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AI produces realistic sounds that fool humans
A video-trained system from MIT’s Computer Science and Artificial Intelligence Lab could help robots understand how objects interact with the world.
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Differentiable Neural Computers
In a recent study in Nature, we introduce a form of memory-augmented neural network called a differentiable neural computer, and show that it can learn to use its memory to answer questions about complex, structured data, including artificially generated stories, family trees, and even a map of the London Underground…
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The Difference Between AI, Machine Learning, and Deep Learning
Explore the progression from AI training to AI inference, and how they both function.
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AI, Deep Learning, and Machine Learning: A Primer [video]
watch time: 45 minutes “One person, in a literal garage, building a self-driving car.” That happened in 2015. Now to put that fact in context, compare this to 2004, when DARPA sponsored the very first driverless car Grand Challenge. Of the 20 entries they received then, the winning entry went 7.2 miles; in 2007, in...
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Amazon Lex – Build Conversational Voice and Text Interfaces
While computers that talk are great, computers that listen and respond are even better! If you have used an Amazon Echo, you know how simple, useful, and powerful the Alexa-powered interaction model can be. Today we are making the same deep learning technologies (ASR – Automatic Speech Recognition NLU – Natural…
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Infrastructure for Deep Learning
Deep learning is an empirical science, and the quality of a group’s infrastructure is a multiplier on progress. Fortunately, today’s open-source ecosystem makes it possible for anyone to build great deep learning infrastructure.
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How to Start Learning Deep Learning
Due to the recent achievements of artificial neural networks across many different tasks (such as face recognition, object detection and Go), deep learning has become extremely popular. This post aims to be a starting point for those interested in learning more about it.
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David J.C. MacKay, Machine Learning pioneer, dies
Thank you so much to lots of lovely friends who have offered to visit me in hospital! Ramesh and I are wanting to keep life really simple...
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What we learned in Seoul with AlphaGo
DeepMind's AlphaGo took on and defeated legendary Go player, Lee Sedol, marking a major milestone for artificial intelligence.
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Why squared error? (2014)
its computational convenience • connections to the Gaussian distribution • differentiability and basis independence • why not squared error • absolute-error methods
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Decoding the Thought Vector
Neural networks have the rather uncanny knack for turning meaning into numbers. Data flows from the input to the output, getting pushed through a series of transformations which process the data into increasingly abstruse vectors of representations. These numbers, the activations of the network, carry useful…
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Jarvis: an Amazon Echo clone in your browser
Javascript and Node.js training and consulting. Get your team up to speed fast or have us build and implement your next web application.
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Video games are essential for inventing artificial intelligence
The most important thing for humanity to do right now is to invent true artificial intelligence (AI): machines or software that can think an...
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Heavy Metal and Natural Language Processing
Edit: Part 2 of this post can be found here , and part 3 here . Most of the code used in these posts is available here .
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Deep Learning Is Easy – Learn Something Harder
Caveat: This post is meant address people who are completely new to deep learning and are planning an entry into this field. The intention is to help them think critically about the complexity of the field, and to help them tell apart things that are trivial from things that are...
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Neural Networks Are Impressively Good at Compression
I’m trying to get into neural networks. There have been a couple big breakthroughs in the field in recent years and suddenly my side project of messing around with programming languages seeme…
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Automatically grading multiple choice exams from photos using Python and OpenCV
Discover how to build a bubble sheet multiple choice scanner and test grader using Optical Mark Recognition (OMR) along with Python, OpenCV, computer vision
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Number plate recognition with Tensorflow
Over the past few weeks I’ve been dabbling with deep learning, in particular convolutional neural networks . One standout paper from recent times is Google’s Multi-digit Number Recognition from Street View . This paper describes a system for extracting house numbers from street view imagery using a single end-to-end…
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Special projects
Impactful scientific work requires working on the right problems—problems which are not just interesting, but whose solutions matter.
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A Visual and Interactive Guide to the Basics of Neural Networks
Discussions: Hacker News (63 points, 8 comments), Reddit r/programming (312 points, 37 comments) Translations: Arabic, French, Spanish Update: Part 2 is now live: A Visual And Interactive Look at Basic Neural Network Math Motivation I’m not a machine learning expert. I’m a software engineer by training and I’ve had…
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Image Compression with Neural Networks
Posted by Nick Johnston and David Minnen, Software EngineersData compression is used nearly everywhere on the internet - the videos you watch onlin...
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Wide and Deep Learning: Better Together with TensorFlow
Posted by Heng-Tze Cheng, Senior Software Engineer, Google ResearchThe human brain is a sophisticated learning machine, forming rules by memorizing...
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Image Completion with Deep Learning in TensorFlow
Content-aware fill is a powerful tool designers and photographers use to fill in unwanted or missing parts of images. Image completion and inpainting are closely related technologies used to fill in missing or corrupted parts of images. There are many ways to do content-aware fill, image completion, and inpainting. In…
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Introducing Google Cloud Natural Language API, Speech API and New Data Center
Following our announcements from GCP NEXT in March, we’re excited to share updates about Cloud Platform expansion and machine learning . Today we’re launching two new Machine Learning APIs into open beta and expanding our footprint in the United States.