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Boosting Sales with Machine Learning
How we use natural language processing to qualify leads
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GPUs for Google Cloud Platform
CPU-based machines in the cloud are terrific for general purpose computing, but certain tasks such as rendering or large-scale simulations are much faster on specialized processors. Graphics Processing Units (GPUs) contain hundreds of times as many computational cores as CPUs and are great at accelerating risk…
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How to Get a Job in Deep Learning
Explore resources and tools created to inspire creativity, perform deep learning, and equip your company with Voice AI best practices.
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Supercharging Style Transfer
Posted by Vincent Dumoulin*, Jonathon Shlens and Manjunath Kudlur, Google Brain TeamPastiche. A French word, it designates a work of art that imita...
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Announcing YouTube-8M: A Large and Diverse Labeled Video Dataset for Research
Posted by Sudheendra Vijayanarasimhan and Paul Natsev, Software EngineersMany recent breakthroughs in machine learning and machine perception have ...
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Found in translation: More accurate, fluent sentences in Google Translate
In 10 years, Google Translate has gone from supporting just a few languages to 103, connecting strangers, reaching across language barriers and even helping people find …
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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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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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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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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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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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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.
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Concrete AI Safety Problems
We (along with researchers from Berkeley and Stanford) are co-authors on today’s paper led by Google Brain researchers, Concrete Problems in AI Safety. The paper explores many research problems around ensuring that modern machine learning systems operate as intended.
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OpenAI technical goals
OpenAI’s mission is to build safe AI, and ensure AI’s benefits are as widely and evenly distributed as possible.
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Zero-Shot Translation with Multilingual Neural Machine Translation System
Posted by Mike Schuster (Google Brain Team), Melvin Johnson (Google Translate) and Nikhil Thorat (Google Brain Team)In the last 10 years, Google Tr...
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Machine Learning in the Cloud, with TensorFlow
Posted by Slaven Bilac, Software Engineer, Google ResearchAt Google, researchers collaborate closely with product teams, applying the latest advanc...
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Machine learning for financial prediction
Machine learning for trading is a huge domain, but not all applications are practical in terms of making your money grow. Let's find out what really works!
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How convolutional neural networks see the world
Note: this post was originally written in January 2016. It is now very outdated. Please see this example of how to visualize convnet filters for an up-to-date alternative, or check out chapter 9 of my book "Deep Learning with Python (2nd edition)".
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Announcing TensorFlow 0.8 – now with distributed computing support
Posted by Derek Murray, Software EngineerGoogle uses machine learning across a wide range of its products. In order to continually improve our mode...