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Introducing the TensorFlow Research Cloud
Posted by Zak Stone, Product Manager for TensorFlowResearchers require enormous computational resources to train the machine learning (ML) models t...
Topics /
AI & Machine Learning
The most popular blog posts about ai & machine learning on Hacker News. 205 posts.
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Welcome to the New AWS AI Blog
If you ask 100 people for the definition of “artificial intelligence,” you’ll get at least 100 answers, if not more. At AWS, we define it as a service or system which can perform tasks that usually require human-level intelligence such as visual perception, speech recognition, decision making, or translation. On this…
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Supercharge your Computer Vision models with the TensorFlow Object Detection API
Posted by Jonathan Huang, Research Scientist and Vivek Rathod, Software Engineer (Cross-posted on the Google Open Source Blog) At Google, we develo...
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How to Get into Natural Language Processing
We’re excited to introduce a new series we’re calling Paths [https://ycombinator.wpengine.com/category/paths/]. Each post will outline an emerging technology and give you clear steps on how to get started in that field. This series was designed with makers and aspiring entrepreneurs in mind. We talked to college…
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The Algorithms Behind Probabilistic Programming
We recently introduced our report on probabilistic programming. The accompanying prototype allows you to explore the past and future of the New York residential real estate market. This post gives a feel for the content in our report by introducing the algorithms and technology that make probabilistic programming…
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An Upgrade to SyntaxNet, New Models and a Parsing Competition
Posted by David Weiss and Slav Petrov, Research ScientistsAt Google, we continuously improve the language understanding capabilities used in applic...
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Challenges you’re going to face when building a chatbot
Developing your own medical chatbot? Challenges and lessons learned from developing Symptomate.
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Attacking machine learning with adversarial examples
Adversarial examples are inputs to machine learning models that an attacker has intentionally designed to cause the model to make a mistake; they’re like optical illusions for machines. In this post we’ll show how adversarial examples work across different mediums, and will discuss why securing systems against them…
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Teaching Machines to Draw
Posted by David Ha, Google Brain ResidentAbstract visual communication is a key part of how people convey ideas to one another. From a young age, c...
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How Adversarial Attacks Work
Emil Mikhailov is the founder of XIX.ai [http://XIX.ai] (YC W17). Roman Trusov is a researcher at XIX.ai. Recent studies by Google Brain have shown that any machine learning classifier can be tricked to give incorrect predictions, and with a little bit of skill, you can get them to give pretty much any result you…
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Making Visible Watermarks More Effective
Posted by Tali Dekel and Michael Rubinstein, Research ScientistsWhether you are a photographer, a marketing manager, or a regular Internet user, ch...
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Tacotron 2: Generating Human-Like Speech from Text
Posted by Jonathan Shen and Ruoming Pang, Software Engineers, on behalf of the Google Brain and Machine Perception TeamsGenerating very natural sou...
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Prodigy: A new tool for radically efficient machine teaching
Machine learning systems are built from both code and data. It's easy to reuse the code but hard to reuse the data, so building AI mostly means doing annotation. This is good, because the examples are how you program the behaviour – the learner itself is really just a compiler. What's not good is the current…
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PhotoScan: Taking Glare-Free Pictures of Pictures
Posted by Ce Liu, Michael Rubinstein, Mike Krainin and Bill Freeman, Research ScientistsYesterday, we released an update to PhotoScan, an app for i...
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AlphaGo's next move
With just three stones on the board, it was clear that this was going to be no ordinary game of Go.
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A novel approach to neural machine translation
Visit the post for more.
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Transformer: A Novel Neural Network Architecture for Language Understanding
Posted by Jakob Uszkoreit, Software Engineer, Natural Language Understanding Neural networks, in particular recurrent neural networks (RNNs), are n...
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Word vectors are awesome but you don’t need a neural network to find them
When I started playing with word2vec four years ago I needed (and luckily had) tons of supercomputer time. But because of advances in our understanding of w...
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Learning a hierarchy
Stay up to speed on the rapid advancement of AI technology and the benefits it offers to humanity.
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Open sourcing Sonnet – a new library for constructing neural networks
It’s now nearly a year since DeepMind made the decision to switch the entire research organisation to using TensorFlow (TF). It’s proven to be a good choice - many of our models learn significantly faster, and the built-in features for distributed training have hugely simplified our code. Along the way, we found that…
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