Kinetics is a large-scale, high-quality dataset of YouTube video URLs which include a diverse range of human focused actions. Our aim in releasing the Kinetics dataset is to help the machine learning community to advance models for video understanding. The dataset consists of approximately 300,000 video clips, and covers 400 human action classes with at least 400 video clips for each action class. Each clip lasts around 10s and is labeled with a single class. All of the clips have been through multiple rounds of human annotation, and each is taken from a unique YouTube video.

That said, I would have thought he was a bit young for a "Director" role. Most other big tech companies have directors of his professors' generation. Not doubting his skill, ability to communicate, or his passion, it just seems a pretty surprising move from a large company. Has Andrej ever managed a team before (beyond running a course or supervising some students)? And does he have any serious SDC experience?

The focus of the DeepMind paper concerns spatial reasoning, in particular the ability to grasp the relation of objects to each other. This may sound simple compared with becoming an expert in chess or the like. But it's only because humans possess something like an "intuitive physics engine," an algorithm for extrapolating three-dimensionality from flat images and comparing objects within it to other objects. This kind of spatial reasoning has proved difficult for computers, at least until now. Using a combination of relational networks and convoluted neural networks, the DeepMind system can answer questions concerning the relation of objects within an image.

This dataset contains over 1.5 million question and answer pairs for a reading comprehension task based on articles from the CNN and Daily Mail. Questions, answers and context are anonymised with random entity markers, thereby forcing systems to answer questions purely based on the context provided. This dataset accompanies the'Teaching Machines to Read and Comprehend' paper.

AI systems continue to get increasingly powerful, but still need far too much hand-holding by their human masters. New research from DeepMind and OpenAI suggests a mere nudge here and there at the outset can be enough to help artificial intelligence accomplish tricky tasks. The team set up a series of experiments in which human participants were given two short clips of an AI's approach to a task. They were then asked to make a snap judgement about which clip appeared to show more promising progress – but without the AI being aware of the desired outcome of the task. One scenario involved the AI learning to play Space Invaders, another involved a virtual robot learning to do backflips.

AI isn't going to be a threat to humanity because it's evil or cruel, AI will be a threat to humanity because we haven't properly explained what it is we want it to do. Consider the classic "paperclip maximizer" thought experiment, in which an all-powerful AI is told, simply, "make paperclips." The AI, not constrained by any human morality or reason, does so, eventually transforming all resources on Earth into paperclips, and wiping out our species in the process. As with any relationship, when talking to our computers, communication is key. That's why a new piece of research published yesterday by Google's DeepMind and the Elon Musk-funded OpenAI institute is so interesting. It offers a simple way for humans to give feedback to AI systems -- crucially, without the instructor needing to know anything about programming or artificial intelligence.

Artificial intelligence is not a contact sport. Currently, algorithms mostly just compete to win old Atari games, or accomplish historic board gaming feats like owning five human Go champions at once. These are just practice rounds, though, for the way more complicated (and practical) goal of teaching robots how to navigate human environments. Vicarious, an AI company, has developed a new AI that is absolutely slammin' at Breakout, the paddle vs. brick arcade classic. Its AI, called Schema Networks, even succeeds at tweaked versions of the game--for instance, when the paddle is moved closer to the bricks.

One step towards building safe AI systems is to remove the need for humans to write goal functions, since using a simple proxy for a complex goal, or getting the complex goal a bit wrong, can lead to undesirable and even dangerous behavior. In collaboration with DeepMind's safety team, we've developed an algorithm which can infer what humans want by being told which of two proposed behaviors is better. We present a learning algorithm that uses small amounts of human feedback to solve modern RL environments. Machine learning systems with human feedback have been explored before, but we've scaled up the approach to be able to work on much more complicated tasks. Our algorithm needed 900 bits of feedback from a human evaluator to learn to backflip -- a seemingly simple task which is simple to judge but challenging to specify.

Artificial intelligence is not a contact sport. Currently, algorithms mostly just compete to win old Atari games, or accomplish historic board gaming feats like pwning five human Go champions at once. These are just practice rounds, though, for the way more complicated (and practical) goal of teaching robots how to navigate human environments. Vicarious, an AI company, has developed a new AI that is absolutely slammin' at Breakout, the paddle vs. brick arcade classic. Its AI, called Schema Networks, even succeeds at tweaked versions of the game--for instance, when the paddle is moved closer to the bricks.