How-To Tutorials - ChatGPT

ChatGPT has shown impressive capabilities in processing and generating human-like text. However, it is not without its imperfections. A primary concern is the model's propensity to produce either inaccurate or obsolete answers, often called "hallucinations."The New York Times recently highlighted this issue in their article, "Here's What Happens When Your Lawyer Uses ChatGPT." It presents a lawsuit where a lawyer leaned heavily on ChatGPT to assist in preparing a court filing for a client suing an airline. The model generated fictional court decisions to back its arguments, which didn't go unnoticed. This incident underscores the need for solutions to ground AI models like ChatGPT and improve their performance.To address this, we propose an approach that focuses on augmenting ChatGPT using a knowledge graph. This method aims to provide a structured context, ensuring the model outputs are accurate but also relevant and up-to-date. By bridging the gap between the unstructured textual world of ChatGPT and the structured clarity of knowledge graphs, we strive to enhance the effectiveness and reliability of AI language models.All the code used in this article is available on Google Colab and on GitHub.What is a knowledge graph?A knowledge graph is a structured format of knowledge representation, usually composed of entities and relationships. In a typical knowledge graph, entities are the nodes, and the relationships between them are the edges. The graph-based representation allows complex relationships to be modeled in a way that's intuitive and closer to human understanding. Here is a simple illustration of a knowledge graph: Source: Wikipedia. CC BY-SA 4.0 Google has been using knowledge graphs since 2012 to provide additional contextual information and sources. The structured representation of data offers a new dimension of context to the AI model, grounding it in validated knowledge.Applying Knowledge Graphs to Improve ChatGPTA crucial limitation of ChatGPT is its lack of real-time information updates. Since the model was last trained using data up until 2021, it doesn't have access to events, data, or context after that year. This leads to ChatGPT having outdated or incomplete information about events, technological advancements, or other critical happenings post-2021.Let's illustrate this limitation by asking ChatGPT about a recent event, When did Apple announce the Vision Pro?. Given the model's knowledge cutoff in 2021, we would expect it to be unaware of this announcement, which happened in 2023.!pip install -q openai langchain import os import openai os.environ['OPENAI_API_KEY'] = "your OpenAI key" openai.api_key = os.environ['OPENAI_API_KEY'] question = "When did apple announced the Vision Pro?" completion = openai.ChatCompletion.create(model="gpt-3.5-turbo", temperature=0, messages=[{"role": "user", "content": question}]) print(completion["choices"][0]["message"]["content"]) As an AI language model, I do not have access to current events or real-time information. However, as of my last training data, Apple has not announced any product called "Vision Pro." It is possible that this product does not exist or has not been announced yet. As expected, ChatGPT is unable to provide the correct answer due to its training data limitations. This clearly highlights the need for constant updates to the model's knowledge base, which can be addressed by integrating it with a continuously updated knowledge graph.By implementing such a knowledge graph, we can ensure that ChatGPT can provide accurate, current, and reliable information, effectively addressing the "hallucination" issues as well as the knowledge cutoff limitations.Sentence-Level Knowledge GraphsTo demonstrate this, we'll use the LangChain library, a powerful tool designed for building frameworks around large language models. The library includes a component called GraphIndexCreator, which can parse a sentence and create a knowledge graph. This component is currently limited and cannot process long corpus of text, but it serves as a perfect starting point for our experiment.Let's start with a straightforward sentence: "Apple announced the Vision Pro in 2023."from langchain.llms import OpenAI from langchain.indexes import GraphIndexCreator from langchain.chains import GraphQAChain from langchain.prompts import PromptTemplate text = "Apple announced the Vision Pro in 2023." index_creator = GraphIndexCreator(llm=OpenAI(temperature=0)) graph = index_creator.from_text(text) graph.get_triples()[('Apple', 'Vision Pro', 'announced'), ('Vision Pro', '2023', 'was announced in')] By feeding this sentence into the GraphIndexCreator, it creates a knowledge graph by identifying the sentence's entities and relationships, forming triplets of information in the format of (source node, relation, and target node). However, the GraphIndexCreator might get confused with the relations and target nodes due to the inherent complexity of natural language.Even though it's a tiny graph based on a single sentence, we can represent it visually using popular Python libraries such as matplotlib and networkx.import networkx as nx import matplotlib.pyplot as plt # Create graph G = nx.DiGraph() G.add_edges_from((source, target, {'relation': relation}) for source, relation, target in graph.get_triples()) # Plot the graph plt.figure(figsize=(8,5), dpi=300) pos = nx.spring_layout(G, k=3, seed=0) nx.draw_networkx_nodes(G, pos, node_size=2000) nx.draw_networkx_edges(G, pos, edge_color='gray') nx.draw_networkx_labels(G, pos, font_size=12) edge_labels = nx.get_edge_attributes(G, 'relation') nx.draw_networkx_edge_labels(G, pos, edge_labels=edge_labels, font_size=10) # Display the plot plt.axis('off') plt.show() Image 2: Visual Pro Graph Now, let's enhance ChatGPT using the knowledge graph. We will use another component of the LangChain library, GraphQAChain, to this end.Initializing the GraphQAChain, we input the same question we asked earlier, "When did Apple announce the Vision Pro?". This time, ChatGPT leverages the knowledge graph we've just built.chain = GraphQAChain.from_llm(OpenAI(temperature=0), graph=graph, verbose=True) chain.run(question)> Entering new GraphQAChain chain...Entities Extracted: Apple, Vision ProFull Context:Apple announced Vision ProVision Pro was announced in 2023> Finished chain. Apple announced Vision Pro in 2023.This time, ChatGPT was able to output the correct information! The good thing is that we don't need any parser to build our knowledge graphs and can use existing ones. In the next experiment, let's try to use a bigger graph and see if it's still as performant.Bigger Knowledge GraphsIn this experiment, we manually create this more complex graph by supplying a list of triplets to the GraphIndexCreator object using the add_triple() method. Each triplet represents a distinct piece of knowledge related to Apple, such as the products it has created or where it is located.from langchain.graphs.networkx_graph import KnowledgeTriple # Knowledge graph kg = [ ('Apple', 'is', 'Company'), ('Apple', 'created', 'iMac'), ('Apple', 'created', 'iPhone'), ('Apple', 'created', 'Apple Watch'), ('Apple', 'created', 'Vision Pro'), ('Apple', 'developed', 'macOS'), ('Apple', 'developed', 'iOS'), ('Apple', 'developed', 'watchOS'), ('Apple', 'is located in', 'USA'), ('Steve Jobs', 'co-founded', 'Apple'), ('Steve Wozniak', 'co-founded', 'Apple'), ('Tim Cook', 'is the CEO of', 'Apple'), ('iOS', 'runs on', 'iPhone'), ('macOS', 'runs on', 'iMac'), ('watchOS', 'runs on', 'Apple Watch'), ('Apple', 'was founded in', '1976'), ('Apple', 'owns', 'App Store'), ('App Store', 'sells', 'iOS apps'), ('iPhone', 'announced in', '2007'), ('iMac', 'announced in', '1998'), ('Apple Watch', 'announced in', '2014'), ('Vision Pro', 'announced in', '2023'), ] graph = index_creator.from_text('') for (node1, relation, node2) in kg: graph.add_triple(KnowledgeTriple(node1, relation, node2))Although we could include many more triplets (real-world knowledge graphs often encompass millions of nodes), the size of our graph for this demonstration is sufficient. When visualized, this more extensive knowledge graph exhibits greater complexity and a richer depiction of information.# Create directed graph G = nx.DiGraph() for node1, relation, node2 in kg: G.add_edge(node1, node2, label=relation) # Plot the graph plt.figure(figsize=(25, 25), dpi=300) pos = nx.spring_layout(G, k=2, iterations=50, seed=0) nx.draw_networkx_nodes(G, pos, node_size=5000) nx.draw_networkx_edges(G, pos, edge_color='gray', edgelist=G.edges(), width=2) nx.draw_networkx_labels(G, pos, font_size=12) edge_labels = nx.get_edge_attributes(G, 'label') nx.draw_networkx_edge_labels(G, pos, edge_labels=edge_labels, font_size=12) # Display the plot plt.axis('off') plt.show()Image 4: Larger GraphWith this larger graph, we once again ask ChatGPT the question: "When did Apple announce the Vision Pro?" Leveraging the GraphQAChain object, ChatGPT processes the information embedded in the knowledge graph.chain = GraphQAChain.from_llm(OpenAI(temperature=0), graph=graph, verbose=True) chain.run(question)> Entering new GraphQAChain chain...Entities Extracted: Apple, Vision ProFull Context:Apple is CompanyApple created iMacApple created iPhoneApple created Apple WatchApple created Vision ProApple developed macOSApple developed iOSApple developed watchOSApple is located in USAApple was founded in 1976Apple owns App StoreVision Pro announced in 2023> Finished chain. Apple announced the Vision Pro in 2023.ChatGPT successfully extracts the correct information from the more expansive knowledge graph. This result demonstrates that our model can not only scale to larger graphs but can also efficiently navigate a more extensive knowledge base.The possibilities for implementing larger and more diverse knowledge graphs are practically endless. They can be populated with data from various sources, such as legal documents, code documentation, scientific literature, and more, enhancing the AI's understanding and response accuracy across multiple domains. The integration of ChatGPT and knowledge graphs thus holds immense promise for future AI development.ConclusionAs seen in our experiments, knowledge graphs can significantly aid in grounding and improving ChatGPT's outputs. A key challenge with large knowledge graphs is finding connections between distant nodes, a problem often referred to as graph completion. Successfully addressing this issue would allow ChatGPT to make insightful connections and propose new ideas based on the information available in the knowledge graph.However, the process of integrating knowledge graphs into language models like ChatGPT is still an evolving field. To further explore the various applications and delve into the details of implementing knowledge graphs, consider the book "Hands-On Graph Neural Networks Using Python", which provides a comprehensive guide on this subject. Through this type of research and experimentation, we can continuously improve AI's ability to understand and generate text, moving us closer to more reliable and grounded AI models.Author BioMaxime Labonne is a senior applied researcher at J.P. Morgan with a Ph.D. in machine learning and cyber security from the Polytechnic Institute of Paris.During his Ph.D., Maxime worked on developing machine learning algorithms for anomaly detection in computer networks. He then joined the AI Connectivity Lab at Airbus, where he applied his expertise in machine learning to improve the security and performance of computer networks. He then joined J.P. Morgan, where he develops techniques to solve a variety of challenging problems in finance and other domains.In addition to his research work, Maxime is passionate about sharing his knowledge and experience with others through Twitter (@maximelabonne) and his personal blog.

IntroductionThe emergence of AI technology, such as ChatGPT and Auto-GPT, has presented a wealth of possibilities for industries across the full spectrum of work. This article introduces ChatGPT and its use cases, followed by analyzing the shortcomings and challenges of ChatGPT integration. Overview of ChatGPT  A chatbot is an artificial intelligence-based computer program designed to simulate conversation with human users through a messaging interface. Chatbots can be a valuable tool to learn about AI and natural language processing, as well as to improve their communication skills. By interacting with a chatbot, beginners can practice their language skills, get feedback, and learn new vocabulary. Additionally, chatbots can provide instant answers to their questions, help them with simple tasks, and guide them through complex processes. For beginners, chatbots can be a useful tool in automating various tasks. Chatbots can also help in providing personalized recommendations, answering frequently asked questions, and offering support.   Technical Use Cases of ChatGPT  ChatGPT can be an excellent resource for experts and technical people in various fields. As a language model, it can provide answers to complex questions, aid in problem-solving, and assist in research. With its vast knowledge database and ability to understand and process natural language, ChatGPT can quickly and efficiently find relevant information and provide accurate answers. This can save time for experts and technical people, allowing them to focus on higher-level tasks that require their expertise. In addition to answering questions and providing information, ChatGPT can also be used by professionals for technical tasks. It can automate processes, such as data analysis, text classification, and language translation, making it an excellent tool for technical people. For example, a data scientist could use ChatGPT to automate the process of analyzing large data sets, while a developer could use it to quickly translate code or troubleshoot technical issues. With its versatility and adaptability, ChatGPT can be a valuable asset to technical people in various fields. Shortcomings and Challenges of ChatGPT  While ChatGPT is a highly advanced and impressive technology, there are still some shortcomings and challenges associated with it.  One of the main challenges is the potential for bias and inaccurate responses based on the data on which it was trained. As with any machine learning model, ChatGPT is only as good as the data it was trained on, so if the training data contains biases or inaccuracies, it may reproduce them in its responses.  Another challenge is the lack of transparency in its decision-making process, which can make it difficult to understand why it generates certain responses.  ChatGPT may struggle with context-dependent conversations and may not always supply accurate or helpful responses to complex or nuanced queries.  Response based on reinforcement learning could potentially be problematic for ChatGPT. Reinforcement learning involves the use of a reward system to incentivize the model to produce certain responses. However, if the feedback supplied is incorrect or biased, it can negatively affect the ChatGPT model's learning and lead to the production of suboptimal responses.  ChatGPT does not provide any confidence score for its response, for example, if an algorithm has multiple functions or sections, it does not provide a confidence score for each section. Hence, it raises questions about the reliability of its responses and how to measure that reliability.  ChatGPT, like any machine learning model, has limitations in its ability to predict the future. While it can generate responses based on patterns it has learned from enormous amounts of data, it cannot anticipate events that have not yet occurred or make predictions beyond its training data. The energy consumption and carbon footprint associated with training and running such a large language model is a concern for its environmental impact.Summary With the ever-growing advancement of AI technology, tools like ChatGPT can assist many professions, from beginners who want to learn a new language, to technical experts who work in the field of data science. Like any other tool, ChatGPT comes with its own shortcomings and challenges. Thus, since ChatGPT is still at its early stage of adoption, it is advisable for the AI community to work on its shortcomings and share its insights and solutions.   About the Author Matt Zand is the director of Coding Bootcamps and High School Technology Services (HSTS) which offers self-paced and live courses related to blockchain, software engineering, and AI.  HSTS is a member of the Linux Foundation and LF AI & Data. He is a leading author of Hands-on Smart Contract Development with Hyperledger Fabric book by O’Reilly Media. He has written many technical articles on blockchain development at sites such as IBM, Alibaba Cloud, Hyperledger, The Linux Foundation, and more. He is also the founder of three tech startups: RealBig, DC Web Makers, and GoReward. Currently, he is the Advisor at Zuma Dating Events. You can connect with him on LinkedIn: https://www.linkedin.com/in/matt-zand-64047871

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 AI_Distilled! This edition brings you key stories on AI, ML, NLP, Gen AI, and more. Our mission is to keep you informed, empowering your skill advancement. Before we embark on the need-to-know updates, let’s take a moment to observe an important perspective from an industry leader. “We’re now seeing a major second wave…let’s acknowledge that without open source, how would AI have made the tremendous progress it has over the last decade” -Jensen Huang, NVIDIA CEO Amidst the uncertainty surrounding Sam Altman's removal and reinstatement at OpenAI, the open-source community emerges as a potential beneficiary. Also, as OpenAI pauses new signups for ChatGPT Plus, enterprises are anticipated to seek stability and long-term impact by turning to open-source AI models such as Llama, Mistral, Falcon, and MPT for their AI application development needs. Both proprietary and open-source models will play roles, but the latter's contributions are crucial for advancing AI technology's impact on work and life. In this week’s edition, we’ll talk about Google DeepMind unveiling an advanced AI music generation model and experiments, Meta releasing Emu Video and Emu Edit, major breakthroughs in generative AI research, Microsoft Ignite 2023 bringing new AI expansions and product announcements, and Galileo's Hallucination Index identifying GPT-4 as the best LLM for different use cases. We’ve also got you your fresh dose of AI secret knowledge and tutorials including how to implement emerging practices for society-centered AI, how to speed up and improve LLM output with skeleton-of-thought, getting started with Llama 2 in 5 steps, and how to build an AI assistant with real-time web access in 100 lines of code using Python and GPT-4. Also, don't forget to check our expert insights column, which covers the interesting concepts of data architecture from the book 'Modern Data Architecture on AWS'. It's a must-read!  Stay curious and gear up for an intellectually enriching experience!  📥 Feedback on the Weekly EditionHey folks!After the stunning OpenAI DevDay, many of us were eager to embark on creating our custom GPT magic. But let's chat about the recent hiccups: the pause on ChatGPT-4 new sign-ups and the shift or reformation in OpenAI's leadership. It's got us all wondering about the future of our handy tools. Quick question: Ever tried ChatGPT's Advanced Data Analysis? Now that it's temporarily on hold for new users, it's got us thinking, right? Share your take on these changes in the comments. Your thoughts count! We're turning the spotlight on you – some of the best insights will be featured in our next issue for our 38K-strong AI-focused community. Don't miss out on the chance to share your views! 🗨️✨ As a big thanks, get our bestselling "The Applied Artificial Intelligence Workshop" in PDF.   Let's make AI_Distilled even more awesome! 🚀 Jump on in! Share your thoughts and opinions here! 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🔳 Sam Altman Is Reinstated as OpenAI’s Chief Executive: OpenAI reinstated CEO Sam Altman, reversing his ouster amid a board shake-up. The revamped board, led by Bret Taylor, includes Lawrence Summers and Adam D'Angelo, with Microsoft's support. Negotiations involved concessions, including an independent investigation into Altman's leadership. Some outgoing members sought to curb Altman's power. Altman's removal sparked a campaign by allies and employees for his return. The board initially stood by its decision but ultimately reinstated Altman for a fresh start. 🔳 Google DeepMind Unveils Advanced AI Music Generation Model and Experiments: Google DeepMind introduces Lyria, an advanced AI music generation model, and collaborates with YouTube on two experiments, "Dream Track" and "Music AI tools," revolutionizing music creation. Lyria excels in maintaining musical continuity, while the experiments support artists and producers in crafting unique soundtracks and enhancing the creative process. 🔳 Meta Unveils Emu Video and Emu Edit: Advancements in Generative AI Research: Meta has unveiled two major advancements in generative AI: Emu Video, a text-to-video platform using diffusion models for high-quality content generation, and Emu Edit, an image editing tool for precise control. Human evaluations favor Emu Video over previous models, showcasing substantial progress in creative and effective generative AI tools. 🔳 Google's AI Search Feature Expands to 120+ Countries: Google's Search Generative Experience (SGE) has expanded to 120+ countries, offering generative AI summaries and language support for Spanish, Portuguese, Korean, and Indonesian. Users can ask follow-up questions and get interactive definitions. The update will initially roll out in the US before expanding globally, enhancing natural language interactions in search results. 🔳 Microsoft Ignite 2023 Brings New AI Expansions and Product Announcements: Microsoft's Ignite 2023 highlighted the company's deepened AI commitment, featuring Bing Chat's rebranding to Copilot, custom AI chips, and new AI tools like Copilot for Azure. Microsoft Teams will offer AI-driven home decoration and voice isolation. The company consolidated planning tools, introduced generative AI copyright protection, Windows AI Studio for local AI deployment, and Azure AI Speech for text-to-speech avatars. The event underscored Microsoft's emphasis on AI integration across its products and services. 🔳 Microsoft Emerges as Ultimate Winner in OpenAI Power Struggle: Microsoft emerged victorious in the OpenAI power struggle by hiring ousted CEO Sam Altman and key staff, including Greg Brockman, to lead a new advanced AI team. This strategic move solidifies Microsoft's dominance in the industry, positioning it as a major player in AI without acquiring OpenAI, valued at $86 billion. The recent turmoil at OpenAI has led to employee threats of quitting and joining Altman at Microsoft, potentially granting Microsoft access to significant AI talent. 🔳 Galileo's Hallucination Index Identifies GPT-4 As the Best LLM for Different Use Cases: San Francisco based Galileo has introduced a Hallucination Index to aid users in selecting the most reliable Large Language Models (LLMs) for specific tasks. Evaluating various LLMs, including Meta's Llama series, the index found GPT-4 excelled, and OpenAI's models consistently performed well, supporting trustworthy GenAI applications. 🔳 Microsoft Releases Orca 2: Small Language Models That Outperform Larger Ones: Orca 2, comprising 7 billion and 13 billion parameter models, excels in intricate reasoning tasks, surpassing larger counterparts. Developed by fine-tuning LLAMA 2 base models on tailored synthetic data, Orca 2 showcases advancements in smaller language model research, demonstrating adaptability across tasks like reasoning, grounding, and safety through post-training with carefully filtered synthetic data. 🔳 NVIDIA CEO Predicts Major Second Wave of AI: Jensen Huang predicts a significant AI surge, citing breakthroughs in language replicated in biology, manufacturing, and robotics, offering substantial opportunities for Europe. Praising France's AI leadership, he emphasizes the importance of region-specific AI systems reflecting cultural nuances and highlights the crucial role of data in regional AI growth. 🔮 Expert Insights from Packt Community Modern Data Architecture on AWS - By Behram Irani Challenges with on-premises data systems As data grew exponentially, so did the on-premises systems. However, visible cracks started to appear in the legacy way of architecting data and analytics use cases. The hardware that was used to process, store, and consume data had to be procured up-front, and then installed and configured before it was ready for use. So, there was operational overhead and risks associated with procuring the hardware, provisioning it, installing software, and maintaining the system all the time. Also, to accommodate for future data growth, people had to estimate additional capacity way in advance. The concept of hardware elasticity didn’t exist.The lack of elasticity in hardware meant that there were scalability risks associated with the systems in place, and these risks would surface whenever there was a sudden growth in the volume of data or when there was a market expansion for the business. Buying all this extra hardware up-front also meant that a huge capital expenditure investment had to be made for the hardware, with all the extra capacity lying unused from time to time. Also, software licenses had to be paid for and those were expensive, adding to the overall IT costs. Even after buying all the hardware upfront, it was difficult to maintain the data platform’s high performance all the time. As data volumes grew, latency started creeping in, which adversely affected the performance of certain critical systems. As data grow into big data, the type of data produced was not just structured data; a lot of business use cases required semi-structured data, such as JSON files, and even unstructured data, such as images and PDF files. In subsequent chapters, we will go through some use cases that specify different types of data. As the sources of data grew, so did the number of ETL pipelines. Managing these pipelines became cumbersome. And on top of that, with so much data movement, data started to duplicate at multiple places, which made it difficult to create a single source of truth for the data. On the flip side, with so many data sources and data owners within an organization, data became siloed, which made it difficult to share across different LOBs in the organization. This content is from the book “Modern Data Architecture on AWS” writtern by Behram Irani (Aug 2023). Start reading a free chapter or access the entire Packt digital library free for 7 days by signing up now. To learn more, click on the button below. Read through the Chapter 1 unlocked here...  🌟 Secret Knowledge: AI/LLM Resources🤖 How to Use Amazon CodeWhisperer for Command Line: Amazon introduces Amazon CodeWhisperer for the command line, enhancing developer productivity with contextual CLI completions and AI-driven natural language-to-bash translation. The tool provides CLI completions and translates natural language instructions into executable shell code snippets, modernizing the command line experience for over thirty million engineers. 🤖 How to Implement Emerging Practices for Society-Centered AI: The post underscores the importance of AI professionals addressing societal implications, advocating for multidisciplinary collaboration. It stresses the significance of measuring AI's impact on society to enhance effectiveness and identify areas for improvement in developing systems that benefit the broader community. 🤖 How to Speed Up and Improve LLM Output with Skeleton-of-Thought: The article introduces the Skeleton-of-Thought (SoT) approach, aiming to enhance the efficiency of Language Models (LLMs) by reducing generation latency and improving answer quality. SoT guides LLMs to generate answer skeletons first, then completes them in parallel, potentially accelerating open-source and API-based models for various question categories. 🤖 Understanding SuperNIC to Enhance AI Efficiency: The BlueField-3 SuperNIC is pivotal in AI-driven innovation, boosting workload efficiency and networking speed in AI cloud computing. With a 1:1 GPU to SuperNIC ratio, it enhances productivity. Integrated with NVIDIA Spectrum-4, it provides adaptive routing, out-of-order packet handling, and optimized congestion control for superior outcomes in enterprise data centers. 🤖 Step-by-step guide to the Evolution of LLMs: The post explores the 12-month evolution of Large Language Models (LLMs), from text completion to dynamic chatbots with code execution and knowledge access. It emphasizes the frequent release of new features, models, and techniques, notably the November 2022 launch of ChatGPT, accelerating user adoption and triggering an AI arms race, while questioning if such rapid advancements are bringing us closer to practical AI agents.  🔛 Masterclass: AI/LLM Tutorials👉 How to Get Started with Llama 2 in 5 Steps: Llama 2, an open-source large language model, is now free for research and commercial use. This blog outlines a five-step guide, covering prerequisites, model setup, fine-tuning, inference, and additional resources for users interested in utilizing Llama 2. 👉 How to Integrate GPT-4 with Python and Java: A Developer's Guide: The article explores integrating GPT-4 with Python and Java, emphasizing Python's compatibility and flexibility. It provides examples, discusses challenges like rate limits, and encourages collaboration for harnessing GPT-4's transformative potential, highlighting the importance of patience and debugging skills. 👉 How to Build an AI Assistant with Real-Time Web Access in 100 Lines of Code Using Python and GPT-4: This article guides readers in creating a Python-based AI assistant with real-time web access using GPT-4 in just 100 lines of code. The process involves initializing clients with API keys, creating the assistant using the OpenAI and Tavily libraries, and implementing a function for retrieving real-time information from the web. The author offers a detailed step-by-step guide with code snippets. 👉 Step-by-step guide to building a real-time recommendation engine with Amazon MSK and Rockset: This tutorial demonstrates building a real-time product recommendation engine using Amazon Managed Streaming for Apache Kafka (Amazon MSK) and Rockset. The architecture allows instant, personalized recommendations critical for e-commerce, utilizing Amazon MSK for capturing high-velocity user data and AWS Managed services for scalability in handling customer requests, API invocations, and data ingestion.  🚀 HackHub: Trending AI Tools💮 protectai/ai-exploits: Collection of real-world AI/ML exploits for responsibly disclosed vulnerabilities, aiming to raise awareness of the amount of vulnerable components in the AI/ML ecosystem. 💮 nlmatics/llmsherpa: Provides strategic APIs to accelerate LLM use cases, includes a LayoutPDFReader that provides layout information for PDF to text parsers, and is tested on a wide variety of PDFs. 💮 QwenLM/Qwen-Audio: Large audio language model proposed by Alibaba Cloud developers can use for speech editing, sound understanding and reasoning, music appreciation, and multi-turn dialogues in diverse audio-oriented scenarios. 💮 langchain-ai/opengpts: Open-source effort creating a similar experience to OpenAI's GPTs and Assistants API. It builds upon LangChain, LangServe, and LangSmith.  Readers’ Feedback! 💬 💭 Anish says, "The growing number of subscribers is really exciting. I particularly appreciate the transformation of 2D images into 3D models from Adobe and going through 'Tackling Hallucinations in LLMs' by Bijit Ghosh. These kinds of practical contexts are truly my preference for the upcoming newsletters." 💭 Tony says, "Very informative, far-reaching, and extremely timely. On point. Just keep it up, keep your eye on news and knowledge, and keep cluing us all once a week please, Merlyn. You're doing a fine job."  Share your thoughts here! Your opinions matter—let's make this space a reflection of diverse perspectives. 

Software architecture acts as a blueprint for the system, using abstraction to control the system's complexity and establish inter-component communication. In the ever-evolving landscape of software architecture, a groundbreaking innovation has emerged, reshaping the way developers design and optimize their systems. Enter ChatGPT, an advanced language model that has revolutionized the field with its remarkable capabilities. With its deep understanding of natural language, ChatGPT is unlocking new horizons in software architecture. From streamlining development processes to enhancing user interactions, this article delves into the transformative potential of ChatGPT, exploring how it is reshaping the very foundations of software architecture as we know it.In this piece, we'll examine the value of software architecture and how chatGPT may help us build it.  Architecture Design with ChatGPTThe entire design of the software, its elements, and its behavior are represented by the software system architect. In a nutshell, it is a visual representation of how software applications are made by connecting their many components. The following are examples of software architecture activities: Implementation Details: It could be architectural artifacts, source code, documentation, repository, etc.Implementation Design decisions: It includes options for technology (cloud or on-premises), architectural style (monolithic or distributed), storage (AWS S3, Azure Blob, ADLS Gen2, etc.), ingestion pattern (batch or real-time streaming pattern), and more.Infrastructure Considerations: Deployment Choices, Component Configurations, etc.Let’s understand in detail about the process of software architecture.Requirement Gathering: Any project should begin with functional and non-functional requirements since they will determine how to create the software architecture and how to prepare the finished product in accordance with the needs of the stakeholders.Create a Helicopter view of the solution: Using the mind map, provide a high-level overview of the system's constituent parts. It is a useful method for capturing your requirements as a diagram.Refine functional and non-functional requirements: Examine the non-functional needs in terms of performance, security, cost-effectiveness, etc. once you have thoroughly refined the functional requirements to comprehend the overall functioning of the program.Design each component in detail: Start with creating the platform's infrastructure and creating the application components while taking into account the functional and non-functional needs.Create a phase-wise approach for the implementation: Once the infrastructure and application implementation design considerations are clear, begin preparing deliverables for a phased rollout. It should include the state of architecture as it is today, architecture in transition, and architecture in the future. Make a visual representation of the application and infrastructure, taking into account networking, security, and interconnection issues.Below are the best practices that can be followed while designing the software architecture for any application:Design your application considering the best and worst-case scenarioDesign your application which should be able to scale up and scale downCreate loosely coupled architecture design for the smooth functioning of the system Create a multi-thread processing to speed up the processing for batch and real-time streaming implementationDesign your application with 7 layers of security implementationMake a choice to store your dataNow, Let’s use ChatGPT to create a software architecture for an application. Our goal is to create a data mesh architecture for the retail industry using the azure technology. The major requirement is also to define and capture data architecture requirements.Image 1 - Part 1, Data mesh responseImage 2 - Part 2, Data mesh responseImage 3- Part 3, Data mesh responseImage 4- Part 4, Data mesh response ChatGPT first offers suggestions for a group of Azure services to be used to implement the batch and real-time streaming patterns after receiving input from the user. The principles for implementing a data mesh architecture are then provided, including domain-driven data ownership, data-driven products, self-service data infrastructure, and data governance, including data discovery and monitoring.Let’s check with chatGPT on how the networking setup should be done for these Azure services:Image 5: Part 1, Networking responseImage 6: Part 2 Networking response Image 7: Part 3 Networking response  The configuration of a VNET, subnet, NSG rules, Azure firewall, VNET peering, VPN gateway, and the private link is advised by chatGPT. These Azure networking components can be used to manage the services' interconnection. Another major requirement to implement data mesh architecture is also to check how domain-specific data will be managed and operationalized for the data mesh architecture. Image 8: Part 1,  Domain-specific Data ManagementImage 9: Part 2,  Domain-specific Data ManagementImage 10: Part 3,  Domain-specific Data ManagementThe goal of chatGPT's proposals is to produce domain-specific cleansed data that end users can utilize directly to extract value from the data. Since these data domain stores are being built by domain experts, they are also in charge of managing, supporting, and operationalizing the data as needed.ConclusionIn this post, we looked in detail at the overall process of software architecture design as well as its sequential process. The best techniques for creating the software architecture for any application were also demonstrated. Later, we used chatGPT to develop a data mesh architecture implementation, complete with networking setup and operationalization, for a retail domain.Author BioSagar Lad is a Cloud Data Solution Architect with a leading organization and has deep expertise in designing and building Enterprise-grade Intelligent Azure Data and Analytics Solutions. He is a published author, content writer, Microsoft Certified Trainer, and C# Corner MVP.Link - Medium , Amazon , LinkedIn  

The application of advanced AI tools, such as ChatGPT, in various industries, particularly finance, has proven transformative due to its extensive language processing capabilities. ChatGPT's functions within the financial sector are diverse and impressive. It can understand financial market dynamics, suggest products, identify specific entities, and generate financial summaries, reports, and forecasts. Furthermore, the potential of training ChatGPT for fraud prevention and detection is an exciting prospect.However, as the integration of ChatGPT into the financial services realm becomes more prevalent, it brings to the fore several ethical challenges. Therefore, the onus is on both researchers and practitioners to ensure that the technology's use is responsible and advantageous to all parties involved. The solutions to these ethical challenges often require a multi-faceted approach, focusing on data exposure, misinformation, technology dependency, privacy concerns, and social engineering.The Ethical Challenges InvolvedOne of the paramount ethical challenges is data exposure. For example, ChatGPT users working with financial data might unintentionally disclose sensitive information. Additionally, during the AI model's training phase, there's a risk of exposing confidential elements such as proprietary code snippets, API keys, or login credentials.ChatGPT can sometimes generate biased or inaccurate responses, causing misinformation. The tool, at present, operates based on data sets that only run up to September 2021, which are sourced online and not always accurate. Therefore, financial professionals must exercise caution while using such advice to prevent the propagation of misinformation.Furthermore, while AI can be a powerful tool for financial decision-making, relying solely on technology can undermine human judgment and intuition. Financial professionals could fall into the trap of misinterpreting or overly depending on ChatGPT's advice, thereby overlooking the importance of human expertise in the financial sector. Therefore, it is crucial to strike a balance between utilizing AI's efficiency and maintaining human critical thinking.As ChatGPT requires an extensive amount of data for training, this raises significant privacy concerns. The information collected could pose serious risks to both individuals and organizations if exposed or used maliciously. In tandem with privacy concerns, social engineering issues arise as well. There is a potential for cybercriminals to misuse ChatGPT, impersonating individuals or organizations to conduct successful phishing attacks.Solving the ProblemAddressing these ethical challenges requires robust solutions. Firstly, the co-creation approach, which emphasizes public participation and stakeholder involvement in designing the AI algorithm. This strategy includes key choices in the algorithm, from the scope of its use to mitigating biases and tackling misinformation. It also ensures that humans keep a certain level of control over the AI tool, thus preventing total dependency on the technology.Secondly, the institutional approach can ensure the ethical use of ChatGPT in finance. This approach demands the establishment of concrete rules for managing ChatGPT, including training policy regulators to scrutinize and audit the AI algorithm and developing regulations. The focus is on creating transparent tools that ensure user privacy and constantly upgrade security measures to prevent breaches by cybercriminals.Lastly, it's vital to maintain a harmonious blend of AI-based decision-making and human intuition. While ChatGPT can crunch data and analyze trends with efficiency, human professionals have the experiential knowledge to make intuitive financial decisions. The amalgamation of both AI and human insight can lead to mutual learning and overall improvement in financial decision-making. It can also help address legal obstacles in financial domains that AI might overlook, thus ensuring the accuracy and reliability of financial decisions.The UK Finance paper on AI Fairness in Financial Services recommends a multi-disciplinary approach:Frontline business must be clear on the objective of the use of AI, the risks to individuals and to the business, and the extent to which risks of unfair treatment will be managed and explained to stakeholders.Data scientists are central to the technical aspects of the use, testing and monitoring of AI.Legal and Compliance need to be involved (including in any preliminary stages) to provide appropriate challenge, to oversee testing and to assist with fair process and related transparency principle.In addition, human application can mitigate the looming threat of job loss due to automation. While technology like ChatGPT can automate many functions, it is essential to preserve roles where human intuition, expertise, and judgment are irreplaceable.While the adoption of ChatGPT in finance is indeed a technological advancement, it comes with ethical challenges that require strategic and thoughtful solutions. Companies must adopt strategies such as co-creation and institutional approaches to ensure ethical usage. Furthermore, they need to strike a balance between AI and human insight to maintain the integrity of financial decisions. By addressing these challenges and implementing relevant strategies, we can ensure a future where AI not only augments the financial sector but also respects the values that we hold dear.Author BioJulian Melanson is one of the founders of Leap Year Learning. Leap Year Learning is a cutting-edge online school that specializes in teaching creative disciplines and integrating AI tools. We believe that creativity and AI are the keys to a successful future and our courses help equip students with the skills they need to succeed in a continuously evolving world. Our seasoned instructors bring real-world experience to the virtual classroom and our interactive lessons help students reinforce their learning with hands-on activities.No matter your background, from beginners to experts, hobbyists to professionals, Leap Year Learning is here to bring in the future of creativity, productivity, and learning!

This article is an excerpt from the book, Building AI Applications with ChatGPT API, by Martin Yanev. This book will help you master ChatGPT, Whisper, and DALL-E APIs by building nine innovative AI projectsIn this section, we will dive into the implementation of the key functions within the essay generator application. These functions are responsible for generating the essay based on user input and saving the generated essay to a file. By understanding the code, you will be able to grasp the inner workings of the application and gain insight into how the essay generation and saving processes are accomplished.We will begin by exploring the generate_essay() function. This function will retrieve the topic entered by the user from the input field. It will then set the engine type for the OpenAI API, create a prompt using the topic, and make a request to the OpenAI API for essay generation. The response received from the API will contain the generated essay, which will be extracted and displayed in the essay output area of the application. To add that functionality, simply remove the pass placeholder and follow the code below.def generate_essay(self): topic = self.topic_input.text()    length = 500    engine = "text-davinci-003"    prompt = f"Write an {length/1.5} words essay on the following topic: {topic} \n\n"    response = openai.Completion.create(engine=engine, prompt=prompt, max_tokens=length)    essay = response.choices[0].text    self.essay_output.setText(essay)Here, we retrieve the topic entered by the user from the topic_input QLineEdit widget and assign it to the topic variable using the text() method. This captures the user's chosen topic for the essay. For now, we can define the length variable and set it to 500. This indicates the desired length of the generated essay. We will modify this value later by adding a dropdown menu with different token sizes to generate essays of different lengths.We can also specify the engine used for the OpenAI API to be text-davinci-003, which will generate the essay. You can adjust this value to utilize different language models or versions based on your requirements. We can also create the prompt variable, which is a string containing the prompt for the essay generation.It is constructed by concatenating the text Write a {length/1.5} essay on the following topic: where the length/1.5 variable specifies how many words our essay should be. We need to divide the tokens number by 1.5, as 1 word in English represents about 1.5 tokes. After specifying the instructions, we can pass the topic variable to the prompt. This prompt serves as the initial input for the essay generation process and provides context for the generated essay.Once all variables are defined, we make a request to the ChatGPT API with the specified engine, prompt, and the maximum number of tokens (in this case, 500). The API processes the prompt and generates a response, which is stored in the response variable. From the response, we extract the generated essay by accessing the text attribute of the first choice. This represents the generated text of the essay. Finally, we can pass the AI response to the essay_output, displaying it in the user interface for the user to read and interact with.Moving on, we will examine the save_essay() function. This function will retrieve the topic and the generated essay. It will utilize the docx library to create a new Word document and add the final essay to the document. The document will then be saved with the filename based on the provided topic, resulting in a Word document that contains the generated essay. After removing the pass keyword, you can implement the described functionality using the code snippet below.def save_essay(self):    topic = self.topic_input.text()    final_text = self.essay_output.toPlainText()    document = docx.Document()    document.add_paragraph(final_text)    document.save(topic + ".docx")Here we retrieve the text entered in the topic_input widget and assign it to the topic variable using the text() method. This captures the topic entered by the user, which will be used as the filename for the saved essay. Next, we use the toPlainText() method on the essay_output widget to retrieve the generated essay text and assign it to the final_text variable. This ensures that the user can edit the ChatGPT-generated essay before saving it. By capturing the topic and the final text, we are now equipped to proceed with the necessary steps to save the essay to a file.We can now use the docx library to create a new Word document by calling docx.Document(), which initializes an empty document. We then add a paragraph to the document by using the add_paragraph() method and passing in the final_text variable, which contains the generated essay text. This adds the generated essay as a paragraph to the document. We can now save the document by calling document.save() and providing a filename constructed by concatenating the topic variable, which represents the topic entered by the user. This saves the document as a Word file with the specified filename.You can now test your Essay Generator by running the code in PyCharm and generating an essay following the steps below (see Figure 8.3):Enter a topic: Write an essay topic of your choice in the Topic Input field. For this example, I have chosen the topic “Ancient Egypt”.Generate Essay: Perform a single click on the Generate Essay button. The app will reach ChatGPT API and within a few seconds, you will have your essay displayed inside the Essay Output field.Edit the Essay: You can edit the essay generated by the Artificial Intelligence before saving it.Save: Perform a single click on the Save button. This action will save the edited essay to a Word document utilizing the save_essay() method. The Word document will be saved in the root directory of your project.Figure 8.3: Essay Generator creating an “Ancient Egypt” essayOnce the essay has been saved to a Word document, you can reshare it with your peers, submit it as a school assignment or use any Word styling options on it.This section discussed the implementation of key functions in our essay generator application using the ChatGPT API. We built the generate_essay() method that retrieved the user’s topic input and sent a request to the ChatGPT API for generating an AI essay. We also developed the save_essay() method that saved the generated essay in a Word document.In the next section, we will introduce additional functionality to the essay generator application. Specifically, we will allow the user to change the number of AI tokens used for generating the essay.Controlling the ChatGPT API TokensIn this section, we will explore how to enhance the functionality of the essay generator application by allowing users to have control over the number of tokens used when communicating with ChatGPT. By enabling this feature, users will be able to generate essays of different lengths, tailored to their specific needs or preferences. Currently, our application has a fixed value of 500 tokens, but we will modify it to include a dropdown menu that provides different options for token sizes.To implement this functionality, we will make use of a dropdown menu that presents users with a selection of token length options. By selecting a specific value from the dropdown, users can indicate their desired length for the generated essay. We will integrate this feature seamlessly into the existing application, empowering users to customize their essay-generation experience.Let's delve into the code snippet that will enable users to control the token length. You can add that code inside the initUI() methods, just under the essay_output resizing:self.essay_output.resize(1100, 500) length_label = QLabel('Select Essay Length:', self) length_label.move(327, 40) self.length_dropdown = QComboBox(self) self.length_dropdown.move(320, 60) self.length_dropdown.addItems(["500", "1000", "2000", "3000", "4000"])The code above introduces a QLabel, length_label, which serves as a visual indication for the purpose of the dropdown menu. It displays the text Select Essay Length to inform users about the functionality.Next, we create a QcomboBox length_dropdown which provides users with a dropdown menu to choose the desired token length. It is positioned below the length_label using the move() method. The addItems() function is then used to populate the dropdown menu with a list of token length options, ranging from 500 to 4000 tokens. Users can select their preferred length from this list.The final step is to implement the functionality that allows users to control the number of tokens used when generating the essay, we need to modify the generate_essay() function. The modified code should be the following:def generate_essay(self):    topic = self.topic_input.text()    length = int(self.length_dropdown.currentText())    engine = "text-davinci-003"    prompt = f"Write an {length/1.5} words essay on the following topic: {topic} \n\n"    response = openai.Completion.create(engine=engine, prompt=prompt, max_tokens=length)    essay = response.choices[0].text   self.essay_output.setText(essay)In the modified code, the length variable is updated to retrieve the selected token length from the length_dropdown dropdown menu. The currentText() method is used to obtain the currently selected option as a string, which is then converted to an integer using the int() function. This allows the chosen token length to be assigned to the length variable dynamically.By making this modification, the generate_essay() the function will utilize the user-selected token length when making the request to the ChatGPT API for essay generation. This ensures that the generated essay will have the desired length specified by the user through the dropdown menu.We can now click on the Run button in PyCharm and verify that the Dropdown menu works properly. As shown in Figure 8.4, a click on the Dropdown menu will show users all options specified by the addItems() function.Figure 8.4: Controlling essay length.The user will be able to choose a token amount between 500 and 4000. Now you can select the 4000 tokens option, resulting in a longer length of the generated essay. We can follow the steps from our previous example and verify that the ChatGPT API generates a longer essay for “Ancient Egypt” when the number of tokens is increased from 500 to 4000.This is how you can enhance the functionality of an essay generator application by allowing users to control the number of tokens used when communicating with ChatGPT. By selecting a specific value from the dropdown menu, users can now indicate their desired length for the generated essay. We achieved that by using the QComboBox to create the dropdown menu itself. The modified generate_essay() method retrieved the selected token length from the dropdown menu and dynamically assigned it to the length variable.SummaryIn conclusion, leveraging the capabilities of ChatGPT API to enhance essay generation opens up a world of interactive creativity. By incorporating practical examples and step-by-step instructions, we have explored how to generate essay-generating elements and make them interact seamlessly with ChatGPT. This powerful combination allows for the production of compelling, coherent, and engaging essays effortlessly. With the ever-evolving potential of AI, the future of essay generation holds immense possibilities. By embracing these techniques, writers and researchers can unlock their full creative potential and revolutionize the way we generate written content.Author BioMartin Yanev is an experienced Software Engineer who has worked in the aerospace and medical industries for over 8 years. He specializes in developing and integrating software solutions for air traffic control and chromatography systems. Martin is a well-respected instructor with over 280,000 students worldwide, and he is skilled in using frameworks like Flask, Django, Pytest, and TensorFlow. He is an expert in building, training, and fine-tuning AI systems with the full range of OpenAI APIs. Martin has dual master's degrees in Aerospace Systems and Software Engineering, which demonstrates his commitment to both practical and theoretical aspects of the industry.LinkedInUdemy

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 back to a new issue of AI_Distilled - your guide to the key advancements in AI, ML, NLP, and GenAI. Let's dive right into an industry expert’s perspective to sharpen our understanding of the field's rapid evolution. "In the near future, anyone who's online will be able to have a personal assistant powered by artificial intelligence that's far beyond today's technology."  - Bill Gates, Co-Founder, Microsoft. In a recent interview, Gates minced no words when he said software is still “pretty dumb” even in today’s day and age. The next 5 years will be crucial, he believes, as everything we know about computing in our personal and professional lives is on the brink of a massive disruption. Even everyday things as simple as phone calls are due for transformation as evident from Samsung unveiling the new 'Galaxy AI' and real-time translate call feature. In this issue, we’ll talk about Google exploring massive investment in AI startup Character.AI, Microsoft's GitHub Copilot user base surging to over a million, OpenAI launching data partnerships to enhance AI understanding, and Adobe researchers’ breakthrough AI that transforms 2D images into 3D models in 5 seconds.We’ve also got you your fresh dose of AI secret knowledge and tutorials. Explore how to scale multimodal understanding to long videos, navigate the landscape of hallucinations in LLMs, read a practical guide to enhancing RAG system responses, how to generate Synthetic Data for Machine Learning and unlock the power of low-code GPT AI apps.  📥 Feedback on the Weekly EditionWe've hit 6 months and 38K subscribers in our AI_Distilled newsletter journey — thanks to you!  The best part? Our emails are opened by 60% of recipients each week. We're dedicated to tailoring them to enhance your Data & AI practice. Let's work together to ensure they fully support your AI efforts and make a positive impact on your daily work.Share your thoughts in a quick 5-minute survey to shape our content. As a big thanks, get our bestselling "The Applied Artificial Intelligence Workshop" in PDF.  Let's make AI_Distilled even more awesome! 🚀 Jump on in! 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🔳 Google Explores Massive Investment in AI Startup Character.AI: Google is reportedly in discussions to invest 'hundreds of millions' in Character.AI, an AI chatbot startup founded by ex-Google Brain employees. The investment is expected to deepen the collaboration between the two entities, leveraging Google's cloud services and Tensor Processing Units (TPUs) for model training. Character.AI, offering virtual interactions with celebrities and customizable chatbots, targets a youthful audience, particularly those aged 18 to 24, constituting 60% of its web traffic.  🔳 AI Actions Empowers AI Platforms with Zapier Integration: AI Actions introduces a tool enabling AI platforms to seamlessly run any Zapier action, leveraging Zapier's extensive repository of 20,000+ searches and actions. The integration allows natural language commands to trigger Zapier actions, eliminating obstacles like third-party app authentication and API integrations. Supported on platforms like ChatGPT, GPTs, Zapier, and customizable solutions, AI Actions provides flexibility for diverse applications. 🔳 Samsung Unveils 'Galaxy AI' and Real-Time Translate Call Feature: Samsung declares its commitment to AI with a preview of "Galaxy AI," a comprehensive mobile AI experience that combines on-device AI with cloud-based AI collaborations. The company introduced an upcoming feature, "AI Live Translate Call," embedded in its native phone app, offering real-time audio and text translations on the device during calls. Set to launch early next year, Galaxy AI is anticipated to debut with the Galaxy S24 lineup.  🔳 Google Expands Collaboration with Anthropic, Prioritizing AI Security and Cloud TPU v5e Accelerators: In an intensified partnership, Google announces its extended collaboration with Anthropic, focusing on elevated AI security and leveraging Cloud TPU v5e chips for AI inference. The collaboration, dating back to Anthropic's inception in 2021, highlights their joint efforts in AI safety and research. Anthropic, utilizing Google's Cloud services like GKE clusters, AlloyDB, and BigQuery, commits to Google Cloud's security services for model deployment. 🔳 Microsoft's GitHub Copilot User Base Surges to Over a Million, CEO Nadella Reports: Satya Nadella announced a substantial 40% growth in paying customers for GitHub Copilot in the September quarter, surpassing one million users across 37,000 organizations. Nadella highlights the rapid adoption of Copilot Chat, utilized by companies like Shopify, Maersk, and PWC, enhancing developers' productivity. The Bing search engine, integrated with OpenAI's ChatGPT, has facilitated over 1.9 billion chats, demonstrating a growing interest in AI-driven interactions. Microsoft's Azure revenue, including a significant contribution from AI services, exceeded expectations, reaching $24.3 billion, with the Azure business rising by 29%.  🔳 Dell and Hugging Face Join Forces to Streamline LLM Deployment: Dell and Hugging Face unveil a strategic partnership aimed at simplifying the deployment of LLMs for enterprises. With the burgeoning interest in generative AI, the collaboration seeks to address common concerns such as complexity, security, and privacy. The companies plan to establish a Dell portal on the Hugging Face platform, offering custom containers, scripts, and technical documentation for deploying open-source models on Dell servers.  🔳 OpenAI Launches Data Partnerships to Enhance AI Understanding: OpenAI introduces Data Partnerships, inviting collaborations with organizations to develop both public and private datasets for training AI models. The initiative aims to create comprehensive datasets reflecting diverse subject matters, industries, cultures, and languages, enhancing AI's understanding of the world. Two partnership options are available: Open-Source Archive for public datasets and Private Datasets for proprietary AI models, ensuring sensitivity and access controls based on partners' preferences. 🔳 Iterate Unveils AppCoder LLM for Effortless AI App Development: California-based Iterate introduces AppCoder LLM, a groundbreaking model embedded in the Interplay application development platform. This innovation allows enterprises to generate functional code for AI applications effortlessly by issuing natural language prompts. Unlike existing AI-driven coding solutions, AppCoder LLM, integrated into Iterate's platform, outperforms competitors, producing better outputs in terms of functional correctness and usefulness.  🔳 Adobe Researchers Unveil Breakthrough AI: Transform 2D Images into 3D Models in 5 Seconds: A collaborative effort between Adobe Research and Australian National University has resulted in a groundbreaking AI model capable of converting a single 2D image into a high-quality 3D model within a mere 5 seconds. The Large Reconstruction Model for Single Image to 3D (LRM) utilizes a transformer-based neural network architecture with over 500 million parameters, trained on approximately 1 million 3D objects. This innovation holds vast potential for industries like gaming, animation, industrial design, AR, and VR.  🔮 Expert Insights from Packt Community Synthetic Data for Machine Learning - By Abdulrahman Kerim Training ML models Developing an ML model usually requires performing the following essential steps: Collecting data. Annotating data. Designing an ML model. Training the model. Testing the model. These steps are depicted in the following diagram: Fig – Developing an ML model process. Now, let’s look at each of the steps in more detail to better understand how we can develop an ML model. Collecting and annotating data The first step in the process of developing an ML model is collecting the needed training data. You need to decide what training data is needed: Train using an existing dataset: In this case, there’s no need to collect training data. Thus, you can skip collecting and annotating data. However, you should make sure that your target task or domain is quite similar to the available dataset(s) you are planning to deploy. Otherwise, your model may train well on this dataset, but it will not perform well when tested on the new task or domain. Train on an existing dataset and fine-tune on a new dataset: This is the most popular case in today’s ML. You can pre-train your model on a large existing dataset and then fine-tune it on the new dataset. Regarding the new dataset, it does not need to be very large as you are already leveraging other existing dataset(s). For the dataset to be collected, you need to identify what the model needs to learn and how you are planning to implement this. After collecting the training data, you will begin the annotation process. Train from scratch on new data: In some contexts, your task or domain may be far from any available datasets. Thus, you will need to collect large-scale data. Collecting large-scale datasets is not simple. To do this, you need to identify what the model will learn and how you want it to do that. Making any modifications to the plan later may require you to recollect more data or even start the data collection process again from scratch. Following this, you need to decide what ground truth to extract, the budget, and the quality you want. This content is from the book “Synthetic Data for Machine Learning” written by Abdulrahman Kerim (Oct 2023). 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🤖 Scaling Multimodal Understanding to Long Videos: A Comprehensive Guide: This guide provides a step-by-step explanation of the challenges associated with modeling diverse modalities like video, audio, and text. Learn about the Mirasol3B architecture, which efficiently handles longer videos, and understand the coordination between time-aligned and contextual modalities. The guide also introduces the Combiner, a learning module to effectively combine signals from video and audio information.  🤖 Mastering AI and ML Workloads: A Guide with Cloud HPC Toolkit: This post, authored by Google Cloud experts, delves into the convergence of HPC systems with AI and ML, highlighting their mutual benefits. They provide instructions on deploying clusters, utilizing preconfigured partitions, and utilizing powerful tools such as enroot and Pyxis for container integration. Discover the simplicity of deploying AI models on Google Cloud with the Cloud HPC Toolkit, fostering innovation and collaboration between HPC and AI communities. 🤖 Mastering the GPT Workflow: A Comprehensive Guide to AI Language Model: From understanding the basics of GPT's architecture and pre-training concept to unraveling the stages of the GPT workflow, including pre-training, fine-tuning, evaluation, and deployment, this guide provides a step-by-step walkthrough. Gain insights into ethical considerations, bias mitigation, and challenges associated with GPT models. Delve into future developments, including model scaling, multimodal capabilities, explainable AI enhancements, and improved context handling.  🤖 Navigating the Landscape of Hallucinations in LLMs: A Comprehensive Exploration: Delve into the intricate world of LLMs and the challenges posed by hallucinations in this in-depth blog post. Gain an understanding of the various types of hallucinations, ranging from harmless inaccuracies to potentially harmful fabrications, and their implications in real-world applications. Explore the root factors leading to hallucinations, such as overconfidence and lack of grounded reasoning, during LLM training.  🤖 Unveiling the Core Challenge in GenAI: Cornell University's Insightful Revelation: Cornell University researchers unveil a pivotal threat in GenAI, emphasizing the crucial role of "long-term memory" and the need for a vector database for contextual retrieval. Privacy issues emerge in seemingly secure solutions, shedding light on the complex challenges of handling non-numerical data in advanced AI models. 🔛 Masterclass: AI/LLM Tutorials👉 Unlocking the Power of Low-Code GPT AI Apps: A Comprehensive Guide. Explore how AINIRO.IO introduces the concept of "AI Apps" by seamlessly integrating ChatGPT with CRUD operations, enabling natural language interfaces to databases. Dive into the intricacies of creating a dynamic AI-based application without extensive coding, leveraging the Magic cloudlet to generate CRUD APIs effortlessly. Explore the significant implications of using ChatGPT for business logic in apps, offering endless possibilities for user interactions. 👉 Deploying LLMs Made Easy with ezsmdeploy 2.0 SDK: This post provides an in-depth understanding of the new capabilities, allowing users to effortlessly deploy foundation models like Llama 2, Falcon, and Stable Diffusion with just a few lines of code. The SDK automates instance selection, configuration of autoscaling, and other deployment details, streamlining the process of launching production-ready APIs. Whether deploying models from Hugging Face Hub or SageMaker Jumpstart, ezsmdeploy 2.0 reduces the coding effort required to integrate state-of-the-art models into production, making it a valuable tool for data scientists and developers. 👉 Enhancing RAG System Responses: A Practical Guide: Discover how to enhance the performance of your Retrieval-Augmented Generation (RAG) systems in generative AI applications by incorporating an interactive clarification component. This post offers a step-by-step guide on improving the quality of answers in RAG use cases where users present vague or ambiguous queries. Learn how to implement a solution using LangChain to engage in a conversational dialogue with users, prompting them for additional details to refine the context and provide accurate responses.  👉 Building Personalized ChatGPT: A Step-by-Step Guide. In this post, you'll learn how to explore OpenAI's GPT Builder, offering a beginner-friendly approach to customize ChatGPT for various applications. With the latest GPT update, users can now create personalized ChatGPT versions, even without technical expertise. The tutorial focuses on creating a customized GPT named 'EduBuddy,' designed to enhance the educational journey with tailored learning strategies and interactive features. 🚀 HackHub: Trending AI Tools💮 reworkd/tarsier: Open-source utility library for multimodal web agents, facilitating interaction with GPT-4(V) by visually tagging interactable elements on a page.  💮 recursal/ai-town-rwkv-proxy: Allows developers to locally run a large AI town using the RWKV model, a linear transformer with low inference costs. 💮 shiyoung77/ovir-3d: Enables open-vocabulary 3D instance retrieval without training on 3D data, addressing the challenge of obtaining diverse annotated 3D categories.  💮 langroid/langroid: User-friendly Python framework for building LLM-powered applications through a Multi-Agent paradigm. 💮 punica-ai/punica: Framework for Low Rank Adaptation (LoRA) to incorporate new knowledge into a pretrained LLM with minimal storage and memory impact. 

This article is an excerpt from the book, Modern Generative AI with ChatGPT and OpenAI Models, by Valentina Alto. This book will help harness the power of AI with innovative, real-world applications, and unprecedented productivity boosts, powered by the latest advancements in AI technology like ChatGPT and OpenAI.A/B testing in marketing is a method of comparing two different versions of a marketing campaign, advertisement, or website to determine which one performs better. In A/B testing, two variations of the same campaign or element are created, with only one variable changed between the two versions. The goal is to see which version generates more clicks, conversions, or other desired outcomes.An example of A/B testing might be testing two versions of an email campaign, using different subject lines, or testing two versions of a website landing page, with different call-to-action buttons. By measuring the response rate of each version, marketers can determine which version performs better and make data-driven decisions about which version to use going forward. A/B testing allows marketers to optimize their campaigns and elements for maximum effectiveness, leading to better results and a higher return on investment.Since this method involves the process of generating many variations of the same content, the generative power of ChatGPT can definitely assist in that.Let’s consider the following example. I’m promoting a new product I developed: a new, light and thin climbing harness for speed climbers. I’ve already done some market research and I know my niche audience. I also know that one great channel of communication for that audience is publishing on an online climbing blog, of which most climbing gyms’ members are fellow readers.My goal is to create an outstanding blog post to share the launch of this new harness, and I want to test two different versions of it in two groups. The blog post I’m about to publish and that I want to be the object of my A/B testing is the following:  Figure 1– An example of a blog post to launch climbing gearHere, ChatGPT can help us on two levels:The first level is that of rewording the article, using different keywords or different attention-grabbing slogans. To do so, once this post is provided as context, we can ask ChatGPT to work on the article and slightly change some elements:Figure 2 – New version of the blog post generated by ChatGPT As per my request, ChatGPT was able to regenerate only those elements I asked for (title, subtitle, and closing sentence) so that I can monitor the effectiveness of those elements by monitoring the reaction of the two audience groups.The second level is working on the design of the web page, namely, changing the collocation of the image rather than the position of the buttons. For this purpose, I created a simple web page for the blog post published in the climbing blog (you can find the code in the book’s GitHub repository at https://github.com/PacktPublishing/The-Ultimate-Guide- to-ChatGPT-and-OpenAI/tree/main/Chapter%207%20-%20ChatGPT%20 for%20Marketers/Code):Figure 3 – Sample blog post published on the climbing blogWe can directly feed ChatGPT with the HTML code and ask it to change some layout elements, such as the position of the buttons or their wording. For example, rather than Buy Now, a reader might be more gripped by an I want one! button.So, let's feed ChatGPT with the HTML source code:Figure 4 – ChatGPT changing HTML code Let’s see what the output looks like:Figure 5 – New version of the websiteAs you can see, ChatGPT only intervened at the button level, slightly changing their layout, position, color, and wording.Indeed, inspecting the source code of the two versions of the web pages, we can see how it differs in the button sections:Figure 6 – Comparison between the source code of the two versions of the website ConclusionIn conclusion, ChatGPT is a valuable tool for A/B testing in marketing. Its ability to quickly generate different versions of the same content can reduce the time to market of new campaigns. By utilizing ChatGPT for A/B testing, you can optimize your marketing strategies and ultimately drive better results for your business.Author BioAfter completing her Bachelor's degree in Finance, Valentina Alto pursued a Master's degree in Data Science in 2021. She began her professional career at Microsoft as an Azure Solution Specialist, and since 2022, she has been primarily focused on working with Data & AI solutions in the Manufacturing and Pharmaceutical industries. Valentina collaborates closely with system integrators on customer projects, with a particular emphasis on deploying cloud architectures that incorporate modern data platforms, data mesh frameworks, and applications of Machine Learning and Artificial Intelligence. Alongside her academic journey, she has been actively writing technical articles on Statistics, Machine Learning, Deep Learning, and AI for various publications, driven by her passion for AI and Python programming.Link - Medium Modern Generative AI with ChatGPT and OpenAI Models

As we stand on the threshold of a new age in Artificial Intelligence,, OpenAI recently announced the rollout of its ChatGPT Code Interpreter plugin for ChatGPT Plus subscribers, marking a significant evolution in the capabilities of AI. This groundbreaking technology is not merely another link in the AI chain but rather an exemplar of the transformative capabilities AI can bring to the table, particularly in programming and data analysis.The ChatGPT Code Interpreter plugin positions itself as an invaluable tool for developers, promising to significantly augment and streamline their workflows. Among its multiple functionalities, three stand out due to their potential impact on the programming landscape and data analysis, which work in tandem to extract valuable insights.Data VisualizationAt its core, the ChatGPT Code Interpreter excels in the domain of data visualization. In a world increasingly reliant on data, the ability to transform complex datasets into visually comprehensible and comprehensive formats is priceless. The plugin simplifies the arduous task of crunching through complex numbers and data sets, producing insightful visualizations without the need for prompt engineering. This proficiency in creatively rendering data echoes the power of platforms like Wolfram, introducing a new era of ease and efficiency in data comprehension.Here’s an example from Justin Fineberg’s TikTok: Click hereFile ConversionThe ChatGPT Code Interpreter extends its versatility into the realm of file conversion. This feature provides a simple solution to the often cumbersome task of converting files from one format to another. Its impressive functionality ranges from changing audio file formats, like MP3 to WAV, to converting an image into a text file. This capability paves the way for more accessible content transformation, such as easily convert PDF documents into editable text files.Here’s an example from Twitter user Riley Goodside: Click herePython Code ExecutionWhat sets the ChatGPT Code Interpreter plugin apart is its prowess in executing Python code within a sandboxed, firewalled execution environment. This essentially means that all the data visualizations are generated using Python, thereby lending the plugin an additional layer of power and versatility.As the plugin is still in its alpha stage, gaining access currently involves joining a waitlist, and  OpenAI has not publicly stated when a large-scale rollout will take place. However, those eager to explore its features have an alternative route via Discord's GPT Assistant bot, which already incorporates the Code Interpreter plugin to enhance its features and functionalities.This revolutionary plugin is not merely an advanced code interpreter; it's a complete tool that uses Python to generate code from natural language input and run it. The results are then presented within the dialogue box. The chatbot’s functionality extends to solving mathematical problems, data analysis and visualization, and file conversion, with an adeptness in these domains that rivals experienced coders.Beyond its immediate capabilities, the ChatGPT Code Interpreter plugin has broader implications for the programming and data analysis industry. It is reminiscent of Github Copilot X in its design, aimed at making workflows more creative and efficient. For instance, when asked to plot a function, the plugin not only generates the graph but also offers the option to 'show work', revealing the exact code it created and executed to generate the graph.The accessibility and user-friendliness of the plugin are expected to democratize the coding landscape, opening up the world of programming to a wider audience. This feature holds tremendous potential to accelerate collaborations, allowing technical and non-technical team members to work together more effectively on data analysis projects.Practical use cases for the ChatGPT Code Interpreter extend beyond the realm of programming, spanning various industries. Marketing teams, for instance, can leverage their capabilities to analyze customer data, segment audiences, and create targeted campaigns. Finance teams can utilize the plugin for tasks like financial modeling, forecasting, and risk analysis. Similarly, human resource teams can use it to analyze employee data, identify performance trends, and make data-driven hiring decisions. Even the healthcare sector stands to benefit, as the tool can analyze patient data, identify patterns in health outcomes, and thus enhance patient care.Accessing ChatGPT Code InterpreterIf you’re selected from the waitlist, here’s a step-by-step guide on how to install the plugin:Ensure you're a ChatGPT Plus subscriber, paying the $20 monthly fee.Log into ChatGPT on the OpenAI website.Click on 'Settings', then the three-dot menu next to your login name.In the 'Beta features' menu, enable 'Plug-ins'. For web browsing access, enable that too.Close the menu, find the language model selector, and choose 'Plugin Store' from the drop-down.Click 'All plug-ins', find 'Code Interpreter' in the list and install it.Now, you can interact with ChatGPT using the Code Interpreter plug-in.SummaryThe ChatGPT Code Interpreter plugin presents a transformative approach to programming and data analysis, automating code generation, facilitating data exploration, and improving code quality. This plugin empowers users to derive more value from their data, aiding in the formulation of strategic insights. As AI continues to evolve, tools like the ChatGPT Code Interpreter will undoubtedly play an instrumental role in shaping the future of data interaction and understanding, ultimately revolutionizing the landscape of data analysis.Author BioJulian Melanson is one of the founders of Leap Year Learning. Leap Year Learning is a cutting-edge online school that specializes in teaching creative disciplines and integrating AI tools. We believe that creativity and AI are the keys to a successful future and our courses help equip students with the skills they need to succeed in a continuously evolving world. Our seasoned instructors bring real-world experience to the virtual classroom and our interactive lessons help students reinforce their learning with hands-on activities.No matter your background, from beginners to experts, hobbyists to professionals, Leap Year Learning is here to bring in the future of creativity, productivity, and learning!

IntroductionCybersecurity professionals face numerous challenges daily, from threat detection to incident response. The advent of AI-powered language models, also called Generative AI such as ChatGPT or Google's Bard, has revolutionized how experts approach their tasks. In this tutorial, we will explore how ChatGPT can assist cybersecurity professionals in performing various tasks efficiently and effectively. From analyzing logs and conducting risk assessments to developing incident response strategies, ChatGPT's capabilities can be harnessed to streamline workflows and enhance productivity. In this blog, let's dive into the practical applications and benefits of integrating Generative AI into (cyber)security operations.In this article, we will cover a tutorial on implementing Azure AD Protection with ChatGPT and also cover certain other areas of cybersecurity where GPT can be beneficial.Implementing Azure AD Identity Protection with ChatGPTAzure AD Identity Protection helps organizations safeguard their Azure Active Directory (Azure AD) identities by detecting and mitigating identity-related risks. In this section, we will explore how ChatGPT can assist in implementing Azure AD Identity Protection through code examples using Python and the Microsoft Graph API.1. Set up the EnvironmentBefore we begin, ensure that you have the following prerequisites in place:Python is installed on your machine.The requests library is installed. You can install it using the following command: pip install requests Azure AD application registered with the appropriate permissions to access Azure AD Identity Protection.2. Acquire Access TokenTo interact with the Microsoft Graph API, we must acquire an access token. Use the following Python code to obtain the access token:```python import requests # Azure AD application details tenant_id = 'YOUR_TENANT_ID' client_id = 'YOUR_CLIENT_ID' client_secret = 'YOUR_CLIENT_SECRET' # Microsoft Graph token endpoint token_url = f'https://login.microsoftonline.com/{tenant_id}/oauth2/v2.0/token' # Request access token payload = {    'grant_type': 'client_credentials',    'client_id': client_id,    'client_secret': client_secret,    'scope': 'https://graph.microsoft.com/.default' } response = requests.post(token_url, data=payload) if response.status_code == 200:    access_token = response.json()['access_token'] else:    print('Error: Failed to obtain access token') ```Make sure to replace the placeholders with your Azure AD application details.3. Query Azure AD Identity Protection Data with ChatGPTNow that we have the access token, we can leverage ChatGPT to query Azure AD Identity Protection data. Use the following code example to interact with the model and retrieve identity protection insights:```python import openai openai.api_key = 'YOUR_OPENAI_API_KEY' def query_model(question):    response = openai.Completion.create(        engine='text-davinci-003',        prompt=question,        max_tokens=100,        temperature=0.5,        n=1,        stop=None,        temperature=0.5,    )    if response.choices:        return response.choices[0].text.strip()    else:        return None # Example question for querying Azure AD Identity Protection data question = "What are the recent risky sign-ins detected by Azure AD Identity Protection?" # Microsoft Graph API endpoint for risky sign-ins graph_api_url = 'https://graph.microsoft.com/v1.0/identityProtection/riskyUsers' # Send API request with the access token headers = {    'Authorization': f'Bearer {access_token}',    'Content-Type': 'application/json' } response = requests.get(graph_api_url, headers=headers) if response.status_code == 200:    risky_sign_ins = response.json()    # Process the response as needed    # ...    # Query the AI model for insights or recommendations    insights = query_model(question)    if insights:        print("Identity Protection Insights:")        print(insights)    else:        print("Error: Failed to obtain insights from the AI model") else:    print("Error: Failed to retrieve risky sign-ins data from Azure AD Identity Protection") ```Ensure you have appropriate permissions and update the `graph_api_url` with the relevant endpoint for the Azure AD Identity Protection data you want to retrieve.4. Interpret and Utilize InsightsOnce you obtain insights from the AI model, interpret, and utilize them to improve your identity protection practices. This could involve taking proactive measures to mitigate risks, investigating suspicious activities, or implementing additional security measures based on the recommendations provided.Remember to adapt the code examples based on your specific requirements and refer to the Microsoft Graph API documentation for available endpoints and data structures. https://learn.microsoft.com/en-us/graph/Other application areas1. Analyzing Log FilesOne of the most important aspects of cybersecurity is analyzing log files for suspicious activity and potential security breaches. Chat can help businesses automate this process. By importing log files into the model, ChatGPT can quickly identify patterns, anomalies, and potentially malicious activities. This analysis allows cybersecurity professionals to focus on the most important issues, saving valuable time and effort. In addition, ChatGPT's ability to create human-readable summaries of log data simplifies the interpretation and communication of findings for stakeholders.2. Conducting Risk AssessmentsConducting a comprehensive risk assessment is essential to understanding an organization's security posture. ChatGPT can help in this process by using its powerful capabilities to provide context and insights. By interacting with the model, organizations can ask specific questions about potential vulnerabilities, attacks, or best practices related to their risk assessments. ChatGPT's feedback provides knowledge of the organization's security environment and offers real value actionable insights that help businesses identify and prioritize risks and remediation tasks.3. Developing Incident Response StrategiesTime is of the essence in a cybersecurity incident. Generative AI can be an invaluable tool for developing effective incident response mechanisms. By leveraging its natural language processing capabilities, businesses can use ChatGPT to brainstorm and optimize response processes. The model can provide recommendations based on historical data, industry standards, and best practices, helping to create robust and efficient incident response systems. Generative AI can understand and generate human-like responses, making it an ideal virtual security analyst for cybersecurity professionals in high-pressure and time-sensitive situations.4. Automating Routine TasksCybersecurity professionals are often faced with increasing volume and velocity of repetitive and time-consuming tasks, such as vulnerability assessments, log analysis, and updating firewall rules. Generative AI can help automate these routine tasks, freeing experts to focus on complex real-value organizational security challenges. By integrating ChatGPT with existing automation frameworks, organizations can create chatbot-like interfaces that interact with the model to perform pre-defined actions. This approach increases productivity and reduces the risk of human error associated with manual processing.5. Enhancing Threat Intelligence AnalysisEffective threat reporting is essential for proactive cybersecurity defenses. Generative AI can enhance threat intelligence data analysis by extracting insights from a vast repository of security information. By asking about emerging threats, known vulnerabilities, or attack techniques, administrators can gain a deeper understanding of the ongoing threat landscape. ChatGPT's ability to understand complex security issues enhances the accuracy and relevance of threat intelligence reports, contributing to timely decision-making.ConclusionIn conclusion, it is easier and more efficient to implement Azure AD in conjunction with ChatGPT. As the cybersecurity landscape continues to evolve, businesses must embrace AI-powered solutions to stay ahead of malicious actors. Generative AI provides valuable support for various cybersecurity tasks, including log analysis, risk assessment, incident response planning, workflow automation, and threat intelligence analysis capabilities, enabling cybersecurity professionals to streamline their workflow, increase productivity, and make more informed decisions. While it is important to exercise proper judgment and credentials when implementing AI models, integrating Generative AI  such as ChatGPT into the cybersecurity industry offers significant opportunities for businesses to manage their tasks faster, more accurately, and more efficiently.Author BioSteve Miles (SMiles) is the CTO responsible for the tools and technologies selection for the cloud practice of a multi-billion turnover IT distributor based in the UK and Ireland. He is also a multi-cloud and hybrid technology strategist with 20+ years of telco, co-location, hosted data center, hybrid, and multi-cloud infrastructure experience. Steve is an Alibaba Cloud MVP (Most Valuable Professional), as well as being a Microsoft Azure MVP (Most Valuable Professional), and MCT (Microsoft Certified Trainer). Published freelance author for Microsoft technologies and certification guides, as well as an editorial and technical reviewer. Amongst many hybrid/cloud-based certifications, he is Alibaba Cloud Certified, with 20+ Cloud/Hybrid based Microsoft certifications with 14 of those being in Azure.His roles have included network security architect, global solutions architect, public cloud security solutions architect, and Azure practice technical lead. He currently works for a leading multi-cloud distributor based in the UK and Dublin in a cloud and hybrid technology leadership role.His first Microsoft certification was on Windows NT. He is an MCP, MCITP, MCSA, and MCSE for Windows Server and many other Microsoft products. He also holds multiple Microsoft Fundamentals, Associate, Expert, and Specialty certifications in Azure Security, Identity, Network, M365, and D365. He also holds multiple security and networking vendor certifications, as well as PRINCE2 and ITIL, and is associated with industry bodies such as the CIF, ISCA, and IISP.Author of the book: Azure Security Cookbook 

This article is an excerpt from the book Modern Generative AI with ChatGPT and OpenAI Models, by Valentina Alto. This book will provide you with insights into the inner workings of the LLMs and guide you through creating your own language models. Information retrieval and competitive intelligence are fields where ChatGPT is a game-changer. It can retrieve information from its knowledge base and reframe it in an original way.One example is using ChatGPT as a search engine to provide summaries, reviews, and recommendations for books:  Alternatively, we could ask for some suggestions for a new book we wish to read based on our preferences:  If we design the prompt with specific information, ChatGPT can serve as a tool for pointing us towards the right references for research or studies. For example, asking ChatGPT to list relevant references for feedforward neural networks:  ChatGPT can also be useful for competitive intelligence. For example, generating a list of existing books with similar content:  Or providing advice on how to be competitive in the market:  ChatGPT can also suggest improvements regarding book content to make it stand out:  Overall, ChatGPT can be a valuable assistant for information retrieval and competitive intelligence. However, it's important to remember that the knowledge base cutoff is 2021, so real-time information may not be available. About the AuthorValentina Alto graduated in 2021 in Data Science. Since 2020 she has been working in Microsoft as Azure Solution Specialist and, since 2022, she focused on Data&AI workloads within the Manufacturing and Pharmaceutical industry. She has been working on customers’ projects closely with system integrators to deploy cloud architecture with a focus on datalake house and DWH, data integration and engineering, IoT and real-time analytics, Azure Machine Learning, Azure cognitive services (including Azure OpenAI Service), and PowerBI for dashboarding. She holds a BSc in Finance and an MSc degree in Data Science from Bocconi University, Milan, Italy. Since her academic journey she has been writing Tech articles about Statistics, Machine Learning, Deep Learning and AI on various publications. She has also written a book about the fundamentals of Machine Learning with Python.  You can connect with Valentina on:LinkedInMedium

Are you weary of trawling through heaps of analysis data in search of meaningful insights? With ChatGPT, the rules will soon alter. ChatGPT may reveal hidden patterns and trends in your data that you never imagined were there because of its sophisticated natural language processing skills. In this blog article, we'll look at how exploratory data analysis with ChatGPT can revolutionize your data and change the way you conduct business.Data Analysis with ChatGPTFor data analysts, ChatGPT can be a useful tool for processing, exploring, communicating, and collaborating on their data-driven ideas. Large volumes of data can be analyzed and processed by ChatGPT fast and effectively. Written inquiries can be interpreted and understood by ChatGPT through its language processing skills, which also allow it to extract pertinent insights from the data. Here are a few benefits that ChatGPT can provide: Data analysts can use ChatGPT to study their data, spot trends, and even produce useful data visualizations. The data is clearly outlined in these graphics, which makes it simpler for analysts to spot trends and insights. Data analysts can utilize ChatGPT to explain their findings to non-technical stakeholders. The chatbot can assist data analysts in providing simple explanations of complicated data ideas and insights by using natural language. Data analysts might benefit from ChatGPT's help in coming up with fresh, insightful queries to pose to their data. Analysts can investigate novel lines of inquiry and unearth previously unconsidered hidden insights by using natural language queries. Let's look at how chatGPT can make data analysis easy and straightforward. As a data modeler, I want to investigate the data's dictionary and metadata first. Image 1: : Data Dictionary Using ChatGPT, Part 1Image 2 : Data Dictionary Using ChatGPT, Part 2ChatGPT gives us thorough details about the data dictionary for each column, including a complete description of each column. The final user will benefit from this guidance on when and how to use the data.Asking chatGPT about the dataset's number of rows and columns will help you better grasp the overall statistics.  Image 2 : Dataset Statistics  As seen in the image above, chatGPT gives us a precise estimate of the dataset's number of rows and columns. After getting a broad overview of the dataset, let's examine the data's quality: Image 3 : Exploratory Data Analysis - Null Value StatisticsHere, we've given the chatGPT an input containing the dataset and requested it to determine the percentage of null values therein in order to determine whether the data can be used for analytics. The dataset does not contain any null values, hence chanGPT responds that the given dataset contains no missing values.Now, we can observe that the data set's header information is absent. Before we can use the data, the columns must contain meaningful data. Image 4 : Dataset Column Naming ConventionLet's ask chatGPT how it can deliver valuable header data. As you can see, the output of chatGPT is a column header with a description and business-specific naming standards. The technical team's and business users' lives are made easier in terms of using this data.We now know that the data quality is good. As this will affect the results of the data analysis, let's look for any outliers in the dataset. Image 5 : Detect Outliers in the DatasetIn this case, chatGPT is carrying out an in-depth analysis at the column level to see whether any outliers are there. It's okay if it doesn't exist. If it does, it also offers advice on what kind of outlier is present and how it can affect the entire data analysis procedure.Let's now look at how to use chatGPT to eliminate those outliers.Image 7 : Remove Outliers from the dataset using python, Part 1 Image 8 : Remove Outliers from the dataset using python, Part 2Therefore, for a given sample dataset, ChatGPT offers a thorough Python code that can be used to automatically eliminate the observed outliers. The team may have business analysts who are unfamiliar with Python. Let's see how chatGPT can assist business analysts with their data analysis work.Image 7 : SQL Query to calculate monthly revenue, Part 1Image 8 : SQL Query to calculate monthly revenueIn this case, chatGPT offers a default query that the business analyst may utilize to figure out the monthly income for a particular dataset. Let's then ask chatGPT to take on the role of a data analyst and offer further insights for a certain dataset. Image 8 : Step by Step Data Analysis using chatGPT, Part 1  Image 9 : Step-by-Step Data Analysis using ChatGPT, Part 2As we can see from the chatGPT's results, it offers us step-by-step advice on various studies and results that may be applied on top of this particular dataset. The execution of each of these tasks using chatGPT is possible for each phase of the overall data analysis process.Let's ask chatGPT to undertake this data analysis work so that it may use Python to analyze prices for the given dataset:Image 9 : Price Analysis using python, Part 1Image 10 : Price Analysis using python, Part 2Image 11 : Price Analysis using Python, Part 2For the purpose of doing price analysis on a given dataset, ChatGPT has developed a Python code and sample output. We can draw a judgment about how the prices are changing over time based on the data points at hand from this output.ConclusionIn this article, we go into great detail on how to use chatGPT for a variety of exploratory data analysis tasks. Additionally, we looked closely at different approaches to carrying out data analysis tasks using Python and SQL. ChatGPT is, in a word, a very useful tool for performing exploratory data analysis tasks while working with massive volumes of data.Author BioSagar Lad is a Cloud Data Solution Architect with a leading organization and has deep expertise in designing and building Enterprise-grade Intelligent Azure Data and Analytics Solutions. He is a published author, content writer, Microsoft Certified Trainer, and C# Corner MVP. Link - Medium , Amazon , LinkedIn 

👋 Hello ,“Artificial intelligence is one of the most profound things we're working on as humanity. It is more profound than fire or electricity.” -Sundar Pichai, Google CEO  Pichai's AI-fire analogy signifies a transformative era; AI and ML will revolutionize education, medicine, and more, reshaping human progress. OpenAI has begun promoting the use of ChatGPT in education, which shouldn’t really come as a surprise as students the world over have been experimenting with the technology. Get ready to dive into the latest AI developments in this edition, AI_Distilled #16, including Baidu launching Ernie chatbot following Chinese government approval, X's Privacy Policy Reveals Plan to Use Public Data for AI Training, Meta releasing FACET Dataset to evaluate AI model fairness, Google’s new Multislice for scalable AI training on cloud TPUs, and Qualcomm's focus on AI and auto amidst NVIDIA's chip dominance. Watch out also for our handpicked collection of fresh AI, GPT, and LLM-focused secret knowledge and tutorials from around the web covering Liquid Neural Networks, Serverless Machine Learning with Amazon Redshift ML, implementing effective guardrails for LLMs, Navigating Generative AI with FMOps and LLMOps, and using Microsoft’s new AI compiler quartet. What 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 & AnalysisMeta Releases FACET Dataset to Evaluate AI Model Fairness: Meta has launched FACET (FAirness in Computer Vision EvaluaTion), a dataset designed to assess the fairness of AI models used for image and video classification, including identifying people. Comprising 32,000 images with 50,000 labeled individuals, FACET includes demographic and physical attributes, allowing for deep evaluations of biases against various classes. Despite previous concerns about Meta's responsible AI practices, the company claims FACET is more comprehensive than previous bias benchmarks. However, concerns have been raised about the dataset's origins and the compensation of annotators. Meta has also released a web-based dataset explorer tool for FACET. You can read the full paper here. Baidu Launches Ernie Chatbot Following Chinese Government Approval: Chinese tech giant Baidu has unveiled its chatbot, Ernie Bot, after receiving government clearance, along with other AI firms. Ernie Bot is now accessible for download via app stores or Baidu's website. Similar to its rival, ChatGPT, users can engage Ernie Bot for queries, market analysis assistance, marketing slogan ideas, and document summaries. While it's accessible globally, registration requires a Chinese number, and the app is only in Chinese on US Android and iOS stores. Baidu has also introduced a plug-in market for Ernie Bot, which quickly garnered over 1 million users within 19 hours of launch. CEO Robin Li expressed plans for further AI-native apps aimed at exploring generative AI's core abilities. Google Introduces Multislice for Scalable AI Training on Cloud TPUs: Google has unveiled Multislice, a comprehensive large-scale training technology that facilitates straightforward, cost-effective, and nearly linear scaling to tens of thousands of Cloud Tensor Processing Units (TPUs) chips. Traditionally, a training run was restricted to a single slice, which meant a maximum of 3072 TPU v4 chips could be used. With Multislice, training can span multiple slices across pods through data center networking, eliminating these limitations. This innovation offers benefits such as efficient scaling for massive models, enhanced developer productivity, automatic compiler optimizations, and cost-efficiency. It promises to revolutionize AI infrastructure by enabling near-linear scaling for AI supercomputing. OpenAI Promotes Use of ChatGPT in Education: OpenAI is encouraging educators to utilize ChatGPT in classrooms. The company showcased six educators, primarily at the university level, using ChatGPT for various purposes, such as role-playing in debates, aiding translation for English-as-a-second-language students, and fact-checking. Despite some schools banning ChatGPT due to concerns about academic integrity, OpenAI believes it can be a valuable tool in education. However, it emphasizes the importance of maintaining human oversight in the assessment process. X's Privacy Policy Reveals Plan to Use Public Data for AI Training: In an update to its privacy policy, X (formerly Twitter) has informed users that it will now collect biometric data, job histories, and education backgrounds. However, another section of the policy reveals a broader plan: X intends to utilize the data it gathers, along with publicly available information, to train its machine learning and AI models. This revelation has attracted attention, particularly due to the connection with X owner Elon Musk's ambitions in the AI market through his company xAI. Musk confirmed the privacy policy change, emphasizing that only public data, not private messages, would be used for AI training.   Qualcomm's Focus on AI and Auto Amidst NVIDIA’s Chip Dominance: As NVIDIA takes the lead as the world's largest fabless chip company, Qualcomm is strategically positioning itself in the AI realm. The company has unveiled in-vehicle generative AI capabilities, expanded into two-wheelers, and forged a partnership with Amazon Web Services. Qualcomm's CEO, Cristiano Amon, believes that generative AI, currently reliant on cloud resources, will transition to local execution, enhancing performance and cost-efficiency. Diversification is also a priority, with Qualcomm's chips powering various smart devices, especially in the automotive sector. Amid uncertainty about its future relationship with Apple, Qualcomm aims to maintain its dominance through innovations in AI and auto tech. InteRecAgent, A Fusion of Language Models and Recommender Systems Introduced: Researchers from the University of Science and Technology of China, in collaboration with Microsoft Research Asia, have introduced InteRecAgent, a cutting-edge framework. This innovation seeks to combine the interactive capabilities of LLMs with the domain-specific precision of traditional recommender systems. Recommender systems play a vital role in various digital domains, but they often struggle with versatile interactions. On the other hand, LLMs excel in conversations but lack domain-specific knowledge. InteRecAgent introduces the "Candidate Memory Bus" to streamline recommendations for LLMs and a "Plan-first Execution with Dynamic Demonstrations" strategy for effective tool interaction. adidas Utilizes AI and NVIDIA RTX for Photorealistic 3D Content: Sportswear giant adidas is partnering with Covision Media, an Italian startup, to revolutionize their online shopping experience. Covision employs AI and NVIDIA RTX technology to develop 3D scanners that allow businesses to create digital twins of their products with stunning realism. This technology can quickly generate 3D scans, capturing textures, colors, and geometry, resulting in lifelike images. adidas is among the first to adopt this technology for automating and scaling e-commerce content production, enhancing their Virtual Try-On feature and replacing traditional product photography with computer-generated content.  🔮 Expert Insights from Packt CommunityServerless Machine Learning with Amazon Redshift ML - By Debu Panda, Phil Bates, Bhanu Pittampally, Sumeet JoshiData analysts and developers use Redshift data with machine learning (ML) models for tasks such as predicting customer behavior. Amazon Redshift ML streamlines this process using familiar SQL commands. A conundrum arises when attempting to decipher these data silos – a formidable challenge that hampers the derivation of meaningful insights essential for organizational clarity. Adding to this complexity, security and performance considerations typically prevent business analysts from accessing data within OLTP systems. The hiccup is that intricate analytical queries weigh down OLTP databases, casting a shadow over their core operations. Here, the solution is the data warehouse, which is a central hub of curated data, used by business analysts and data scientists to make informed decisions by employing the business intelligence and machine learning tools at their disposal. These users make use of Structured Query Language (SQL) to derive insights from this data trove. Here’s where Amazon Redshift Serverless comes in. It’s a key option within Amazon Redshift, a well-managed cloud data warehouse offered by Amazon Web Services (AWS). With cloud-based ease, Amazon Redshift Serverless lets you set up your data storage without infrastructure hassles or cost worries. You pay based on what you use for compute and storage. Amazon Redshift Serverless goes beyond convenience, propelling modern data applications that seamlessly connect to the data lake. The above content is extracted from the book Serverless Machine Learning with Amazon Redshift ML written by Debu Panda, Phil Bates, Bhanu Pittampally, Sumeet Joshi and published in Aug 2023. 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. Keep Calm, Start Reading! 🌟 Secret Knowledge: AI/LLM ResourcesUnderstanding Liquid Neural Networks: A Primer on AI Advancements: In this post, you'll learn how liquid neural networks are transforming the AI landscape. These networks, inspired by the human brain, offer a unique and creative approach to problem-solving. They excel in complex tasks such as weather prediction, stock market analysis, and speech recognition. Unlike traditional neural networks, liquid neural networks require significantly fewer neurons, making them ideal for resource-constrained environments like autonomous vehicles. These networks excel in handling continuous data streams but may not be suitable for static data. They also provide better causality handling and interpretability. Navigating Generative AI with FMOps and LLMOps: A Practical Guide: In this informative post, you'll gain valuable insights into the world of generative AI and its operationalization using FMOps and LLMOps principles. The authors delve into the challenges businesses face when integrating generative AI into their operations. You'll explore the fundamental differences between traditional MLOps and these emerging concepts. The post outlines the roles various teams play in this process, from data engineers to data scientists, ML engineers, and product owners. The guide provides a roadmap for businesses looking to embrace generative AI. AI Compiler Quartet: A Breakdown of Cutting-Edge Technologies: Explore Microsoft’s groundbreaking "heavy-metal quartet" of AI compilers: Rammer, Roller, Welder, and Grinder. These compilers address the evolving challenges posed by AI models and hardware. Rammer focuses on optimizing deep neural network (DNN) computations, improving hardware parallel utilization. Roller tackles the challenge of memory partitioning and optimization, enabling faster compilation with good computation efficiency. Welder optimizes memory access, particularly vital as AI models become more memory-intensive. Grinder addresses complex control flow execution in AI computation. These AI compilers collectively offer innovative solutions for parallelism, compilation efficiency, memory, and control flow, shaping the future of AI model optimization and compilation.  💡 MasterClass: AI/LLM Tutorials Exploring IoT Data Simulation with ChatGPT and MQTTX: In this comprehensive guide, you'll learn how to harness the power of AI, specifically ChatGPT, and the MQTT client tool, MQTTX, to simulate and generate authentic IoT data streams. Discover why simulating IoT data is crucial for system verification, customer experience enhancement, performance assessment, and rapid prototype design. The article dives into the integration of ChatGPT and MQTTX, introducing the "Candidate Memory Bus" to streamline data testing. Follow the step-by-step guide to create simulation scripts with ChatGPT and efficiently simulate data transmission with MQTTX.  Revolutionizing Real-time Inference: SageMaker Unveils Streaming Support for Generative AI: Amazon SageMaker now offers real-time response streaming, transforming generative AI applications. This new feature enables continuous response streaming to clients, reducing time-to-first-byte and enhancing interactive experiences for chatbots, virtual assistants, and music generators. The post guides you through building a streaming web application using SageMaker real-time endpoints for interactive chat use cases. It showcases deployment options with AWS Large Model Inference (LMI) and Hugging Face Text Generation Inference (TGI) containers, providing a seamless, engaging conversation experience for users. Implementing Effective Guardrails for Large Language Models: Guardrails are crucial for maintaining trust in LLM applications as they ensure compliance with defined principles. This guide presents two open-source tools for implementing LLM guardrails: Guardrails AI and NVIDIA NeMo-Guardrails. Guardrails AI offers Python-based validation of LLM responses, using the RAIL specification. It enables developers to define output criteria and corrective actions, with step-by-step instructions for implementation. NVIDIA NeMo-Guardrails introduces Colang, a modeling language for flexible conversational workflows. The guide explains its syntax elements and event-driven design. Comparing the two, Guardrails AI suits simple tasks, while NeMo-Guardrails excels in defining advanced conversational guidelines. 🚀 HackHub: Trending AI Toolscabralpinto/modular-diffusion: Python library for crafting and training personalized Diffusion Models with PyTorch.  cofactoryai/textbase: Simplified Python chatbot development using NLP and ML with Textbase's on_message function in main.py. microsoft/BatteryML: Open-source ML tool for battery analysis, aiding researchers in understanding electrochemical processes and predicting battery degradation. facebookresearch/co-tracker: Swift transformer-based video tracker with Optical Flow, pixel-level tracking, grid sampling, and manual point selection. explodinggradients/ragas: Framework evaluates Retrieval Augmented Generation pipelines, enhancing LLM context with external data using research-based tools. 

As the relentless tide of technological advancement swells, the insurance industry, among many others, is facing a pivotal transformation. The inception and evolution of insurance technology or "insurtech" mandates that insurance agents, brokers, and companies diligently adapt and assimilate novel tools and methodologies to augment their operational efficiency and competitiveness. Of the emerging technologies, the innovative language model ChatGPT, conceived and developed by OpenAI, is showing significant potential to redefine the landscape of the insurance industry.This powerful AI model offers a diverse suite of services, each capable of improving the lives of insurance agents in numerous ways. From perfecting customer service and streamlining underwriting to improving data analytics and fraud detection, ChatGPT opens a Pandora's box of possibilities. Yet, the efficacy and feasibility of these innovative solutions call for a judicious understanding of the technology's strengths and limitations.The advantages of AI in InsuranceFirstly, customer service, the linchpin of the insurance business, is an area that stands to gain substantially from the implementation of ChatGPT. Insurance products and processes, notoriously labyrinthine to the average consumer, are sources of frequent queries and uncertainties. By employing ChatGPT, insurance firms can automatically answer routine questions related to policy details, billing, claims statuses, and more, in an array of languages. In doing so, it significantly alleviates the burden on customer service agents and concurrently boosts customer engagement.Such automated systems also find favor among modern consumers, with reports suggesting a notable preference for chatbot interactions. ChatGPT, with its impressive capabilities in generating human-like text responses, can amplify the effectiveness of customer service chatbots. These enhancements invariably lead to increased customer satisfaction, freeing up human agents to tackle more complex customer concerns. Furthermore, ChatGPT's natural language processing prowess can be harnessed to guide customers on suitable insurance products and services, digitizing sales and distribution.The underwriting process, a traditionally time-consuming task characterized by risk evaluation, is another sector ripe for the automation that ChatGPT brings. While Artificial Intelligence (AI) and Machine Learning (ML) models have previously been employed to improve the accuracy of risk assessment, gaps in data and information remain problematic. ChatGPT addresses this issue by enhancing data collection and analysis, investigating digital resources for analogous cases, and speeding up the identification of risk patterns.Through this sophisticated data analysis, ChatGPT can evaluate factors like a customer's age, financial status, occupation, and lifestyle, thereby determining their risk profile. This information enables insurers to offer personalized coverage and pricing, improving customer experience, and streamlining underwriting. In addition, it can alert insurers about high-risk individuals or circumstances, proactively averting potential losses. This automatic evaluation brings with it many questions around AI and ethics (which you can read more about here) but the advantages of getting such a system working are clear.Claims processing, an insurance operation infamous for its high cost and low level of digitization, is another area primed for disruption by ChatGPT. The AI model can proficiently extract and categorize information from claims forms and other documents, drastically reducing the laborious and time-intensive task of manual data entry.A significant advantage arising from automating claims processing is its potential in fraud detection. With estimates by the FBI suggesting that insurance fraud costs American families hundreds of dollars each year in premium hikes, the value of early fraud detection cannot be overstated. ChatGPT can help in showing patterns of inconsistencies in claim forms, then flagging suspicious entries for human review. By alerting insurers to both overt and covert attempts at fraud, ChatGPT can help save billions of dollars annually.Reasons to be carefulAs a caveat, while the utility and advantages of ChatGPT in the insurance industry are substantial, one must consider the nascent stage of this technology. Its real-world impact will be contingent on its successful integration within existing processes and wide-ranging adoption. Moreover, while AI systems offer remarkable capabilities, they are not infallible and require human supervision. Thus, the technology's explainability, its transparency, and its limitations should be carefully considered and understood.In summary, the potential of ChatGPT to transform the insurance industry is vast, promising efficiency gains, cost reductions, and enhanced customer service. But realizing these advantages requires industry-wide receptiveness, careful integration, and judicious application, along with a respect for the limitations of the technology.Author BioJulian Melanson is one of the founders of Leap Year Learning. Leap Year Learning is a cutting-edge online school that specializes in teaching creative disciplines and integrating AI tools. We believe that creativity and AI are the keys to a successful future and our courses help equip students with the skills they need to succeed in a continuously evolving world. Our seasoned instructors bring real-world experience to the virtual classroom and our interactive lessons help students reinforce their learning with hands-on activities.No matter your background, from beginners to experts, hobbyists to professionals, Leap Year Learning is here to bring in the future of creativity, productivity, and learning!

ChatGPT has been making the rounds recently in the world of programming.  In short, there is a lot of hoopla about whether or not it will replace programmers, how well it writes code, how well it troubleshoots issues, and more importantly how programmers can leverage it to automate code production. One area where ChatGPT can be of particular use is in the field of automation.  As all automation programmers know, the automation industry is very fast-paced.  This means that being able to quickly produce quality code is vital to the success of not only the programmer but the company as a whole.  However, it is often the case that some of the simplest code that a developer will need to produce is often the most time-consuming to write.  Two areas that come to mind are large case statements and sorting algorithms.  As such, the following article is going to explore how ChatGPT can be used to produce code that would otherwise be monotonous or time-consuming to write.  More specifically, the following is going to explore how ChatGPT can be used to generate the necessary code for a case statement that displays a motor’s health and how it can be used to select and generate the code for a sorting algorithm to sort part sizes. A CASE Statement to Detect Motor HealthA very daunting and time-consuming task that all automation programmers will perform regularly is creating large case statements.  It is not uncommon for automation programmers to have to build things like state machines that have 10s or even 100s of different cases.  As anyone who has built these before knows, building structures like these is time-consuming and monotonous at best.  For example, suppose a developer is working on a function block that will display a custom message about a motor error in an HMI.  Assume in this example the developer needs a code block that can handle the input/output exchange for the following table,Input valueString Message1Cable Error2Motor Overheated3Under CurrentDepending on the experience level of the developer a simple case statement program that can handle the requirements would take about five minutes.  However, when there are more case statements, for example, 30 or 40 inputs, the timeframe for completion could drastically increase.  Simply put, since there are more cases there will be more code to write.  In turn, this means that it will take the developer more time to type out the code and there will be a higher probability of introducing a bug or bugs in the code due to human error.  On the other hand, to quickly generate a quality code block that can meet these requirements all one would need to do is enter the following into ChatGPT:write an IEC 61131-3 code block that will set a message variable to "cable error" when the input is 1, "motor overheated" when the input is 2, and "under current" when the input is 3This statement will output the following:FUNCTION_BLOCK SetMessage VAR_INPUT    Input: INT; // Input value END_VAR VAR_OUTPUT    Message: STRING(50); // Output message END_VAR METHOD SetMessage VAR    TempMessage: STRING(50); // Temporary message variable END_VAR CASE Input OF    1:        TempMessage := 'Cable error';    2:        TempMessage := 'Motor overheated';    3:        TempMessage := 'Under current';    ELSE        TempMessage := 'Invalid input'; // Optional: default message for other input values END_CASE Message := TempMessage; // Assign the temporary message to the output message END_METHOD From the time it took to input and run the query on ChatGPT to the final code output was about one minute.  The code that was generated is quality code that looks like it was produced by an experienced programmer, it is documented, and above all else works.  The code even has an optional else statement to catch invalid inputs.  Overall, the autogenerated code is no less than what would be expected from a human developer.  With that, let’s explore how ChatGPT can be used to generate a more complex piece of software.   An Efficient Sorting Algorithm to Sort Parts by SizeIt is not uncommon for developers to be tasked with finding the smallest/largest or lightest/heaviest part produced on a production run.  For a task like this, it is common to store all the sizes, weights, or whatever it may be in an array and at one point or another sort the array to find the greatest and least size.  Depending on the size of the array, or in the case of this scenario, the number of part sizes to sort, having an efficient array is vital.  For this task, an efficient sorting algorithm will need to be picked and the code will need to be implemented. Assuming the PLC programmer is knowledgeable of sorting algorithms and Big O notations selecting an algorithm may only take a few minutes.  However, for a PLC programmer that is not versed in these topics, such as a person that is perhaps and mechanical engineer or a technician of some type, this task will probably require a Google search which ideally will take about five minutes.  Regardless, of whether the programmer is experienced or not with the algorithm it will take roughly 10 to 20 minutes for the person to type in the code.  As such, on a good day, it would take roughly 10 to 25 minutes for someone to implement a sorting algorithm. On the other hand, if a person used ChatGPT all they would have to do is type in the following query:write an IEC 61131-3 Structured Text sorting algorithm that can sort the elements in an array with 100 entries with the best possible Big O timeWhich will generate the following:FUNCTION_BLOCK QuickSort VAR_INPUT    arr: ARRAY[1..100] OF INT; END_VAR METHOD SortArray : BOOL    SortPartition(arr, 1, 100);    SortArray := TRUE; END_METHOD METHOD SortPartition(arr: ARRAY[1..100] OF INT; low: INT; high: INT) VAR    pivot: INT;    partitionIndex: INT; END_VAR    IF low < high THEN        pivot := Partition(arr, low, high, low); // Choosing pivot as the first element        SortPartition(arr, low, pivot - 1);        SortPartition(arr, pivot + 1, high);    END_IF; END_METHOD METHOD Partition(arr: ARRAY[1..100] OF INT; low: INT; high: INT; pivotIndex: INT) : INT VAR    pivotValue: INT;    i: INT;    partitionIndex: INT;    temp: INT; END_VAR    // Swap pivot element to the end    temp := arr[high];    arr[high] := arr[pivotIndex];    arr[pivotIndex] := temp;    pivotValue := arr[high];    partitionIndex := low;    FOR i := low TO high - 1 DO        IF arr[i] <= pivotValue THEN            // Swap elements            temp := arr[i];            arr[i] := arr[partitionIndex];            arr[partitionIndex] := temp;            partitionIndex := partitionIndex + 1;        END_IF;    END_FOR;    // Move pivot element to its correct position    temp := arr[partitionIndex];    arr[partitionIndex] := arr[high];    arr[high] := temp;    Partition := partitionIndex; END_METHOD END_FUNCTION_BLOCKAll of this code was generated in about one minute.  As can be seen, ChatGPT generated roughly 60 lines of code not counting the code that utilizes the example.  The only overhead that would be required by the programmer is to create the necessary files and paste the code in.  In terms of efficiency, ChatGPT rendered the Quicksort algorithm which has a Big O of n*log(n).  So, in the end, the programmer is probably looking at around five minutes' worth of work to implement an efficient algorithm that would otherwise take up to half an hour by hand.The Key to using ChatGPTAs can be seen, the key to using ChatGPT as a programming assistant is simply learning to talk to it.  Unlike programming languages that have a unique syntax, ChatGPT can interpret normal, human language.  However, to get the most out of ChatGPT the programmer needs to learn to ask detailed questions. The general rule of thumb is the more detailed a statement is the more detailed the solution will be.  As was seen, in the examples, all the code was produced with a single sentence.  Depending on the resolution needed, a simple query can produce great results, but it should be noted that if specs like specific ports, and so on need to be addressed the user should specify those.  Though novel now, it is likely that the art of talking to these systems will soon appear to generate optimal code with a query. SummaryIn all, ChatGPT can be used as a tool to help speed up the development process.  As was explored with the motor health code block and the sorting algorithm, ChatGPT can turn a simple phrase into workable code that would take a human a considerable amount of time to type out.  In short, even if a PLC programmer is knowledgeable of both programming principles, algorithms, and other computer science concepts they will always be bottlenecked by having to implement the code if they cannot simply cut and paste it from another source. When used in a way that was explored, ChatGPT is a great productivity tool.  It can be used to greatly reduce the amount of time needed to implement and if necessary find a solution.  Overall, ChatGPT needs guidance to arrive at a proper solution and the person driving the AI needs to be competent enough to implement the solution.  However, when in the right hands ChatGPT and similar AI systems can greatly improve development time.Author BioM.T. White has been programming since the age of 12. His fascination with robotics flourished when he was a child programming microcontrollers such as Arduino. M.T. currently holds an undergraduate degree in mathematics, and a master's degree in software engineering, and is currently working on an MBA in IT project management. M.T. is currently working as a software developer for a major US defense contractor and is an adjunct CIS instructor at ECPI University. His background mostly stems from the automation industry where he programmed PLCs and HMIs for many different types of applications. M.T. has programmed many different brands of PLCs over the years and has developed HMIs using many different tools.Author of the book: Mastering PLC Programming