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Sci-fi story coauthored by GPT-3, including in-character human/AI chats
Short stories about the singularity written in collaboration with GPT-3.
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Training GPT-2 to Play Chess
Almost 25 years after Kasparov vs. Deep Blue, another seminal man vs. machine matchup: Neither competitor has much to be proud of here. White has a poor opening. Black screws up and loses his queen…
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Logistic regression from scratch
Deep dives into Cryptography and Privacy.
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Algorithms Are Now Commodities
When I first started writing software, developers had to implement most of the algorithms they used; yes, hardware vendors provided libraries, but the culture was one of self-reliance (except for maths functions, which were technical and complicated).
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Gradient Boosted Decision Trees
From zero to gradient boosted decision trees
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Do NLP Beyond English
7000+ languages are spoken around the world but NLP research has mostly focused on English. This post outlines why you should work on languages other than English.
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Differentiable Dithering
Problem Let's say we want to reduce the number of colors in an image. For example consider the picture of fruit below:
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Generating MIDI Music with GPT-2
Generating Irish/folk/classical music in ABC format using GPT-2-117M, with good results.
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Machine Learning, Kolmogorov Complexity, and Squishy Bunnies (2019)
Computer Science, Machine Learning, Programming, Art, Mathematics, Philosophy, and Short Fiction
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Understanding Convolution in Deep Learning (2015)
Convolution is probably the most important concept in deep learning right now. It was convolution and convolutional nets that catapulted deep learning to the forefront of almost any machine learning task there is. But what makes convolution so powerful? How does it work? In this blog post I will explain convolution…
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Transformers Are Graph Neural Networks
Engineer friends often ask me: Graph Deep Learning sounds great, but are there any big commercial success stories? Is it being deployed in practical applications? Besides the obvious ones–recommendation systems at Pinterest, Alibaba and Twitter–a slightly nuanced success story is the Transformer architecture, which…
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Why neural networks struggle with the Game of Life
The Game of Life is based on very simple rules, but artificial neural networks struggle to learn its rules through the classic deep learning training process.
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AlphaFold2 at CASP14: “It feels like one’s child has left home.”
The past week was a momentous occasion for protein structure prediction, structural biology at large, and in due time, may prove to be so for the whole of life sciences. CASP14, the conference for …
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Deep Chernoff Faces
One of my favorite ¹ concepts for multi-dimensional data visualization is the Chernoff Face . The idea here is that for a dataset with many...
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Building a simple neural net in Java
Building a simple neural net in Java.
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Slack's core flaws require topic modeling to fix
Chatops is a ladder
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Julia GPU
Lambdaclass's blog about distributed systems, machine learning, compilers, operating systems, security and cryptography.
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Self Supervised Learning in NLP
An overview of self-supervised pretext tasks in Natural Language Processing
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Formulog: ML + Datalog + SMT
If you read a description of a static analysis in a paper, what might you find? There’ll be some cute model of a language. Maybe some inference rules describing the analysis itself, but those rules probably rely on a variety of helper functions. These days, the analysis likely involves some logical reasoning: about…
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Explaining RNNs without neural networks
This article explains how recurrent neural networks (RNN's) work without using the neural network metaphor. It uses a visually-focused data-transformation perspective to show how RNNs encode variable-length input vectors as fixed-length embeddings. Included are PyTorch implementation notebooks that use just linear…
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Scaling Down Deep Learning
A blog by Sam Greydanus
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Are Humans Intelligent? An AI Op-Ed
After seeing so many people debate whether computers can be intelligent I thought it only fair to ask an AI. As with previous posts, I picked the best responses, but everything after the bolded pro…
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An Overview of Bayesian Inference (2019)
A few weeks ago I wrote about Kuhn’s theory of paradigm shifts and how it relates to Bayesian inference. In this post I want to back up a little bit and explain what Bayesian inference is, and eventually rediscover the idea of a paradigm shift just from understanding how Bayesian inference works.
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CuPy Accelerates NumPy on the GPU? Hold My Cider, Here's Clojure
Everyone knows that Java is slow and Clojure is not a great choice for number crunching, but as soon as I dig anywhere with my software, I discover that the ...