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It’s not a self-driving car unless you can sleep in it
GROUND TRUTH is an inside look at the development and deployment of self-driving technology, from the simulation labs to city streets.
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The latest blog posts to take off on Hacker News. 6,335 posts.
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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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New algorithm flies drones faster than human racing pilots
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Useful algorithms that are not optimized by Jax, PyTorch, or TensorFlow
In some previous blog posts we described in details how one can generalize automatic differentiation to give automatically stability enhancements and all sorts of other niceties by incorporating graph transformations into code generation. However, one of the things which we didn’t go into too much is the limitation of…
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AlphaFold 2 is here: what’s behind the structure prediction miracle
Nature has now released that AlphaFold 2 paper , after eight long months of waiting. The main text reports more or less what we have known for nearly a year, with some added tidbits, although it is accompanied by a painstaking description of the architecture in the supplementary information . Perhaps more importantly…
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GitHub Copilot: First Impressions
A glimpse of the upcoming paradigm shift in how we do development
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Reducing the Computational Cost of Deep Reinforcement Learning Research
Posted by Pablo Samuel Castro, Staff Software Engineer, Google Research It is widely accepted that the enormous growth of deep reinforcement learni...
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Mike Levin on using bioelectricity to study how cells form (2019)
By Lindsay Brownell At first glance, the lab of Wyss Associate Faculty member Mike Levin looks like any standard biology lab: graduate students in white lab coats pipette liquids into tubes at benches surrounded by centrifuges, freezers, racks of graduated cylinders hanging over sinks, and shelves of petri dishes…
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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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What Are Diffusion Models?
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Gaussian Processes from Scratch (2019)
This post explores some concepts behind Gaussian processes, such as stochastic processes and the kernel function. We will build up deeper understanding of Gaussian process regression by implementing them from scratch using Python and NumPy.
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What DeepMind’s AlphaFold 2 really achieved (2020)
Disclaimer: this post is an opinion piece based on the experience and opinions derived from attending the CASP14 conference as a doctoral student researching protein modelling. When provided, quotes have been extracted from my notes of the event, and while I hope to have captured them as accurately as possible, I…
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EleutherAI One Year Retrospective
A look back at the first year of EleutherAI.
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Ideas in statistics that have powered AI
Andrew Gelman, a statistics professor at Columbia, and Aki Vehtari, a computer science professor at Finland’s Aalto University, recently published a list of the most important statistical ideas in the last 50 years. Here, they break it down in easy-to-understand terms.
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YouTube algorithm recommends videos that violate its own policies: study
Conducted using data donated by thousands of YouTube users, research reveals the algorithm is recommending videos with misinformation, violent content, …
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AI Explorables: big ideas in machine learning, simply explained
People + AI Research (PAIR) is a multidisciplinary team at Google that explores the human side of AI.
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Probably Approximately Correct – A Formal Theory of Learning (2014)
In tackling machine learning (and computer science in general) we face some deep philosophical questions. Questions like, “What does it mean to learn?” and, “Can a computer learn?” and, “How do you define simplicity?” and, “Why does Occam’s Razor work? (Why do simple hypotheses do well at modelling reality?)” In a…
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Ray: A Distributed Framework for Emerging AI Applications
Ray: A Distributed Framework for Emerging AI Applications Moritz, Nishihara, Wang et. al.
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Reasonable Effectiveness of the Multiplicative Weights Update Algorithm (2017)
papad Hard to believe Sanjeev Arora and his coauthors consider it “a basic tool [that should be] taught to all algorithms students together with divide-and-conquer, dynamic programming, and random sampling.” Christos Papadimitriou calls it “so hard to believe that it has been discovered five times and forgotten.” It…
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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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Wav2vec Overview: Semi and Unsupervised Speech Recognition
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Alien Dreams: An Emerging Art Scene
Machine Learning at Berkeley is a student organization at UC Berkeley. Click to read ML@B Blog, by Machine Learning at Berkeley, a Substack publication with thousands of subscribers.
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Fun and dystopia with AI-based code generation using GPT-J-6B
At the least, AI-generated code is much more readable than the average human’s.
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FastText is the best text classification library for a quick baseline
Text classification is a very frequent use case for machine learning (ML) and natural language processing (NLP). It’s used for things like spam detection in emails, sentiment analysis for social media posts, or intent detection in chat bots. In this series I am going to compare several libraries that can be used to…