Someday, it might be perfectly normal to watch an AI system fight with itself. The concept comes from researchers at OpenAI, a nonprofit founded by several Silicon Valley luminaries, including Y Combinator partner Sam Altman, LinkedIn chair Reid Hoffman, Facebook board member and Palantir founder Peter Thiel, and Tesla and SpaceX head Elon Musk. The OpenAI researchers have previously shown that AI systems that train themselves can sometimes develop unexpected and unwanted habits. For example, in a computer game, an agent may figure out how to "glitch" its way to a higher score. In some cases it may be possible for a person to supervise the training process.

Q-learning (which doesn't stand for anything, by the way) is centered around creating a "virtual table" that accounts for how much reward is assigned to each possible action given the current state of the environment. Let's break that down one step at a time: What do we mean by "virtual table?" Imagine that for each possible configuration of the input space, you have a table that assigns a score for each of the possible actions you can take. If this were magically possible, then it would be extremely easy for you to "beat" the environment: simply choose the action that has the highest score! Two points to note about this score.

Although machine learning has been successfully used to propose novel molecules that satisfy desired properties, it is still challenging to explore a large chemical space efficiently. In this paper, we present a conditional molecular design method that facilitates generating new molecules with desired properties. The proposed model, which simultaneously performs both property prediction and molecule generation, is built as a semi-supervised variational autoencoder trained on a set of existing molecules with only a partial annotation. We generate new molecules with desired properties by sampling from the generative distribution estimated by the model. We demonstrate the effectiveness of the proposed model by evaluating it on drug-like molecules. The model improves the performance of property prediction by exploiting unlabeled molecules, and efficiently generates novel molecules fulfilling various target conditions.

This work exploits translation data as a source of semantically relevant learning signal for models of word representation. In particular, we exploit equivalence through translation as a form of distributed context and jointly learn how to embed and align with a deep generative model. Our EmbedAlign model embeds words in their complete observed context and learns by marginalisation of latent lexical alignments. Besides, it embeds words as posterior probability densities, rather than point estimates, which allows us to compare words in context using a measure of overlap between distributions (e.g. KL divergence). We investigate our model's performance on a range of lexical semantics tasks achieving competitive results on several standard benchmarks including natural language inference, paraphrasing, and text similarity.

Researchers in artificial intelligence can stand to make a ton of money. But this week, we actually know just how much some A.I. experts are being paid -- and it's a lot, even at a nonprofit. OpenAI, a nonprofit research lab, paid its lead A.I. expert, Ilya Sutskever, more than $1.9 million in 2016, according to a recent public tax filing. Another researcher, Ian Goodfellow, made more than $800,000 that year, even though he was only hired in March, the New York Times reported. As the publication points out, the figures are eye-opening and offer a bit of insight on how much A.I. researchers are being paid across the globe.

Elon Musk's OpenAI is paying big money for the world's best AI researchers. There's been a lot of speculation in the last couple of years about how much money technology firms are paying the world's top artificial intelligence (AI) experts but concrete numbers have been hard to come by. That changed this week when Cade Metz, a journalist for The New York Times, revealed that he had stumbled upon a tax filing from OpenAI -- an AI research lab set up by Tesla CEO Elon Musk -- that included staff salaries and bonuses. The numbers are high, especially when you consider the fact that Open AI is a non-profit organisation. The company, which says it is working to ensure AI benefits all of humanity, was founded in San Francisco in 2015.

We're releasing an experimental metalearning approach called Evolved Policy Gradients, a method that evolves the loss function of learning agents, which can enable fast training on novel tasks. Agents trained with EPG can succeed at basic tasks at test time that were outside their training regime, like learning to navigate to an object on a different side of the room from where it was placed during training. EPG trains agents to have a prior notion of what constitutes making progress on a novel task. Rather than encoding prior knowledge through a learned policy network, EPG encodes it as a learned loss function[1]. Agents are then able to use this loss function, defined as a temporal-convolutional neural network, to learn quickly on a novel task. We've shown that EPG can generalize to out of distribution test time tasks, exhibiting behavior qualitatively different from other popular metalearning algorithms.

One of the poorest kept secrets in Silicon Valley has been the huge salaries and bonuses that experts in artificial intelligence can command. Now, a little-noticed tax filing by a research lab called OpenAI has made some of those eye-popping figures public. OpenAI paid its top researcher, Ilya Sutskever, more than $1.9m (£1.35m) in 2016. It paid another leading researcher, Ian Goodfellow, more than $800,000 (£570,000) – even though he was not hired until March of that year. Both were recruited from Google.

One of the poorest-kept secrets in Silicon Valley has been the huge salaries and bonuses that experts in artificial intelligence can command. Now, a little-noticed tax filing by a research lab called OpenAI has made some of those eye-popping figures public. OpenAI paid its top researcher, Ilya Sutskever, more than $1.9 million in 2016. It paid another leading researcher, Ian Goodfellow, more than $800,000 -- even though he was not hired until March of that year. Both were recruited from Google.

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