👋 Hello , 🎉 Joyous 2024! Wishing you a year as delightful as your dreams! Dive into the new year with our outstanding edition, filled with essential features to boost your AI practice. “The speed at which people will be able to come up with an idea, to test the idea, to make something, it’s going to be so accelerated…You don’t need to have a degree in computer science to do that.” - Matthew Candy, IBM’s global managing partner for generative AI Coding without coding is a revolutionary idea indeed. What might have been previously perceived as unbelievable is a living reality and new features like Github’s Copilot Chat make it all the more seamless. The real possibilities of AI expand far beyond computing, with the technology making waves in healthcare, finance, and supply chain management. Starting from this edition, we’ll bring you fresh updates from each of these sectors, so stay tuned! Let's kick things off by tapping into the latest news and developments. AI Launches & Industry Updates: Microsoft Copilot Integrates with GenAI Music App Suno for Song Composition Google Plans Potential Layoffs Amidst AI Integration in Ad Sales GitHub Expands Copilot Chat Availability for Developers AI in Healthcare: AI Streamlining Health Insurance Shopping Process Revolutionizing Healthcare with AI Stethoscope on Smartphones Generative AI's Impact on Mental Health Counseling AI in Finance: Next-Gen Banks to Leverage AI for Financial Influence and Support Invest Qatar Introduces Cutting-Edge Azure OpenAI GPT-Powered Chatbot AI in Supply Chain Management: AI Safeguards Supply Chains Amidst Holiday Challenges AI-SaaS Integration Revolutionizes E-commerce Analytics Here are some handpicked GPT and LLM resources, tutorials, and secret knowledge that’ll come in handy for your next project: Understanding the Prompt Development Life Cycle Building Platforms with LLMs: Overcoming Challenges in Summarization as a Service Understanding the Risks of Prompt Injection in LLM Applications Creating an Open Source LLM Recommender System: Mastering Prompt Iteration and Optimization Looking for hands-on tips and strategies straight from the developer community? We’ve got you covered: Exploring Google's Gemini Pro Vision LLM with Javascript: A Practical Guide Accelerating AI Application Productionization: A Guide with SageMaker JumpStart, Amazon Bedrock, and TruEra Quantizing LLMs with Activation-aware Weight Quantization (AWQ) Unlocking Your MacBook's AI Potential: Running 70B LLM Models Without Quantization Check out our curated list of smoking hot GitHub repositories: Giskard-AI/giskard CopilotKit/CopilotKit chengzeyi/stable-fast ml-explore/mlx |
New Launches & Industry Updates: ⭐ Microsoft Copilot Integrates with GenAI Music App Suno for Song Composition: Microsoft Copilot has partnered with GenAI music app Suno, enabling users to create complete songs including lyrics, instrumentals, and singing voices. Accessible via Microsoft Edge, the integration aims to make music creation inclusive and enjoyable. However, ethical and legal concerns persist, with some artists uncomfortable with AI algorithms learning from their work without consent or compensation. Suno attempts to address such issues by blocking certain prompts and preventing the generation of covers using existing lyrics. Read Microsoft’s official blog here. ⭐ Google Plans Potential Layoffs Amidst AI Integration in Ad Sales: Google is reportedly considering laying off around 30,000 employees within its ad sales division due to the implementation of internal AI, aiming for improved operational efficiency. The restructuring primarily targets the ad sales team, reflecting Google's exploration of AI benefits in operational processes. Earlier in 2023, Google had already laid off 12,000 employees, emphasizing the need for organizational adaptation amidst evolving global dynamics. Read about other significant 2023 layoffs here. ⭐ GitHub Expands Copilot Chat Availability for Developers: GitHub is extending the availability of Copilot Chat, a programming-centric chatbot powered by GPT-4, to all users. The tool was initially launched for Copilot for Business subscribers and later in beta for $10 per month users. Integrated into Microsoft's IDEs, Visual Studio Code and Visual Studio, it's included in GitHub Copilot's paid tiers and free for verified teachers, students, and maintainers of specific open-source projects. Developers can prompt Copilot Chat in natural language, seeking real-time guidance on code-related tasks. Know more about Copilot Chat here. AI in Healthcare: ⭐ AI Streamlining Health Insurance Shopping Process: Companies are utilizing AI to simplify the often complex and tedious task of shopping for health insurance, aiming to guide consumers to better and more affordable options. With many Americans sticking to their health plans due to the difficulty of predicting their future healthcare needs, AI-powered tools gather individual information and predict the most suitable health plans. Alight, a cloud-based HR services provider, reports that 95% of its served employers use AI technology, including a virtual assistant, for employee health benefits selection. ⭐ Revolutionizing Healthcare with AI Stethoscope on Smartphones: A startup AI Health Highway is addressing the challenge of limited access to specialists in healthcare by introducing an innovative solution, AI Steth, which combines traditional stethoscope use with cutting-edge signal processing and AI. Targeting the early detection and prediction of heart and lung disorders, the device transforms sound patterns into visual representations on smartphones, allowing non-specialists like family physicians and nurses to examine patients effectively. AI Steth has shown exceptional accuracy in murmur detection, paving the way for more objective and efficient diagnoses. Discover AI Health Highway’s work here. ⭐ Generative AI's Impact on Mental Health Counseling: Generative AI is finding use in mental health counseling, sparking discussions about its potential to assist or even replace human therapists. Recent research testing ChatGPT on mental health counseling questions has raised questions about the technology's role in therapy. AI therapy has evolved from basic chatbots to sophisticated entities capable of nuanced emotional responses, offering accessible mental health support 24/7. While the benefits are evident, challenges such as risk, coverage, and ethical considerations must be addressed for responsible implementation. AI in Finance: ⭐ Next-Gen Banks to Leverage AI for Financial Influence and Support: Experts predict that next-generation banks will harness generative AI to impact various aspects of financial services, ranging from influencing customer decisions to identifying vulnerable clients. Tom Merry, Head of Banking Strategy at Accenture, suggests that generative AI could significantly influence banking operations, touching nearly every aspect. While the UK banking industry has been utilizing AI for fraud detection and risk analysis, the introduction of generative AI, capable of creating novel solutions based on extensive data, is gaining traction. ⭐ Invest Qatar Introduces Cutting-Edge Azure OpenAI GPT-Powered Chatbot: Invest Qatar, in collaboration with Microsoft, has launched Ai.SHA, an innovative AI-powered chatbot utilizing GPT capabilities through the Azure OpenAI service. This move positions Invest Qatar as a pioneer among investment promotion agencies globally, embracing advanced technology to transform interactions between investors and businesses in Qatar. Ai.SHA acts as a comprehensive resource, providing information on business opportunities, the investment ecosystem, and business setup in Qatar. AI in Supply Chain Management: ⭐ AI Safeguards Supply Chains Amidst Holiday Challenges: Businesses face unique challenges in managing complex supply chains amid the holiday season, from counterfeit airplane parts to recalls affecting festive foods. The reliance on suppliers underscores the need for transparency and visibility to prevent disruptions caused by supplier misconduct. Leveraging AI in contracts offers a solution, allowing businesses to streamline due diligence, enhance visibility, conduct predictive analytics, and align with environmental, social, and governance (ESG) regulations. AI-powered contracts emerge as vital tools to proactively address supply chain challenges and ensure customer trust during the holiday season and beyond. ⭐ AI-SaaS Integration Revolutionizes E-commerce Analytics: In the logistics sector, where precision and speed are critical, SaaS coupled with AI is transforming traditional approaches. This integration allows for real-time data processing and learning from it, offering unprecedented insights and optimization capabilities. Learn how AI-SaaS integration streamlines inventory, boosts operational efficiency, and fortifies against fraud, becoming the recipe for e-commerce success in a hypercompetitive landscape. |
Architectural Patterns and Techniques for Developing IoT Solutions - By Jasbir Singh Dhaliwal Unique requirements of IoT use cases IoT use cases tend to have very unique requirements concerning power consumption, bandwidth, analytics, and more. Additionally, the inherent complexity of IoT implementations (computationally challenged field devices on one end of the spectrum vis-à-vis almost infinite capacity of the cloud on the other) forces architects to make difficult architectural decisions and implementation choices. Before presenting the various IoT patterns, it is worth mentioning the unique expectations from IoT architectures that are different from non-IoT architectures: Sensing events and actuation commands have a wide range of latency expectations – from real-time to fire and forget. Data analysis results need to be reported/visualized/consumed on a variety of consumer devices – mobiles, desktops, tablets, and more. Similarly, data consumers have diverse backgrounds, data needs, and application roles (personas). One is often forced to integrate with legacy as well as cutting-edge devices and/or external systems – very few trivial use cases have isolated/standalone architectures. There is a considerable difference in the way the data is extracted from legacy versus non-legacy systems – legacy systems may internally collate the data and then push it to the external port (file transfer), whereas newer systems may push the data in a continuous stream (time-series data). This variability is one of the critical considerations when choosing a particular IoT architectural pattern. Varied deployment requirements – edge, on-premise, hybrid, the cloud, and more. Adherence to strict regulatory compliances, especially in medical and aeronautical domains. There are expectations considering immediate payback, return on investment (ROI), business outcomes, and new service business models. Continuous innovation, which results in new services or offerings (especially by cloud vendors), forcing IoT architectures to be in continuous sync mode with these new offerings or services. This is an excerpt from the book Architectural Patterns and Techniques for Developing IoT Solutions written by Jasbir Singh Dhaliwal and published in Sep ‘23. To see what's inside the book, read the entire chapter here or try a 7-day free trial to access the full Packt digital library. To discover more, click the button below. |
⭐ Understanding the Prompt Development Life Cycle: Explore PDLC and gain insights into how prompt engineering mirrors software development. The primer unfolds a step-by-step guide, beginning with the Initial Build phase where an imperfect prompt is crafted, incorporating techniques like zero-shot and few-shot. The Optimization stage strategically refines prompts based on historical data. Finally, the Fine-tune phase demonstrates the refinement of models, emphasizing the importance of continuous tracking. ⭐ Building Platforms with LLMs: Overcoming Challenges in Summarization as a Service: Get to know more about Summarization as a Service, a platform designed by a Microsoft team for Viva Engage. Learn about the complexities of prompt design, ensuring accuracy and grounding, addressing privacy and compliance concerns, managing performance, cost, and availability of LLM services, and integrating outputs seamlessly with the Copilot and other Viva Engage features. ⭐ Understanding the Risks of Prompt Injection in LLM Applications: Explore the intricacies of prompt injection in LLM applications. The author emphasizes the critical security implications and potential impacts, citing the OWASP Top 10 for LLM Applications. Drawing parallels to injection vulnerabilities like A03 in traditional security, the article illustrates potential risks through a thought experiment involving a robotic server. ⭐ Creating an Open Source LLM Recommender System: Mastering Prompt Iteration and Optimization: Open Recommender is an open-source YouTube video recommendation system adept at tailoring content to your interests based on Twitter feed analysis. Discover its data pipeline, utilizing GPT-4, and the transition towards cost-effective open-source models using OpenPipe. Explore the challenges faced during prompt iteration, with a focus on better prompt engineering tools, including the introduction of a TypeScript library, Prompt Iteration Assistant. |
⭐ Exploring Google's Gemini Pro Vision LLM with Javascript: A Practical Guide: The blog introduces the concept of multi-modal LLMs capable of interpreting various data modes, including images. Learn how to utilize Google's multi-modal Gemini Pro Vision LLM with Javascript. The tutorial guides you through creating an AI-powered nutrition-fact explainer app using the newly released LLM. The tutorial covers prerequisites, such as installing node.js and obtaining a Gemini LLM API key. ⭐ Accelerating AI Application Productionization: A Guide with SageMaker JumpStart, Amazon Bedrock, and TruEra: The post emphasizes the importance of observability in LLM applications and provides insights into evaluating responses for honesty, harmlessness, and helpfulness. You'll learn how to deploy, fine-tune, and iterate on foundation models for LLM applications using Amazon SageMaker JumpStart, Amazon Bedrock, and TruEra. ⭐ Quantizing LLMs with Activation-aware Weight Quantization (AWQ): Explore the application of Activation-aware Weight Quantization (AWQ) to democratize LLMs like Llama-2, making them more accessible for deployment on regular CPUs or less powerful GPUs. The process involves setting up a GPU instance, installing necessary packages like AutoAWQ and transformers, and saving the quantized model. The tutorial further covers the model upload to the Hugging Face Model Hub and concludes with the successful reduction of the Llama-2 model from ~27GB to ~4GB, enhancing its efficiency for diverse applications. ⭐ Unlocking Your MacBook's AI Potential: Running 70B LLM Models Without Quantization: Discover how to unleash the hidden AI power of your 8GB MacBook as this post explores the latest 2.8 version of AirLLM. Without the need for quantization or model compression, an ordinary MacBook can now efficiently run top-tier 70 billion parameter models. Explore the MacBook's AI capabilities, understanding Apple's role in AI evolution through its M1, M2, and M3 series GPUs, which offer competitive performance in the era of generative AI. Gain insights into GPU capabilities, memory advantages, and the open-source MLX platform. |
⭐ Giskard-AI/giskard: Specialized testing framework for ML models, covering a range from tabular to LLMs. Developers can efficiently scan AI models using just four lines of code. ⭐ CopilotKit/CopilotKit: Build in-app AI chatbots that seamlessly interact with the app state, execute actions within the app, and communicate with both frontend, backend, and third-party services via plugins, serving as an AI "second brain" for users. ⭐ chengzeyi/stable-fast: Leverage stable-fast for efficient and high-performance inference on various diffuser models while enjoying fast model compilation and out-of-the-box support for dynamic shape, LoRA, and ControlNet. ⭐ ml-explore/mlx: Array framework for machine learning on Apple silicon by Apple's ML research, offering familiar Python and C++ APIs closely aligned with NumPy and PyTorch. |
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