How-To Tutorials - ChatGPT

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

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

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

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

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

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

Dive deeper into the world of AI innovation and stay ahead of the AI curve! Subscribe to our AI_Distilled newsletter for the latest insights. Don't miss out – sign up today!This article is an excerpt from the book, Power BI Machine Learning and OpenAI, by Greg Beaumont. Master core data architecture design concepts and Azure Data & AI services to gain a cloud data and AI architect’s perspective to developing end-to-end solutions IntroductionIn this article, we delve into the exciting world of Power BI integration with OpenAI and Azure OpenAI. Data-driven decision-making is at the core of modern business, and harnessing the capabilities of AI models for generating text adds an invaluable dimension to your insights. Whether you're new to OpenAI or exploring the power of Azure OpenAI, we'll guide you through the technical requirements, API key setup, resource management, and dataflow optimization to seamlessly infuse AI-generated content into your Power BI projects. Let's embark on a journey to supercharge your data analytics capabilities and stay ahead in the ever-evolving world of data science.Technical requirementsFor this article, you’ll need the following:An account with the original open source OpenAI: https://openai.com/. • Optional – Azure OpenAI as part of your Azure subscription: https://azure.microsoft. com/en-us/products/cognitive-services/openai-service. The book is written so this is optional since it is not available to everyone at the time of publication.FAA Wildlife Strike data files from either the FAA website or the Packt GitHub site.• A Power BI Pro license.• One of the following Power BI licensing options for access to Power BI dataflows:Power BI PremiumPower BI Premium Per UserConfiguring OpenAI and Azure OpenAI for use in your Power BI solutionPrior to proceeding with the configuration of OpenAI and Azure OpenAI, it is important to note that OpenAI is still a nascent technology at the time of writing this book. In the future, the integration of OpenAI with Power BI may become less technical, as advancements in the technology continue to be made. However, the use cases that will be demonstrated in this chapter will remain applicable.As such, the instructions provided in this chapter will showcase how this integration can be used to enhance your data analytics capabilities in the context of Power BI.Configuring OpenAIYou can create an account in OpenAI (if you do not have one already) from this link: https:// chat.openai.com/auth/login. At the time of writing, new accounts are granted trial credits to begin using OpenAI. If you run out of trial credits, or if the trial is no longer offered after this book has been written, you may need to pay for the use of OpenAI. Pricing details can be found at this link: https://openai.com/pricing.Once you have an OpenAI account, you will need to create an API key that will be used to authenticate your API calls. An API key can be easily created at this link: https://platform.openai.com/ account/api-keys. Clicking on Create new secret key will allow you to create a new key for API calls that you make later in this chapter. This book will use abc123xyz as an example key for the sample code. Be sure to use the actual Key from OpenAI, and not the Key Name.Once you have an account and an API key, you are ready to go with OpenAI for this book!Configuring Microsoft Azure OpenAIOpenAI is also available as a service in Microsoft Azure. By using the Microsoft Azure OpenAI Service, users can leverage large-scale AI models with the benefits of Azure, such as role-based access security, private networks, and comprehensive security tools that integrate with other Microsoft tools in Azure. Billing and governance can be centralized for large organizations to help ensure the responsible use of AI.For the purposes of this book, Azure OpenAI is optional as an alternative to the original OpenAI. Azure OpenAI may not be available to everyone since it is a new technology with high demand. All of the content for the workshop can be done with either OpenAI or Azure OpenAI.Instructions for setting up Azure OpenAI can be found at this link: https://learn.microsoft. com/en-us/azure/cognitive-services/openai/how-to/create-resource/.Once you’ve created a resource, you can also deploy a model per the instructions at that link. As noted in Chapter 12, you will be using the text-davinci-003 model for the workshop associated with this chapter. OpenAI is evolving rapidly, and you may be able to choose different models at the time you are reading this book. Take note of the following values when walking through these steps; they will be needed later in this chapter:Resource name: Note the name of your Azure OpenAI resource in your subscription. This book will use PBI_OpenAI_project for the examples in this chapter.Deployment name: This is the name of the resource for the text-davinci-003 model deployment. This book will use davinci-PBIML for names of deployments in examples of code.Next, you’ll need to create a key for your Azure OpenAI API calls. From your Azure OpenAI resource, named PBI_OpenAI_project for this book, go to Resource management | Keys and endpoint, and your keys will be on that page. This book will use abc123xyz as an example key for the sample code.Once you have either OpenAI or Azure OpenAI set up and ready to go, you can add some new generative text capabilities to your project using FAA Wildlife Strike data!Preparing a Power BI dataflow for OpenAI and Azure OpenAIIn Chapter 12, you decided to use OpenAI for two use cases with your FAA Wildlife Strike database project:Generating descriptions of airplane models and the operator of the aircraft, for each incidentSummarizing the free text remarks provided in the report for each incidentSince OpenAI is still new at the time of writing this book, Power BI does not yet have connectors built into the product. But you can still call OpenAI and Azure OpenAI APIs from both Power Query and Power BI dataflows using custom M scripts. Let’s get started!First, you will create a new dataflow for use with OpenAI and Cognitive Services in Power BI:1. From your Power BI workspace, on the ribbon, select New | Dataflow.2. Select Define new tables | Link tables from other dataflows.3. Sign in and click Next.4. Expand your workspace.5. Expand the Strike Reports dataflow and check Strike Reports Curated New.6. Click Transform Data.7. Create a group named Sources and move Strike Reports Curated New into that group.8. Right-click Strike Reports Curated New and unselect Enable load.Next, you will create a version of the query that will be used with OpenAI and Cognitive Services:1. Right-click on Strike Reports Curated New and select Reference.2. Rename the new query Strike Reports Curated New OpenAI.3. Create a group named OpenAI and move Strike Reports Curated New OpenAI into the group.In Chapter 12, you decided to use the FAA Wildlife Strike Operator, Aircraft, Species, and Remarks database columns as part of your OpenAI prompts. Filtering out blank and unknown values from Strike Reports Curated New OpenAI will help produce better results for your testing. Note that you may need to select Load more... if the values all come up empty or UNKNOWN:1. For the Operator column, filter out the UNKNOWN, UNKNOWN COMMERCIAL, BUSINESS, and PRIVATELY OWNED values.2. For the Aircraft column, filter out UNKNOWN.3. For the Species column, filter out Unknown bird, Unknown bird – large, Unknown bird – medium, Unknown bird – small, and Unknown bird or bat.For the Remarks column, filter out (blank).Finally – this step is optional – you can filter the number of rows for testing purposes. Both OpenAI and Azure OpenAI can run up a bill, so limiting the number of calls for this workshop makes sense. For the example in this book, the Strike Reports Curated New OpenAI table will be filtered to events happening in or after December 2022, which can be filtered using the Incident Date column.Now you are ready to add OpenAI and Cognitive Services content to your data!ConclusionIn conclusion, configuring OpenAI and Azure OpenAI for integration with Power BI offers valuable enhancements to your data analytics capabilities. While OpenAI is still an evolving technology, the instructions provided in this article remain relevant and applicable. Whether you choose OpenAI or Azure OpenAI, both options empower you to leverage AI models effectively within Power BI.Setting up these services involves creating API keys, resources, and deployments, as outlined in the article. Additionally, preparing your Power BI dataflow for OpenAI and Azure OpenAI is a crucial step. You can filter and optimize your data to improve the quality of AI-generated content.As AI continues to advance, the potential for enhancing data analytics with OpenAI grows, and these configurations provide a strong foundation for leveraging generative text capabilities in your projects.Author BioGreg Beaumont is a Data Architect at Microsoft; Greg is an expert in solving complex problems and creating value for customers. With a focus on the healthcare industry, Greg works closely with customers to plan enterprise analytics strategies, evaluate new tools and products, conduct training sessions and hackathons, and architect solutions that improve the quality of care and reduce costs. With years of experience in data architecture and a passion for innovation, Greg has a unique ability to identify and solve complex challenges. He is a trusted advisor to his customers and is always seeking new ways to drive progress and help organizations thrive. For more than 15 years, Greg has worked with healthcare customers who strive to improve patient outcomes and find opportunities for efficiencies. He is a veteran of the Microsoft data speaker network and has worked with hundreds of customers on their data management and analytics strategies.

Dive deeper into the world of AI innovation and stay ahead of the AI curve! Subscribe to our AI_Distilled newsletter for the latest insights. Don't miss out – sign up today!This article is an excerpt from the book, Natural Language Understanding with Python, by Deborah A. Dahl. Combine natural language technology, deep learning, and large language models to create human-like language comprehension in computer systemsIntroductionIn the world of artificial intelligence, ChatGPT stands as a versatile conversational agent, adept at handling generic information interactions. While customization can be a challenge at present, ChatGPT offers a unique avenue for developers and AI enthusiasts alike. Beyond chat-based dialogue, it holds the potential to streamline the often time-consuming process of generating training data for conventional applications. In this article, we delve into the capabilities of ChatGPT and explore the journey of fine-tuning GPT-3 for specific use cases. By the end, you'll be equipped to harness the power of these language models, from data generation to AI customization, in your projects. Let's embark on this exciting AI journey together.ChatGPTChatGPT (https://openai.com/blog/chatgpt/) is a system that can interact with users about generic information in a very capable way. Although at the time of writing, it is hard to customize ChatGPT for specific applications, it can be useful for other purposes than customized natural language applications. For example, it can very easily be used to generate training data for a conventional application. If we wanted to develop a banking application using some of the techniques discussed earlier in this book, we would need training data to provide the system with examples of how users might ask the system questions. Typically, this involves a process of collecting actual user input, which could be very time-consuming. ChatGPT could be used to generate training data instead, by simply asking it for examples. For example, for the prompt give me 10 examples of how someone might ask for their checking balance, ChatGPT responded with the sentences in Figure 11.3:Figure 11.3 – GPT-3 generated training data for a banking applicationMost of these seem like pretty reasonable queries about a checking account, but some of them don’t seem very natural. For that reason, data generated in this way always needs to be reviewed. For example, a developer might decide not to include the second to the last example in a training set because it sounds stilted, but overall, this technique has the potential to save developers quite a bit of time.Applying GPT-3Another well-known LLM, GPT-3, can also be fine-tuned with application-specific data, which should result in better performance. To do this, you need an OpenAI key because using GPT-3 is a paid service. Both fine-tuning to prepare the model and using the fine-tuned model to process new data at inference time will incur a cost, so it is important to verify that the training process is performing as expected before training with a large dataset and incurring the associated expense.OpenAI recommends the following steps to fine-tune a GPT-3 model.1. Sign up for an account at https://openai.com/ and obtain an API key. The API key will be used to track your usage and charge your account accordingly.2.  Install the OpenAI command-line interface (CLI) with the following command:! pip install --upgrade openaiThis command can be used at a terminal prompt in Unix-like systems (some developers have reported problems with Windows or macOS). Alternatively, you can install GPT-3 to be used in a Jupyter notebook with the following code:!pip install --upgrade openaiAll of the following examples assume that the code is running in a Jupyter notebook:1. Set your API key:api_key =<your API key> openai.api_key = api_key2. The next step is to specify the training data that you will use for fine-tuning GPT-3 for your application. This is very similar to the process of training any NLP system; however, GPT-3 has a specific format that must be used for training data. This format uses a syntax called JSONL, where every line is an independent JSON expression. For example, if we want to fine-tune GPT-3 to classify movie reviews, a couple of data items would look like the following (omitting some of the text for clarity):{"prompt":"this film is extraordinarily horrendous and i'm not going to waste any more words on it . ","completion":" negative"} {"prompt":"9 : its pathetic attempt at \" improving \" on a shakespeare classic . 8 : its just another piece of teen fluff . 7 : kids in high school are not that witty . … ","completion":" negative"} {"prompt":"claire danes , giovanni ribisi , and omar epps make a likable trio of protagonists , …","completion":" negative"}Each item consists of a JSON dict with two keys, prompt and completion. prompt is the text to be classified, and completion is the correct classification. All three of these items are negative reviews, so the completions are all marked as negative.It might not always be convenient to get your data into this format if it is already in another format, but OpenAI provides a useful tool for converting other formats into JSONL. It accepts a wide range of input formats, such as CSV, TSV, XLSX, and JSON, with the only requirement for the input being that it contains two columns with prompt and completion headers. Table 11.2 shows a few cells from an Excel spreadsheet with some movie reviews as an example:promptcompletionkolya is one of the richest films i’ve seen in some time . zdenek sverak plays a confirmed old bachelor ( who’s likely to remain so ) , who finds his life as a czech cellist increasingly impacted by the five-year old boy that he’s taking care of …positivethis three hour movie opens up with a view of singer/guitar player/musician/ composer frank zappa rehearsing with his fellow band members . all the rest displays a compilation of footage , mostly from the concert at the palladium in new york city , halloween 1979 …positive`strange days’ chronicles the last two days of 1999 in los angeles . as the locals gear up for the new millenium , lenny nero ( ralph fiennes ) goes about his business …positiveTable 11.2 – Movie review data for fine-tuning GPT-3To convert one of these alternative formats into JSONL, you can use the fine_tunes.prepare_ data tool, as shown here, assuming that your data is contained in the movies.csv file:!openai tools fine_tunes.prepare_data -f ./movies.csv -qThe fine_tunes.prepare_data utility will create a JSONL file of the data and will also provide some diagnostic information that can help improve the data. The most important diagnostic that it provides is whether or not the amount of data is sufficient. OpenAI recommends several hundred examples of good performance. Other diagnostics include various types of formatting information such as separators between the prompts and the completions.After the data is correctly formatted, you can upload it to your OpenAI account and save the filename:file_name = "./movies_prepared.jsonl" upload_response = openai.File.create( file=open(file_name, "rb"), purpose='fine-tune' ) file_id = upload_response.idThe next step is to create and save a fine-tuned model. There are several different OpenAI models that can be used. The one we’re using here, ada, is the fastest and least expensive, and does a good job on many classification tasks:openai.FineTune.create(training_file=file_id, model="ada") fine_tuned_model = fine_tune_response.fine_tuned_modelFinally, we can test the model with a new prompt:answer = openai.Completion.create( model = fine_tuned_model, engine = "ada", prompt = " I don't like this movie ", max_tokens = 10, # Change amount of tokens for longer completion temperature = 0 ) answer['choices'][0]['text']In this example, since we are only using a few fine-tuning utterances, the results will not be very good. You are encouraged to experiment with larger amounts of training data.ConclusionIn conclusion, ChatGPT and GPT-3 offer invaluable tools for AI enthusiasts and developers alike. From data generation to fine-tuning for specific applications, these models present a world of possibilities. As we've seen, ChatGPT can expedite the process of creating training data, while GPT-3's customization can elevate the performance of your AI applications. As the field of artificial intelligence continues to evolve, these models hold immense promise. So, whether you're looking to streamline your development process or take your AI solutions to the next level, the journey with ChatGPT and GPT-3 is an exciting one filled with untapped potential. Embrace the future of AI with confidence and innovation.Author BioDeborah A. Dahl is the principal at Conversational Technologies, with over 30 years of experience in natural language understanding technology. She has developed numerous natural language processing systems for research, commercial, and government applications, including a system for NASA, and speech and natural language components on Android. She has taught over 20 workshops on natural language processing, consulted on many natural language processing applications for her customers, and written over 75 technical papers. This is Deborah’s fourth book on natural language understanding topics. Deborah has a PhD in linguistics from the University of Minnesota and postdoctoral studies in cognitive science from the University of Pennsylvania.

Dive deeper into the world of AI innovation and stay ahead of the AI curve! Subscribe to our AI_Distilled newsletter for the latest insights. Don't miss out – sign up today!👋 Hello ,“Scientific experimentation involves an iterative process of creating hypotheses, designing experiments, running experiments, and analyzing the results. Can we build AI research agents to perform these long-horizon tasks? To take a step towards building and evaluating research agents on such open-ended decision-making tasks -- we propose MLAgentBench, a suite of ML tasks for benchmarking AI research agents.” - from the paper Benchmarking Large Language Models as AI Research Agents (arXivLabs, Oct 2023), proposed by Qian Huang, Jian Vora, Percy Liang, Jure Leskovec. Stanford University researchers are addressing the challenge of evaluating AI research agents with free-form decision-making abilities through MLAgentBench, a pioneering benchmark. This framework provides research tasks with task descriptions and required files, allowing AI agents to mimic human researchers' actions like reading, writing, and running code. The evaluation assesses proficiency, reasoning, research process, and efficiency.Welcome to AI_Distilled #21, your weekly source for the latest breakthroughs in AI, ML, GPT, and LLM. In this edition, we’ll talk about Microsoft and Google introducing new AI initiatives for healthcare, OpenAI unveiling the beta version of Python SDK for enhanced API access, IBM’s enhancement of PyTorch for AI inference, targeting enterprise deployment, and AMD working on enhancing its AI capabilities with the acquisition of Nod.ai and getting a quick look at OpenAI’s ambitious new ventures in AI chipmaking to tackle the global chip shortage. We know how much you love our curated collection of AI tutorials and secret knowledge. We’ve packed some great knowledge resources in this issue covering recent advances in enhancing content safety with Azure ML, understanding autonomous agents for problem solving with LLMs, and enhancing code quality and security with Generative AI, Amazon Bedrock, and CodeGuru. 📥 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  ⚡ TechWave: AI/GPT News & AnalysisMicrosoft and Google Introduce New Gen AI Initiatives for Healthcare: Microsoft and Alphabet's Google have unveiled separate AI initiatives to assist healthcare organizations in improving data access and information management. Google's project, powered by Google Cloud, aims to simplify the retrieval of patient data, including test results and prescriptions, in one central location. It also intends to help healthcare professionals with administrative tasks that often lead to work overload and burnout. Meanwhile, Microsoft's initiative is focused on enabling healthcare entities to efficiently aggregate data from various doctors and hospitals, eliminating the time-consuming search for information.  OpenAI Mulls Chip Independence Due to Rising Costs: OpenAI, known for its ChatGPT AI model, is considering developing its own AI chips due to the growing costs of using Nvidia's hardware. Each ChatGPT query costs OpenAI around 4 cents, and the company reportedly spends $700,000 daily to run ChatGPT. Nvidia accounts for over 70% of AI chip sales but is becoming costly for OpenAI. The organization has been in discussions about making its own chips but has not made a final decision. Microsoft is also exploring in-house chip development, potentially competing with Nvidia's H100 GPU. OpenAI may remain dependent on Nvidia for the time being. Microsoft May Unveil AI Chip at Ignite 2023 to Tackle GPU Shortage: Microsoft is considering debuting its own AI chip at the upcoming Ignite 2023 conference due to the high demand for GPUs, with NVIDIA struggling to meet this demand. The chip would be utilized in Microsoft's data center servers and to enhance AI capabilities within its productivity apps. This move reflects Microsoft's commitment to advancing AI technology following a substantial investment in OpenAI. While Microsoft plans to continue purchasing NVIDIA GPUs, the development of its own AI chip could increase profitability and competitiveness with tech giants like Amazon and Google, who already use their custom AI chips. OpenAI Unveils Beta Version of Python SDK for Enhanced API Access: OpenAI has released a beta version of its Python SDK, aiming to improve access to the OpenAI API for Python developers. This Python library simplifies interactions with the OpenAI API for Python-based applications, providing an opportunity for early testing and feedback ahead of the official version 1.0 launch. The SDK streamlines integration by offering pre-defined classes for API resources and ensuring compatibility across different API versions. OpenAI encourages developers to explore the beta version, share feedback, and shape the final release. The library supports various tasks, including chat completions, text model completions, embeddings, fine-tuning, moderation, image generation, and audio functions.  IBM Enhances PyTorch for AI Inference, Targeting Enterprise Deployment: IBM is expanding the capabilities of the PyTorch machine learning framework beyond model training to AI inference. The goal is to provide a robust, open-source alternative for inference that can operate on multiple vendor technologies and both GPUs and CPUs. IBM's efforts involve combining three techniques within PyTorch: graph fusion, kernel optimizations, and parallel tensors to speed up inference. Using these optimizations, they achieved impressive inference speeds of 29 milliseconds per token for a large language model with 70 billion parameters. While these efforts are not yet ready for production, IBM aims to contribute these improvements to the PyTorch project for future deployment, making PyTorch more enterprise-ready. AMD Enhances AI Capabilities with Acquisition of Nod.ai: AMD has announced its intention to acquire Nod.ai, a startup focused on optimizing AI software for high-performance hardware. This acquisition underlines AMD's commitment to the rapidly expanding AI chip market, which is projected to reach $383.7 billion by 2032. Nod.ai's software, including the SHARK Machine Learning Distribution, will accelerate the deployment of AI models on platforms utilizing AMD's architecture. By integrating Nod.ai's technology, AMD aims to offer open software solutions to facilitate the deployment of highly performant AI models, thereby enhancing its presence in the AI industry.   🔮 Expert Insights from Packt Community Machine Learning Engineering with MLflow - By Natu Lauchande Developing your first model with MLflow From the point of view of simplicity, in this section, we will use the built-in sample datasets in sklearn, the ML library that we will use initially to explore MLflow features. For this section, we will choose the famous Iris dataset to train a multi-class classifier using MLflow. The Iris dataset (one of sklearn's built-in datasets available from https://scikit-learn.org/stable/datasets/toy_dataset.html) contains the following elements as features: sepal length, sepal width, petal length, and petal width. The target variable is the class of the iris: Iris Setosa, Iris Versocoulor, or Iris Virginica: Load the sample dataset: from sklearn import datasets from sklearn.model_selection import train_test_split dataset = datasets.load_iris() X_train, X_test, y_train, y_test = train_test_split(dataset.data, dataset.target, test_size=0.4) Next, let's train your model. Training a simple machine model with a framework such as scikit-learn involves instantiating an estimator such as LogisticRegression and calling the fit command to execute training over the Iris dataset built in scikit-learn: from sklearn.linear_model import LogisticRegression clf = LogisticRegression() clf.fit(X_train, y_train) The preceding lines of code are just a small portion of the ML Engineering process. As will be demonstrated, a non-trivial amount of code needs to be created in order to productionize and make sure that the preceding training code is usable and reliable. One of the main objectives of MLflow is to aid in the process of setting up ML systems and projects. In the following sections, we will demonstrate how MLflow can be used to make your solutions robust and reliable. Then, we will add MLflow. With a few more lines of code, you should be able to start your first MLflow interaction. In the following code listing, we start by importing the mlflow module, followed by the LogisticRegression class in scikit-learn. You can use the accompanying Jupyter notebook to run the next section: import mlflow from sklearn.linear_model import LogisticRegression mlflow.sklearn.autolog() with mlflow.start_run():    clf = LogisticRegression()    clf.fit(X_train, y_train) The mlflow.sklearn.autolog() instruction enables you to automatically log the experiment in the local directory. It captures the metrics produced by the underlying ML library in use. MLflow Tracking is the module responsible for handling metrics and logs. By default, the metadata of an MLflow run is stored in the local filesystem. The above content is extracted from the book Machine Learning Engineering with MLflow written by Natu Lauchande and published in Aug 2021. To get a glimpse of the book's contents, make sure to read the free chapter provided here, or if you want to unlock the full Packt digital library free for 7 days, try signing up now! To learn more, click on the button below.   Read through the Chapter 1 unlocked here...  🌟 Secret Knowledge: AI/LLM ResourcesBoosting Model Inference Speed with Quantization: In the realm of deploying deep learning models, efficiency is key. This post offers a primer on quantization, a technique that significantly enhances the inference speed of hosted language models. Quantization involves reducing the precision of data types used for weights and activations, such as moving from 32-bit floating point to 8-bit integers. While this may slightly affect model accuracy, the benefits are substantial: reduced memory usage, faster inference times, lower energy consumption, and the ability to deploy models on edge devices. The post explains two common approaches for quantization: Post-Training Quantization (PTQ) and Quantization-Aware Training (QAT), helping you understand how to implement them effectively.  Unlocking Database Queries with Text2SQL: A Historical Perspective and Current Advancements: In this post, you'll explore the evolution of Text2SQL, a technology that converts natural language queries into SQL for interacting with databases. Beginning with rule-based approaches in the 1960s, it has transitioned to machine learning-based models, and now, LLMs like BERT and GPT have revolutionized it. Discover how LLMs enhance Text2SQL, the challenges it faces, and prominent products like Microsoft LayoutLM, Google TAPAS, Stanford Spider, and GuruSQL. Despite challenges, Text2SQL holds great promise for making database querying more convenient and intelligent in practical applications. Enhancing Content Safety with Azure ML: Learn how to ensure content safety in Azure ML when using LLMs. By setting up Azure AI Content Safety and establishing a connection within Prompt Flow, you'll scrutinize user input before directing it to the LLM. The article guides you through constructing the flow, including directing input to content safety, analyzing results, invoking the LLM, and consolidating the final output. With this approach, you can prevent unwanted responses from LLM and ensure content safety throughout the interaction.  💡 Masterclass: AI/LLM TutorialsUnderstanding Autonomous Agents for Problem Solving with LLMs: In this post, you'll explore the concept of autonomous LLM-based agents, how they interact with their environment, and the key modules that make up these agents, including the Planner, Reasoner, Actioner, Executor, Evaluator, and more. Learn how these agents utilize LLMs' inherent reasoning abilities and external tools to efficiently solve intricate problems while avoiding the limitations of fine-tuning.Determining the Optimal Chunk Size for a RAG System with LlamaIndex: When working with retrieval-augmented generation (RAG) systems, selecting the right chunk size is a crucial factor affecting efficiency and accuracy. This post introduces LlamaIndex's Response Evaluation module, providing a step-by-step guide on how to find the ideal chunk size for your RAG system. Considering factors like relevance, granularity, and response generation time, the optimal balance typically found around 1024 for a RAG system.Understanding the Power of Rouge Score in Model Evaluation: Evaluating the effectiveness of fine-tuned language models like Mistral 7B Instruct Model requires a reliable metric, and the Rouge Score is a valuable tool. This article provides a step-by-step guide on how to use the Rouge Score to compare finetuned and base language models effectively. This assesses the similarity of words generated by a model to reference words provided by humans using unigrams, bigrams, and n-grams. Mastering this metric, you'll be able to make informed decisions when choosing between different model versions for specific tasks. Enhancing Code Quality and Security with Generative AI, Amazon Bedrock, and CodeGuru: In this post, you'll learn how to use Amazon CodeGuru Reviewer, Amazon Bedrock, and Generative AI to enhance the quality and security of your code. Amazon CodeGuru Reviewer provides automated code analysis and recommendations, while Bedrock offers insights and code remediation. The post outlines a detailed solution involving CodeCommit, CodeGuru Reviewer, and Bedrock.  Exploring Generative AI with LangChain and OpenAI: Enhancing Amazon SageMaker Knowledge: In this post, the author illustrates the process of hosting a Machine Learning Model with the Generative AI ecosystem, using LangChain, a Python framework that simplifies Generative AI applications, and OpenAI's LLMs. The goal is to see how well this solution can answer SageMaker-related questions, addressing the challenge of LLMs lacking access to specific and recent data sources.   🚀 HackHub: Trending AI Toolsleptonai/leptonai: ̉Python library for simplifying AI service creation, offering a Pythonic abstraction (Photon) for converting research code into a service, simplified model launching, prebuilt examples, and AI-specific features. okuvshynov/slowllama: Enables developers to fine-tune Llama2 and CodeLLama models, including 70B/35B, on Apple M1/M2 devices or Nvidia GPUs, emphasizing fine-tuning without quantization. yaohui-wyh/ctoc: A lightweight tool for analyzing codebases at the token level, which is crucial for understanding and managing the memory and conversation history of LLMs.  eric-ai-lab/minigpt-5: ̉A model for interleaved vision-and-language generation using generative vokens to enable the simultaneous generation of images and textual narratives, particularly in the context of multimodal applications.

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 recent years, we've witnessed a technological revolution in the field of artificial intelligence. One of the most groundbreaking developments has been the advent of Large Language Models (LLMs). Since the release of ChatGPT, people have been both shocked and excited by the capabilities of this AI.Countless experiments have been conducted to push the boundaries and explore the full potential of LLMs. Traditionally, these experiments have involved incorporating AI as part of a larger pipeline. However, what if we told you that the entire process could be automated by the AI itself? Imagine just setting the goal of a task and then sitting back and relaxing while the AI takes care of everything, from scraping websites for information to summarizing content and executing connected plugins. Fortunately, this vision is no longer a distant dream. Welcome to the world of AutoGPT!AutoGPT is an experimental open-source application that showcases the remarkable capabilities of the GPT-4 language model. This program, driven by GPT-4, connects the dots between LLM "thoughts" to autonomously achieve whatever goal you set. It represents one of the first examples of GPT-4 running fully autonomously, effectively pushing the boundaries of what is possible with AI.AutoGPT comes packed with an array of features that make it a game-changer in the world of AI automation. Let's take a closer look at what sets this revolutionary tool apart:Internet Access for Searches and Information Gathering: AutoGPT has the power to access the internet, making it a formidable tool for information gathering. Whether you need to research a topic, gather data, or fetch real-time information, AutoGPT can navigate the web effortlessly.Long-Term and Short-Term Memory Management: Just like a human, AutoGPT has memory. It can remember context and information from previous interactions, enabling it to provide more coherent and contextually relevant responses.GPT-4 Instances for Text Generation: With the might of GPT-4 behind it, AutoGPT can generate high-quality text that is coherent, contextually accurate, and tailored to your specific needs. Whether it's drafting an email, writing code, or crafting a compelling story, AutoGPT has got you covered.Access to Popular Websites and Platforms: AutoGPT can access popular websites and platforms, interacting with them just as a human user would. This opens up endless possibilities, from automating routine tasks on social media to retrieving data from web applications.File Storage and Summarization with GPT-3.5: AutoGPT doesn't just generate text; it also manages files and can summarize content using the GPT-3.5 model. This means it can help you organize and understand your data more efficiently.Extensibility with Plugins: AutoGPT is highly extensible, thanks to its plugin architecture. You can customize its functionality by adding plugins tailored to your specific needs. Whether it's automating tasks in your business or streamlining personal chores, plugins make AutoGPT endlessly adaptable. For more information regarding plugins, you can check the official repo.Throughout this article, we’ll learn how to install AutoGPT and run it on your local computer. Moreover, we’ll also learn how to utilize it to build your own personal investment valuation analyst! Without wasting any more time, let’s take a deep breath, make yourselves comfortable, and be ready to learn all about AutoGPT!Setting Up AutoGPTLet’s go through the process of setting up AutoGPT, whether you choose to use Docker or Git, setting up AutoGPT is pretty straightforward. But before we delve into the technical details, let's start with the most crucial step: obtaining an API key from OpenAI.Getting an API KeyTo use AutoGPT effectively, you'll need an API key from OpenAI. You can obtain this key by visiting the OpenAI API Key page at https://platform.openai.com/account/api-keys. It's essential to note that for seamless operation and to prevent potential crashes, we recommend setting up a billing account with OpenAI.Free accounts come with limitations, allowing only three API calls per minute. A paid account ensures a smoother experience. You can set up a paid account by following these steps:Go to "Manage Account."Navigate to "Billing."Click on "Overview."Setting up AutoGPT with DockerBefore you begin, make sure you have Docker installed on your system. If you haven't installed Docker yet, you can find the installation instructions here. Now, let’s start setting up AutoGPT with Docker.1. Open your terminal or command prompt.2. Create a project directory for AutoGPT. You can name it anything you like, but for this guide, we'll use "AutoGPT".mkdir AutoGPT cd AutoGPT3. In your project directory, create a file called `docker-compose.yml` and populate it with the following contents:version: "3.9" services: auto-gpt:    image: significantgravitas/auto-gpt    env_file:      - .env    profiles: ["exclude-from-up"]    volumes:      - ./auto_gpt_workspace:/app/auto_gpt_workspace      - ./data:/app/data      - ./logs:/app/logsThis configuration file specifies the settings for your AutoGPT Docker container, including environment variables and volume mounts.4. AutoGPT requires specific configuration files. You can find templates for these files in the AutoGPT repository. Create the necessary configuration files as needed.5. Before running AutoGPT, pull the latest image from Docker Hubdocker pull significantgravitas/auto-gpt6. With Docker Compose configured and the image pulled, you can now run AutoGPT:docker compose run --rm auto-gptThis command launches AutoGPT inside a Docker container, and it's all set to perform its AI-powered magic.Setting up AutoGPT with GitIf you prefer to set up AutoGPT using Git, here are the steps to follow:1. Ensure that you have Git installed on your system. You can download it from https://git-scm.com/downloads.2. Open your terminal or command prompt.3. Clone the AutoGPT repository using Git:git clone -b stable https://github.com/Significant-Gravitas/AutoGPT.git4. Navigate to the directory where you downloaded the repository:cd AutoGPT/autogpts/autogpt5. Run the startup scripta. On Linux/MacOS:./run.shb. On Windows:.\run.batIf you encounter errors, ensure that you have a compatible Python version installed and meet the requirements outlined in the documentation.AutoGPT for Your Personal Investment Valuation AnalystIn our previous article, we explored the exciting use case of building a personal investment news analyst with LLM. However, making sound investment decisions based solely on news articles is only one piece of the puzzle.To truly understand the potential of an investment, it's crucial to dive deeper into the financial health of the companies you're considering. This involves analyzing financial statements, including balance sheets, income statements, and cash flow statements. Yet, the sheer volume of data within these documents can be overwhelming, especially for newbie retail investors.Let’s see how AutoGPT is in action! Once the AutoGPT is up, we’ll be shown a welcome message and it will ask us to give the name of our AI, the role, and also the goals that we want to achieve. In this case, we’ll give the name of AI as “Personal Investment Valuation Analyst”. As for the role and goals, please see the attached image below.After we input the role and the goals, our assistant will start planning all of the things that it needs to do. It will give some thoughts along with the reasoning before creating a plan. Sometimes it’ll also criticize itself with the aim to create a better plan. Once the plan is laid out, it will ask for confirmation from the user. If the user is satisfied with the plan, then they can give their approval by typing “y”.Then, AutoGPT will execute each of the planned tasks. For example, here, it is browsing through the internet with the “official source of Apple financial statements” query.Based on the result of the first task, it learned that it needs to visit the corporate website of Apple, visit the invertor relations page, and then search for the required documents, which are the balance sheet, cashflow statement, and income statement. Look at this! Pretty amazing, right?The process then continues by searching through the investor relations page on the Apple website as planned in the previous step. This process will continue until the goals are achieved, which is to give recommendations to the user on whether to buy, sell, or hold the Apple stock based on valuation analysis.ConclusionCongratulations on keeping up to this point! Throughout this article, you have learned what is AutoGPT, how to install and run it on your local computer, and how to utilize it as your personal investment valuation analyst. Hope the best for your experiment with AutoGPT and see you in the next article!Author BioLouis Owen is a data scientist/AI engineer from Indonesia who is always hungry for new knowledge. Throughout his career journey, he has worked in various fields of industry, including NGOs, e-commerce, conversational AI, OTA, Smart City, and FinTech. Outside of work, he loves to spend his time helping data science enthusiasts to become data scientists, either through his articles or through mentoring sessions. He also loves to spend his spare time doing his hobbies: watching movies and conducting side projects.Currently, Louis is an NLP Research Engineer at Yellow.ai, the world’s leading CX automation platform. Check out Louis’ website to learn more about him! Lastly, if you have any queries or any topics to be discussed, please reach out to Louis via LinkedIn.

Dive deeper into the world of AI innovation and stay ahead of the AI curve! Subscribe to our AI_Distilled newsletter for the latest insights. Don't miss out – sign up today!IntroductionIn a world where technology is progressing at an exponential rate, the concept of a personal assistant is no longer confined to high-profile executives with hectic schedules. Today, due to the incredible advancements in artificial intelligence (AI), each one of us has the chance to take advantage of a personal assistant's services, even for tasks that may have appeared beyond reach just a few years ago. Imagine having an entity that can aid you in conducting research, examining your daily financial expenditures, organizing your travel itinerary, and much more. This entity is known as AI and, more precisely, it is embodied in AgentGPT.You have likely heard of AI's incredible capabilities, ranging from diagnosing diseases to defeating world-class chess champions. While AI has undoubtedly made significant strides, here's the caveat: unless you possess technical expertise, devising the workflow to fully utilize AI's potential can be an intimidating endeavor. This is where the concepts of "tools" and "agents" become relevant, and AgentGPT excels in this domain.An "agent" is essentially the mastermind behind your AI assistant. It's the entity that “thinks”, strategizes, and determines how to achieve your objectives based on the available "tools." These "tools" represent the skills your agent possesses, such as web searching, code writing, generating images, retrieving knowledge from your personal data, and a myriad of other capabilities. Creating a seamless workflow where your agent utilizes these tools effectively is no simple task. It entails connecting the agent to the tools, managing errors that may arise, devising prompts to guide the agent, and more.Fortunately, there's a game-changer in the world of AI personal assistants, and it goes by the name of AgentGPT. Without wasting any more time, let’s take a deep breath, make yourselves comfortable, and be ready to learn how to utilize AgentGPT to build your personal assistant!What is AgentGPT?AgentGPT is an open-source project that streamlines the intricate process of creating and configuring AI personal assistants. This powerful tool enables you to deploy Autonomous AI agents, each equipped with distinct capabilities and skills. You can even name your AI, fostering a sense of personalization and relatability. With AgentGPT, you can assign your AI any mission you can conceive, and it will strive to accomplish it.The magic of AgentGPT lies in its ability to empower your AI agent to think, act, and learn. Here's how it operates:Select the Tools: You start by selecting the tools for the agent. It can be web searching, code writing, generating images, or even retrieving knowledge from your personal dataSetting the Goal: You then need to define the goal you want your AI to achieve. Whether it's conducting research, managing your finances, or planning your dream vacation, the choice is yours.Task Generation: Once the goal is set and the tools are selected, your AI agent "thinks" about the tasks required to accomplish it. This involves considering the available tools and formulating a plan of action.Task Execution: Your AI agent then proceeds to execute the tasks it has devised. This can include searching the web for information, performing calculations, generating content, and more.Learning and Adaptation: As your AI agent carries out its tasks, it learns from the results. If something doesn't go as planned, it adapts its approach for the future, continuously improving its performance.In a world where time is precious and efficiency is crucial, AgentGPT emerges as a ray of hope. It's a tool that empowers individuals from all walks of life to harness the might of AI to streamline their daily tasks, realize their goals, and amplify their productivity. Thus, whether you're a business professional seeking to optimize your daily operations or an inquisitive individual eager to explore the boundless possibilities of AI, AgentGPT stands ready to propel you into a new era of personalized assistance.Initialize AgentGPTTo build your own personal assistant with AgetnGPT, you can just follow the following simple instructions. Or even, you can also just go to the website and try the demo.Open Your Terminal: You can usually access the terminal from a 'Terminal' tab or by using a shortcut.Clone the Repository: Copy and paste the following command into your terminal and press Enter. This will clone the AgentGPT repository to your local machine.a. For Max/Linux usersgit clone https://github.com/reworkd/AgentGPT.git cd AgentGPT ./setup.sh                b. For Windows usersgit clone https://github.com/reworkd/AgentGPT.git cd AgentGPT ./setup.batFollow Setup Instructions: The setup script will guide you through the setup process. You'll need to add the appropriate API keys and other required information as instructed.Access the Web Interface: Once all the services are up and running, you can access the AgentGPT web interface by opening your web browser and navigating to http://localhost:3000.Build Your Own Assistant with AgentGPTLet’s start with an example of how to build your own assistant. First and foremost, let’s select the tools for our agent. Here, we’re selecting image generation, web search, and code writing as the tools. Once we finish selecting the tools, we can define the goal for our assistant. AgentGPT provides three templates for us:ResearchGPT: Create a comprehensive report of the Nike companyTravelGPT: Plan a detailed trip to HawaiiPlatformerGPT: Write some code to make a platformer gameNote that we can also create our own assistant name with a specific goal apart from these three templates. For now, let’s select the PlatformerGPT template.Once the goal is defined, then the agent will generate all tasks required to accomplish the goal. This involves considering the available tools and formulating a plan of action.Then, based on the generated tasks, the Agent will execute each task and learn through the results of each of the tasks.This process will continue until the goal is achieved, or in this case, until the Agent succeeds in writing the code for a platformer game. If something doesn't go as planned, it adapts its approach for the future, continuously improving its performance.ConclusionCongratulations on keeping up to this point! Throughout this article, you have learned what AgentGPT is capable of and how to build your own personal assistant with it. I wish the best for your experiment in creating your personal assistant and see you in the next article!Author BioLouis Owen is a data scientist/AI engineer from Indonesia who is always hungry for new knowledge. Throughout his career journey, he has worked in various fields of industry, including NGOs, e-commerce, conversational AI, OTA, Smart City, and FinTech. Outside of work, he loves to spend his time helping data science enthusiasts to become data scientists, either through his articles or through mentoring sessions. He also loves to spend his spare time doing his hobbies: watching movies and conducting side projects.Currently, Louis is an NLP Research Engineer at Yellow.ai, the world’s leading CX automation platform. Check out Louis’ website to learn more about him! Lastly, if you have any queries or any topics to be discussed, please reach out to Louis via LinkedIn.

Dive deeper into the world of AI innovation and stay ahead of the AI curve! Subscribe to our AI_Distilled newsletter for the latest insights. Don't miss out – sign up today!IntroductionIn the evolving world of digital creativity, the collaboration between Canva and ChatGPT ushers a new era. Canva is a popular graphic design platform that allows users to create a wide variety of visual content, such as social media posts, presentations, posters, videos, banners, and many more. Whereas ChatGPT is an extensive language model that is capable of writing many types of creative material like poems, stories, essays, and songs, generating code snippets, translating languages, and providing you with helpful answers to your queries.In this article, we examine the compelling reasons for embracing these two cutting-edge platforms and reveal the endless possibilities they offer.Why use Canva on ChatGPT?Using Canva and ChatGPT individually can be a great way to create content, but there are several benefits to using them.You can get the best of both platforms by integrating Canva on ChatGPT. The creativity and flexibility of ChatGPT are dynamic while the functionality and simplicity of Canva are user-friendly.You can optimize your workflow and save time and effort by integrating Canva with ChatGPT. When you submit your design query to ChatGPT, It will quickly start the process of locating and producing the best output within less time.You can get ideas and get creative by using Canva on ChatGPT. By altering the description or the parameters in ChatGPT, you can experiment with various options and styles for your graphic.How to use Canva on ChatGPT?Follow the below steps to get started for the Canva plugin:Step-1:To use GPT-4 and Canva Plugin you will need to upgrade to the Plus version. So for that go to the ChatGPT website and log in to your account. Then navigate to top of your screen, then you will be able to find the GPT-4 button.Step-2:Once clicked, then press the Upgrade to Plus button. On the Subscription page, enter your email address, payment method, and billing address. Click the Subscribe button. Once your payment has been processed, you will be upgraded to ChatGPT Plus. Step-3:Now, move to the “GPT-4” model and choose “Plugins” from the drop-down menu.Step-4:After that, you will be able to see “Plugin store” in which you can access different kinds of plugins and explore them.Step-5:Here, you must search “Canva” and click on the install button to download the plugin in ChatGPT.  Step-6:Once installed, make sure the “Canva” plugin is enabled via the drop-down menu.  Step-7:Now, go ahead and enter the prompt for the image, video, banner, poster, and presentation you wish to create. For example, you can ask ChatGPT to generate, “I'm performing a keynote speech presentation about advancements in Al technology. Create a futuristic, modern, and innovative presentation template for me to use” and it generated some impressive results within a minute.  Step-8:By clicking the link in ChatGPT's response you will be redirected toward the Canva editing page then you can customize the design, without even signing in. Once you are finished editing your visual content, you can download it from Canva and share it with others.So overall, you may utilize the Canva plugin in ChatGPT to quickly realize your ideas if you want to create an automated Instagram or YouTube channel with unique stuff. The user's engagement is minimal and effortless.Here are some specific examples of how you can use the Canva plugin on ChatGPT to create amazing content: Create presentations: Using your topic and audience, ChatGPT can generate presentation outlines for you. Once you have an outline, Canva can be used to make interactive and informative presentations.Generate social media posts: Using ChatGPT, you can come up with ideas for social media posts depending on your objectives and target audience. Once you have a few ideas, you may use Canva to make visually beautiful and interesting social media posts.Design marketing materials: You may utilize ChatGPT to come up with concepts for blog articles, infographics, and e-books, among other types of marketing materials. You may use Canva to create visually appealing and informative marketing materials.Make educational resources: ChatGPT can be used to create worksheets, flashcards, and lesson plans, among other types of educational materials. Once you've collected some resources, you can utilize Canva to make interesting and visually appealing educational materials.Things you must know about Canva on ChatGPTBe specific in your prompts. The more specific you are in your prompts, the better ChatGPT will be able to generate the type of visual content you want. Use words and phrases that are appropriate for your visual material. In order to come up with visual content ideas, ChatGPT searches for terms that are relevant to your prompt.Test out several templates and prompts. You may use Canva in a variety of ways on ChatGPT, so don't be hesitant to try out various prompts and templates to see what works best for you.Use ChatGPT's other features. ChatGPT can do more than just generate visual content. You can also use it to translate languages, write different kinds of creative content, and answer your questions in an informative way.ConclusionOverall, using Canva on ChatGPT has a number of advantages, including simplicity, strength, and adaptability. You can save a tonne of time and work by using the Canva plugin to create and update graphic material without using ChatGPT. With ChatGPT's AI capabilities, you can produce more inventive and interesting visual material than you could on your own. You also have a lot of versatility when generating visual material because to Canva's wide variety of templates and creative tools. So we got to know that, whether you are a content creator, a marketing manager, or a teacher, using the Canva plugin on ChatGPT can help you create amazing content that will engage the audience and help you to achieve your goals.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.

👋 Hello ,“AI is everywhere. It's not that big, scary thing in the future. AI is here with us.” -Fei-Fei Li, American computer scientist and ImageNet visionary.  It’s easy enough to relegate AI as a technology of the distant future, still too immature to warrant enough thought in the present. Nothing could be further from the truth, as AI is already making colossal leaps everywhere. Amazon’s recently announced $4 billion investment in ChatGPT rival Anthropic and Microsoft unveiling its AI companion across its ecosystem speak volumes about what the tech behemoths have in mind.  Here we are with a fresh new issue of your favorite AI-focused newsletter, AI_Distilled#19! We’re here to bring you cutting-edge developments in the field of ML, LLM, NLP, GPT, and Gen AI. In this edition, we’ll talk about ChatGPT’s introduction of voice and image capabilities for enhanced user interaction, a new AI algorithm that shows promise in detecting signs of life on other planets, OpenAI enhancing DALL-E Art Generator with ChatGPT integration, Forester study predicting AI will boost enterprise efficiency by 50% in 2024, Microsoft’s AutoGen: A Framework for Streamlining Large Language Model Workflows and DeepMind's OPRO: a novel approach using AI language models as optimizers. If you’ve been looking for some inspiration, follow our curated collection of featured secret knowledge and tutorials covering LoRA Fine-Tuning for GPT and BERT, mastering customer segmentation with LLM, and building LLMs from scratch.  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  📥 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! ⚡ TechWave: AI/GPT News & AnalysisAmazon Invests $4 Billion in ChatGPT Rival Anthropic to Advance Safer AI Models: Amazon has leveled up the AI race with an investment amounting up to $4 billion in Anthropic to develop safer AI models. As part of this collaboration, AWS will become Anthropic's primary cloud provider for critical workloads. AWS will provide access to its compute infrastructure, including Trainium and Inferentia chips. Amazon will also expand its support for Amazon Bedrock, allowing developers and engineers to build on top of Anthropic's models. These models, including Claude 2, can be used for various tasks, from dialogue generation to complex reasoning. The partnership aims to promote responsible AI development and deployment and includes support for safety best practices. ChatGPT Introduces Voice and Image Capabilities for Enhanced User Interaction: OpenAI is introducing new voice and image capabilities in ChatGPT, offering users a more intuitive interface. With these additions, users can engage in voice conversations and share images with ChatGPT, opening new possibilities. For instance, users can discuss landmarks while traveling, plan meals by scanning their fridge, or even assist children with math problems using photos. Voice conversations are powered by text-to-speech models, featuring five different voices, while image understanding is facilitated by multimodal GPT models. OpenAI is gradually deploying these features, aiming to ensure their responsible and safe usage. Plus, and Enterprise users will have early access, with broader availability in the future. Microsoft Unveils 'Copilot,' an AI Companion Across Its Ecosystem: Microsoft is introducing a new AI companion called "Microsoft Copilot" designed to enhance user interactions across its ecosystem. This AI will incorporate web context, work data, and real-time PC activity to provide personalized assistance while prioritizing user privacy and security. It will be seamlessly integrated into Windows 11, Microsoft 365, Edge, and Bing, accessible through a right-click or as an app. Copilot will evolve over time, expanding its capabilities and connections to various applications. Additionally, Microsoft is releasing a significant update for Windows 11, along with enhancements in Bing and Edge, delivering more personalized and productive AI-driven experiences to users.  New AI Algorithm Shows Promise in Detecting Signs of Life on Other Planets: Researchers have developed an AI algorithm that can detect subtle molecular patterns indicating the presence of biological signals in samples, even if they are hundreds of millions of years old, with a 90% accuracy rate. This method could revolutionize the search for signs of life on other planets. By training the machine learning algorithm with a set of biotic and abiotic samples, it successfully identified biotic samples, including ancient life preserved in fossils, and abiotic samples, such as lab-created amino acids and carbon-rich meteorites. The AI system has the potential to be used in robotic space explorers and spacecraft orbiting potentially habitable worlds.  AutoGen: A Framework for Streamlining Large Language Model Workflows: Microsoft Research has introduced AutoGen, a framework designed to simplify the orchestration, optimization, and automation of workflows involving LLMs like GPT-4. AutoGen offers customizable agents that can converse and coordinate tasks, integrating LLMs, humans, and tools. By defining agents and their interactions, developers can build complex multi-agent conversation systems, reducing manual effort and coding. AutoGen's agent-centric design handles ambiguity, feedback, and collaboration, making it versatile for various applications, including conversational chess. It's available as a Python package and aims to enable the development of next-generation LLM applications by streamlining workflow management.  OpenAI Enhances DALL-E Art Generator with ChatGPT Integration: OpenAI has unveiled DALL-E 3, an improved version of its text-to-image tool, which now incorporates ChatGPT to simplify the prompt generation process. Subscribers of OpenAI's premium ChatGPT plans can request, and fine-tune image prompts directly within the chat application, receiving results with enhanced descriptions and guidance. DALL-E 3 not only produces higher-quality images, especially with longer prompts, but also handles challenging content like textual descriptions and depictions of human hands more effectively. The model includes safety mechanisms, rejecting requests for images resembling the work of living artists or public figures. OpenAI plans to introduce DALL-E 3 to premium ChatGPT users first, with broader availability to follow. AI Predicted to Boost Enterprise Efficiency by 50% in 2024, Says Forrester: According to Forrester's Predictions 2024 report, AI initiatives are expected to enhance productivity and problem-solving in enterprises by 50% in IT operations. The report also highlights the role of AI in unlocking creative potential and emphasizes the need for responsible AI deployment. While current AI projects have led to up to 40% improvement in software development, the report advises visionary tech executives to strategically realign IT resources to promote innovation and interdisciplinary teamwork. It also notes that AI deployments will require budget spending, and despite a predicted recession in 2024, tech spending is expected to grow.  DeepMind's OPRO: A Novel Approach Using AI Language Models as Optimizers: Researchers from DeepMind have introduced a novel approach called "Optimization by PROmpting" (OPRO), which leverages LLMs like AI models to optimize tasks defined in natural language rather than mathematical terms. The method begins with a "meta-prompt" that describes the task, and the LLM generates candidate solutions based on this description. OPRO then evaluates these solutions, refines them based on past performance, and continues iterating until an optimal solution is found. This approach showed promise in solving mathematical optimization problems. OPRO's strength lies in its ability to optimize LLM prompts for maximum task accuracy.  🔮 Looking for a New Book from Packt’s Expert Community? Learn Ethereum - Second Edition - By Xun (Brian) Wu, Zhihong Zou, Dongying Song Are you eager to refine your coding skills in smart contracts? "Learn Ethereum, 2nd Edition" is your ultimate guide to mastering Ethereum. Dive deep into the realm of blockchain with this comprehensive book, which covers everything from the fundamentals of smart contracts to the cutting-edge technologies in Ethereum.Gain insights into Ethereum's intricate mechanics, delve into Ethereum 2.0 and the Ethereum Virtual Machine, and grasp essential concepts like gas and proof of stake. Take control of L1/L2 scaling solutions, explore DeFi protocols, and understand EVM-compatible blockchains. Additionally, explore advanced topics such as sharding, DAOs, the Metaverse, and NFTs. By the end, you'll be well-prepared to create smart contracts, develop decentralized applications (DApps), and confidently navigate the Ethereum ecosystem. Read the free chapter by clicking the button below!Read through the Chapter 1 unlocked here...  🌟 Secret Knowledge: AI/LLM ResourcesA Primer on Leveraging LLM Techniques: Prompt Engineering, Retrieval Augmented Generation, and Fine Tuning In this post, you'll learn how to navigate the world of LLMs effectively. The article explores three key strategies: Prompt Engineering, Retrieval Augmented Generation, and Fine Tuning, providing insights into when and how to employ these techniques. Prompt Engineering focuses on crafting precise queries to optimize model responses. Retrieval Augmented Generation combines LLMs with external knowledge sources for contextually rich output. Fine Tuning tailors LLMs to specific domains, enhancing their efficiency. Understanding when to use these techniques is vital for harnessing the potential of LLMs in your projects, each offering unique advantages and considerations. Understanding LoRA Fine-Tuning for GPT and BERT: A Visualized Implementation Guide In this post, you'll learn how to implement LoRA (Low-Rank Adaption of Large Language Models) fine-tuning techniques for models like GPT and BERT. Fine-tuning is essential for preparing these models for production, but LoRA offers an efficient way to do it. LoRA involves adding low-parameter weights to pre-trained model weights, significantly reducing the number of parameters to update during training. This guide provides a visualized implementation of LoRA, breaking down the process step by step, and it covers both BERT and GPT implementations. It's a valuable resource for researchers and practitioners looking to enhance their understanding of efficient fine-tuning methods for large language models. Building LLMs from Scratch: Understanding the Process and Costs In this comprehensive article, you'll gain insights into the process of creating LLMs from the ground up. The text delves into the technical aspects of LLM development, focusing on models like GPT-3, Llama, and Falcon. It begins by emphasizing the historical shift from LLM development as an esoteric task to a growing interest among businesses and organizations. The article also provides a cost estimation for training LLMs, considering GPU hours and commercial cloud computing expenses. By reading this post, you'll explore the key considerations and financial aspects of embarking on the journey of building custom LLMs, gaining a deeper understanding of when and why it's worthwhile. 💡 Masterclass: AI/LLM TutorialsMaximizing Throughput for Large Language Models with Batching Techniques: In this informative guide, you'll discover strategies to enhance the throughput performance of LLMs like Llama v2 using batching techniques. The text explains the challenges associated with LLMs, including memory limitations and compute constraints, and introduces three key batching methods: Dynamic Batching, Continuous Batching, and PagedAttention Batching. Each method is thoroughly explained with configuration details, enabling you to optimize LLM inference on platforms like Amazon SageMaker. Through a comparative analysis, you'll gain insights into the significant throughput improvements achieved by these batching techniques, enhancing your understanding of LLM model serving for text generation. Improving LLMs with RLHF on Amazon SageMaker: This text provides a comprehensive guide on enhancing the performance of large language models LLMs using Reinforcement Learning from Human Feedback (RLHF) on Amazon SageMaker. RLHF is crucial for ensuring LLMs produce truthful and helpful content, aligning them with human objectives. The text covers the complexities of RLHF, including training reward models and fine-tuning LLMs, and then demonstrates a step-by-step process for implementing RLHF on Amazon SageMaker. It also explains how to perform human evaluation to quantify improvements in model outputs. The article emphasizes the effectiveness of RLHF in reducing toxicity and highlights the advantages of using Amazon SageMaker for customized LLM development, making it a valuable resource for researchers seeking to optimize LLMs. Mastering Customer Segmentation with LLM: Unlock advanced customer segmentation techniques using LLMs and improve your clustering models with advanced techniques In this post, you'll learn how to employ advanced techniques for customer segmentation, going beyond cluster definition to in-depth analysis. This article is tailored for data scientists aiming to bolster their clustering abilities. Three methods are explored: Kmeans, K-Prototype, and LLM + Kmeans, each dissected for comprehensive understanding. Notably, you'll delve into dimensionality reduction with techniques like PCA, t-SNE, and MCA. The dataset used is a public Kaggle dataset on banking, offering both numerical and categorical data, expanding segmentation possibilities. The post provides insights into data preprocessing, outlier detection using Python Outlier Detection (PyOD) library, and the process of building a Kmeans model. It further covers model evaluation metrics, visualization, and the importance of PCA and t-SNE. Finally, the article analyzes feature importance and cluster characteristics, emphasizing the need for diverse tools in real-world projects for effective customer segmentation.  🚀 HackHub: Trending AI Toolskornia/kornia: PyTorch-based differentiable computer vision library offering a collection of routines and modules to address various computer vision tasks, leveraging PyTorch's efficiency and auto-differentiation capabilities for gradient computation. confident-ai/deepeval: Tool for unit testing LLMs, providing metrics to assess the relevance, consistency, lack of bias, and non-toxicity of LLM responses. It offers a Python-friendly approach for offline evaluations and a user-friendly web UI for analysis. aiwaves-cn/agents: Open-source framework for building autonomous language agents with advanced features like long-short term memory, tool usage, web navigation, multi-agent communication, human-agent interaction, and symbolic control.  OpenBMB/AgentVerse: Versatile framework designed for creating custom multi-agent environments for LLMs with ease, allowing researchers to focus on their research without getting caught up in implementation details.  hpcaitech/ColossalAI: Offers parallel components and user-friendly tools to simplify the process of writing and deploying distributed deep learning models, making it as straightforward as working on a local machine. 

Dive deeper into the world of AI innovation and stay ahead of the AI curve! Subscribe to our AI_Distilled newsletter for the latest insights. Don't miss out – sign up today!This article is an excerpt from the book, Modern Generative AI with ChatGPT and OpenAI Models, by Valentina Alto. Master core data architecture design concepts and Azure Data & AI services to gain a cloud data and AI architect’s perspective to developing end-to-end solutions.IntroductionIn the ever-evolving landscape of digital marketing, the emergence of AI-powered tools has redefined the way businesses engage with their audience. At the forefront of this transformation is ChatGPT, a versatile language model that is proving to be a game changer in two critical domains: Search Engine Optimization (SEO) and Sentiment Analysis. In this article, we embark on a journey to explore how ChatGPT is revolutionizing SEO strategies, enabling businesses to soar in search rankings, and how it wields its prowess in sentiment analysis to decipher customer feedback and enhance product quality.Boosting Search Engine Optimization (SEO)Another promising area for ChatGPT to be a game changer is Search Engine Optimization (SEO). This is the key element behind ranking in search engines such as Google or Bing and it determines whether your websites will be visible to users who are looking for what you promote.DefinitionSEO is a technique used to enhance the visibility and ranking of a website on search engine results pages (SERPs). It is done by optimizing the website or web page to increase the amount and quality of organic (unpaid) traffic from search engines. The purpose of SEO is to attract more targeted visitors to the website by optimizing it for specific keywords or phrases.Imagine you run an e-commerce company called Hat&Gloves, which only sells, as you might have guessed, hats and gloves. You are now creating your e-commerce website and want to optimize its ranking. Let’s ask ChatGPT to list some relevant keywords to embed in our website:Figure 7.18 – Example of SEO keywords generated by ChatGPTAs you can see, ChatGPT was able to create a list of keywords of different kinds. Some of them are pretty intuitive, such as Hats and Gloves. Others are related, with an indirect link. For example, Gift ideas are not necessarily related to my e-commerce business, however, it could be very smart to include it in my keywords, so that I can widen my audience.Another key element of SEO is search engine intent. Search engine intent, also known as user intent, refers to the underlying purpose or goal of a specific search query made by a user in a search engine. Understanding search engine intent is important because it helps businesses and marketers create more targeted and effective content and marketing strategies that align with the searcher’s needs and expectations.There are generally four types of search engine intent:Informational intent: The user is looking for information on a particular topic or question, such as What is the capital of France? or How to make a pizza at home.Navigational intent: The user is looking for a specific website or web page, such as Facebook login or Amazon.com. Commercial intent: The user is looking to buy a product or service, but may not have made a final decision yet. Examples of commercial intent searches include best laptop under $1000 or discount shoes online.Transactional intent: The user has a specific goal to complete a transaction, which might refer to physical purchases or subscribing to services. Examples of transactional intent could be buy iPhone 13 or sign up for a gym membership.By understanding the intent behind specific search queries, businesses, and marketers can create more targeted and effective content that meets the needs and expectations of their target audience. This can lead to higher search engine rankings, more traffic, and ultimately, more conversions and revenue.Now, the question is, will ChatGPT be able to determine the intent of a given request? Before answering, it is worth noticing that the activity of inferring the intent of a given prompt is the core business of Large Language Models (LLMs), including GPT. So, for sure, ChatGPT is able to capture prompts’ intents.The added value here is that we want to see whether ChatGPT is able to determine the intent in a precise domain with a precise taxonomy, that is, the one of marketing. That is the reason why prompt design is once again pivotal in guiding ChatGPT in the right direction.                                                                      Figure 7.19 – Example of keywords clustered by user intent by ChatGPTFinally, we could also go further and leverage once more the Act as… hack, which we already mentioned in Chapter 4. It would be very interesting indeed to understand how to optimize our website so that it reaches as many users as possible. In marketing, this analysis is called an SEO audit. An SEO audit is an evaluation of a website’s SEO performance and potential areas for improvement. An SEO audit is typically conducted by SEO experts, web developers, or marketers, and involves a comprehensive analysis of a website’s technical infrastructure, content, and backlink profile.During an SEO audit, the auditor will typically use a range of tools and techniques to identify areas of improvement, such as keyword analysis, website speed analysis, website architecture analysis, and content analysis. The auditor will then generate a report outlining the key issues, opportunities for improvement, and recommended actions to address them.Let’s ask ChatGPT to act as an SEO expert and instruct us on what an SEO audit report should look like and which metrics and KPIs should include:We can also ask you to give us an example of one of ChatGPT’s suggestions as follows:Figure 7.20 – Example of ChatGPT acting as an SEO expertChatGPT was able to generate a pretty accurate analysis, with relevant comments and suggestions. Overall, ChatGPT has interesting potential for SEO-related activities, and it can be a good tool whether you are building your website from scratch or you want to improve existing ones.Sentiment analysis to improve quality and increase customer satisfactionSentiment analysis is a technique used in marketing to analyze and interpret the emotions and opinions expressed by customers toward a brand, product, or service. It involves the use of natural language processing (NLP) and machine learning (ML) algorithms to identify and classify the sentiment of textual data such as social media posts, customer reviews, and feedback surveys.By performing sentiment analysis, marketers can gain insights into customer perceptions of their brand, identify areas for improvement, and make data-driven decisions to optimize their marketing strategies. For example, they can track the sentiment of customer reviews to identify which products or services are receiving positive or negative feedback and adjust their marketing messaging accordingly.Overall, sentiment analysis is a valuable tool for marketers to understand customer sentiment, gauge customer satisfaction, and develop effective marketing campaigns that resonate with their target audience.Sentiment analysis has been around for a while, so you might be wondering what ChatGPT could bring as added value. Well, besides the accuracy of the analysis (it being the most powerful model on the market right now), ChatGPT differentiates itself from other sentiment analysis tools since it is artificial general intelligence (AGI).This means that when we use ChatGPT for sentiment analysis, we are not using one of its specific APIs for that task: the core idea behind ChatGPT and OpenAI models is that they can assist the user in many general tasks at once, interacting with a task and changing the scope of the analysis according to the user’s request.So, for sure, ChatGPT is able to capture the sentiment of a given text, such as a Twitter post or a product review. However, ChatGPT can also go further and assist in identifying specific aspects of a product or brand that are positively or negatively impacting the sentiment. For example, if customers consistently mention a particular feature of a product in a negative way, ChatGPT can highlight that feature as an area for improvement. Or, ChatGPT might be asked to generate a response to a particularly delicate review, keeping in mind the sentiment of the review and using it as context for the response. Again, it can generate reports that summarize all the negative and positive elements found in reviews or comments and cluster them into categories.Let’s consider the following example. A customer has recently purchased a pair of shoes from my e-commerce company, RunFast, and left the following review:I recently purchased the RunFast Prodigy shoes and have mixed feelings about them. On one hand, the shoes are incredibly comfortable and provide great support for my feet during my daily runs. The cushioning is top-notch and my feet feel less fatigued after my runs than with my previous shoes. Additionally, the design is visually appealing and I received several compliments on them.However, on the other hand, I’ve experienced some durability issues with these shoes. The outsole seems to wear down rather quickly and the upper material, while breathable, is showing signs of wear after only a few weeks of use. This is disappointing, considering the high price point of the shoes.Overall, while I love the comfort and design of the RunFast Prodigy shoes, I’m hesitant to recommend them due to the durability issues I’ve experienced.Let’s ask ChatGPT to capture the sentiment of this review:Figure 7.21 – ChatGPT analyzing a customer reviewFrom the preceding figure, we can see how ChatGPT didn’t limit itself to providing a label: it also explained both the positive and negative elements characterizing the review, which has a mixed feeling and hence can be labeled as neutral overall.Let’s try to go deeper into that and ask some suggestions about improving the product:Figure 7.22 – Suggestions on how to improve my product based on customer feedbackFinally, let’s generate a response to the customer, showing that we, as a company, do care about customers’ feedback and want to improve our products.Figure 7.23 – Response generated by ChatGPTThe example we saw was a very simple one with just one review. Now imagine we have tons of reviews, as well as diverse sales channels where we receive feedback. Imagine the power of tools such as ChatGPT and OpenAI models, which are able to analyze and integrate all of that information and identify the pluses and minuses of your products, as well as capturing customer trends and shopping habits. Additionally, for customer care and retention, we could also automate review responses using the writing style we prefer. In fact, by tailoring your chatbot’s language and tone to meet the specific needs and expectations of your customers, you can create a more engaging and effective customer experience.Here are some examples:Empathetic chatbot: A chatbot that uses an empathetic tone and language to interact with customers who may be experiencing a problem or need help with a sensitive issueProfessional chatbot: A chatbot that uses a professional tone and language to interact with customers who may be looking for specific information or need help with a technical issueConversational chatbot: A chatbot that uses a casual and friendly tone to interact with customers who may be looking for a personalized experience or have a more general inquiryHumorous chatbot: A chatbot that uses humor and witty language to interact with customers who may be looking for a light-hearted experience or to diffuse a tense situationEducational chatbot: A chatbot that uses a teaching style of communication to interact with customers who may be looking to learn more about a product or serviceIn conclusion, ChatGPT can be a powerful tool for businesses to conduct sentiment analysis, improve their quality, and retain their customers. With its advanced natural language processing capabilities, ChatGPT can accurately analyze customer feedback and reviews in real-time, providing businesses with valuable insights into customer sentiment and preferences. By using ChatGPT as part of their customer experience strategy, businesses can quickly identify any issues that may be negatively impacting customer satisfaction and take corrective action. Not only can this help businesses improve their quality but it can also increase customer loyalty and retention.ConclusionIn this article, we learned to enhance SEO analysis, and capture the sentiment of reviews, social media posts, and other customer feedback.As ChatGPT continues to advance and evolve, it is likely that we will see even more involvement in the marketing industry, especially in the way companies engage with their customers. In fact, relying heavily on AI allows companies to gain deeper insights into customer behavior and preferences.The key takeaway for marketers is to embrace these changes and adapt to the new reality of AI-powered marketing in order to stay ahead of the competition and meet the needs of their customers.Author BioValentina Alto graduated in 2021 in data science. Since 2020, she has been working at Microsoft as an Azure solution specialist, and since 2022, she has been focusing on data and AI workloads within the manufacturing and pharmaceutical industry. She has been working closely with system integrators on customer projects to deploy cloud architecture with a focus on modern data platforms, data mesh frameworks, IoT and real-time analytics, Azure Machine Learning, Azure Cognitive Services (including Azure OpenAI Service), and Power BI for dashboarding. Since commencing her academic journey, she has been writing tech articles on statistics, machine learning, deep learning, and AI in various publications and has authored a book on the fundamentals of machine learning with Python.