This week I’m highlighting a new and very interesting episode from Lex Fridman’s podcast, a set of articles on large language model training, LangChain, Semantic Kernel and a full-day workshop on Azure Computer Vision. Also, information about Qdrant, a vector similarity search engine, and several interesting articles. Enjoy!

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Podcasts

Lex Fridman Podcast

Episode 372: Simone Giertz: Queen of Sh*tty Robots, Innovative Engineering, and Design
Simone Giertz is an inventor, designer, engineer, and roboticist famous for a combination of humor and brilliant creative design in the systems and products she creates. This is an awesome conversation that goes through many different topics, from robots, AI, tumors, fear of death or weapons. Highly recommended.

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Model Training

DeepSpeed
DeepSpeed empowers ChatGPT-like model training with a single click, offering 15x speedup over SOTA RLHF systems with unprecedented cost reduction at all scales. DeepSpeed enables world’s most powerful language models like MT-530B and BLOOM. It is an easy-to-use deep learning optimization software suite that powers unprecedented scale and speed for both training and inference.

How to train your own Large Language Models
In this blog post, we’ll provide an overview of how we train LLMs, from raw data to deployment in a user-facing production environment. We’ll discuss the engineering challenges we face along the way, and how we leverage the vendors that we believe make up the modern LLM stack: Databricks, Hugging Face, and MosaicML.

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Azure Computer Vision

Azure Computer Vision in a day workshop
In this technical workshop, you will receive a thorough introduction to Azure Computer Vision and Azure Vision Studio. You will be taught how to utilize the new capabilities of Azure Computer Vision 4 to analyze images, including its multimodal features (Florence). Moreover, you will be able to investigate pre-existing solution accelerators and best practices for prototyping and deploying end-to-end use cases. Finally, the workshop will conclude with a Q&A session and a wrap-up.

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Vector Database

Qdrant Vector Database
Qdrant (read: quadrant ) is a vector similarity search engine. It provides a production-ready service with a convenient API to store, search, and manage points – vectors with an additional payload. Qdrant is tailored to extended filtering support. It makes it useful for all sorts of neural network or semantic-based matching, faceted search, and other applications.

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Useful Stuff

LangChain explained – The hottest new Python framework
LangChain explained in 3 minutes – LangChain is a Python framework for developing applications powered by language models. In this video we take a look at LangChain, see how it works, and what you can do with it!

Semantic Kernel Tools
We are excited to announce the release of Semantic Kernel Tools, the first Semantic Kernel extension for Visual Studio Code. With this extension, developers can develop their own semantic skills on Semantic Kernel faster and with greater ease.

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Interesting Stuff

10 Graphs That Sum Up the State of AI in 2023
The Stanford Institute for Human-Centered Artificial Intelligence (HAI) has assembled a year’s worth of AI data providing a comprehensive picture of today’s AI world, as it has done annually for six years. And I do mean comprehensive—this year’s report came in at 302 pages. That’s a nearly 60 percent jump from the 2022 report, thanks in large part to the 2022 boom in generative AI demanding attention and an increasing effort to gather data on AI and ethics.

The AI revolution: Google’s developers on the future of artificial intelligence | 60 Minutes
Competitive pressure among tech giants is propelling society into the future of artificial intelligence, ready or not. Scott Pelley dives into the world of AI with Google CEO Sundar Pichai.

The Dangers of Irresponsible AI Adoption and the Importance of Microsoft’s Responsible AI Toolbox
As artificial intelligence (AI) continues to revolutionize various industries and aspects of our lives, the incredible power of models like OpenAI’s GPT-4 becomes increasingly undeniable. However, with this immense power comes great responsibility. Unfortunately, many companies are jumping on the AI bandwagon without adhering to responsible AI principles, leading to unintended consequences such as biased algorithms, privacy violations, and unethical data usage. These consequences can have far-reaching implications on society, and it’s time to address this issue head-on.

Introducing Copilot in Microsoft Viva—A new way to boost employee engagement and performance
Building on last month’s announcement of Microsoft 365 Copilot, we’re excited to announce Copilot in Microsoft Viva, along with the introduction of Microsoft Viva Glint, to help organizations create a more engaged and productive workforce. With Copilot, Microsoft Viva takes advantage of next-generation AI to accelerate this new performance equation, where engagement and productivity lead to better business outcomes and success. Copilot in Viva is built on the Microsoft 365 Copilot System, which combines the power of large language models (LLMs) with your data in the Microsoft Graph and the Viva apps to give leaders an entirely new way to understand and engage their workforce.

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Have a fantastic week!