Vindula Jayawardana

No one likes sitting at a red light, or wasting fuel and producing emissions while idling. What if motorists could pace themselves to arrive when the light is green? A LIDS team’s new machine-learning approach could keep traffic flowing smoothly. mitsha.re/OZTC50J9Xg0

meet @mostik_ai! what happens when you put 12 PhDs in one room for four months? first place on the ARC-AGI leaderboard, which I can't say much about while the competition is still running. and this, which I can. everyone's arguing about whether open models will catch up to frontier models. we think it's the wrong question. here's the one we pose: why does a frontier model have to generate your answer at all, when the only thing you need from it is the reasoning? we do this by enabling models to communicate in latent space. through our protocol, hidden states pass straight from a frontier model into a small one running on your infrastructure -- no text between them, and neither model is fine-tuned. two models from different families, sharing reasoning, both left untouched. how do we know it works? we tested it on a setup where a 753B model reads the problem, and a 4B edge-class model writes the answer. with this approach, we get results 80% as accurate as the frontier model, but at 20x faster performance. we're committed to preventing frontier model lock-in and are already partnering with inference providers to accelerate open-weight adoption. we've done this between 15 of us, in four months, 12 PhDs and a Fields medalist, backed by @generalcatalyst WIRED has the first external account of the company and the work: wired.com/story/russian-star… full writeup, the setup, and all the numbers: mostik.ai/read-more

🚨Paper‼️Alert 🔈 Work from my lovely internship 🚢 at @ToyotaResearch is out 💃🏻 A thread 🧵⬇️ arxiv.org/abs/2604.26083

We upgraded Tabracadabra 🎉 to bring an entire context-aware assistant (not just tab to autocomplete!) to any textbox. It's pretty great if you hate switching between the chat interface and what you're working on. We're also open-sourcing, so you can try it out!🧵

Reinforcement learning (RL) is surprisingly brittle to contextual variations in tasks. Our new method (NeurIPS 2024) for solving contextual RL problems achieves 5-50x better sample efficiency on standard & traffic benchmarks. Featured today by MIT news! news.mit.edu/2024/mit-resear…

The 𝐀𝐮𝐭𝐨𝐧𝐨𝐦𝐨𝐮𝐬 𝐕𝐞𝐡𝐢𝐜𝐥𝐞𝐬 𝐀𝐜𝐫𝐨𝐬𝐬 𝐒𝐜𝐚𝐥𝐞𝐬 (𝐀𝐕𝐀𝐒) 𝐖𝐨𝐫𝐤𝐬𝐡𝐨𝐩 at RSS 2024 is seeking submissions! We will bring together researchers across the three ‘scales’ of AV research – ego vehicle, traffic, & network. Deadline is May 24th (4-8 pages).

If you're worried about AGI & would like to work on 𝗻𝗮𝗿𝗿𝗼𝘄 AI, come work with us on AI in transportation! 🚗📱🚂🤖🚌 Why? 1) Hugely complex distributed systems in need of better multi-agent coordination. 2) Positive impact to environment, economy, safety, public health. 🧵

#PhD Positions I am looking for #PhD students with a background in #MachineLearning, #DeepLearning, or #Robotics to start in Fall'24. DM me with a link to your CV. @phd_position @AcademicChatter #AcademicChatter #AcademicTwitter @PostdocVoice #PostdocVoice @PhDVoice #PhDVoice