Quick updates from NeurIPS 2024
And Humanoids Summit coming up
The biggest academic conference in Machine Learning, NeurIPS 2024, is ongoing in Vancouver, B.C., this week. This conference is huge with over 10000 attendees each year. It’s very hard to browse through its proceedings and find papers of interest. Luckily, there is a third party website which provides a great visualization of paper topics in a grid, and allows you to browse through your areas of interest. I could then pick up “robotics” as my topic of interest, which filtered me 50 different papers, mostly concentrated in areas of 3D object detection and reinforcement learning. (See a picture snapshot below). I am still going through the papers, so will save talking about them in a different post.
One interesting work that caught my attention was BricksRL, which allows you to train custom LEGO robots by interfacing them with the TorchRL library for reinforcement learning agents. The authors built three different robots using Lego parts and small components such as motors and camera sensors, and then trained them using a Reinforcement Learning to enable these robots achieve simple tasks such as driving along a line. It sure sounds fun to design your own robot out of Legos and make them accomplish tasks.
O1-mini
In a previous post, I discussed a demo of Vector robot answering questions with the help of o1-preview, the latest and best Large Language Model (LLM) from OpenAI. Subsequently, I made another video with Vector using the o1-mini model. The o1-mini model is smaller much cheaper than o1-preview, but to my surprise, I liked the answers given by o1-mini better. I posted the two videos on the r/AnkiVector subreddit, and asked users which video they liked better. The results were across the spectrum, with many people not liking either video, with one user complaining that the answers were too long. But o1-mini polled the highest votes in the Reddit poll. Here are the results:
Since OpenAI’s release, Alibaba has come up with an open-source model which can reason, think, decide and then answer. This model is called QWQ (Qwen with Questions). Early results are impressive, and many providers such as Together.ai are supporting this model. I will soon test how Vector robot performs with this model. Research in this space in exploding, and I am sure that there will be multiple developments before the end of the year (even though we are so close to it). If you have tried these models, definitely share your experience or send me a note.
Humanoids Summit
I will be at the Humanoids Summit in Mountain View, CA tomorrow… where there is an exciting lineup of illustrious speakers and humanoid robots. If you are a paid subscriber, you can expect some live posts and commentary. If you are not a paid subscriber and are interested in content from the Humanoids summit, I am offering a 14-day free-trial to this newsletter. If you work, many employers will reimburse you the cost of a subscription, since this newsletter mainly carries educational content. If your organization requires a letter to justify the expense, please use this template provided by The Pragmatic Engineer.
I hope to bring you some great updates tomorrow.