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AI Update, Late 2019
It's been 7 months since my last commentary on the field, and as it became regular appearance in this blog (and in fact many people apparently enjoy this form and keep asking for it),
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The latest blog posts to take off on Hacker News. 3,092 posts.
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Deep Learning in the Real World: Dealing with Non-Differentiable Loss Functions
Over the past few years, deep learning has been taking by storm many industries. From voice recognition to image analysis and synthesis, neural networks have turned out to be very efficient at solv…
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ML from Scratch, Part 2: Logistic Regression (2018)
In this second installment of the machine learning from scratch we switch the point of view from regression to classification: instead of estimating a number, we will be trying to guess which of 2 possible classes a given input belongs to. A modern example is looking at a photo and deciding if its a cat or a dog.
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Quantum supremacy: the gloves are off
Links:Google paper in NatureNew York Times articleIBM paper and blog post responding to Google’s announcementBoaz Barak’s new post: “Boaz’s inferior classical inferiority FA…
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Weaponizing and Gamifying AI for WiFi Hacking: Presenting Pwnagotchi 1.0.0
This is the story of a summer project that started out of boredom and that evolved into something incredibly fun and unique. It is also the story of how that p…
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ML From Scratch, Part 1: Linear Regression
To kick off this series, will start with something simple yet foundational: linear regression via ordinary least squares. While not particularly exciting, linear regression finds widespread use both as a standalone learning algorithm and as a building block in more advanced learning algorithms.
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Google and Ambient Computing
Google presented a vision of ambient computing that goes beyond the smartphone. The company is well-placed, but faces challenges both in the marketplace and in the mirror.
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A dumb reason computer vision apps aren’t working: Exif Orientation
I’ve written about lots of computer vision and machine learning projects like object recognition systems and face recognition projects. I…
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Successful machine learning models: lessons learned at Booking.com
150 successful machine learning models: 6 lessons learned at Booking.com Bernadi et al., KDD’19 Here’s a paper that will reward careful study for many organisations. We’ve previously looked a…
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New chips for machine intelligence
This note summarises details of some of the new silicon chips for machine intelligence. Its aim is to distil the most important implementation and architectural details (at least those that are currently available), to highlight the main differences between them. I’m focusing on chips designed for training since they…
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Quantum computers: amazing progress, but probably false supremacy claims
A 2017 cartoon from this post. After the embargo update (Oct 25): Now that I have some answers from the people involved let me make a quick update: 1) I still find the paper unconvincing, specific…
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Deep Learning: Our Miraculous Year 1990-1991
Many of the basic ideas behind the Deep Learning Revolution were published three decades ago within fewer than 12 months in our "Annus Mirabilis" 1990-91
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Principal Component Analysis
In the previous article in this series we distinguished between two kinds of unsupervised learning (cluster analysis and dimensionality reduction) and discussed the former in some detail. In this installment we turn our attention to the later. In dimensionality reduction we seek a function $f : \mathbb{R}^n \mapsto…
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A Gentle Introduction to Bayes’ Theorem for Machine Learning
Bayes Theorem provides a principled way for calculating a conditional probability. It is a deceptively simple calculation, although it can be used to easily calculate the conditional probability of events where intuition often fails. Although it is a powerful tool in the field of probability, Bayes Theorem is also…
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Scott’s Supreme Quantum Supremacy FAQ
You’ve seen the stories—in the Financial Times, Technology Review, CNET, Facebook, Reddit, Twitter, or elsewhere—saying that a group at Google has now achieved quantum computation…
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AI competitions don’t produce useful models
Ai competitions are fun, community building, talent scouting, brand promoting, and attention grabbing. But competitions are not intended to develop useful models.
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Gaussian Processes, not quite for dummies
Before diving inFor a long time, I recall having this vague impression about Gaussian Processes (GPs) being able to magically define probability distributions over sets of functions, yet I procrastinated reading up about them for many many moons. However, as always, I’d like to think that this is not just due to my…
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Dungeon crawling or lucid dreaming?
I’ve done several experiments with a text-generating neural network called GPT-2. Trained at great expense by OpenAI (to the tune of tens of thousands of dollars worth of computing power), GPT-2 learned to imitate all kinds of text from the internet. I’ve interacted with the basic model, discovering its abilities to…
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Fast Tensors in Clojure – A Sneak Peek
There's a lot going on behind the scenes, since I am not only writing the books; at the same time I have to create the software to write about. For example, ...
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Learning to prove theorems via interacting with proof assistants
Learning to prove theorems via interacting with proof assistants Yang & Deng, ICML’19 Something a little different to end the week: deep learning meets theorem proving! It’s been a while …
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Transformers from Scratch
Repository for personal website
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OpenGPT-2: We Replicated GPT-2 Because You Can Too
By Aaron Gokaslan* and Vanya Cohen*
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The Essential Tools of Scientific Machine Learning
Scientific machine learning is a burgeoning discipline which blends scientific computing and machine learning. Traditionally, scientific computing focuses on large-scale mechanistic models, usually differential equations, that are derived from scientific laws that simplified and explained phenomena. On the other hand…