The latest blog posts to take off on Hacker News. 3,092 posts.
This class is a general introduction to computer vision. It covers standard techniques in image processing like filtering, edge detection, stereo, flow, etc. (old-school vision), as well as newer, machine-learning based computer vision. It was originally offered in the spring of 2018 at the University of Washington .
It's put up or shut up time for Data Science teams. When the business depends on our work for revenue growth, success is expected. Failure is fatal.
Training the car to do self-parking using a genetic algorithm
[Hacker News discussion, Twitter thread]
Why this is the first rule, some baseline heuristics, and when to move on to machine learning.
There is a subfield of robotics research called “sim-to-real” (sim2real) whereby one attempts to solve a robotic task in simulation, and the...
It is not the fault of Turing, though.
Guess the movie based on an AI’s visual interpretation of the plot.
A couple of days back, Ganesh Venkataramanan, leader of the Tesla Dojo project, announced the Dojo machine learning training system. It’s an unusually network rich, power dense, and memory light design. I’ve summarized the architecture of the system below but I found three aspects of the system particularly…
Technological debt is insidious, a kind of socio-infrastructural subprime crisis that's unfolding around us in slow motion. Our digital infrastructure is built atop layers and layers and layers of code that's insecure due to a combination of bad practices and bad frameworks.
Transcript of interview on how tech changes the world
I just sat down this morning and organized all deep learning related videos I recorded in 2021. I am sure this will be a useful reference for my future…
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.
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…
A glimpse of the upcoming paradigm shift in how we do development
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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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.
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…
Ray: A Distributed Framework for Emerging AI Applications Moritz, Nishihara, Wang et. al.
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…
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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At the least, AI-generated code is much more readable than the average human’s.
