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DALL·E: Creating Images from Text
We’ve trained a neural network called DALL·E that creates images from text captions for a wide range of concepts expressible in natural language.
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ImageNet contains naturally occurring Apple NeuralHash collisions
Apple’s NeuralHash: real-world collisions, false positives, and implications for CSAM detection. See what this means for system reliability.
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I do not agree with Github's use of copyrighted code as training for Copilot
I do not agree with GitHub's unauthorized and unlicensed use of copyrighted source code as training data for their ML-powered GitHub Copilot product. This product injects source code derived from copyrighted sources into the software of their customers without informing them of the license of the original source code…
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The First Rule of Machine Learning: Start Without Machine Learning
Why this is the first rule, some baseline heuristics, and when to move on to machine learning.
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Mozilla Common Voice Adds 16 New Languages and 4,600 New Hours of Speech
The Mozilla Common Voice initiative has released a new, expanded data set featuring 16 new languages — like Basaa and Kazakh — and 4,622 new hours of sp…
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Monkey MindPong
Get the latest on neurotech directly from our experts. Check out the Neuralink blog for news, insights, and behind-the-scenes looks at our work.
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We were promised Strong AI, but instead we got metadata analysis
How simple structured data trumps clever machine learning
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Machine Learning: The Great Stagnation
The bureaucrats are running the asylum
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How a Kalman filter works, in pictures
I have to tell you about the Kalman filter, because what it does is pretty damn amazing.
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Autonomous drone navigates independently through the forest at 40 km/h
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“AI promised to revolutionize radiology but so far its failing”
Geoffrey Hinton is a legendary computer scientist . . . Naturally, people paid attention when Hinton declared in 2016, “We should stop training radiologists now, it’s just completely obvious within five years deep learning is going to do better than radiologists.” The US Food and Drug Administration (FDA) approved the…
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Simulating worlds on the GPU: Four billion years in four minutes
This post delves into the implementation of my procedural earth simulation, written entirely in GLSL fragment shaders. It simulates the complete history of an earth-like planet in a few minutes, with the simulation updating at 60 frames per second.
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OpenAI Codex
We’ve created an improved version of OpenAI Codex, our AI system that translates natural language to code, and we are releasing it through our API in private beta starting today.
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Find anything fast with Google's vector search technology
Recently, Google Cloud partner Groovenauts, Inc. published a live demo of MatchIt Fast . As the demo shows, you can find images and text similar to a selected sample from a collection of millions in a matter of milliseconds:
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Open-sourcing the MuJoCo physics simulator for robotics
When you walk, your feet make contact with the ground. When you write, your fingers make contact with the pen. Physical contacts are what makes interaction with the world possible. Yet, for such a common occurrence, contact is a surprisingly complex phenomenon. Taking place at microscopic scales at the interface of…
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The neural network of the Stockfish chess engine
Last time, we briefly mentioned the high-level differences between Stockfish and Leela Chess. To recap, Stockfish evaluates about 100 million positions per second using rudimentary heuristics, wher…
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Turing Oversold?
It is not the fault of Turing, though.
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1931: Kurt Gödel shows limits of math, logic, computing, AI
His famous 1931 paper identified the fundamental limits of math, logics, theorem proving, computing, AI.
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Triton: Open-Source GPU Programming for Neural Networks
We’re releasing Triton 1.0, an open-source Python-like programming language which enables researchers with no CUDA experience to write highly efficient GPU code—most of the time on par with what an expert would be able to produce.
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The art of asking nicely
There are upsides to working with a neural net that trained on a huge collection of internet images and text. One is that, instead of ominous grey geometric blobs [https://aiweirdness.com/post/177091486527/this-ai-is-bad-at-drawing-but-will-try-anyways] when it doesn't understand your
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A Year at Google Brain (2020)
Reflections from my time at Google Brain, and why I left for a PhD.
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OpenAI’s API now available with no waitlist
Wider availability made possible by safety progress.
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Redefining what a map can be with new information and AI
Here's a snapshot of how we're using AI to make Maps work better for you with a number of updates coming this year.