Felipe Rodríguez retweeted
Throwback: MIT prof. Peter Shor gives advice to aspiring computer scientists.
Full video: bit.ly/3Myh123
Felipe Rodríguez retweeted
After co-inventing ChatGPT, I kept asking myself: why have superhuman chat models not led to AGI?
I’ve spent the last 2 years in stealth building a new way to train models (RLCD), and a new type of frontier AI model that we are releasing today: Jev
• 20-200x faster
• 40-400x cheaper (w/ output tokens free)
• Frontier composable intelligence optimized for decisions
AFAICT the shortest path to AI-based economic revolution
Felipe Rodríguez retweeted
Is AI good? Or is it bad? Will it displace jobs? Create jobs?
These are the biggest questions today in AI and society.
@spandan_madan and I offer what we believe is a better frame: not AGI, but Augmented Human Intelligence (AHI). Technology should and will improve the human condition.
Felipe Rodríguez retweeted
Replying to @PessimistsArc
Right. Dario was already claiming that GPT2 was too dangerous to open source back in 2019.
I made fun of them then.
Everyone should make fun of them now.
Felipe Rodríguez retweeted
Does your company need more attention or investment? Just tell the world that there's a 10% chance your product will kill us all.
Felipe Rodríguez retweeted
Thanks @rohanpaul_ai for highlighting our work!
The paper introduces MCP-Bench, a tough benchmark that tests real tool using LLM agents on complex workflows.
28 servers and 250 tools, wired through the Model Context Protocol, power cross domain tasks.
Earlier benchmarks stitch isolated APIs, reveal tool names, and stop at short workflows.
MCP-Bench connects production servers designed to interoperate, so inputs and outputs actually line up.
Tasks are built by discovering dependency chains, then rewritten into fuzzy requests that hide tools and steps.
Each task adds distractor servers to test retrieval, and agents must ground claims in tool outputs.
Evaluation mixes strict schema checks with an LLM judge scoring completion, tool choice, and planning.
Across 20 models, schema handling is near perfect, yet long horizon planning stays weak, especially with multiple servers.
Top systems stay stable as servers grow while smaller ones drop, exposing the real bottleneck, cross server, multi step reasoning.
Overall, MCP-Bench pressures agents to pick the right tools, coordinate them, and support answers with evidence.
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Paper – arxiv. org/abs/2508.20453
Paper Title: "MCP-Bench: Benchmarking Tool-Using LLM Agents with Complex Real-World Tasks via MCP Servers"
Felipe Rodríguez retweeted
Very proud of our new preprint introducing reverse predictivity — a two-way test of AI–brain alignment. We find a striking asymmetry: models & brains don’t map to each other equally, while brain-to-brain mappings are symmetric 🧠🤖
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Felipe Rodríguez retweeted
Who invented convolutional neural networks (CNNs)?
1969: Fukushima had CNN-relevant ReLUs [2].
1979: Fukushima had the basic CNN architecture with convolution layers and downsampling layers [1]. Compute was 100 x more costly than in 1989, and a billion x more costly than today.
1987: Waibel applied Linnainmaa's 1970 backpropagation [3] to weight-sharing TDNNs with 1-dimensional convolutions [4].
1988: Wei Zhang et al. applied "modern" backprop-trained 2-dimensional CNNs to character recognition [5].
All of the above was published in Japan 1979-1988.
1989: LeCun et al. applied CNNs again to character recognition (zip codes) [6,10].
1990-93: Fukushima’s downsampling based on spatial averaging [1] was replaced by max-pooling for 1-D TDNNs (Yamaguchi et al.) [7] and 2-D CNNs (Weng et al.) [8].
2011: Much later, my team with Dan Ciresan made max-pooling CNNs really fast on NVIDIA GPUs. In 2011, DanNet achieved the first superhuman pattern recognition result [9]. For a while, it enjoyed a monopoly: from May 2011 to Sept 2012, DanNet won every image recognition challenge it entered, 4 of them in a row. Admittedly, however, this was mostly about engineering & scaling up the basic insights from the previous millennium, profiting from much faster hardware.
Some "AI experts" claim that "making CNNs work" (e.g., [5,6,9]) was as important as inventing them. But "making them work" largely depended on whether your lab was rich enough to buy the latest computers required to scale up the original work. It's the same as today. Basic research vs engineering/development - the R vs the D in R&D.
REFERENCES
[1] K. Fukushima (1979). Neural network model for a mechanism of pattern recognition unaffected by shift in position — Neocognitron. Trans. IECE, vol. J62-A, no. 10, pp. 658-665, 1979.
[2] K. Fukushima (1969). Visual feature extraction by a multilayered network of analog threshold elements. IEEE Transactions on Systems Science and Cybernetics. 5 (4): 322-333. This work introduced rectified linear units (ReLUs), now used in many CNNs.
[3] S. Linnainmaa (1970). Master's Thesis, Univ. Helsinki, 1970. The first publication on "modern" backpropagation, also known as the reverse mode of automatic differentiation. (See Schmidhuber's well-known backpropagation overview: "Who Invented Backpropagation?")
[4] A. Waibel. Phoneme Recognition Using Time-Delay Neural Networks. Meeting of IEICE, Tokyo, Japan, 1987. Backpropagation for a weight-sharing TDNN with 1-dimensional convolutions.
[5] W. Zhang, J. Tanida, K. Itoh, Y. Ichioka. Shift-invariant pattern recognition neural network and its optical architecture. Proc. Annual Conference of the Japan Society of Applied Physics, 1988. First backpropagation-trained 2-dimensional CNN, with applications to English character recognition.
[6] Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, L. D. Jackel: Backpropagation Applied to Handwritten Zip Code Recognition, Neural Computation, 1(4):541-551, 1989. See also Sec. 3 of [10].
[7] K. Yamaguchi, K. Sakamoto, A. Kenji, T. Akabane, Y. Fujimoto. A Neural Network for Speaker-Independent Isolated Word Recognition. First International Conference on Spoken Language Processing (ICSLP 90), Kobe, Japan, Nov 1990. A 1-dimensional convolutional TDNN using Max-Pooling instead of Fukushima's Spatial Averaging [1].
[8] Weng, J., Ahuja, N., and Huang, T. S. (1993). Learning recognition and segmentation of 3-D objects from 2-D images. Proc. 4th Intl. Conf. Computer Vision, Berlin, pp. 121-128. A 2-dimensional CNN whose downsampling layers use Max-Pooling (which has become very popular) instead of Fukushima's Spatial Averaging [1].
[9] In 2011, the fast and deep GPU-based CNN called DanNet (7+ layers) achieved the first superhuman performance in a computer vision contest. See overview: "2011: DanNet triggers deep CNN revolution."
[10] How 3 Turing awardees republished key methods and ideas whose creators they failed to credit. Technical Report IDSIA-23-23, Swiss AI Lab IDSIA, 14 Dec 2023. See also the YouTube video for the Bower Award Ceremony 2021: J. Schmidhuber lauds Kunihiko Fukushima.
Felipe Rodríguez retweeted
Excited to release what we’ve been working on at Amaranth Foundation, our latest whitepaper, NeuroAI for AI safety! A detailed, ambitious roadmap for how neuroscience research can help build safer AI systems while accelerating both virtual neuroscience and neurotech. 1/N
Felipe Rodríguez retweeted
Why do video models handle motion so poorly? It might be lack of motion equivariance.
Very excited to introduce: Flow Equivariant RNNs (FERNNs), the first sequence models to respect symmetries over time.
Paper: arxiv.org/abs/2507.14793
Blog: kempnerinstitute.harvard.edu…
1/🧵
Felipe Rodríguez retweeted
Vision transformers have high-norm outliers that hurt performance and distort attention. While prior work removed them by retraining with “register” tokens, we find the mechanism behind outliers and make registers at ✨test-time✨—giving clean features and better performance! 🧵
Felipe Rodríguez retweeted
Last week, our Triangle splatting paper was quietly released, and since then the tech community ignited fierce debates about it !
It was trending on @hackernews !
Today we released the code!
A deep dive into the epic “comeback” of Triangles to the throne of 3D
🧵
1/n
Felipe Rodríguez retweeted
A robot hand grasp over 500 totally new objects without fail? Zero-shot, single-view & super reliable
⬇️ + Paper
Grasping random objects is hard for robots, especially when shapes, weights, and materials vary.
RobustDexGrasp solves this with a smart new way of seeing and controlling the hand, leading to near-perfect grasping, even in noisy or cluttered scenes.
Thank you for sharing, @Hui_Zhang_eth 🙏
Follow him!!
What makes it special
✅ Grabs 500+ unseen objects with 94.6% success using only single-view input
✅ Learns local shapes, not full geometry, for better generalization
✅ Trained with just 35 objects in sim but works in the real world with hundreds more
✅ Adapts to noise, unexpected forces, and even plays chess with VLM planning
It shows that smart sensing and adaptive control can take dexterous grasping to the next level.
Project: zdchan.github.io/Robust_DexG…
Paper: arxiv.org/abs/2504.05287
Felipe Rodríguez retweeted
265 pages of everything you need to know about building AI agents.
5 things that stood out to me about this report:
Felipe Rodríguez retweeted
Meet SO-101, next-gen robot arm for all, by @huggingface 🤗
Enables smooth takeover to boost AI capabilities, faster assembly (20mn), same affordable price ($100 per arm) 🤯
Get yours today! Links in thread below 👇
Felipe Rodríguez retweeted
This preprint is now published at @Nature. With current and former DeepMinders @yuvaltassa, Josh Merel, Matt Botvinick, and my @HHMIJanelia colleagues @vaxenburg, Igor Siwanowicz, @KristinMBranson, @MichaelBReiser, Gwyneth Card and more
🪰By infusing a virtual fruit fly with #AI, Janelia & @GoogleDeepMind scientists created a computerized insect that can walk & fly just like the real thing➡️ hhmi.news/3Rwop0w
🤖Read more about this work, first published in a #preprint in 2024➡️ hhmi.news/4cGAVUW
Felipe Rodríguez retweeted
Happy birthday Joseph Fourier, whose 1822 equation allows us to listen to mp3s today: bit.ly/22kbNfi
Felipe Rodríguez retweeted
A banger just got released 💥
Here is a snapshot of L2D, the biggest self-driving dataset by far!
- 90 TeraBytes of data
- 5000 hours of driving
- 6 surrounding HD cameras
- OPENLY AVAILABLE
- Train your car to drive like @Tesla at home
🧵 More details in thread





























