Akash Srivastava @variational_i
Director, Core AI, IBM. Chief Architect https://t.co/lo3JOcuDFA . Founder, Red Hat AI Innovation Team. PI @MITIBMLab. ❤️ Density Ratios. ai-innovation.team Cambridge, MA Joined July 2009-
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What does it take to scale AI beyond the lab? At #RedHatSummit, @ishapuri101 and I spoke with Red Hat CEO Matt Hicks & CTO Chris Wright on inference-time scaling, open infra (LLMD), and making AI affordable for enterprise. 🎧 youtu.be/mj1dwrPfvb4 #NoMathAI @RedHat_AI
🚀 How is generative AI transforming the way we design cars, planes, and entire systems? In Ep 2 of No Math AI, @ishapuri101 and I chat with Dr. @_faezahmed (@MIT DeCoDE Lab) about how AI boosts creativity, cuts design time, and works with engineers—not against them.
🚀 How is generative AI transforming the way we design cars, planes, and entire systems? In Ep 2 of No Math AI, @ishapuri101 and I chat with Dr. @_faezahmed (@MIT DeCoDE Lab) about how AI boosts creativity, cuts design time, and works with engineers—not against them.
SQuat: KV-Cache for making reasoning models go 🚀 📄paper: lnkd.in/emKhAVZu 💻 code: lnkd.in/e8TJ7N3R From my awesome collaborators @RedHat_AI
SQuat: KV-Cache for making reasoning models go 🚀 📄paper: lnkd.in/emKhAVZu 💻 code: lnkd.in/e8TJ7N3R From my awesome collaborators @RedHat_AI
Excited to share our preliminary work on customizing reasoning models using Red Hat AI Innovation’s Synthetic Data Generation (SDG) package! 📄 Turn your documents into training data for LLMs. 🧵👇
had a great time giving a talk about probabilistic inference scaling and the power of small models at the IBM Research ML Seminar Series - the best talks end with tons of questions, and it was great to see everyone so engaged : ) youtube.com/watch?v=--3rsQ…
Come along and help us build reasoning in small LLMs
Excited to share our latest work with @ishapuri101 et al.! 🚀 We introduce a probabilistic inference approach for inference-time scaling of LLMs using particle-based Monte Carlo methods—achieving 4–16x better scaling on math reasoning tasks and O1-level performance on MATH500.
Excited to share our latest work with @ishapuri101 et al.! 🚀 We introduce a probabilistic inference approach for inference-time scaling of LLMs using particle-based Monte Carlo methods—achieving 4–16x better scaling on math reasoning tasks and O1-level performance on MATH500.
🧩 Why do task vectors exist in pretrained LLMs? Our new research uncovers how transformers form internal abstractions and the mechanisms behind in-context learning(ICL).
Neural activity is correlated among animals performing the same task and across sequential trials. Led by @zhang_yizi and @hl3616, we develop an reduced-rank model that exploits shared structure across animals to improve neural decoding. biorxiv.org/content/10.110…
What will a foundation model for the brain look like? We argue that it must be able to solve a diverse set of tasks across multiple brain regions and animals. Check out our preprint where we introduce a multi-region, multi-animal, multi-task model (MtM): arxiv.org/abs/2407.14668
🚀 Stronger, simpler, and better! 🚀 Introducing Value Augmented Sampling (VAS) - our new algorithm for LLM alignment and personalization that outperforms existing methods!
Excited to give a talk on our hottest, newest work “Value Augmented Sampling for Language Model Alignment and Personalization” at 2:30p Halle A3 in #ICLR2024 Reliable and Responsible Foundation Models Workshop 🥳🥳
Excited to give a talk on our hottest, newest work “Value Augmented Sampling for Language Model Alignment and Personalization” at 2:30p Halle A3 in #ICLR2024 Reliable and Responsible Foundation Models Workshop 🥳🥳
Attending #ICLR2024, interested in continual learning and like probabilistic modeling? Lazar from the @MITIBMLab, will be presenting our latest work that takes a probabilistic approach to modular continual learning on Tuesday, 7 May, Halle B #222 (iclr.cc/virtual/2024/p…).
Attending #ICLR2024, interested in continual learning and like probabilistic modeling? Lazar from the @MITIBMLab, will be presenting our latest work that takes a probabilistic approach to modular continual learning on Tuesday, 7 May, Halle B #222 (iclr.cc/virtual/2024/p…).
Check out our work titled "From Automation to Augmentation: Redefining Engineering Design and Manufacturing in the Age of NextGen-AI", where we highlight the requirements for NextGenAI suitable for design, engineering, and manufacturing. mit-genai.pubpub.org/pub/9s6690gd/r…
Check out our work titled "From Automation to Augmentation: Redefining Engineering Design and Manufacturing in the Age of NextGen-AI", where we highlight the requirements for NextGenAI suitable for design, engineering, and manufacturing. mit-genai.pubpub.org/pub/9s6690gd/r…
New work from @MITIBMLab researchers on large scale alignment of LLMs. Check out the models at HF huggingface.co/ibm/merlinite-…
New work from @MITIBMLab researchers on large scale alignment of LLMs. Check out the models at HF huggingface.co/ibm/merlinite-…
❤️ #NeurIPS2023. After 4 years, met my adviser and my adviser's advisor at the same time.
Uniform sampling hampers offline RL. How to fix it? Check our paper at #NeurIPS2023. Time: Wed 13 Dec 5 p.m. CST — 7 p.m. CST Location: Great Hall & Hall B1+B2 (level 1) #1908 Paper: openreview.net/forum?id=TW99H… Code: github.com/Improbable-AI/…
Interested in learning how generative models can help with constrained design generation and topology optimization? Come to poster 540, 10:45am session today neurips.cc/virtual/2023/p… where @georgosgeorgos will be presenting our work on aligning TO with diffusion models #NeurIPS23

Dustin Tran @dustinvtran
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Naomi Saphra @nsaphra
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antonio vergari ⚔�... @tetraduzione
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