Last week, I watched an interview of Aravind Srinivas, the CEO of Perplexity AI (https://www.perplexity.ai). It is a three-hour interview done by Lex Fridman where Aravind talked about the major breakthroughs in AI that brought us to LLMs, the mission of Perplexity, how the technology works, his vision of the future of search and web in general, and some valuable advice for startup founders and young people.
Fascinating interview - highly recommended for everyone to watch.
Perplexity AI (https://www.perplexity.ai) has been gaining attention in the world of chatbots and large language models. I had heard about it in a few forums and mentioned by industry leaders like Jensen Huang and Kelsey Hightower. In fact, I had created an account and tried it out a few times earlier this year, but didn’t take it much seriously.
All that changed last week when I watched this recent interview of Perplexity CEO Aravind Srinivas by Lex Fridman.
The standout feature unveiled at this week’s Apple WWDC 2024 event was Apple Intelligence, a personal intelligence system that will be integrated into multiple platforms - iOS 18, iPadOS 18 and macOS Sequoia.
What is Apple Intelligence? Apple Intelligence comprises of multiple highly-capable and efficient generative models - large language models and diffusion models. These models include on-device models as well as server-based foundation models.
The foundation models are trained on Apple’s open-source AXLearn library for deep learning, built on top of JAX (Python library for accelerated computing and transformation) and XLA (Accelerated Linear Algebra, an open-source ML compiler).
Today, Meta AI announced they are releasing a new model Code Llama 70B, a higher performing LLM to generate code. This was exciting news and we have to try it out immediately.
In this post, I will do a walk-through of how to download and use the new model and how it compares to other code generating models like GPT-4.
As usual, the best way to run the inference on any model locally is to run Ollama.
This is the fast follow (Part 2) of the previous post OpenAI DevDay 2023 - Highlights (aka Part 1) where I shared the highlights and the announcements made at OpenAI DevDay 2023.
In this post right here, I will share my observations - about the event and OpenAI tech - and my learnings - from talking to people during the event and by trying out the tech hands-on. So, let’s jump in!
This week I attended the Open AI Dev Day on 6 Nov 2023 at SVN West, San Francisco. This event was special in many ways - for me, this is the first conference I attended since the pandemic and the first one during my new journey; for OpenAI, this is their first developer conference ever! So I think it deserves a dedicated blog post to share the highlights, key takeaways and my observations, right?
OpenAI published the GPT-4 Technical Report on 27 Mar 2023. I wanted to read it immediately but was intimidated by the fact that it is a 100-page document. It turns out that it was not as difficult as I anticipated, it can be easily read over a weekend. The best part is that this report describes the key terminologies and methodologies used in the development of GPT-4. It’s always beneficial to obtain this information directly from the source.
Note - This post is written by ChatGPT expanding on the outline of my original post An Introduction to ChatGPT - section by section. This is a fun exercise to demonstrate the potential of ChatGPT and how it can change how we create content, art and code. You can see the full results of the experiment at Blog about ChatGPT in three different ways.
Have you ever heard of ChatGPT? It’s a large language model that has taken the AI world by storm.