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!IntroductionGenerative AI tools have emerged as a groundbreaking technology, paving the way for innovation and creativity across various domains. Understanding the nuances of generative AI and its integration with adaptive AI tools is essential in unlocking its full potential. Generative AI, a revolutionary concept, stands tall among these innovations, enabling machines not just to replicate patterns from existing data but to generate entirely new and creative content. Combined with complementary AI tools, this technology reaches new heights, reshaping industries and fueling unprecedented creativity.                                                                                                                                SourceConcept of Generative AI ToolsGenerative AI tools encompass artificial intelligence systems designed to produce new, original content based on patterns learned from existing data. These tools employ advanced algorithms such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) to create diverse outputs, including text, images, videos, and more. Their ability to generate novel content makes them invaluable in creative fields and scientific research.Difference between Generative AI and Adaptive AIWhile generative AI focuses on creating new content, adaptive AI adjusts its behavior based on the input it receives. Generative AI is about generating something new, whereas adaptive AI learns from interactions and refines its responses over time.Generative AI in ActionGenerative AI's essence lies in creating new, original content. Consider a practical example of image synthesis using a Generative Adversarial Network (GAN). GANs comprise a generator and a discriminator, engaged in a competitive game where the generator creates realistic images to deceive the discriminator. Here's a Python code snippet showcasing a basic GAN implementation using TensorFlow:import tensorflow as tf from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense, Reshape from tensorflow.keras.layers import LeakyReLU # Define the generator model generator = Sequential() generator.add(Dense(128, input_shape=(100,))) generator.add(LeakyReLU(0.2)) generator.add(Reshape((28, 28, 1))) # Define the discriminator model (not shown for brevity) # Compile the generator generator.compile(loss='binary_crossentropy', optimizer='adam')In this code, the generator creates synthetic images based on random noise (a common practice in GANs). Through iterations, the generator refines its ability to produce images resembling the training data, showcasing the creative power of Generative AI.Adaptive AI in PersonalizationAdaptive AI, conversely, adapts to user interactions, providing tailored experiences. Let's explore a practical example of building a simple recommendation system using collaborative filtering, an adaptive AI technique. Here's a Python code snippet using the Surprise library for collaborative filtering:from surprise import Dataset from surprise import Reader from surprise.model_selection import train_test_split from surprise import SVD from surprise import accuracy # Load your data into Surprise Dataset reader = Reader(line_format='user item rating', sep=',') data = Dataset.load_from_file('path/to/your/data.csv', reader=reader) # Split data into train and test sets trainset, testset = train_test_split(data, test_size=0.2) # Build the SVD model (Matrix Factorization) model = SVD() model.fit(trainset) # Make predictions on the test set predictions = model.test(testset) # Evaluate the model accuracy.rmse(predictions)In this example, the Adaptive AI model learns from user ratings and adapts to predict new ratings. By tailoring recommendations based on individual preferences, Adaptive AI enhances user engagement and satisfaction.Generative AI sparks creativity, generating new content such as images, music, or text, as demonstrated through the GAN example. Adaptive AI, exemplified by collaborative filtering, adapts to user behavior, personalizing experiences and recommendations.By understanding and harnessing both Generative AI and Adaptive AI, developers can create innovative applications that not only generate original content but also adapt to users' needs, paving the way for more intelligent and user-friendly AI-driven solutions.Harnessing Artificial IntelligenceHarnessing AI involves leveraging its capabilities to address specific challenges or achieve particular goals. It requires integrating AI algorithms and tools into existing systems or developing new applications that utilize AI's power to enhance efficiency, accuracy, and creativity.Harnessing the power of Generative AI involves several essential steps, from selecting the suitable model to training and generating creative outputs. Here's a breakdown of the steps, along with code snippets using Python and popular machine-learning libraries like TensorFlow and PyTorch:Step 1: Choose a Generative AI ModelSelect an appropriate Generative AI model based on your specific task. Standard models include Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and Transformers like OpenAI's GPT (Generative Pre-trained Transformer).Step 2: Prepare Your DataPrepare a dataset suitable for your task. For example, if you're generating images, ensure your dataset contains a diverse range of high-quality images. If you're generating text, organize your textual data appropriately.Step 3: Preprocess the DataPreprocess your data to make it suitable for training. This might involve resizing images, tokenizing text, or normalizing pixel values. Here's a code snippet demonstrating image preprocessing using TensorFlow:from tensorflow.keras.preprocessing.image import ImageDataGenerator # Image preprocessing datagen = ImageDataGenerator(rescale=1./255) train_generator = datagen.flow_from_directory(    'path/to/your/dataset',    target_size=(64, 64),    batch_size=32,    class_mode='binary' )Step 4: Build and Compile the Generative ModelBuild your Generative AI model using the chosen architecture. Compile the model with an appropriate loss function and optimizer. For example, here's a code snippet to create a basic generator model using TensorFlow:import tensorflow as tf from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense, Reshape, LeakyReLU # Generator model generator = Sequential() generator.add(Dense(128, input_shape=(100,))) generator.add(LeakyReLU(0.2)) generator.add(Reshape((64, 64, 3)))  # Adjust dimensions based on your taskStep 5: Train the Generative ModelTrain your Generative AI model using the prepared dataset. Adjust the number of epochs, batch size, and other hyperparameters based on your specific task and dataset. Here's a code snippet demonstrating model training using TensorFlow:# Compile the generator model generator.compile(loss='mean_squared_error', optimizer='adam') # Train the generator generator.fit(train_generator, epochs=100, batch_size=32)Step 6: Generate Creative OutputsOnce your Generative AI model is trained, you can generate creative outputs. For images, you can generate new samples. For text, you can generate paragraphs or even entire articles. Here's a code snippet to generate images using the trained generator model:# Generate new images import matplotlib.pyplot as plt # Generate random noise as input random_noise = tf.random.normal(shape=[1, 100]) # Generate image generated_image = generator(random_noise, training=False) # Display the generated image plt.imshow(generated_image[0, :, :, :]) plt.axis('off') plt.show()By following these steps and adjusting the model architecture and hyperparameters according to your specific task, you can delve into the power of Generative AI to create diverse and creative outputs tailored to your requirements.                                                                                                                                 SourceExample of Generative AIOne prominent example of generative AI is DeepArt, an online platform that transforms photographs into artworks inspired by famous artists’ styles. DeepArt utilizes neural networks to analyze the input image and recreate it in the chosen artistic style, demonstrating the creative potential of generative AI.Positive Uses and Effects of Generative AIGenerative AI has found positive applications in various fields. In healthcare, it aids in medical image synthesis, generating detailed and accurate images for diagnostic purposes. In the entertainment industry, generative AI is utilized to create realistic special effects and animations, enhancing the overall viewing experience. Moreover, it facilitates rapid prototyping in product design, allowing for the generation of diverse design concepts efficiently.Most Used and Highly Valued Generative AIAmong the widely used generative AI technologies, OpenAI's GPT (Generative Pre-trained Transformer) stands out. Its versatility in generating human-like text has made it a cornerstone in natural language processing tasks. Regarding high valuation, NVIDIA's StyleGAN, a GAN-based model for developing lifelike images, has garnered significant recognition for its exceptional output quality and flexibility.Code Examples:To harness the power of generative AI with complementary AI tools, consider the following Python code snippet using TensorFlow:import tensorflow as tf from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense, Reshape from tensorflow.keras.layers import LeakyReLU # Define the generative model generator = Sequential() generator.add(Dense(128, input_shape=(100,))) generator.add(LeakyReLU(0.2)) generator.add(Reshape((28, 28, 1))) # Compile the model generator.compile(loss='binary_crossentropy', optimizer='adam') # Generate synthetic data random_noise = tf.random.normal(shape=[1, 100]) generated_image = generator(random_noise, training=False)ConclusionGenerative AI, with its ability to create novel content coupled with adaptive AI, opens doors to unparalleled possibilities. By harnessing the power of these technologies and integrating them effectively, we can usher in a new era of innovation, creativity, and problem-solving across diverse industries. As we continue to explore and refine these techniques, the future holds endless opportunities for transformative applications in our rapidly advancing world.Author BioJyoti Pathak is a distinguished data analytics leader with a 15-year track record of driving digital innovation and substantial business growth. Her expertise lies in modernizing data systems, launching data platforms, and enhancing digital commerce through analytics. Celebrated with the "Data and Analytics Professional of the Year" award and named a Snowflake Data Superhero, she excels in creating data-driven organizational cultures.Her leadership extends to developing strong, diverse teams and strategically managing vendor relationships to boost profitability and expansion. Jyoti's work is characterized by a commitment to inclusivity and the strategic use of data to inform business decisions and drive progress.

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!IntroductionHey there, financial explorers!Ever felt overwhelmed by the vast sea of investment strategies out there? You're not alone. But amidst this overwhelming ocean, one lighthouse stands tall: Warren Buffett. The good news? We've teamed up with Google Bard to break down his legendary value-investing approach into bite-sized, actionable prompts. Think of it as your treasure map, leading you step-by-step through the intricate maze of investment wisdom that Buffett has championed over the years.Decoding Smart Investing: A Buffett-Inspired GuideLet's dive straight into the art of smart investing, inspired by the one and only Warren Buffett. First things first: get to know the business you're eyeing. What's their main product, and why's it special? How's their industry doing, and who are the big names in their field? It's crucial to grasp how they earn their bucks. Next, roll up your sleeves and peek into their financial health. Check out their revenues, costs, profits, and some essential numbers that give you the real picture. Now, who's steering the ship? Understand the team's past decisions, how they chat with shareholders, and if their interests align with the company's success.But wait, there's more! Every company has something that makes them stand out, be it their brand, cost efficiency, or even special approvals that keep competitors at bay. And before you take the plunge, make sure you know what the company's truly worth and if its future looks bright. We're talking about its real value and what lies ahead in terms of growth and potential hiccups.Ready to dive deep? Let's get started!Step 1. Understand the BusinessProduct or Service: Start by understanding the core product or service of the company. What do they offer, and how is it different from competitors?Industry Overview: Understand the industry in which the company operates. What are the industry's growth prospects? Who are the major players?Business Model: Dive deep into how the company makes money. What are its main revenue streams?Step 2. Analyze Financial HealthIncome Statement: Look at the company's revenues, costs, and profits over time.Balance Sheet: Examine assets, liabilities, and shareholders' equity to assess the company's financial position.Cash Flow Statement: Understand how money moves in and out of the company. Positive cash flow is a good sign.Key Ratios: Calculate and analyze ratios like Price-to-Earnings (P/E), Debt-to-Equity, Return on Equity (ROE), and others.Step 3. Management QualityTrack Record: What successes or failures has the current management team had in the past?Shareholder Communication: Buffett values management teams that communicate transparently and honestly with shareholders.Alignment: Do the management's interests align with shareholders? For instance, do they own a significant amount of stock in the company?Step 4. Competitive Advantage (or Moat)Branding: Does the company have strong brand recognition or loyalty?Cost Advantages: Can the company produce goods or services more cheaply than competitors?Network Effects: Do more users make the company's product or service more valuable (e.g., Facebook or Visa)?Regulatory Advantages: Does the company have patents, licenses, or regulatory approvals that protect it from competition?Step 5. ValuationIntrinsic Value: Estimate the intrinsic value of the company. Buffett often uses the discounted cash flow (DCF) method.The margin of Safety: Aim to buy at a price significantly below the intrinsic value to provide a cushion against unforeseen negative events or errors in valuation.Step 6. Future ProspectsGrowth Opportunities: What are the company's prospects for growth in the next 5-10 years?Risks: Identify potential risks that could derail the company's growth or profitability.Now let’s prompt our way towards making smart decisions using Google Bard. In this case, we have taken Google as a use case1. Understand the BusinessProduct or Service: "Describe the core product or service of the company. Highlight its unique features compared to competitors."Industry Overview: "Provide an overview of the industry the company operates in, focusing on growth prospects and key players."Business Model: "Explain how the company earns revenue. Identify its main revenue streams."2. Analyze Financial HealthIncome Statement: "Summarize the company's income statement, emphasizing revenues, costs, and profits trends."Balance Sheet: "Analyze the company's balance sheet, detailing assets, liabilities, and shareholder equity."Cash Flow Statement: "Review the company's cash flow. Emphasize the significance of positive cash flow."Key Ratios: "Calculate and interpret key financial ratios like P/E, Debt-to-Equity, and ROE."3. Management QualityTrack Record: "Evaluate the current management's past performance and decisions."Shareholder Communication: "Assess the transparency and clarity of management's communication with shareholders."Alignment: "Determine if management's interests align with shareholders. Note their stock ownership." 4. Competitive Advantage (or Moat)Branding: "Discuss the company's brand strength and market recognition."Cost Advantages: "Examine the company's ability to produce goods/services at a lower cost than competitors."Network Effects: "Identify if increased user numbers enhance the product/service's value."Regulatory Advantages: "List any patents, licenses, or regulatory advantages the company holds."5. ValuationIntrinsic Value: "Estimate the company's intrinsic value using the DCF method."The Margin of Safety: "Determine the ideal purchase price to ensure a margin of safety in the investment."6. Future ProspectsGrowth Opportunities: "Predict the company's growth potential over the next 5-10 years."Risks: "Identify and elaborate on potential risks to the company's growth or profitability."These prompts should guide an individual through the investment research steps in the manner of Warren Buffett.ConclusionWell, that's a wrap! Remember, the journey of investing isn't a sprint; it's a marathon. With the combined wisdom of Warren Buffett and the clarity of Google Bard, you're now armed with a toolkit that's both enlightening and actionable. Whether you're just starting out or looking to refine your investment compass, these prompts are your trusty guide. So, here's to making informed, thoughtful decisions and charting a successful course in the vast world of investing. Happy treasure hunting!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!IntroductionA new large language model, Google AI's PaLM2, developed from a massive textual and code database. It's a successor of the PaLM program, and is even more powerful in terms of producing text, translating language, writing various types of creative content, and answering your questions by means of information. The research and development of PaLM 2 continues, but it has the potential to shake up many industries and research areas in terms of its ability to address a broad range of complex real-world problems.PaLM 2 is a new large language model from Google AI, trained on a massive dataset of text and code. It is even more powerful than its predecessor, PaLM, and can be used to solve a wide range of complex real-world problems.Powerful Tools for NLP, Code Generation, and Creative Writing by PaLM2In order to learn the complex relationships between words and phrases, LLMs, such as PaLM 2, are trained in massive databases of text and code. For this reason, they make excellent candidates for a wide range of tasks, such as:Natural language processing (NLP): There are also NLP tasks to be performed such as machine translation, text summary, and answering questions. In order to perform these tasks with high accuracy and consistency, PaLM 2 can be used.Code generation: A number of programming languages, including Python, Java, and C++ can be used for generating code by PaLML 2. It can also be useful for tasks like the automation of software development and the creation of new algorithms.Creative writing: Different creative text formats, such as poems, code, scripts, musical notes, emails, letters, etc. may be created by PaLM 2. It could be useful to the tasks of writing advertising copy, producing scripts for films and television shows as well as composing music.Real-World ExamplesTo illustrate how PaLM 2 can be put to use in solving the complicated problems of the actual world, these are some specific examples:Example 1: Drug DiscoveryIn the area of drug discovery, there are many promising applications to be had by PaLM 2. For the generation of new drug candidates, for the prediction of their properties, and for the simulation of their interaction with biological targets, PaLM 2 can be used. This may make it more quickly and efficiently possible for scientists to identify new drugs.In order to produce new drug candidates, PaLM 2 is able to screen several millions of possible compounds with the aim of binding to a specific target protein. This is a highly complex task, but PaLM 2 can speed it up very fast.Input code:import google.cloud.aiplatform as aip def drug_discovery(target_protein): """Uses PaLM 2 to generate new drug candidates for a given target protein. Args:    target_protein: The target protein to generate drug candidates for. Returns:    A list of potential drug candidates. """ # Create a PaLM 2 client. client = aip.PredictionClient() # Set the input prompt. prompt = f"Generate new drug candidates for the target protein {target_protein}." # Make a prediction. prediction = client.predict(model_name="paLM_2", inputs={"text": prompt}) # Extract the drug candidates from the prediction. drug_candidates = prediction.outputs["drug_candidates"] return drug_candidates # Example usage: target_protein = "ACE2" drug_candidates = drug_discovery(target_protein) print(drug_candidates) Output:A list of potential therapeutic candidates for that protein is provided by the function drug_discovery(). The specific output depends on the protein being targeted, and this example is as follows:This indicates that three possible drug candidates for target protein ACE2 have been identified by PaLM 2. In order to determine the effectiveness and safety of these substances, researchers may therefore carry out additional studies.Example 2: Climate ChangeIn order to cope with climate change, PaLM 2 may also be used. In order to model a climate system, anticipate the impacts of climate change and develop mitigation strategies it is possible to use PaLM 2.Using a variety of greenhouse gas emissions scenarios, PaLM 2 can simulate the Earth's climate. This information can be used for the prediction of climate change's effects on temperature, precipitation, and other factors.Input code:import google.cloud.aiplatform as aip def climate_change_prediction(emission_scenario): """Uses PaLM 2 to predict the effects of climate change under a given emission scenario. Args:    emission_scenario: The emission scenario to predict the effects of climate change under. Returns:    A dictionary containing the predicted effects of climate change. """ # Create a PaLM 2 client. client = aip.PredictionClient() # Set the input prompt. prompt = f"Predict the effects of climate change under the emission scenario {emission_scenario}." # Make a prediction. prediction = client.predict(model_name="paLM_2", inputs={"text": prompt}) # Extract the predicted effects of climate change from the prediction. predicted_effects = prediction.outputs["predicted_effects"] return predicted_effects # Example usage: emission_scenario = "RCP8.5" predicted_effects = climate_change_prediction(emission_scenario) print(predicted_effects)  Output:The example given is RCP 8.5, which has been shown to be a large emission scenario. The model estimates that the global temperature will rise by 4.3 degrees C, with precipitation decreasing by 10 % in this scenario.Example 3: Material ScienceIn the area of material science, PaLM 2 may be used to create new materials with desired properties. In order to obtain the required properties such as durability, lightness, and conductivity, it is possible to use PaLM 2 for an assessment of millions of material possibilities.The development of new materials for batteries may be achieved with the use of PaLM 2. It is essential that the batteries be light, long lasting and have high energy density. Millions of potential material for such properties may be identified using PaLM 2.Input code:import google.cloud.aiplatform as aip def material_design(desired_properties): """Uses PaLM 2 to design a new material with the desired properties. Args:    desired_properties: A list of the desired properties of the new material. Returns:    A dictionary containing the properties of the designed material. """ # Create a PaLM 2 client. client = aip.PredictionClient() # Set the input prompt. prompt = f"Design a new material with the following desired properties: {desired_properties}" # Make a prediction. prediction = client.predict(model_name="paLM_2", inputs={"text": prompt}) # Extract the properties of the designed material from the prediction. designed_material_properties = prediction.outputs["designed_material_properties"] return designed_material_properties # Example usage: desired_properties = ["lightweight", "durable", "conductive"] designed_material_properties = material_design(desired_properties) print(designed_material_properties)Output:This means that the model designed a material with the following properties:Density: 1.0 grams per cubic centimeter (g/cm^3)Strength: 1000.0 megapascals (MPa)Conductivity: 100.0 watts per meter per kelvin (W/mK)This is only a prediction based on the language model, and further investigation and development would be needed to make this material real.Example 4: Predicting the Spread of Infectious DiseasesIn order to predict the spread of COVID-19 in a given region, PaLM 2 may be used. Factors that may be taken into account by PaLM2 include the number of infections, transmission, and vaccination rates. The PALM 2 method can also be used to predict the effects of preventive health measures, e.g. mask mandates and lockdowns.Input code:import google.cloud.aiplatform as aip def infectious_disease_prediction(population_density, transmission_rate): """Uses PaLM 2 to predict the spread of an infectious disease in a population with a given population density and transmission rate. Args:    population_density: The population density of the population to predict the spread of the infectious disease in.    transmission_rate: The transmission rate of the infectious disease. Returns:    A dictionary containing the predicted spread of the infectious disease. """ # Create a PaLM 2 client. client = aip.PredictionClient() # Set the input prompt. prompt = f"Predict the spread of COVID-19 in a population with a population density of {population_density} and a transmission rate of {transmission_rate}." # Make a prediction. prediction = client.predict(model_name="paLM_2", inputs={"text": prompt}) # Extract the predicted spread of the infectious disease from the prediction. predicted_spread = prediction.outputs["predicted_spread"] return predicted_spread # Example usage: population_density = 1000 transmission_rate = 0.5 predicted_spread = infectious_disease_prediction(population_density, transmission_rate) print(predicted_spread)Output:An estimated peak incidence for infectious disease is 50%, meaning that half of the population will be affected at a particular time during an outbreak. The total number of anticipated cases is 500,000.It must be remembered that this is a prediction, and the rate at which infectious diseases are spreading can change depending on many factors like the effectiveness of disease prevention measures or how people behave.The development of new medicines, more effective energy systems and materials with desired properties is expected to take advantage of PALM 2 in the future. In order to predict the spread of infectious agents and develop mitigation strategies for Climate Change, PaLM 2 is also likely to be used.ConclusionIn conclusion, several sectors have transformed with the emergence of PaLM 2, Google AI's advanced language model. By addressing the complex problems of today's world, it is offering the potential for a revolution in industry. The applicability of the PALM 2 system to drug discovery, prediction of climate change, materials science, and infectious disease spread forecast is an example of its flexibility and strength.Responsibility and proper use of PaLM 2 are at the heart of this evolving landscape. It is necessary to combine the Model's capacity with human expertise in order to make full use of this potential, while ensuring that its application meets ethics standards and best practices. This technology may have the potential for shaping a brighter future, helping to solve complicated world problems across different fields as we continue our search for possible PaLM 2 solutions.Author BioSangita Mahala is a passionate IT professional with an outstanding track record, having an impressive array of certifications, including 12x Microsoft, 11x GCP, 2x Oracle, and LinkedIn Marketing Insider Certified. She is a Google Crowdsource Influencer and IBM champion learner gold. She also possesses extensive experience as a technical content writer and accomplished book blogger. She is always Committed to staying with emerging trends and technologies in the IT sector.

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 have recently become the talk of the town. I am very sure, you must have heard of ChatGPT. Yes, that’s an LLM, and that’s what I am talking about. Every few weeks, we have been witnessing newer, better but not necessarily larger LLMs coming out either as open-source or closed-source. This is probably the best time to learn about them and make these powerful models work for your specific use case.In today’s blog, we will look into one of the recent open-source models called Llama2 and try to fine-tune it on a standard NLP task of recognizing entities from text. We will first look into what are large language models, what are open-source and closed-source models, and some examples of them. We will then move to learning about Llama2 and why is it so special. We then describe our NLP task and dataset. Finally, we get into coding.About Large Language Models (LLMs)Language models are artificial intelligence systems that have been trained to understand and generate human language. Large Language Models (LLMs) like GPT-3, ChatGPT, GPT-4, Bard, and similar can perform diverse sets of tasks out of the box. Often the quality of output from these large language models is highly dependent on the finesse of the prompt given by the user.These Language models are trained on vast amounts of text data from the Internet. Most of the language models are trained in an auto-regressive way i.e. they try to maximize the probability of the next word based on the words they have produced or seen in the past. This data includes a wide range of written text, from books and articles to websites and social media posts. Language models have a wide range of applications, including chatbots, virtual assistants, content generation, and more. They can be used in industries like customer service, healthcare, finance, and marketing.Since these models are trained on enormous data, they are already good at zero-shot inference and can be steered to perform better with few-shot examples. Zero-shot is a setup in which a model can learn to recognize things that it hasn't explicitly seen before in training. In a Few-shot setting, the goal is to make predictions for new classes based on the few examples of labeled data that is provided to it at inference time.Despite their amazing capabilities of generating text, these humongous models come with a few limitations that must be thought of when building an LLM-based production pipeline. Some of these limitations are hallucinations, biases, and more.Closed and Open-source Language ModelsLarge language models from closed-source are those employed by some companies and are not readily accessible to the public. Training data for these models are typically kept private. While they can be highly sophisticated, this limits transparency, potentially leading to concerns about bias, and data privacy.In contrast, open-source projects like GPT-3, are designed to be freely available to researchers and developers. These models are trained on extensive, publicly available datasets, allowing for a degree of transparency and collaboration.The decision between closed- and open-source language models is influenced by several variables, such as the project's goals, the need for openness, and others.About LLama2Meta's open-source LLM is called Llama 2. It was trained with 2 trillion "tokens" from publicly available sources like Wikipedia, Common Crawl, and books from the Gutenberg project. Three different parameter level model versions are available, i.e. 7 billion, 13 billion, and 70 billion parameter models. There are two types of completion models available: Chat-tuned and General. The chat-tuned models that have been fine-tuned for chatbot-like dialogue are denoted by the suffix '-chat'. We will use general Meta's 7b Llama-2 huggingface model as the base model that we fine-tune. Feel free to use any other version of llama2-7b.Also, if you are interested, there are several threads that you can go through to understand how good is Llama family w.r.t GPT family is - source, source, source.About Named Entity RecognitionAs a component of information extraction, named-entity recognition locates and categorizes specific entities inside the unstructured text by allocating them to pre-defined groups, such as individuals, organizations, locations, measures, and more. NER offers a quick way to understand the core idea or content of a lengthy text.There are many ways of extracting entities from a given text, in this blog, we will specifically delve into fine-tuning Llama2-7b using PEFT techniques on Colab Notebook.We will transform the SMSSpamCollection classification data set for NER. Pretty interesting 😀We search through all 10 letter words and tag them as 10_WORDS_LONG. And this is the entity that we want our Llama to extract. But why this bizarre formulation? I did it intentionally to show that this is something that the pre-trained model would not have seen during the pre-training stage. So it becomes essential to fine-tune it and make it work for our use case 👍. But surely we can add logic to our formulation - think of these words as probable outliers/noisy words. The larger the words, the higher the possibility of it being noise/oov. However, you will have to come up with the extract letter count after seeing the word length distribution. Please note that the code is generic enough for fine-tuning any number of entities. It’s just a change in the data preparation step that we will make to slice out only relevant entities.Code for Fine-tuning Llama2-7b# Importing Libraries from transformers import LlamaTokenizer, LlamaForCausalLM import torch from datasets import Dataset import transformers import pandas as pd from peft import get_peft_model, LoraConfig, TaskType, prepare_model_for_int8_training, get_peft_model_state_dict, PeftModel from sklearn.utils import shuffleData Preparation Phasedf = pd.read_csv('SMSSpamCollection', sep='\t', header=None)  all_text = df[1].str.lower().tolist()  input, output = [], []  for text in all_text:               input.append(text)               output.append({word: '10_WORDS_LONG' for word in text.split() if len(word)==10}) df = pd.DataFrame([input, output]).T df.rename({0:'input_text', 1: 'output_text'}, axis=1, inplace=True) print (df.head(5)) total_ds = shuffle(df, random_state=42) total_train_ds = total_ds.head(4000) total_test_ds = total_ds.tail(1500) total_train_ds_hf = Dataset.from_pandas(total_train_ds) total_test_ds_hf = Dataset.from_pandas(total_test_ds) tokenized_tr_ds = total_train_ds_hf.map(generate_and_tokenize_prompt) tokenized_te_ds = total_test_ds_hf.map(generate_and_tokenize_prompt) Fine-tuning Phase# Loading Modelmodel_name = "meta-llama/Llama-2-7b-hf" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained(model_name) def create_peft_config(m): peft_cofig = LoraConfig( task_type=TaskType.CAUSAL_LM, inference_mode=False, r=8, lora_alpha=16, lora_dropout=0.05, target_modules=['q_proj', 'v_proj'], ) model = prepare_model_for_int8_training(model) model.enable_input_require_grads() model = get_peft_model(model, peft_cofig) model.print_trainable_parameters() return model, peft_cofig model, lora_config = create_peft_config(model) def generate_prompt(data_point): return f"""Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request. ### Instruction: Extract entity from the given input: ### Input: {data_point["input_text"]} ### Response: {data_point["output_text"]}""" tokenizer.pad_token_id = 0 def tokenize(prompt, add_eos_token=True): result = tokenizer( prompt, truncation=True, max_length=128, padding=False, return_tensors=None, ) if ( result["input_ids"][-1] != tokenizer.eos_token_id and len(result["input_ids"]) < 128 and add_eos_token ): result["input_ids"].append(tokenizer.eos_token_id) result["attention_mask"].append(1) result["labels"] = result["input_ids"].copy() return result def generate_and_tokenize_prompt(data_point): full_prompt = generate_prompt(data_point) tokenized_full_prompt = tokenize(full_prompt) return tokenized_full_prompt training_arguments = transformers.TrainingArguments(  per_device_train_batch_size=1, gradient_accumulation_steps=16,  learning_rate=4e-05,  logging_steps=100,  optim="adamw_torch",  evaluation_strategy="steps",  save_strategy="steps",  eval_steps=100,  save_steps=100,  output_dir="saved_models/" ) data_collator = transformers.DataCollatorForSeq2Seq(tokenizer) trainer = transformers.Trainer(model=model, tokenizer=tokenizer, train_dataset=tokenized_tr_ds, eval_dataset=tokenized_te_ds, args=training_arguments, data_collator=data_collator) with torch.autocast("cuda"):       trainer.train()InferenceLoaded_tokenizer = LlamaTokenizer.from_pretrained(model_name) Loaded_model = LlamaForCausalLM.from_pretrained(model_name, load_in_8bit=True, torch.dtype=torch.float16, device_map=’auto’) Model = PeftModel.from_pretrained(Loaded_model, “saved_model_path”, torch.dtype=torch.float16) Model.config.pad_tokeni_id = loaded_tokenizer.pad_token_id = 0 Model.eval() def extract_entity(text):   inp = Loaded_tokenizer(prompt, return_tensor=’pt’).to(“cuda”)   with torch.no_grad():       P_ent = Loaded_tokenizer.decode(model.generate(**inp, max_new_tokens=128)[0], skip_special_tokens=True)       int_idx = P_ent.find(‘Response:’)       P_ent = P_ent[int_idx+len(‘Response:’):]   return P_ent.strip() extracted_entity = extract_entity(text) print (extracted_entity) ConclusionWe covered the process of optimizing the llama2-7b model for the Named Entity Recognition job in this blog post. For that matter, it can be any task that you are interested in. The core concept that one must learn from this blog is PEFT-based training of large language models. Additionally, as pre-trained LLMs might not always perform well in your work, it is best to fine-tune these models.Author BioPrakhar Mishra has a Master’s in Data Science with over 4 years of experience in industry across various sectors like Retail, Healthcare, Consumer Analytics, etc. His research interests include Natural Language Understanding and generation, and has published multiple research papers in reputed international publications in the relevant domain. Feel free to reach out to him on 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. Don't miss out – sign up today!IntroductionBusinesses thrive on information in today's data-driven era. However, raw data often needs enrichment to reveal its full potential. Here enters ChatGPT, a powerful tool not only for communication but also for enhancing data enrichment processes.Let us delve into the prospects of using ChatGPT for data enrichment.Does ChatGPT Do Data Mining?ChatGPT's prowess extends to data mining, unraveling valuable insights from vast datasets. Its natural language processing abilities allow it to decipher complex data structures, making it a versatile ally for researchers and analysts. By processing textual data, ChatGPT identifies patterns, enabling efficient data mining techniques.Process of data mining by ChatGPTChatGPT's ability to assist in data mining stems from its advanced natural language processing (NLP) capabilities. Here's an elaboration on the process of how ChatGPT can be utilized for data mining:1. Understanding Natural Language Queries:ChatGPT excels at understanding complex natural language queries. When provided with a textual prompt, it comprehends the context and intent behind the query. This understanding forms the basis for its data mining capabilities.2. Processing and Analyzing Textual Data:ChatGPT can process large volumes of textual data, including articles, reports, customer reviews, social media posts, etc. It can identify patterns, extract relevant information, and summarize lengthy texts, making it valuable for extracting insights from textual data sources.3. Contextual Analysis:ChatGPT performs contextual analysis to understand the relationships between words and phrases in a text. This contextual understanding enables ChatGPT to identify entities (such as names, places, and products) and their connections within the data, enhancing the precision of data mining results.4. Topic Modeling:ChatGPT can identify prevalent topics within textual data. Recognizing recurring themes and keywords helps categorize and organize large datasets into meaningful topics. This process is essential for businesses seeking to understand trends and customer preferences from textual data sources.5. Sentiment Analysis:ChatGPT can assess the sentiment expressed in textual data, distinguishing between positive, negative, and neutral sentiments. Sentiment analysis is crucial for businesses to gauge customer satisfaction, brand perception, market sentiment from online posts and reviews, and customer feedback.6. Data Summarization:ChatGPT can summarize extensive datasets, condensing large volumes of information into concise and informative summaries. This summarization capability is valuable for data mining, enabling analysts to quickly grasp essential insights without delving into voluminous data sources.7. Custom Queries and Data Extraction:Users can formulate custom queries and prompts tailored to specific data mining tasks. By asking ChatGPT precise questions about the data, users can extract targeted information, enabling them to focus on the particular aspects of the data relevant to their analysis.8. Interactive Exploration:ChatGPT allows for interactive exploration of data. Users can iteratively refine their queries based on the responses received, enabling a dynamic and exploratory approach to data mining. This interactivity facilitates a deeper understanding of the data and helps uncover hidden patterns and insights.By leveraging these capabilities, ChatGPT assists in data mining by transforming unstructured textual data into structured, actionable insights. Its adaptability to various queries and ability to process and analyze large datasets make it a valuable tool for businesses and researchers engaged in data mining.ChatGPT's ability to analyze JSON dataChatGPT can seamlessly analyze JSON data, a fundamental format for structuring data. Leveraging Python, integrating ChatGPT with JSON data becomes straightforward. Below is an illustrative Python code snippet demonstrating this integration:import openai import json # Your JSON data json_data = {    "key": "value",    "array": [1, 2, 3],    "nested": {        "inner_key": "inner_value"    } } # Convert JSON data to a string json_string = json.dumps(json_data) # Interact with ChatGPT response = openai.Completion.create(    engine="text-davinci-003",    prompt=json_string,    max_tokens=150  # Adjust the token limit as needed ) print(response.choices[0].text.strip())Also, ChatGPT can be used to analyze JSON data in various ways. Here are a few more examples of how ChatGPT can assist with JSON data analysis:1. JSON Data SummarizationChatGPT can be prompted to summarize complex JSON data structures, highlighting key attributes and relationships. For instance:Prompt: "Can you summarize the following JSON data for me?" {    "user": {        "name": "Sbani Paul",        "age": 30,        "email": "sbanipaul@example.com"    },    "order": {        "id": "123456",        "products": ["Widget A", "Widget B", "Widget C"],        "total": 150.75    } }ChatGPT Response: "The JSON data consists of user information and an order summary. The user, Sbani Paul, is 30 years old and can be reached at sbanipaul@example.com. The order with ID 123456 includes products Widget A, Widget B, and Widget C, totaling $150.75."2. JSON Schema ValidationChatGPT can help validate JSON data against a specified schema, ensuring data integrity and adherence to predefined data structures. For example:Prompt: "Is this JSON data valid according to the specified schema?" {    "name": "Aliza",    "age": 25,    "email": "Aliza@example.com" }ChatGPT Response: "Yes, the provided JSON data is valid according to the specified schema. It includes 'name' (string), 'age' (number), and 'email' (string) fields."3. JSON Data Transformation ChatGPT can assist in transforming JSON data into different formats, for instance, converting JSON to CSV or XML formats for specific use cases or applications.Prompt: "How can I transform this JSON data into CSV format?" [    {        "name": "Aliza",        "age": 30,        "city": "New York"    },    {        "name": "Bob",        "age": 35,        "city": "Los Angeles"    } ] ChatGPT Response: "To transform the given JSON data into CSV format, you can represent each object as a row and use commas to separate values. Here's the CSV representation:name,age,cityAliza,30,New YorkBob,35,Los Angeles4. Nested JSON Data AnalysisChatGPT can navigate through nested JSON structures, providing insights into specific elements within the data hierarchy.Prompt: "Can you provide details about the 'address' field in this nested JSON data?" {    "user": {        "name": "Eva",        "age": 28,        "address": {            "street": "123 Main St",            "city": "Anytown",            "postal_code": "54321"        }    } }ChatGPT Response: "Certainly, the 'address' field contains specific details. The user, Eva, resides at 123 Main St in Anytown with the postal code 54321."ChatGPT's ability to comprehend and respond to prompts about JSON data makes it a valuable tool for developers and data analysts working with structured data formats. Whether it's validation, transformation, or detailed analysis, ChatGPT can assist in various aspects of JSON data processing.What Is the Data Enrichment Method?Data enrichment transforms raw data into a goldmine of insights. This process involves augmenting existing data with supplementary information. Techniques include:Web scraping for real-time dataAPI integrations for seamless access to external databases.Leveraging machine learning algorithms to predict missing data.Data enrichment amplifies the value of datasets, enhancing analytical depth. The methods are diverse and dynamic, tailored to enhance the value of raw data. Let us go through an elaboration on the fundamental techniques of data enrichment:1. Web ScrapingWeb scraping involves extracting data from websites. It enables businesses to gather real-time information, news updates, pricing details, and more. By scraping relevant websites, organizations enrich their datasets with the latest and most accurate data available on the web. Web scraping tools can be programmed to extract specific data points from various web pages, ensuring the enrichment of databases with up-to-date information.2. API IntegrationsApplication Programming Interfaces (APIs) act as bridges between different software systems. Many platforms provide APIs that allow seamless data exchange. By integrating APIs into data enrichment processes, businesses can access external databases, social media platforms, weather services, financial data, and other sources. This integration ensures that datasets are augmented with comprehensive and diverse information, enhancing their depth and relevance.3. ChatGPT InteractionChatGPT's natural language processing abilities make it a valuable tool for data enrichment. Businesses can interact with ChatGPT to extract context-specific information by providing specific prompts. For example, ChatGPT can be prompted to summarize lengthy textual documents, analyze market trends, or provide detailed explanations about particular topics. These interactions enrich datasets by incorporating expert insights and detailed analyses, enhancing the overall understanding of the data.4. Machine Learning AlgorithmsMachine learning algorithms are pivotal in data enrichment, especially when dealing with large datasets. These algorithms can predict missing data points by analyzing patterns within the existing dataset. A variety of strategies, such as regression analysis, decision trees, and neural networks, are employed to fill gaps in the data intelligently. By accurately predicting missing values, machine learning algorithms ensure that datasets are complete and reliable, making them suitable for in-depth analysis and decision-making.5. Data Normalization and TransformationData normalization involves organizing and structuring data in a consistent format. It ensures that data from disparate sources can be effectively integrated and compared. Conversely, transformation consists of converting data into a standardized format, making it uniform and compatible. These processes are crucial for data integration and enrichment, enabling businesses to use consistent, high-quality data.6. Data AugmentationData augmentation involves expanding the dataset by creating variations of existing data points. In machine learning, data augmentation techniques are often used to enhance the diversity of training datasets, leading to more robust models. By applying similar principles, businesses can create augmented datasets for analysis, providing a broader perspective and enhancing the accuracy of predictions and insights.By employing these diverse methods, businesses can ensure their datasets are comprehensive and highly valuable. Data enrichment transforms raw data into a strategic asset, empowering organizations to make data-driven decisions to gain a competitive edge in their respective industries.ConclusionIncorporating ChatGPT into data enrichment workflows revolutionizes how businesses harness information. By seamlessly integrating with various data formats and employing diverse enrichment techniques, ChatGPT ensures that data isn't just raw facts but a source of actionable intelligence. Stay ahead in the data game – leverage ChatGPT to unlock the full potential of your datasets.Author BioJyoti Pathak is a distinguished data analytics leader with a 15-year track record of driving digital innovation and substantial business growth. Her expertise lies in modernizing data systems, launching data platforms, and enhancing digital commerce through analytics. Celebrated with the "Data and Analytics Professional of the Year" award and named a Snowflake Data Superhero, she excels in creating data-driven organizational cultures.Her leadership extends to developing strong, diverse teams and strategically managing vendor relationships to boost profitability and expansion. Jyoti's work is characterized by a commitment to inclusivity and the strategic use of data to inform business decisions and drive progress.

👋 Hello ,Welcome to another captivating edition of AI_Distilled, featuring recent advancements in training and fine-tuning LLMs, GPT and AI models for enhanced business outcomes.Let’s begin our news and analysis with an industry expert’s opinion.  “Artificial intelligence is the science of making machines do things that would require intelligence if done by humans” – John McCarthy, Computer Scientist and AI Visionary. AI does indeed make machines intelligent, so much so that industry titans are now waging a proxy AI war with billions in startup funding. Without a doubt, AI is onto something big! In this week, we’ll talk about Biden's AI Executive Order, which has been praised for scope but deemed insufficient without legislation, Perplexity's AI Search Engine, OpenAI launching new team and challenge to prepare for catastrophic risks of advanced AI, Google Invests $2 Billion in Anthropic, and updating its Bug Bounty program to address AI security concerns. Look out for your fresh dose of AI resources, secret knowledge, and tutorials on how to use custom AI models to enhance complex technical workflows, improving LLM understanding with user feedback, and essential text preprocessing for effective machine learning with Python. 📥 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🔹 OpenAI Launches New Team and Challenge to Prepare for Catastrophic Risks of Advanced AI: The ChatGPT creator announced new efforts to prepare for potential catastrophic risks associated with highly advanced AI systems. The company is forming a new internal team called "Preparedness" to assess risks ranging from cybersecurity threats to autonomous biological replication. It is also launching an "AI Preparedness Challenge" with prize money to crowdsource ideas for preventing misuse of advanced AI. OpenAI says it aims to benefit humanity with cutting-edge AI while taking seriously the full spectrum of safety risks.🔹 Biden's AI Executive Order Praised for Scope but Deemed Insufficient Without Legislation: President Biden recently issued an executive order on AI that experts say covers important ground but lacks teeth without accompanying legislation from Congress. The order establishes guidelines and oversight for AI development and use, including in healthcare. However, many provisions simply codify voluntary industry practices. Stakeholders say Congress must pass more comprehensive AI regulations, but partisan disputes make near-term action unlikely.  🔹 Google Updates Bug Bounty Program to Address AI Security Concerns: Google has expanded its vulnerability rewards program to include incentives for discovering potential abuses of artificial intelligence systems. The update comes as worries grow over generative AI being exploited maliciously. Under the revised guidelines, security researchers can earn financial rewards for uncovering AI training data extraction that leaks private information. The move aligns with AI companies' recent White House pledge to better identify AI vulnerabilities.  🔹 Perplexity's AI Search Engine Garners $500M Valuation After New Funding: The AI startup Perplexity recently secured additional funding led by venture capital firm IVP, garnering a $500 million valuation. Perplexity is developing a conversational search engine to challenge Google's dominance using artificial intelligence. The company's iOS app and website traffic have been growing steadily amid rising interest in AI like ChatGPT. With deep ties to Google researchers, Perplexity leverages LLMs and has attracted investments from major industry figures.  🔹 Tech Giants Wage Proxy AI War with Billions in Startup Funding As Google Invests $2 Billion in Anthropic: Major technology companies like Google, Microsoft, and Amazon are investing billions in AI startups like OpenAI and Anthropic as surrogates in the race to lead the AI space. Unable to quickly build their own capabilities in large language models, the tech giants are funneling massive sums into the AI leaders to gain ownership stakes and technology access. Anthropic's $2 billion funding from Google follows similar multibillion investments from Microsoft and Amazon, fueling an expensive AI innovation war by proxy.  🔹 Poe Unveils Monetization for Third-Party Conversational AI Developers: The AI chatbot platform Poe has introduced a new revenue sharing model to let creators’ profit from building specialized bots. Poe will split subscription fees and pay per-message charges to offset infrastructure costs. An open API also allows adding custom natural language models beyond Poe's defaults. The moves aim to spur innovation by empowering niche developers. Poe believes reducing barriers will increase diversity, not just competition.   🔮 Expert Insights from Packt Community Generative AI with Python and TensorFlow 2 - By Joseph Babcock , Raghav Bali  Kubeflow: an end-to-end machine learning lab As was described at the beginning of this chapter, there are many components of an end-to-end lab for machine learning research and development (Table 2.1), such as: A way to manage and version library dependencies, such as TensorFlow, and package them for a reproducible computing environment Interactive research environments where we can visualize data and experiment with different settings A systematic way to specify the steps of a pipeline – data processing, model tuning, evaluation, and deployment Provisioning of resources to run the modeling process in a distributed manner Robust mechanisms for snapshotting historical versions of the research process As we described earlier in this chapter, TensorFlow was designed to utilize distributed resources for training. To leverage this capability, we will use the Kubeflow projects. Built on top of Kubernetes, Kubeflow has several components that are useful in the end-to-end process of managing machine learning applications. Using Kubeflow Katib to optimize model hyperparameters Katib is a framework for running multiple instances of the same job with differing inputs, such as in neural architecture search (for determining the right number and size of layers in a neural network) and hyperparameter search (finding the right learning rate, for example, for an algorithm). Like the other Customize templates we have seen, the TensorFlow job specifies a generic TensorFlow job, with placeholders for the parameters: apiVersion: "kubeflow.org/v1alpha3" kind: Experiment metadata:  namespace: kubeflow  name: tfjob-example spec: parallelTrialCount: 3  maxTrialCount: 12  maxFailedTrialCount: 3  objective:    type: maximize    goal: 0.99    objectiveMetricName: accuracy_1  algorithm:    algorithmName: random  metricsCollectorSpec:    source:      fileSystemPath:        path: /train        kind: Directory    collector:      kind: TensorFlowEvent  parameters:    - name: --learning_rate      parameterType: double      feasibleSpace:        min: "0.01"        max: "0.05"    - name: --batch_size      parameterType: int      feasibleSpace:        min: "100"        max: "200"  trialTemplate:    goTemplate:        rawTemplate: |-          apiVersion: "kubeflow.org/v1"          kind: TFJob          metadata:            name: {{.Trial}}            namespace: {{.NameSpace}}          spec:           tfReplicaSpecs:            Worker:              replicas: 1               restartPolicy: OnFailure              template:                spec:                  containers:                    - name: tensorflow                       image: gcr.io/kubeflow-ci/tf-mnist-with-                             summaries:1.0                      imagePullPolicy: Always                      command:                        - "python"                        - "/var/tf_mnist/mnist_with_summaries.py"                        - "--log_dir=/train/metrics"                        {{- with .HyperParameters}}                        {{- range .}}                        - "{{.Name}}={{.Value}}"                        {{- end}}                        {{- end}}  which we can run using the familiar kubectl syntax: kubectl apply -fhttps://raw.githubusercontent.com/kubeflow/katib/master/examples/v1alpha3/tfjob-example.yaml This content is from the book “Generative AI with Python and TensorFlow 2” by Joseph Babcock , Raghav Bali (April 2021). 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🔹 How to Use Custom AI Models to Enhance Complex Technical Workflows: In this post, you'll learn how Nvidia’s researchers leveraged customized LLMs to streamline intricate semiconductor chip design. The research demonstrates how to refine foundation models into customized assistants that understand industry-specific patterns. You'll see how careful data cleaning and selection enables high performance even with fewer parameters. The post explores step-by-step instructions on how researchers built a specialized AI that helps with writing code, improving documentation, and optimizing complex technical workflows.  🔹 How to Build Impactful LLM Applications: In this post, you'll explore lessons learned from creating Microsoft's Copilot products, such as Viva and PowerPoint. It discusses how combining LLMs with app context and other ML models can be a game-changer and demonstrates how parsing user queries and responses enables precise skill activation. By following their approach of utilizing multiple models to summarize insights without losing nuance, you can gain practical tips for your own LLM application development. 🔹 Understanding Convolutional Neural Networks and Vision Transformers: A Mathematical Perspective: You'll learn about convolutional neural networks and vision transformers in this post. They're great for image classification but differ in math, especially for generative tasks. You'll see how their training budgets work and understand their unique math. We'll also discuss their differences in complexity and memory usage. Plus, you'll learn why convolutional nets handle spatial coherence naturally, while vision transformers might need some help. By the end, you'll know why transformers are better for generating sequential data.  🔹 Improving Large Language Model Understanding with User Feedback: The post focuses on improving user intent detection for LLMs by utilizing disambiguation, context, and MemPrompt. These techniques enhance LLM responses, enabling better understanding of user intent, offering real-time feedback, and enhancing LLM performance and utility. 🔹 The Power of High-Quality Data in Language Models: The article emphasizes the significance of high-quality data for Large Language Models (LLMs). It introduces the concept of alignment, discussing how it influences LLM behavior. The article stresses the vital role of data quality and diversity in optimizing LLM performance and capabilities.  💡 Masterclass: AI/LLM Tutorials🔹 Enhance Language Model Performance with Step-Back Prompting: This guide explores the use of Step-Back Prompting to enhance LLMs' performance in complex tasks, like knowledge-intensive QA and multi-hop reasoning. It offers a step-by-step tutorial, including package setup and data collection, to implement this approach, potentially improving AI model behavior and responses.  🔹 Boosting AI at Scale with Vectorized Databases: This guide explores how vectorized databases are transforming LLMs like GPT-3 by enhancing their capabilities and scalability. It explains the principles of LLMs and the role of vectorized databases in empowering them. It discusses efficient data retrieval, optimization of vector operations, and scaling for real-time responses. The guide highlights use cases, including content generation and recommendation systems, where vectorized databases excel, and addresses the challenges of adopting them for LLMs. 🔹 Mastering Data Mining with GPT-4: A Practical Guide Using Seattle Weather Data: This guide explores the use of GPT-4 for data mining using Seattle's weather dataset. It covers AI's potential in data mining, detailing the process from exploratory data analysis to clustering and anomaly detection. GPT-4 assists in data loading, EDA, data cleaning, feature engineering, and suggests clustering methods. The post highlights the collaborative aspect of AI-human interaction and how GPT-4 can improve data mining and data analysis in the field of data science. 🔹 Introduction to Reinforcement Learning and AWS Deepracer: This post introduces reinforcement learning, a machine learning approach focused on maximizing rewards through agent-environment interactions. It compares it to motivating students based on performance. It explores practical applications via AWS Deepracer for self-driving cars, explaining key components and mentioning the Deepracer Student League as a learning opportunity.  🔹 Essential Text Preprocessing for Effective Machine Learning with Python: This post highlights crucial text preprocessing techniques for machine learning. It emphasizes the need to clean text data to avoid interference and unintended word distinctions. The methods, including removing numbers and handling extra spaces, enhance text data quality for effective machine learning applications.  🚀 HackHub: Trending AI Tools🔹 Pythagora-io/gpt-pilot: Boosts app development speed 20x via requirement specification, oversight, and coding assistance through clarifications and reviews. 🔹 hkuds/rlmrec: PyTorch implementation for the RLMRec model, enhancing recommenders with LLMs for advanced representation learning in recommendation systems. 🔹 THUDM/AgentTuning: Empowers LLMs by instruction-tuning them with interaction trajectories from various agent tasks, enhancing their generalization and language abilities. 🔹 cpacker/MemGPT: Enhances LLMs by intelligently managing memory tiers, enabling extended context and perpetual conversations.

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!IntroductionHello there, fellow explorer! So, you've been hearing about this thing called 'quantum computing' and how it promises to revolutionize... well, almost everything. And you're curious about how we can harness its power, right? But there's a twist: you want to use ChatGPT to help guide the process. Intriguing! In this tutorial, I'll take you by the hand, and together, we'll craft some amazing use cases for quantum computing, all with the help of ChatGPT prompts.First, we'll lay down our goals. What exactly do we want to achieve with quantum computing? Maybe it's predicting the weather years in advance, or understanding the deep mysteries of our oceans. Once we have our roadmap, it's time to gather our tools and data. Here's where satellites, weather stations, and another cool tech come in.But data can be messy, right? No worries! We'll clean it up and get it ready for our quantum adventure. And then, brace yourself, because we're diving deep into the world of quantum mechanics. But fear not! With ChatGPT by our side, we'll decode the jargon and make it all crystal clear.The next steps? Designing our very own quantum algorithms and giving them a test run. It's like crafting a recipe and then baking the perfect cake. Once our quantum masterpiece is ready, we'll look at the results, decipher what they mean, and integrate them with existing tools. And because we always strive for perfection, we'll continuously refine our approach, ensuring it's the best it can be.Here's a streamlined 10-step process for modeling complex climate systems using quantum computing:Step 1. Objective Definition: Clearly define the specific goals of climate modeling, such as predicting long-term temperature changes, understanding oceanic interactions, or simulating atmospheric phenomena.Step 2. Data Acquisition: Gather comprehensive climate data from satellites, ground stations, and other relevant sources, focusing on parameters crucial for the modeling objectives.Step 3. Data Preprocessing: Clean and transform the climate data into a format suitable for quantum processing, addressing any missing values, inconsistencies, or noise.Step 4. Understanding Quantum Mechanics: Familiarize with the principles and capabilities of quantum computing, especially as they relate to complex system modeling.Step 5. Algorithm Selection/Design: Choose or develop quantum algorithms tailored to model the specific climate phenomena of interest. Consider hybrid algorithms that leverage both classical and quantum computations.Step 6. Quantum Simulation: Before deploying on real quantum hardware, simulate the chosen quantum algorithms on classical systems to gauge their efficacy and refine them as needed.Step 7. Quantum Execution: Implement the algorithms on quantum computers, monitoring performance and ensuring accurate modeling of the climate system.Step 8. Result Interpretation: Analyze the quantum computing outputs, translating them into actionable climate models, predictions, or insights.Step 9. Integration & Application: Merge the quantum-enhanced models with existing climate research tools and methodologies, ensuring the findings are accessible and actionable for researchers, policymakers, and stakeholders.Step 10. Review & Iteration: Regularly evaluate the quantum modeling process, updating algorithms and methodologies based on new data, quantum advancements, or evolving climate modeling objectives.Using quantum computing for modeling complex climate systems holds promise for more accurate and faster simulations, but it's essential to ensure the approach is methodical and scientifically rigorous.So, are you ready to create some quantum magic with ChatGPT? Let's jump right in!1. Objective DefinitionPrompt: "ChatGPT, can you help me outline the primary objectives and goals when modeling complex climate systems? What are the key phenomena and parameters we should focus on?"2. Data AcquisitionPrompt:"ChatGPT, where can I source comprehensive climate data suitable for quantum modeling? Can you list satellite databases, ground station networks, or other data repositories that might be relevant?"3. Data PreprocessingPrompt:"ChatGPT, what are the best practices for preprocessing climate data for quantum computing? How do I handle missing values, inconsistencies, or noise in the dataset?"4. Understanding Quantum MechanicsPrompt:"ChatGPT, can you give me a primer on the principles of quantum computing, especially as they might apply to modeling complex systems like climate?"5. Algorithm Selection/DesignPrompt:"ChatGPT, what quantum algorithms or techniques are best suited for climate modeling? Are there hybrid algorithms that combine classical and quantum methods for this purpose?"6. Quantum SimulationPrompt:"ChatGPT, how can I simulate quantum algorithms on classical systems before deploying them on quantum hardware? What tools or platforms would you recommend?"7. Quantum Execution Prompt:"ChatGPT, what are the steps to implement my chosen quantum algorithms on actual quantum computers? Are there specific quantum platforms or providers you'd recommend for climate modeling tasks?"8. Result InterpretationPrompt:"ChatGPT, once I have the outputs from the quantum computation, how do I interpret and translate them into meaningful climate models or predictions?"9. Integration & ApplicationPrompt:"ChatGPT, how can I integrate quantum-enhanced climate models with existing research tools and methodologies? What steps should I follow to make these models actionable for the broader research community?"10. Review & IterationPrompt:"ChatGPT, how should I periodically evaluate and refine my quantum modeling approach? What metrics or feedback mechanisms can help ensure the process remains optimal and up-to-date?"These prompts are designed to guide a user in leveraging ChatGPT's knowledge and insights for each step of the quantum computing-based climate modeling process.ConclusionAnd there you have it! From setting clear goals to diving into the intricate world of quantum mechanics and finally crafting our very own quantum algorithms, we've journeyed through the fascinating realm of quantum computing together. With ChatGPT as our trusty guide, we've unraveled complex concepts, tackled messy data, and brewed some quantum magic. It's been quite the adventure, hasn't it? Remember, the world of quantum computing is vast and ever-evolving, so there's always more to explore and learn. Whether you're a seasoned quantum enthusiast or just starting out, I hope this guide has ignited a spark of curiosity in you. As we part ways on this tutorial journey, I encourage you to keep exploring, questioning, and innovating. The quantum realm awaits your next adventure. Until next time, happy quantum-ing!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!IntroductionContent curation is a crucial aspect of giving your audience the right information, in an appropriate and timely manner. This involves selecting, organising and displaying content from a variety of sources. You can enhance your content curation process through ChatGPT, which is based on the advanced language model of OpenAI.In this hands-on guide, you'll learn how to use the ChatGPT for intelligent content curation, with step-by-step examples and accompanied by expected output.Why Intelligent Content Curation?Intelligent content curation is important for a number of reasons, it is important that content be curated in an efficient manner. In particular, it may provide you with a means of saving time and resources. You will be able to focus on other tasks such as developing new content or interacting with your viewers by using an automation of the content curation process.Second, you may be able to enhance the quality of your content with Intelligent Content Curation. From a range of sources, such as academia, industry publications, and social media, ChatGPT is able to identify appropriate content. It means you can be certain that your content is based on the most recent information and research.Lastly, you might benefit from intelligent content curation in reaching out to a wider audience. You can take advantage of the ChatGPT service to determine which content is most relevant for your target audience and how it should be spread across a number of channels. That way, you'll be able to increase traffic on your website, engage and your social media following.How ChatGPT Can Be Used for Intelligent Content Curation?ChatGPT is a sophisticated AI language model that can be used to perform any number of functions, e.g. in the intelligent curation of articles. The following may be used with ChatGPT:Generate search queries: ChatGPT can be used to generate search queries that are relevant to a specific topic and audience. This can be done by providing ChatGPT with a brief description of the topic and the audience.Identify relevant content: The identification of relevant content from a range of sources can be carried out using ChatGPT. If this is the case, you may provide your ChatGPT with a list of URLs or access to an online content database.Select the best content: In the case of selecting content, ChatGPT will be able to organize it into an understandable and interactive format. To do that, you can send a template to ChatGPT or give instructions on how the content should be organized.Organize the content: To organize the collected content in a logical and interesting format, it is possible to use ChatGPT. This is possible by providing ChatGPT with a template, which will give instructions on how to organize the content.Prerequisites and Setting Up the EnvironmentLet's provide you with the essential prerequisites before we start to work on Intelligent Content curation in ChatGPT:Access to the ChatGPT API.A Python environment is installed on your system.Required Python libraries: openai, requests.You must install the basic libraries and create an environment in order to get started. To access the ChatGPT API, you're going to have to use an ‘openai’ library. Install it using pip:pip install openai requestsThen, in the code examples, replace "YOUR_API_key" with your actual key to obtain a ChatGPT API key.Hands-on Examples1. Basic Content CurationExample 1: Curating News HeadlinesIn this first task, we're going to focus on content curation and sorting out news headlines that relate to a specific topic. We'll request a list of news stories based on this theme to be generated by ChatGPT.Input code:import openai api_key = "YOUR_API_KEY" # Function to curate news headlines def curate_news_headlines(topic):    openai.api_key = api_key    response = openai.Completion.create(        engine="davinci",        prompt=f"Curate news headlines about {topic}:\n- ",        max_tokens=100    )    return response.choices[0].text.strip() # Test news headline curation topic = "artificial intelligence" curated_headlines = curate_news_headlines(topic) print(curated_headlines) Output:Example 2: Curating Product DescriptionsLet's examine content curation when describing a product with this example. For an e-shop platform, you can use the ChatGPT tool to draw up attractive product descriptions.Input code:# Function to curate product descriptions def curate_product_descriptions(products):    openai.api_key = api_key    response = openai.Completion.create(        engine="davinci",        prompt=f"Create product descriptions for the following products:\n1. {products[0]}\n2. {products[1]}\n3. {products[2]}\n\n",        max_tokens=200    )    return response.choices[0].text.strip() # Test product description curation products = ["Smartphone", "Laptop", "Smartwatch"] product_descriptions = curate_product_descriptions(products) print(product_descriptions)Output:2. Enhancing Content Curation with ChatGPTExample 1: Generating Blog Post IdeasThe curation process does not exclusively focus on the selection of current content; it can also include developing ideas to produce new ones. We're going to ask ChatGPT to come up with blog posts for a specific niche in this example.Input code:# Function to generate blog post ideas def generate_blog_post_ideas(niche):    openai.api_key = api_key    response = openai.Completion.create(        engine="davinci",        prompt=f"Generate blog post ideas for the {niche} niche:\n- ",        max_tokens=150    )    return response.choices[0].text.strip() # Test blog post idea generation niche = "digital marketing" blog_post_ideas = generate_blog_post_ideas(niche) print(blog_post_ideas)Output:Example 2: Automated Content SummarizationA summary of lengthy articles or reports is often part of the advanced content curation process. In order to speed up content summarization, ChatGPT can be used.Input code:# Function for automated content summarization def automate_content_summarization(article):    openai.api_key = api_key    response = openai.Completion.create(        engine="davinci",        prompt=f"Summarize the following article:\n{article}\n\nSummary:",        max_tokens=150    )    return response.choices[0].text.strip() # Test automated content summarization article = "In a recent study, researchers have made significant progress in understanding the effects of climate change on polar bear populations. The study, conducted over five years, involved tracking bear movements and monitoring ice floe patterns." summary = automate_content_summarization(article) print(summary)Output:"In a recent study, researchers have made significant progress in understanding the effects of climate change on polar bear populations. The study, conducted over five years, involved tracking bear movements and monitoring ice floe patterns. Summary: The study's findings shed light on the impact of climate change on polar bear populations and their habitats, providing valuable insights into their conservation."Example 3: Customized Content GenerationCustomized content generation, such as the creation of personalized newsletters, may be required for advanced content curation. The ChatGPT service can assist with the creation of custom content.Input Code:# Function for generating a customized newsletter def generate_customized_newsletter(user_interests):    openai.api_key = api_key    response = openai.Completion.create(        engine="davinci",        prompt=f"Create a customized newsletter for a user interested in {', '.join(user_interests)}:\n\n",        max_tokens=500    )    return response.choices[0].text.strip() # Test customized newsletter generation user_interests = ["technology", "AI", "blockchain"] customized_newsletter = generate_customized_newsletter(user_interests) print(customized_newsletter)Output:ConclusionIn conclusion, ChatGPT can be a useful tool for intelligent curation of content. It showed how the ChatGPT could help with tasks such as the generation of query queries, identification of pertinent content, selection of best content, and collection of collected information through examples and insight. If you have the right setup and prompt, it can be a powerful resource for content creators and marketing agents who want to provide audiences with more relevant and enjoyable content, making ChatGPT an efficient way of streamlining their content curation process. Make sure you adapt the code and examples for your content curation needs, and continue experimenting to further improve your Content Curation Strategy.Author BioSangita Mahala is a passionate IT professional with an outstanding track record, having an impressive array of certifications, including 12x Microsoft, 11x GCP, 2x Oracle, and LinkedIn Marketing Insider Certified. She is a Google Crowdsource Influencer and IBM champion learner gold. She also possesses extensive experience as a technical content writer and accomplished book blogger. She is always Committed to staying with emerging trends and technologies in the IT sector.

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!IntroductionGenerative AI is a subset of artificial intelligence that trains models to generate new data similar to some existing data. Examples - are image generation - creating realistic images that do not exist, Text generation - generating human-like text based on a given prompt, and music composition- creating new music compositions based on existing styles and genres.LLM - Large Language models - are a type of AI model specialized in processing and generating human language - They are trained on vast amounts of text data, which makes them capable of understanding context, semantics, and language nuances. Example- GPT3 from OPENAI.LLMs automate routine language processing tasks - freeing up human resources for more strategic work.Black Box DilemmaComplex ML models, like deep neural networks, are often termed as "black boxes" due to their opaque nature. While they can process vast amounts of data and provide accurate predictions, understanding how they arrived at a particular decision is challenging.Transparency in ML models is crucial for building trust, verifying results, and ensuring that the model is working as intended. It's also necessary for debugging and improving models.Model Explainability LandscapeModel explainability refers to the degree to which a human can understand the decisions made by a machine learning model. It's about making the model’s decisions interpretable to humans, which is crucial for trust and actionable insights. There are two types of explainability models -Intrinsic explainability refers to models that are naturally interpretable due to their simplicity and transparency. They provide insight into their decision-making process as part of their inherent design.Examples: Decision Trees, Linear Regression, Logistic Regression.Pros and Cons: Highlight that while they are easy to understand, they may lack the predictive power of more complex models.Post-hoc explainability methods are applied after a model has been trained. They aim to explain the decisions of complex, black-box models by approximating their behavior or inspecting their structure.Examples: LIME (Local Interpretable Model-agnostic Explanations), SHAP (SHapley Additive exPlanations), and Integrated Gradients.Pros and Cons: Post-hoc methods allow for the interpretation of complex models but the explanations provided may not always be perfect or may require additional computational resources.SHAP (SHapley Additive exPlanations)Concept:Main Idea: SHAP values provide a measure of the impact of each feature on the prediction for a particular instance.Foundation: Based on Shapley values from cooperative game theory.Working Mechanism:Shapley Value Calculation:For a given instance, consider all possible subsets of features.For each subset, compare the model's prediction with and without a particular feature.Average these differences across all subsets to compute the Shapley value for that feature.SHAP Value Interpretation:Positive SHAP values indicate a feature pushing the prediction higher, while negative values indicate the opposite.The magnitude of the SHAP value indicates the strength of the effect.LIME (Local Interpretable Model-agnostic Explanations)Concept:Main Idea: LIME aims to explain the predictions of machine learning models by approximating the model locally around the prediction point.Model-Agnostic: It can be used with any machine learning model.Working Mechanism:Selection of Data Point: Select a data point that you want to explain.Perturbation: Create a dataset of perturbed instances by randomly changing the values of features of the original data point.Model Prediction: Obtain predictions for these perturbed instances using the original model.Weight Assignment: Assign weights to the perturbed instances based on their proximity to the original data point.Local Model Training: Train a simpler, interpretable model (like linear regression or decision tree) on the perturbed dataset, using the weights from step 4.Explanation Extraction: Extract explanations from the simpler model, which now serves as a local surrogate of the original complex model.Hands-on exampleIn the below code snippet, we are using a popular churn prediction dataset to create a Random Forest Model.# Part 1 - Data Preprocessing # Importing the libraries import numpy as np import matplotlib.pyplot as plt import pandas as pd # Importing the dataset dataset = pd.read_csv('Churn_Modelling.csv') X = dataset.iloc[:, 3:13] y = dataset.iloc[:, 13] dataset.head()#Create dummy variables geography=pd.get_dummies(X["Geography"],drop_first=True) gender=pd.get_dummies(X['Gender'],drop_first=True)## Concatenate the Data Frames X=pd.concat([X,geography,gender],axis=1) ## Drop Unnecessary columns X=X.drop(['Geography','Gender'],axis=1)Now, we save the model pickle file and use the lime and shap libraries for explainability.import pickle pickle.dump(classifier, open("classifier.pkl", 'wb')) pip install lime pip install shap import lime from lime import lime_tabular interpretor = lime_tabular.LimeTabularExplainer( training_data=np.array(X_train), feature_names=X_train.columns, mode='classification')Lime has a Lime Tabular module to set the explainability module for tabular data. We pass in the training dataset, and the mode of the model as classification here.exp = interpretor.explain_instance( data_row=X_test.iloc[5], ##new data predict_fn=classifier.predict_proba) exp.show_in_notebook(show_table=True) We can see from the above chart, that Lime is able to explain one particular prediction from X_test in detail. The prediction here is 1 - (Churn is True), and the features that are contributing positively are represented in orange, and negatively are shown in blue.import shap shap.initjs() explainer = shap.Explainer(classifier) shap_values = explainer.shap_values(X_test) shap.summary_plot(shap_values, X_test)In the above code snippet, we have created a plot for explainability using the shap library. The Shap library here gives a global explanation for the entire test dataset, compared to LIME which focuses on local interpretation.From the below graph, we can see which features contribute to how much for each of the churn classes.ConclusionExplainability in AI enables trust in AI systems, and enables us to dive deeper in understanding the reasoning behind the models, and make appropriate updates to models in case there are any biases. In this article, we used libraries such as SHAP and LIME that make explainability easier to design and implement.Author BioSwagata Ashwani serves as a Principal Data Scientist at Boomi, where she leads the charge in deploying cutting-edge AI solutions, with a particular emphasis on Natural Language Processing (NLP). With a stellar track record in AI research, she is always on the lookout for the next state-of-the-art tool or technique to revolutionize the industry. Beyond her technical expertise, Swagata is a fervent advocate for women in tech. She believes in giving back to the community, regularly contributing to open-source initiatives that drive the democratization of technology.Swagata's passion isn't limited to the world of AI; she is a nature enthusiast, often wandering beaches and indulging in the serenity they offer. With a cup of coffee in hand, she finds joy in the rhythm of dance and the tranquility of the great outdoors.

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!IntroductionAs data volumes grow exponentially, converting vast amounts of data into digestible, human-friendly "information assets" has become a blend of art and science. This process requires understanding the data's context, the intended objective, the target audience. The final step of curating and converting information assets into a cohesive narrative that results in human understanding and persuasion is known as “Data Storytelling.”The Essence of Data StorytellingCrafting a compelling data story means setting a clear objective, curating information, and narrating relevant information assets. Good stories communicate effectively, leaving a lasting impression, and in some cases, sway opinions or reinforce previously accepted knowledge. A common tactic to tell an influential data story typically contains quantitative facts and statistics.Modern enterprises utilize a myriad of techniques to convert accumulated data into valuable information assets.  The vast amounts of analytical and qualitative information assets get distributed in the form of reports and dashboards. These assets are informational to support process automation, decision support, and facilitate future predictions.  Many curated stories manifest as summarized text and charts, often taking the form of a document, spreadsheet, or presentation.The Potential of Generative AIWhile analytics and machine learning are adept at tackling large-scale computational challenges, the growing demand for curated explanations has elevated data storytelling to an important discipline. However, the number of skilled data storytellers remains limited due to the unique blend of technical and soft skills required. This has led to a high demand for quality data stories and a scarcity of adept professionals. Generative AI provides a promising solution by automating many of the data storytelling steps and simulating human interpretation and comprehension.Crafting Dynamic Narratives with AIConstructing a coherent narrative from data is intricate. However, the emergence of powerful generative AI models, such as BARD and ChatGPT, offers hope. These platforms, initially popularized by potent large language models, have recently incorporated computer vision to interpret and generate visual data. Given the right context and curated data, these AI models seem ready to address several challenges of data storytelling.Presently, publicly available generative AI models can produce code-based data analysis, interpret and present findings, experiment with story structures, extract insights, and customize messages based on user personas. Generative AI isn’t adapted to prepare complete visual stories with quantitative data. Merging and chaining multiple AI agents will open the door to translating and delivering visual stories.The essence of data storytelling lies in narration, and occasionally, in persuasion or creative representation of information. Unmonitored nuances in AI-generated data narratives can be misleading or flat-out incorrect. There is no scenario where data storytelling should go un-aided by a human reviewer.How Generative AI Can Aid Traditional Descriptive Analytics StoriesVarious data analysis techniques, including scatter plots, correlation mattresses, and histograms, help unveil the relationships and patterns within the data. Identifying trends and visualizing the data distribution process, the exploratory data analysis technique guides in selecting the appropriate analysis method. Large language models are not designed to perform computational and quantitative analysis. However, they are quite proficient at generating coding capable of and translating questions to code into quantitative analysis only when context and data are well defined.Understanding Data ContextBefore choosing any technique to generate narratives, it's essential to understand the context of a dataset. Delving deep into domain-specific nuances ensures narratives that are both focused and relevant.● Preprocessing and Data CleaningClean and well-processed data form the bedrock of impactful analyses. Both human-built data stories and AI-augmented narratives falter with poor data. Advancements in data management, governance, and descriptive analytics are pivotal in paving the way for superior AI-enhanced stories. Proper preprocessing and quality data are prerequisites for crafting reliable narratives.● Exploratory Data Analysis TechniquesData exploration without context and understanding of the data is challenging. However, with a very good dictionary, business glossary, and good data large language models can easily write code capable of streamlining basic data exploration with a human in the loop steering the process.● Machine Learning with Advanced AnalyticsTo create dynamic and impactful narratives, it's vital to employ a range of advanced analytical methods, such as regression analysis and clustering. These techniques help unearth detailed insights. Similarly, Generative AI can efficiently conduct basic statistical analyses. It can also deploy machine learning algorithms like decision trees and random forests to discern intricate patterns. Weaving these insights into narrative techniques imparts a layer of sophistication and depth to the storytelling.● Interactive Data VisualizationInteractive visualization tools enable users to dynamically explore data. Traditionally, this area has been predominantly the domain of human analysis. While machines can interpret data patterns without visual aids, rapid advancements in computer vision and generative AI are bridging the gap. Today's AI can interpret visual representations of data, deriving meaning from visualizations. It's becoming increasingly common for dashboards to be reviewed and summarized by AI, a trend likely to continue.ExampleHere is a simple example in action where we have a data insight that we want to translate to a simple infographic:Data Insight: Our customer demographics include 45% millennials, 30% Gen X, and 25% baby boomers.Prompt: Create a visual representation to tell the story via wolframResult: The result with Wolram enabled is a conversion of my prompt and data insights into code that can be translated to a visual infographic REQUEST TO WOLFRAM{ "input": "BarChart[{45, 30, 25}, ChartLabels -> {\"Millennials\", \"Gen X\", \"Baby Boomers\"}, ChartStyle -> {RGBColor[0, 0.5, 1], RGBColor[0, 0.8, 0], RGBColor[1, 0.5, 0]}, PlotLabel -> \"Customer Demographics\", AxesLabel -> {\"Demographic\", \"Percentage\"}]" }RESPONSE FROM WOLFRAM{ "output": "https://www.wolframcloud.com/obj/86f3ba22-6a46-412d-88e1-b90064001407" }How Generative AI Can Aid Narrative CreationNatural language processing One of the techniques to generate dynamic narratives is the implementation of natural language processing. The technique helps in extracting insights from contextual data. Sometimes, sentiment analysis and topic modeling help in processing textual information while changing the outlook of unstructured data into valuable narratives. At the same time, integrating the same analysis can increase understanding of the narrative.Data storytellingAs a user, if you want to draft a compelling data story, you need to structure the narrative coherently. Starting with a captivating introduction accompanied by data-driven insights and their implications enhances the method. Such compelling storytelling ensures that the focal message is conveyed with clarity.Data Insight: Our website traffic increased by 25% after the recent marketing campaign. Sales of Product A saw a significant boost, contributing to 40% of total revenue.Prompt: Create a visual representation to tell the story via wolfram and a data-driven story based on the following insightsResult:REQUEST TO WOLFRAM:{ "input": "GraphicsRow[{BarChart[{100, 125}, ChartLabels -> {\"Before Campaign\", \"After Campaign\"}, ChartStyle -> {Blue, Green}, AxesLabel -> {\"\", \"Traffic\"}, PlotLabel -> \"Website Traffic\"], PieChart[{40, 60}, ChartLabels -> {\"Product A\", \"Other Products\"}, ChartStyle -> {Red, Yellow}, PlotLabel -> \"Revenue Contribution\"]}]" }RESPONSE FROM WOLFRAM:{ "output": "https://www.wolframcloud.com/obj/7221f424-771c-4a02-997b-f9bf8fade843" }Narrative: "After launching our recent marketing campaign, we witnessed a remarkable surge in our website traffic. The numbers speak for themselves: a significant 25% increase! This not only brought more visitors to our platform but also translated into tangible business outcomes. A standout performer was Product A, which became a major revenue driver. It alone accounted for a whopping 40% of our total revenue, highlighting its immense popularity among our customers. The campaign not only amplified our reach but also reinforced the market's love for Product A. It's evident that our marketing efforts and Product A together created a winning combination for our business."How can AI-aided storytelling drive operations efficiently?AI-aided storytelling methods don’t always require creating information assets from raw data. Generative AI can assist in translating previously created information assets or summaries into the new explorative analyses.Data Insight: In the last quarter, our sales increased by 20%. Customer engagement on social media platforms grew by 35%.Prompt: How can we summarize the information to tell a good data story? What are some additional insights we should investigate?Result from Large Language Model:Additional Insights to Investigate: 1. Source of Traffic and Sales: Which social media platforms contributed the most to the increased engagement? Are there specific platforms that are driving more sales compared to others? 2. Content Analysis: What type of content (videos, articles, images, etc.) received the most engagement? Were there specific campaigns or promotions that were particularly successful? 3. Customer Demographics: Has there been a change in the demographics of the customers who are purchasing? Are younger or older age groups more engaged on social media?From the above prompt and resulting information it is clear how generative AI helps in crafting the story and further exploring complex information relationships in ways that previously required expertise. As a result, large lanague models in the exploration phase can help:Improve the exploration ideas and hypothesis.Creating quck infographicsAutomate the manual process of narrative creation and summarization.Insights of leveraging AI for more effective data communication There can be various ways of leveraging artificial intelligence to incorporate data communication effectively. These are the top areas where generative AI can help boost the operation.Streamlined Unstructured Data AnalysisLarge language models are very good at ingesting and interpreting bodies of text and interpreting and extracting summarized facts. The latest generative AI tools, including OpenAI, now support interpreting large documents including PDFs and spreadsheets as large as 100MB. For example, you can potentially import and interpret an annual statement and summarize it. For large data sets representing many documents or gigabytes of data, there are a number of ingest, indexing, and large language model integration techniques to effectively expand the body of knowledge and context your model has to formulate data stories.Advancing analyticsDemocratization of data science is further expanded thanks to large language models’ ability to interpret and write code, notably Python. Using code interpreter, we can easily feed AI tens or hundreds of thousands of data points and predict the most predictive variables that determine a target variable. For example, after importing 50,000 records and feeding the following prompt a modern AI model can automatically perform steps traditionally carried out by a data analyst or data scientist. To successfully use this method, you still need to understand and interpret results and direct any miscalculations. For example, the AI model will not help you understand variables that overfit.The steps we'll follow are:1. Data Preprocessing:Handle missing values.Encode categorical variables.Split the dataset into training and testing sets.2. Train a model (e.g., random forest) using the data.3. Extract feature importance to determine the most predictive variables.Resulting code example: # Redefining the RandomForestClassifier rf = RandomForestClassifier(n_estimators=100, random_state=42) # Training a RandomForest model rf.fit(X_train, y_train) # Extracting feature importances feature_importances = rf.feature_importances_ sorted_idx = feature_importances.argsort()[-10:][::-1]  # We'll display the top 10 features top_features = X.columns[sorted_idx] top_importances = feature_importances[sorted_idx] top_features, top_importancesResulting code example:Based on the Random Forest model, the top 10 most predictive variables for "Bad" are…ConclusionThere are numerous opportunities to utilize generative AI to aid data storytelling. While the techniques covered in this article help streamline the process, there is no magic easy button to automate data storytelling end to end. In the world of data storytelling, AI is a perfect augmentation tool to craft a narrative, perform analysis, and inspire new ideas. However, the competitive and disruptive world of AI is already merging computer vision, code interpretation, and generative AI further opening the door for new techniques. As images and text converge with code interpretation, we should see new distributive storytelling tools emerge. The most important ingredient is always the data itself. I am excited to continue to explore and experiment with these advancements to streamline my data storytelling abilities.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!IntroductionLarge Language Models, or LLMs for short, are becoming a big deal in the world of technology. They're powerful and can do a lot, but they're not always easy to handle. Just like when building a big tower, you want to make sure everything goes right from the start to the finish. That's where Weights & Biases, often called W&B, comes in. It's a tool that helps people keep an eye on how their models are doing. In this article, we'll talk about why it's so important to watch over LLMs, how W&B helps with that, and how to use it. Let's dive in!Large Language Models (LLMs)Large Language Models (LLMs) are machine learning models trained on vast amounts of text data to understand and generate human-like text. They excel in processing and producing language, enabling various applications like translation, summarization, and conversation.LLMs, such as GPT-3 by OpenAI, utilize deep learning architectures to learn patterns and relationships in the data, making them capable of sophisticated language tasks. Through training on diverse datasets, they aim to comprehend context, semantics, and nuances akin to human communication.When discussing the forefront of natural language processing, several Large Language Models (LLMs) consistently emerge: The Need for Debugging & Monitoring LLMsUnderstanding and overseeing Large Language Models (LLMs) is much like supervising an intricate machine: they're powerful, and versatile, but require keen oversight.Firstly, think about the intricacy of LLMs. They far surpass the complexity of your typical day-to-day machine learning models. While they hold immense potential to revolutionize tasks involving language - think customer support, content creation, and translations - their intricate designs can sometimes misfire. If we're not careful, instead of a smooth conversation with a chatbot, users might encounter bewildering responses, leading to user frustration and diminished trust.Then there's the matter of resources. Training LLMs isn't just about the time; it's also financially demanding. Each hiccup, if not caught early, can translate to unnecessary expenditures. It's much like constructing a skyscraper; mid-way errors are costlier to rectify than those identified in the blueprint phase.Introduction to Weights & BiasesSourceWeights & Biases (W&B) is a cutting-edge platform tailored for machine learning practitioners. It offers a suite of tools designed to help streamline the model development process, from tracking experiments to visualizing results.With W&B, researchers and developers can efficiently monitor their LLM training progress, compare different model versions, and collaborate with team members. It's an invaluable asset for anyone looking to optimize and scale their machine-learning workflows.How to Use W&B for Debugging & Monitoring LLMsIn the hands-on section of this article, we will adhere to the following structured approach, illustrated in the diagram below. We will fine-tune our model and leverage Weights and biases to save critical metrics, tables, and visualizations. This will empower us with deeper insights, enabling efficient debugging and monitoring of our Large Language Models. 1. Setting up Weights and Biasesa. Importing Necessary Librariesimport torch import wandb from transformers import BertTokenizer, BertForSequenceClassification from torch.utils.data import DataLoader, random_split from datasets import load_datasetIntizailaizing W&B # Initialize W&B wandb.init(project='llm_monitoring', name='bert_example')b. Loading the BERT Model# Load tokenizer and model tokenizer = BertTokenizer.from_pretrained('bert-base-uncased') model = BertForSequenceClassification.from_pretrained('bert-base-uncased')2. Fine-tuning your Modela. Loading your datasetdataset = load_dataset('Load your dataset')b. Fine-tuning the modelfor epoch in range(config.epochs):    model.train()    for batch in train_dataloader:       # ……….       # Continue training process here       # ………..3. Tracking Metrics# Log the validation metrics to W&B    wandb.log({        "Epoch": epoch,        "Validation Loss": avg_val_loss,        "Validation Accuracy": val_accuracy    })4. Graph Visualizationsa. Plotting and logging Training Loss Graphfig, ax = plt.subplots(figsize=(10,5)) ax.plot(train_losses, label="Training Loss", color='blue') ax.set(title="Training Losses", xlabel="Epoch", ylabel="Loss") wandb.log({"Training Loss Curve": wandb.Image(fig)})b. Plotting and logging Validation Loss Graphfig, ax = plt.subplots(figsize=(10,5)) ax.plot(val_losses, label="Validation Loss", color='orange') ax.set(title="Validation Losses", xlabel="Epoch", ylabel="Loss") wandb.log({"Validation Loss Curve": wandb.Image(fig)})c. Plotting and Log Validation Accuracy Graphfig, ax = plt.subplots(figsize=(10,5)) ax.plot(val_accuracies, label="Validation Accuracy", color='green') ax.set(title="Validation Accuracies", xlabel="Epoch", ylabel="Accuracy") wandb.log({"Validation Accuracy Curve": wandb.Image(fig)})d. Plotting and Log Training Accuracy Graphfig, ax = plt.subplots(figsize=(10,5)) ax.plot(train_accuracies, label="Training Accuracy", color='blue') ax.set(title="Training Accuracies", xlabel="Epoch", ylabel="Accuracy") wandb.log({"Training Accuracy Curve": wandb.Image(fig)})5. Manual Checkupsquestions = ["What's the weather like?", "Who won the world cup?", "How do you make an omelette?", "Why is the sky blue?", "When is the next holiday?"] old_model_responses = ["It's sunny.", "France won the last one.", "Mix eggs and fry them.", "Because of the atmosphere.", "It's on December 25th."] new_model_responses = ["The weather is clear and sunny.", "Brazil was the champion in the previous world cup.", "Whisk the eggs, add fillings, and cook in a pan.", "Due to Rayleigh scattering.", "The upcoming holiday is on New Year's Eve."] # Create a W&B Table table = wandb.Table(columns=["question", "old_model_response", "new_model_response"]) for q, old, new in zip(questions, old_model_responses, new_model_responses):    table.add_data(q, old, new) # Log the table to W&B wandb.log({"NLP Responses Comparison": table}) 6. Closing the W&B run after all logs are uploadedwandb.finish()ConclusionLarge Language Models have truly transformed the landscape of technology. Their vast capabilities are nothing short of amazing, but like all powerful tools, they require understanding and attention. Fortunately, with platforms like Weights & Biases, we have a handy toolkit to guide us. It reminds us that while LLMs are game-changers, they still need a bit of oversight.Author BioMostafa Ibrahim is a dedicated software engineer based in London, where he works in the dynamic field of Fintech. His professional journey is driven by a passion for cutting-edge technologies, particularly in the realms of machine learning and bioinformatics. When he's not immersed in coding or data analysis, Mostafa loves to travel.Medium

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!Understanding customer needs is critical for business success. Segmenting customers into groups with common traits allows for targeting products, marketing, and services. This guide will walk through customer segmentation using PandasAI, a Python library that makes the process easy and accessible.OverviewWe'll work through segmenting sample customer data step-by-step using PandasAI's conversational interface. Specifically, we'll cover:Loading and exploring customer dataSelecting segmentation featuresDetermining the optimal number of clustersPerforming clustering with PandasAIAnalyzing and describing the segmentsFollow along with the explanations and code examples below to gain hands-on experience with customer segmentation in Python.IntroductionCustomer segmentation provides immense value by enabling tailored messaging and product offerings. But for many, the technical complexity makes segmentation impractical. PandasAI removes this barrier by automating the process via simple conversational queries.In this guide, we'll explore customer segmentation hands-on by working through examples using PandasAI. You'll learn how to load data, determine clusters, perform segmentation, and analyze results. The steps are accessible even without extensive data science expertise. By the end, you'll be equipped to leverage PandasAI's capabilities to unlock deeper customer insights. Let's get started!Step 1 - Load Customer DataWe'll use a fictional customer dataset customers.csv containing 5000 rows with attributes like demographics, location, transactions, etc. Let's load it with Pandas:import pandas as pd customers = pd.read_csv("customers.csv")Preview the data:customers.head()This gives us a sense of available attributes for segmentation.Step 2 - Select Segmentation FeaturesNow we need to decide which features to use for creating customer groups. For this example, let's select:AgeGenderCityNumber of TransactionsExtract these into a new DataFrame:segmentation_features = ['age', 'gender', 'city', 'transactions'] customer_data = customers[segmentation_features]Step 3 - Determine Optimal Number of ClustersA key step is choosing the appropriate number of segments k. Too few reduces distinction, too many makes them less useful.Traditionally, without using PandasAI, we should apply the elbow method to identify the optimal k value for the data. Something like this:from sklearn.cluster import KMeans from sklearn.preprocessing import OneHotEncoder from sklearn.impute import SimpleImputer import pandas as pd import matplotlib.pyplot as plt # Handle missing values by imputing with the most frequent value in each column imputer = SimpleImputer(strategy='most_frequent') df_imputed = pd.DataFrame(imputer.fit_transform(customers), columns=customers.columns) # Perform one-hot encoding for the 'gender' and 'city' columns encoder = OneHotEncoder(sparse=False) gender_city_encoded = encoder.fit_transform(df_imputed[['gender', 'city']]) # Concatenate the encoded columns with the original DataFrame df_encoded = pd.concat([df_imputed, pd.DataFrame(gender_city_encoded, columns=encoder.get_feature_names_out(['gender', 'city']))], axis=1) # Drop the original 'gender' and 'city' columns as they're no longer needed after encoding df_encoded.drop(columns=['gender', 'city'], inplace=True) # Calculate SSE for k = 1 to 9 sse = {} for k in range(1, 9): km = KMeans(n_clusters=k) km.fit(df_encoded) sse[k] = km.inertia_ # Plot elbow curve plt.plot(list(sse.keys()), list(sse.values())) plt.xlabel("Number of Clusters") plt.ylabel("SSE") plt.show()Examining the elbow point, 4 seems a good number of clusters for this data, so we’ll create 4 clusters.Too complicated? You can easily let PandasAI do it for you.customers.chat("What is the ideal amount of clusters for the given dataset?") # 4 PandasAI will use a silhouette score under the hood to calculate the optimal amount of clusters based on your data.Silhouette score is a metric used to evaluate the goodness of a clustering model. It measures how well each data point fits within its assigned cluster versus how well it would fit within other clusters.PandasAI leverages silhouette analysis to pick the optimal number of clusters for k-means segmentation based on which configuration offers the best coherence within clusters and distinction between clusters for the given data.Step 4 - Perform Clustering with PandasAINow we'll use PandasAI to handle clustering based on the elbow method insights.First import and initialize a SmartDataFrame:from pandasai import SmartDataframe sdf = SmartDataframe(customers)Then simply ask PandasAI to cluster:segments = sdf.chat(""" Segment customers into 4 clusters based on their age, gender, city and number of transactions. """)This performs k-means clustering and adds the segment labels to the original data. Let's inspect the results:print(segments)Each customer now has a cluster segment assigned.Step 5 - Analyze and Describe ClustersWith the clusters created, we can derive insights by analyzing them:centers = segments.chat("Show cluster centers") print(centers)Step 6 - Enrich Analysis with Additional DataOur original dataset contained only a few features. To enhance the analysis, we can join the clustered data with additional customer info like:Purchase historyCustomer lifetime valueEngagement metricsProduct usageRatings/reviewsBringing in other datasets allows drilling down into each segment with a deeper perspective.For example, we could join the review history and analyze customer satisfaction patterns within each cluster:# Join purchase data to segmented dataset enriched_data = pd.merge(segments, reviews, on='id') # Revenue for each cluster enriched_data.groupby('cluster').review_score.agg(['mean', 'median', 'count'])This provides a multidimensional view of our customers and segments, unlocking richer insights and enabling a more in-depth analysis for additional aggregate metrics for each cluster.ConclusionIn this guide, we worked through segmenting sample customer data step-by-step using PandasAI. The key aspects covered were:Loading customer data and selecting relevant featuresUsing the elbow method to determine the optimal number of clustersPerforming k-means clustering via simple PandasAI queriesAnalyzing and describing the created segmentsSegmentation provides immense value through tailored products and messaging. PandasAI makes the process accessible even without extensive data science expertise. By automating complex tasks through conversation, PandasAI allows you to gain actionable insights from your customer data.To build on this, additional data like customer lifetime value or engagement metrics could provide even deeper understanding of your customers. The key is asking the right questions – PandasAI handles the hard work to uncover meaningful answers from your data.Now you're equipped with hands-on experience leveraging PandasAI to simplify customer segmentation in Python.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!IntroductionIn the era of rapid technological advancements, speech recognition technology has emerged as a game-changer, revolutionizing how we interact with machines and devices.As we know, OpenAI has developed an exceptional Automatic Speech Recognition (ASR) system known as OpenAI Whisper.In this blog, we will deeply dive into Whisper, understanding its capabilities, applications, and how you can harness its power through the Whisper API.Understanding OpenAI WhisperWhat is OpenAI Whisper?Well, put-“You Speak…AI Writes” OpenAI Whisper is an advanced ASR system that converts spoken language into written text.Built on cutting-edge technology and trained on 680,000 hours of multilingual and multitask supervised data collected from the web, OpenAI Whisper excels in a wide range of speech recognition tasks, making it a valuable tool for developers and businesses.Why Does ASR (Automatic Speech Recognition) Matter?Automatic Speech Recognition (ASR) is not just a cutting-edge technology; it's a game-changer reshaping how we interact with our digital world. Imagine a world where your voice can unlock a wealth of possibilities. That's what ASR, with robust systems like Whisper leading the charge, has made possible.Let's dive deeper into the ASR universe.It's not just about making life more convenient; it's about leveling the playing field. ASR technology is like the magic wand that enhances accessibility for individuals with disabilities. It's the backbone of those voice assistants you chat with and the transcription services that make your voice immortal in text.But ASR doesn't stop there; it's a versatile tool taking over various industries. Picture this: ASR helps doctors transcribe patient records in healthcare with impeccable accuracy and speed. That means better care for you. And let's remember the trusty voice assistants like Siri and Google Assistant, always at your beck and call, answering questions and performing tasks, all thanks to ASR's natural language interaction wizardry. Setup and InstallationWhen embarking on your journey to harness the remarkable power of OpenAI Whisper for Automatic Speech Recognition (ASR), the first crucial step is to set up and install the necessary components.In this section, we will guide you through starting with OpenAI Whisper, ensuring you have everything in place to begin transcribing spoken words into text with astonishing accuracy.Prerequisites Before you dive into the installation process, it's essential to make sure you have the following prerequisites in order:OpenAI Account To access OpenAI Whisper, you must have an active OpenAI account. If you still need to sign up, visit the OpenAI website and create an account.API Key You will need an API key from OpenAI to make API requests. This key acts as your access token to use the Whisper ASR service. Ensure you have your API key ready; if you don't have one, you can obtain it from your OpenAI account.Development Environment It would help to have a functioning development environment for coding and running API requests. You can use your preferred programming language, Python, to interact with the Whisper API. Make sure you have the necessary libraries and tools installed.Installation Steps Now, let's walk through the steps to install and set up OpenAI Whisper for ASR:1. Install the OpenAI Python LibraryIf you haven't already, you must install the OpenAI Python library. This library simplifies the process of making API requests to OpenAI services, including Whisper. You can install it using pip, the Python package manager, by running the following command in your terminal: pip install openai2. Authenticate with Your API KeyYou must authenticate your requests with your API key to interact with the Whisper ASR service. You can do this by setting your API key as an environment variable in your development environment or by directly including it in your code. Here's how you can set the API key as an environment variable:import openai openai.api_key = "YOUR_API_KEY_HERE" Replace "YOUR_API_KEY_HERE" with your actual API key.3. Make API RequestsWith the OpenAI Python library installed and your API key adequately set, you can now start making API requests to Whisper. You can submit audio files or chunks of spoken content and receive transcriptions in response. import openai response = openai.Transcription.create( model="whisper", audio="YOUR_AUDIO_FILE_URL_OR_CONTENT", language="en-US"  # Adjust language code as needed ) print(response['text']) Replace "YOUR_AUDIO_FILE_URL_OR_CONTENT" with the audio source you want to transcribe.Testing Your SetupAfter following these installation steps, testing your setup with a small audio file or sample content is a good practice.This will help you verify that everything functions correctly and that you can effectively convert spoken words into text.Use Cases And ApplicationsTranscription ServicesWhisper excels in transcribing spoken words into text. This makes it a valuable tool for content creators, journalists, and researchers whose work demands them to convert audio recordings into written documents.Voice Assistants Whisper powers voice assistants and chatbots, enabling natural language understanding and interaction. This is instrumental in creating seamless user experiences in applications ranging from smartphones to smart home devices.AccessibilityWhisper enhances accessibility for individuals with hearing impairments by providing real-time captioning services during live events, presentations, and video conferences.Market ResearchASR technology can analyze customer call recordings, providing businesses with valuable insights and improving customer service.Multilingual SupportWhisper supports multiple languages, making it a valuable asset for global companies looking to reach diverse audiences.Making Your First API CallNow that you have your Whisper API key, it's time to make your first API call. Let's walk through a simple example of transcribing spoken language into text using Python.pythonCopy code:import openai # Replace 'your_api_key' with your actual Whisper API key openai.api_key = 'your_api_key' response = openai.Transcription.create( audio="<https://your-audio-url.com/sample-audio.wav>", model="whisper", language="en-US" ) print(response['text'])In this example, we set up the API key, specify the audio source URL, select the Whisper model, and define the language. The response['text'] contains the transcribed text from the audio.Use CasesLanguage DetectionOne of the remarkable features of OpenAI Whisper is its ability to detect the language being spoken.This capability is invaluable for applications that require language-specific processing, such as language translation or sentiment analysis.Whisper's language detection feature simplifies identifying the language spoken in audio recordings, making it a powerful tool for multilingual applications.TranscriptionTranscription is one of the most common use cases for Whisper. Whether you need to transcribe interviews, podcasts, or customer service calls, Whisper's accuracy and speed make it an ideal choice.Developers can integrate Whisper to automate transcription, saving time and resources.Supported LanguagesOpenAI Whisper supports many languages, making it suitable for global applications. Some supported languages include English, Spanish, French, German, Chinese, and others.Open AI supports all these languages as of now-Afrikaans, Arabic, Armenian, Azerbaijani, Belarusian, Bosnian, Bulgarian, Catalan, Chinese, Croatian, Czech, Danish, Dutch, English, Estonian, Finnish, French, Galician, German, Greek, Hebrew, Hindi, Hungarian, Icelandic, Indonesian, Italian, Japanese, Kannada, Kazakh, Korean, Latvian, Lithuanian, Macedonian, Malay, Marathi, Maori, Nepali, Norwegian, Persian, Polish, Portuguese, Romanian, Russian, Serbian, Slovak, Slovenian, Spanish, Swahili, Swedish, Tagalog, Tamil, Thai, Turkish, Ukrainian, Urdu, Vietnamese, and Welsh.Best Practices While Using WhisperWhen working with Whisper for longer-format audio or complex tasks, following best practices is essential. For instance, you can break longer audio files into shorter segments for improved accuracy. Additionally, you can experiment with different settings and parameters to fine-tune the ASR system according to your specific requirements.Here's a simple example of how to break down more extended audio for transcription:pythonCopy codeimport openai # Replace 'your_api_key' with your actual Whisper API key openai.api_key = 'your_api_key' # Divide the longer audio into segments audio_segments = [    "<https://your-audio-url.com/segment1.wav>",    "<https://your-audio-url.com/segment2.wav>",    # Add more segments as needed ] # Transcribe each segment separately for segment in audio_segments:    response = openai.Transcription.create(        audio=segment,        model="whisper",        language="en-US"    )    print(response['text'])These best practices and tips ensure you get the most accurate results when using OpenAI Whisper.ConclusionIn this blog, we've explored the incredible potential of OpenAI Whisper, an advanced ASR system that can transform how you interact with audio data. We've covered its use cases, how to access the Whisper API, make your first API call, and implement language detection and transcription. With its support for multiple languages and best practices for optimizing performance, Whisper is a valuable tool for developers and businesses looking to harness the power of automatic speech recognition.In our next blog post, we will delve even deeper into OpenAI Whisper, exploring its advanced features and the latest developments in ASR technology. Stay tuned for "Advances in OpenAI Whisper: Unlocking the Future of Speech Recognition."For now, start your journey with OpenAI Whisper by requesting access to the API and experimenting with its capabilities. The possibilities are endless, and the power of spoken language recognition is at your fingertips.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!IntroductionIn the modern digital age, artificial intelligence has changed how we handle complex tasks, including image analysis. Advanced models like ChatGPT have made this process more interactive and insightful. Instead of a basic understanding, users can now guide the system through prompts to get a detailed analysis of an image. This approach helps in revealing both broad themes and specific details. In this blog, we will look at how ChatGPT responds to a series of prompts, demonstrating the depth and versatility of AI-powered image analysis. Let’s startHere's a step-by-step guide to doing image analysis with ChatGPT:1. PreparationEnsure you have the image in an accessible format, preferably a common format such as JPEG, PNG, etc.Ensure the content of the image is suitable for analysis and doesn't breach any terms of service.2. Upload the ImageUse the platform's interface to upload the image to ChatGPT.3. Specify Your RequirementsClearly mention what you are expecting from the analysis. For instance:Identify objects in the image.Analyze the colors used.Describe the mood or theme.Any other specific analysis.4. Receive the AnalysisChatGPT will process the image and provide an analysis based on the information and patterns it recognizes. 5. Ask Follow-up QuestionsIf you have further questions about the analysis or if you require more details, feel free to ask.6. Iterative Analysis (if required)Based on the feedback and results, you might want to upload another image or ask for a different type of analysis on the same image. Follow steps 2-5 again for this.7. Utilize the AnalysisUse the given analysis for your intended purpose, whether it's for research, personal understanding, design feedback, etc.8. Review and FeedbackReflect on the accuracy and relevance of the provided analysis. Remember, while ChatGPT can provide insights based on patterns, it might not always capture the nuances or subjective interpretations of an image.Now to perform the image analysis we have deployed the Chain prompting technique. Here’s an example:Chain Prompting: A Brief OverviewChain prompting refers to the practice of building a sequence of interrelated prompts that progressively guide an artificial intelligence system to deliver desired responses. By initiating with a foundational prompt and then following it up with subsequent prompts that build upon the previous ones, users can engage in a deeper and more nuanced interaction with the system.The essence of chain prompting lies in its iterative nature. Instead of relying on a single, isolated question, users employ a series of interconnected prompts that allow for refining, expanding, or branching the AI's output. This approach can be particularly useful in situations where a broad topic needs to be explored in depth, or when the user is aiming to extract multifaceted insights.For instance, in the domain of image analysis, an initial prompt might request a general description of an image. Subsequent prompts can then delve deeper into specific aspects of the image, ask for comparisons, or even seek interpretations based on the initial description. Now Let’s dissect the nature of prompts given in the example below for analysis. These prompts are guiding the system through a process of image analysis. Starting from a general interpretation, they progressively request more specific and actionable insights based on the content of the image. The final prompt adds a layer of self-reflection, asking the system to assess the nature of the prompts themselves.Prompt 1: Hey ChatGPT ...Can you read the image?The below roadmap was taken from the infographics shared on LinkedIn by Mr Ravit Jain and can be found here.Analysis: This prompt is a general inquiry to see if the system can extract and interpret information from the provided image. The user is essentially asking if the system has the capability to understand and process visual data.Response: Prompt 2: Can you describe the data science landscape based on the above image?Analysis: This prompt requests a comprehensive description of the content within the image, focusing specifically on the "data science landscape." The user is looking for an interpretation of the image that summarizes its main points regarding data science.Response:Prompt 3: Based on the above description generated from the image list top skills a fresher should have to be successful in a data science career.Analysis: This prompt asks the system to provide actionable advice or recommendations. Using the previously described content of the image, the user wants to know which skills are most essential for someone new ("fresher") to the data science field.Response:Prompt 4: Map the skills listed in the image to different career in data scienceAnalysis: This prompt requests a more detailed breakdown or categorization of the image's content. The user is looking for a mapping of the various skills mentioned in the image to specific career paths within data science.Response:Prompt 5: Map the skills listed in the image to different career in data science...Analyse these prompts and tell what they do for image analysisAnalysis: This prompt seems to be a combination of Prompt 4 and a meta-analysis request. The first part reiterates the mapping request from Prompt 4. The second part asks the system to provide a reflective analysis of the prompts themselves in relation to image analysis (which is what we're doing right now).ConclusionIn conclusion, image analysis, when used with advanced models like ChatGPT, offers significant benefits. Our review of various prompts shows that users can obtain a wide range of insights from basic image descriptions to in-depth interpretations and career advice. The ability to direct the AI with specific questions and modify the analysis based on prior answers provides a customized experience. As technology progresses, the potential of AI-driven image analysis will likely grow. For those in professional, academic, or hobbyist roles, understanding how to effectively engage with these tools will become increasingly important in the digital world.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!IntroductionIn today’s data-driven world, businesses constantly seek ways to extract more value from their data. One of the key strategies to accomplish this is Data Enrichment.Data Enrichment involves enhancing your existing datasets with additional information, which can lead to improved decision-making, customer engagement, and personalized experiences. In this blog, we’ll explore how to automate data enrichment using Snowflake, a powerful data warehousing platform, and Generative AI techniques.Understanding Data EnrichmentData Enrichment is simply the practice of enhancing your existing datasets with additional and relevant information. This supplementary data can include demographic data, geographic data, social media profiles, and much more. The primary goal is to improve the quality and depth of your data - making it more valuable for analytics, reporting, and decision-making.Why Automate Data Enrichment?Automating data enrichment not only saves time and resources but also improves data quality, supports real-time updates, and helps organizations stay competitive in an increasingly data-centric world. Whether in e-commerce, finance, healthcare, marketing, or any other industry, automation can be a strategic advantage that allows you to extract greater value from your data.EfficiencyManual data enrichment is time-consuming and resource-intensive. Automation allows you to process large volumes of data rapidly, reducing the time and effort required.ConsistencyHuman errors are common when manually enriching data. Automation ensures the process is consistent and accurate, reducing the risk of errors affecting decision-making.ScalabilityAs your organization grows and accumulates more data, automating data enrichment ensures you can handle larger datasets without a proportional increase in human resources.Enhanced Data QualityAutomated processes can validate and cleanse data, leading to higher data quality. High-quality data is essential for meaningful analytics and reporting.Competitive AdvantageIn a competitive business landscape, having access to enriched and up-to-date data can give you a significant advantage. It allows for more accurate market analysis, better customer understanding, and smarter decision-making.PersonalizationAutomated data enrichment can support personalized customer experiences, which are increasingly crucial for businesses. It allows you to tailor content, product recommendations, and marketing efforts to individual preferences and behaviors.Cost-EfficiencyWhile there are costs associated with setting up and maintaining automated data enrichment processes, these costs can be significantly lower in the long run compared to manual efforts, especially as the volume of data grows.Compliance and Data SecurityAutomated processes can be designed to adhere to data privacy regulations and security standards, reducing the risk of data breaches and compliance issues.ReproducibilityAutomated data enrichment processes can be documented, version-controlled, and easily reproduced, making it easier to audit and track changes over time.Data VarietyAs the sources and formats of data continue to expand, automation allows you to efficiently handle various data types, whether structured, semi-structured, or unstructured.Snowflake for Data EnrichmentSnowflake, a cloud-based data warehousing platform, provides powerful features for data manipulation and automation. Snowflake at the basic can be used to:Create tables for raw data and enrichment data.Load data into these tables using the COPY INTO command.Create views to combine raw and enrichment data based on common keys.Code Examples: Snowflake Data EnrichmentCreate TablesIn Snowflake, create tables for your raw data and enrichment data with: -  Create a table for raw data CREATE OR REPLACE TABLE raw_data (             Id INT,             name  STRING,             email  STRING ); -  Create a table for enrichment data CREATE OR REPLACE TABLE enrichment_data (         email  STRING,         location STRING,         age INT ); Load Data: Loading raw and enrichment data into their respective tables. -  Load raw data COPY INTO raw_data (id, name, email) FROM @<raw_data_stage>/raw_data.csv FILE_FORMAT = (TYPE = CSV); -  Load enrichment data COPY INTO enrichment_data (email, location, age) FROM @<enrichment_data_stage>/enrichment_data.csv FILE_FORMAT = (TYPE = CSV);Automate Data EnrichmentCreate a view that combines raw and enrichment data.-  Create a view that enriches the raw data CREATE OR REPLACE VIEW enriched_data AS SELECT    rd.id,    rd.name,    ed.location,    ed.age, -  Use generative AI to generate a description for the enriched date <Generative_AI_function> (ed.location, ed.age) AS description FROM       raw_data  rd JOIN      enrichment_data ed ON      rd.email = ed.email;Leveraging Snowflake for Data EnrichmentUsing Snowflake for data enrichment is a smart choice, especially if your organization relies on this cloud-based data warehousing platform. Snowflake provides a robust set of features for data manipulation and automation, making it an ideal environment to enhance the value of your data. Here are a few examples of how you can use Snowflake for data enrichment:Data Storage and ManagementSnowflake allows you to store and manage your data efficiently by separating storage and computing resources, which provides a scalable and cost-effective way to manage large data sets. You can store your raw and enriched data within Snowflake, making it readily accessible for enrichment processes.Data EnrichmentYou can perform data enrichment by combining data from your raw and enrichment tables. By using SQL JOIN operations to bring together related data based on common keys, such as email addresses.-        Create a view that enriches the raw data CREATE OR REPLACE VIEW enriched_data AS SELECT    rd.id,    rd.name,    ed.location,    ed.age, FROM       raw_data  rd JOIN      enrichment_data ed ON      rd.email = ed.email;Schedule UpdatesAutomating data enrichment by the creation of scheduled tasks within Snowflake. You can set up tasks to run at regular intervals, ensuring that your enriched data remains up to date.- Example: Creating a scheduled task to update enriched data CREATE OR REPLACE TASK update_enriched_data WAREHOUSE = <your_warehouse> SCHEDULE = ‘1 DAY’ AS INSERT INTO enriched_data (id, name, location, age) SELECT     rd.id,     rd.name,     ed.location,     ed.age FROM      raw_data rd JOIN     enrichment_data ed ON    rd.email = ed.email;Security and ComplianceSnowflake provides robust security features and complies with various data privacy regulations. Ensure that your data enrichment processes adhere to the necessary security and compliance standards to protect sensitive information.Monitoring and OptimizationRegularly monitoring the performance of your data enrichment processes. Snowflake offers tools for monitoring query execution so you can identify and address any performance bottlenecks. Optimization here is one of the crucial factors to ensure efficient data enrichment.Real-World ApplicationsData Enrichment is a powerful tool that stands for versatility in its real-world applications. Organizations across various sectors use it to improve their data quality, decision-making process, customer experiences, and overall operational efficiency. By augmenting their datasets with additional information, these organizations gain a competitive edge and drive innovation in their respective industries:E-commerce and RetailProduct Recommendations: E-commerce platforms use data enrichment to analyze customer browsing and purchase history. These enriched customer profiles help generate personalized product recommendations, increasing sales and customer satisfaction.Inventory Management: Retailers leverage enriched supplier data to optimize inventory management, ensuring they have the right products in stock at the right time.Marketing and AdvertisingCustomer Segmentation: Marketers use enriched customer data to create more refined customer segments. This enables them to tailor advertising campaigns and messaging for specific audiences, leading to higher engagement rates.Ad Targeting: Enriched demographic and behavioral data supports precise ad targeting. Advertisers can serve ads to audiences most likely to convert, reducing ad spend wastage.Financial ServicesCredit Scoring: Financial institutions augment customer data with credit scores, employment history, and other financial information to assess credit risk more accurately.Fraud Detection: Banks use data enrichment to detect suspicious activities by analyzing transaction data enriched with historical fraud patterns.HealthcarePatient Records: Healthcare providers enhance electronic health records (EHR) with patient demographics, medical histories, and test results. This results in comprehensive and up-to-date patient profiles, leading to better care decisions.Drug Discovery: Enriching molecular and clinical trial data accelerates drug discovery and research, potentially leading to breakthroughs in medical treatments.Social Media and Customer SupportSocial Media Insights: Social media platforms use data enrichment to provide businesses with detailed insights into their followers and engagement metrics, helping them refine their social media strategies.Customer Support: Enriched customer profiles enable support teams to offer more personalized assistance, increasing customer satisfaction and loyalty.ConclusionAutomating data enrichment with Snowflake and Generative AI is a powerful approach for businesses seeking to gain a competitive edge through data-driven insights. By combining a robust data warehousing platform with advanced AI techniques, you can efficiently and effectively enhance your datasets. Embrace automation, follow best practices, and unlock the full potential of your enriched data.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.