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New
The latest blog posts to take off on Hacker News. 292 posts.
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Neural scene representation and rendering
There is more than meets the eye when it comes to how we understand a visual scene: our brains draw on prior knowledge to reason and to make inferences that go far beyond the patterns of light that hit our retinas. For example, when entering a room for the first time, you instantly recognise the items it contains and…
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AI Nationalism
For the past 9 months I have been presenting versions of this talk to AI researchers, investors, politicians and policy makers. I felt it was time to share these ideas with a wider audience. Thanks to the Ditchley conference on Machine Learning in 2017 for giving me a fantastic platform to get ear
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Faster Natural Language Processing in Python
How to take advantage of spaCy & a bit of Cython for blazing fast NLP
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Improving Language Understanding with Unsupervised Learning
Stay up to speed on the rapid advancement of AI technology and the benefits it offers to humanity.
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Machine learning can run on tiny, low-power chips
Photo by Kevin Steinhardt When Azeem asked me to give a talk at CogX, he asked me to focus on just a single point that I wanted the audience to take away. A few years ago my priority would have bee…
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Attacks against machine learning – an overview
This blog post surveys the attacks techniques that target AI (Artificial Intelligence) systems and how to protect against them.
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AI at Google: our principles
We’re announcing seven principles to guide our work in AI.
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How The New York Times Uses Software to Recognize Members of Congress
Even if you’ve covered Congress for The New York Times for a decade, it can be hard to recognize which member you’ve just spoken with…
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Mozilla Announces $225,000 for Art and Advocacy Exploring A.I.
Mozilla’s latest awards will support people and projects that examine the effects of AI on society At Mozilla, one way we support a healthy internet i
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MLflow: An Open Source Machine Learning Platform
MLflow is an open-source platform to manage the ML lifecycle, offering tools for data preparation, model training, and deployment at scale.
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Predicting Stock Performance with Natural Language Deep Learning
We developed an NLP deep learning model using a one-dimensional convolutional neural network to predict future stock market performance of companies using Azure ML Workbench and Keras with open source for you to replicate.
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AI winter is well on its way
Deep learning has been at the forefront of the so called AI revolution for quite a few years now, and many people had believed that it is the silver bullet that will take us to
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Empiricism and the limits of gradient descent
This post is actually about artificial intelligence, and argues a position that many AI researchers will disagree with. Specifically, it arg...
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Dissecting Reinforcement Learning
Explaining the basic ideas behind reinforcement learning. In particular, Markov Decision Process, Bellman equation, Value iteration and Policy Iteration algorithms, policy iteration through linear algebra methods. It includes full working code written in Python.
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Gym Retro
Stay up to speed on the rapid advancement of AI technology and the benefits it offers to humanity.
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ML Beyond Curve Fitting: An Intro to Causal Inference and Do-Calculus
Since writing this post back in 2018, I have extended this to a 4-part series on causal inference: * ➡️️ Part 1: Intro to causal inference and do-calculus [https://www.inference.vc/untitled] * Part 2: Illustrating Interventions with a Toy Example…
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3D Face Reconstruction with Position Map Regression Networks
Position Map Regression Networks (PRN) is a method to jointly regress dense alignment and 3D face shape in an end-to-end manner. In this article, I’ll provide a short explanation and discuss its applications in computer vision. When I was a… Continue reading 3D Face Reconstruction with Position Map Regression Networks
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Letting neural networks be weird
Machine learning algorithms are not like other computer programs. In the usual sort of programming, a human programmer tells the computer exactly what to do. In machine learning, the human programmer merely gives the algorithm the problem to be solved, and through trial-and-error the algorithm has to figure out how to…