TWIL: May 7, 2023
This week I highly recommend Lex Fridman’s conversation with Robert Playter (Boston Dynamics CEO) because it is really interesting. Also, an episode of Azure Podcast on Data API Builder, a free MIT course on Deep Learning and an article explaining the power of Vector Databases. Finally, the repo for Bark, a new generative AI model for text-to-audio and two other repos with architectures for GPT-based enterprise solutions. Enjoy!
Lex Fridman Podcast
Episode 374: Robert Playter: Boston Dynamics CEO on Humanoid and Legged Robotics
Robert Playter is CEO of Boston Dynamics, a legendary robotics company that over 30 years has created some of the most elegant, dextrous, and simply amazing robots ever built, including the humanoid robot Atlas and the robot dog Spot.
The Azure Podcast
Episode 457: Data API Builder
The team talks to Ayush Agarwal, Davide Mauri, and Sean Leonard to learn about how the open-source Data API Builder can be used to quickly and easily create CRUD APIs on top of multiple database platforms in Azure for use in many types of applications and languages.
MIT: Introduction to Deep Learning
MIT’s introductory program on deep learning methods with applications to computer vision, natural language processing, biology, and more! Students will gain foundational knowledge of deep learning algorithms and get practical experience in building neural networks in TensorFlow. Program concludes with a project proposal competition with feedback from staff and panel of industry sponsors. Prerequisites assume calculus (i.e. taking derivatives) and linear algebra (i.e. matrix multiplication), we’ll try to explain everything else along the way! Experience in Python is helpful but not necessary.
How vector databases can revolutionize our relationship with generative AI
Generative AI has received a lot of attention already this year in the tech world and beyond. Whether it’s ChatGPT’s prose or Stable Diffusion’s art, 2022 provided an insight into the potential for AI to disrupt creative industries. But behind the headlines, 2022 brought an even more important development in AI: the rise of the vector database.
Bark is a transformer-based text-to-audio model created by Suno. Bark can generate highly realistic, multilingual speech as well as other audio – including music, background noise and simple sound effects. The model can also produce nonverbal communications like laughing, sighing and crying. To support the research community, we are providing access to pretrained model checkpoints, which are ready for inference and available for commercial use. Samples can be found here: Bark Samples
Azure Open AI
Enterprise Search with OpenAI Architecture
The purpose of this repo is to accelerate the deployment of a Python-based Knowledge Mining solution with OpenAI that will ingest a Knowledge Base, generate embeddings using the contents extracted, store them in a vector search engine (Redis), and use that engine to answer queries / questions specific to that Knowledge Base.
ChatGPT + Enterprise data with Azure OpenAI
This sample demonstrates a few approaches for creating ChatGPT-like experiences over your own data. It uses Azure OpenAI Service to access the ChatGPT model (gpt-35-turbo and gpt3), and vector store (Pinecone, Redis and others) or Azure cognitive search for data indexing and retrieval. The repo provides a way to upload your own data so it’s ready to try end to end.
I’ve tested Google Bard vs ChatGPT and I’m Shocked: Where did Google spend All the Money over the last 10 years?
In today’s article, we’re diving headfirst into a showdown between Google and OpenAI. Just last night, I managed to get my hands on Google’s mysterious and highly anticipated AI Assistant, Bard. You might be thinking, “Well, that’s fantastic, but how does it stack up against the already very popular OpenAI’s ChatGPT?” That is precisely the question I aimed to answer.
Have a great week!