1
Google Duplex: An AI System for Accomplishing Real World Tasks Over the Phone
Posted by Yaniv Leviathan, Principal Engineer and Yossi Matias, Vice President, Engineering, Google A long-standing goal of human-computer intera...
Topics /
AI & Machine Learning
The most popular blog posts about ai & machine learning on Hacker News. 292 posts.
2
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
3
4
Facial recognition: It’s time for action
In July, we shared our views about the need for government regulation and responsible industry measures to address advancing facial recognition technology. As we discussed, this technology brings important and even exciting societal benefits but also the potential for abuse. We noted the need for broader study and…
5
6
Markov Chains Explained Visually (2014)
Markov chains, named after Andrey Markov , are mathematical systems that hop from one "state" (a situation or set of values) to another. For example, if you made a Markov chain model of a baby's behavior, you might include "playing," "eating", "sleeping," and "crying" as states, which together with other behaviors…
7
8
Learning Math for Machine Learning
Vincent Chen is a student at Stanford University studying Computer Science. He is also a Research Assistant at the Stanford AI Lab. -------------------------------------------------------------------------------- It’s not entirely clear what level of mathematics is necessary to get started in machine learning…
9
Amazon’s own ‘Machine Learning University’ now available to all developers
Today, I’m excited to share that, for the first time, the same machine learning courses used to train engineers at Amazon are now available to all developers through AWS. We’ve been using machine learning across Amazon for more than 20 years. With thousands of engineers focused on machine learning across the company…
10
OpenAI Five
Stay up to speed on the rapid advancement of AI technology and the benefits it offers to humanity.
11
AI at Google: our principles
We’re announcing seven principles to guide our work in AI.
12
The Matrix Calculus You Need for Deep Learning
Most of us last saw calculus in school, but derivatives are a critical part of machine learning, particularly deep neural networks, which are trained by optimizing a loss function. This article is an attempt to explain all the matrix calculus you need in order to understand the training of deep neural networks. We…
13
14
15
16
17
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…
18
19
End-to-end implementation of a machine learning pipeline (2017)
In the fall of 2016, I was a Teaching Fellow (Harvard's version of TA) for the graduate class on "Advanced Topics in Data Science (CS209/109)" at Harvard University. I was in-charge of designing the class project given to the students, and this tutorial has been built on top of the project I designed for the class.
20
21
Shipping a Neural Network on iOS with CoreML, PyTorch, and React Native
This is the story of how I trained a simple neural network to solve a well-defined yet novel challenge in a real iOS app. The problem is unique, but most of what I cover should apply to any task in any iOS app. That’s the beauty of neural networks.
22
Facebook open-sources a speech-recognition system and a machine learning library
WHAT THE RESEARCH IS: A new fully convolutional approach to automatic speech recognition and wav2letter++, the fastest state-of-the-art end-to-end speech recognition system available. The approach …
23
On generative algorithms
An illustrated essay on generative algorithms written by Anders Hoff.
24
Neural Networks as Ordinary Differential Equations
Recently I found a paper being presented at NeurIPS this year, entitled Neural Ordinary Differential Equations, written by Ricky Chen, Yulia Rubanova, Jesse Bettencourt, and David Duvenaud from the University of Toronto. The core idea is that certain types of neural networks are analogous to a discretized differential…
25
Listening for illegal logging chainsaws using TensorFlow
Rainforest Connection founder and CEO Topher White shares how TensorFlow, Google’s open-source machine learning framework, aids in their efforts.
26
Learning Dexterity
Stay up to speed on the rapid advancement of AI technology and the benefits it offers to humanity.
27
28
29
30
