indonesia who is always hungry for new knowledge. Throughout his career journey, he has worked in various fields of industry, including NGOs, e-commerce, conversational AI, OTA, Smart City, and FinTech. Outside of work, he loves to spend his time helping data science enthusiasts to become data scientists, either through his articles or through mentoring sessions. He also loves to spend his spare time doing his hobbies: watching movies and conducting side projects.Currently, Louis is an NLP Research Engineer at Yellow.ai, the world’s leading CX automation platform. Check out Louis’ website to learn more about him! Lastly, if you have any queries or any topics to be discussed, please reach out to Louis via LinkedIn.

Dive deeper into the world of AI innovation and stay ahead of the AI curve! Subscribe to our AI_Distilled newsletter for the latest insights and books. Don't miss out – sign up today!IntroductionWhat Power Developers Know About ChatGPT's Capabilities That You Don't?You've tinkered with ChatGPT, got some fun replies, and maybe even used it for some quick Q&A.But there's a feeling of missing out, isn't there?ChatGPT feels like a vast ocean, and you've only skimmed the surface.Deep down, you know there's more. What's the secret sauce?It's like having a sports car and only driving in the first gear. ChatGPT is built for more, way more.Hold on to your coding hat, because there's a blueprint, a set of hidden levers and buttons that power users are pressing.Ready to get in on the secret?Envision a world where you're not just using ChatGPT but mastering it.Every challenge, every coding puzzle, you've got a secret weapon.Welcome to the world of Power Developers.Here are 3 advanced prompts you can use to up your AI skills so you can harness ChatGPT like never beforePowerPointYou are about to experience how to create customized, memorable presentations.I will show you how to use ChatGPT to automate your presentation outline generation and generate jaw-dropping content that keeps your viewers engaged.Instead of starting off with blank slides, we will use a format from one of the best Presentation trainers Jason Teteak.Here is the full megaprompt , now don’t get overwhelmed with the length. You just need to replace the TOPIC and AUDIENCE parts.TOPIC= Why do we need Spring framework AUDIENCE= Junor developers who know Java Create a presentation outline for {TOPIC} and {AUDIENCE} by using Famous presentation framework from Jason Teteak from his book Rule the room Make sure to Identify what Audience Wants • What are your biggest concerns or worries? • What are the biggest challenges you have with those areas? • What are the problems they are causing? • What's your ideal outcome? • What would getting that outcome do for vou? Use takeaways Start with an action verb. The trick to doing this is to mentally insert the words "As a result of my presentation, you will be able to..." at the beginning of the phrase. • Use seven words or less. A string of seven items is the maximum number people can hold in their short-term memorv. • Use familiar words. Avoid what I call cliquespeak-using words or assuming a grasp of concepts people new to or unfamiliar to vour field won't understand Identify pain and pleasure pointes, and say how the takleways relieve pain points and enhance pleasure points Define how the takeaways offer happiness, success and/or freedom Create title according to formula Start with an action verb, use 7 words or less, and use familiar words Use the following format For slides use markdown Title is h1 Content is using bulletpoints For what you say use italic and add "You say:" Give your credentials Tell the audience how what you do will help them. Example: "I help community bankers find new income sources. Deliver the main hook Example: "I'm going to offer you a new source of income with less risk plus the expertise you need to expand services to old customers and attract new ones." Main Agenda slide - Complete list of takeaways Highlighted Takeway #1 slide Task Slide #1 - Complete list of tasks for takeaway #1 What you say: Takeway #1 hook sentence Example slide What you say Highlighted Takeway #2 slide Task Slide #2 - Complete list of tasks for takeaway #2 What you say: Takeway #2 hook sentence Highlighted Takeway #3 slide Task Slide #3 - Complete list of tasks for takeaway #3 What you say: Takeway #3 hook sentence Example slide Summary Slide - Complete list of takeaways What you say: Takeway #3 hook sentence Final Slide What you say - offer to stay for individual questions - Thank the audience - add a pleasantry to conclude the presentation (e.g. Have a great day) Here is the full conversation: https://chat.openai.com/share/e116d8c4-b267-466e-9d9e-39799f073e24Here is what you can get from this prompt:Simulate running an appLet’s imagine you want to demo a backend running up.You need to present it to coworkers, or just verify how the final app might work.You would need:have a working coderunning server (locally or in the cloud)running storage (e.g. database)and tools to engage (create GET or POST requests to interact)What if I told you that ChatGPT can do all for you with only 1 prompt?Here is a full prompt, you can just replace the APP part:APP: Spring rest application that persist list of conferences in mysql database, it exposes GET and POST mapping Imagine there is mysql database already running with conferences table. An application can be accessed by invoking GET or POST requests I want you to act as a Linux terminal. I will type commands and you will reply with what the terminal should show. Imagine for a given {APP} we are in the directory where directory which contains full application code. I want you to only reply with the terminal output inside one unique code block, and nothing else. Do no write explanations. Do not type commands unless I instruct you to do so. When I need to tell you something in English I will do so by putting text inside curly brackets {like this}. My first command is pwd. Here is the chat: https://chat.openai.com/share/74dad74d-8a59-43e8-8c5c-042dfcecda99You get an output of starting an app, or making a POST request to add a conference.ChatGPT did not actually run the code, but frankly, it did an excellent job of simulating everything.Creating Educational OutlineEver noticed how most educational content out there feels like it’s either too basic or way over your head?It's like there's no middle ground.Endless hours scrolling, and reading, but in the end, you're still at square one.That's not learning; that's a wild goose chase.But wait, what if there's a different way?A formula, perhaps, to craft content that resonates, educates, and empowers?Imagine diving into educational material that sparks curiosity, drives understanding, and equips you with actionable insights.It’s time to revolutionize educational content for developers.Be authentic, be clear, and always keep the learner at the heart of your content.Now replace COURSE NAME and AUDIENCE according to your needs.COURSE NAME= How to start writing that are fun and easy Java tests AUDIENCE= Junior developers You are an expert developer in crafting authentic, clear training outline that always keeps the learner at the heart of your content. It sparks curiosity, drives understanding, and equips you with actionable insights. I need you to create an outline for a 5-part educational course called {COURSE NAME} Give this course 3 examples of compelling course names For context, this audience are {AUDIENCE} Your output should be formatted like this: # NAME OF THE COURSE with 3 examples ## PART OF THE COURSE ### Idea 1 - Sub point 1 - Sub point 2 - Sub point 3 ### Idea 2 - Sub point 1 - Sub point 2 - Sub point 3 ### Idea 3 - Sub point 1 - Sub point 2 - Sub point 3 Every PART should be a headline for the respective part Every Idea is one Heading inside that PART Every Sub point is supportive of the above idea Here is the link: https://chat.openai.com/share/096f48c4-8886-4d4c-a051-49eb1516b730And screenshot of the outputConclusionIn conclusion, ChatGPT holds the key to a new realm of coding mastery. By delving into the advanced prompts and hidden techniques, you're poised to become a true Power Developer. Embrace this journey, unleash ChatGPT's potential, and pave the way for a future where you're not just using AI but shaping it to your advantage. With a mix of storytelling, real-world examples, and interactivity, you can craft content that developers crave.Author BioJakov Semenski is an IT Architect working at IBMiX with almost 20 years of experience.He is also a ChatGPT Speaker at the WeAreDevelopers conference and shares valuable tech stories on LinkedIn.