Zero-shot learning (ZSL) is regarded as an effective way to construct classification models for target classes which have no labeled samples available. The basic framework is to transfer knowledge from (different) auxiliary source classes having sufficient labeled samples with some attributes shared by target and source classes as bridge. Attributes play an important role in ZSL but they have not gained sufficient attention in recent years. Previous works mostly assume attributes are perfect and treat each attribute equally. However, as shown in this paper, different attributes have different properties, such as their class distribution, variance, and entropy, which may have considerable impact on ZSL accuracy if treated equally. Based on this observation, in this paper we propose to use a subset of attributes, instead of the whole set, for building ZSL models. The attribute selection is conducted by considering the information amount and predictability under a novel joint optimization framework. To our knowledge, this is the first work that notices the influence of attributes themselves and proposes to use a refined attribute set for ZSL. Since our approach focuses on selecting good attributes for ZSL, it can be combined to any attribute based ZSL approaches so as to augment their performance. Experiments on four ZSL benchmarks demonstrate that our approach can improve zero-shot classification accuracy and yield state-of-the-art results.

A classic approach toward zero-shot learning (ZSL) is to map the input domain to a set of semantically meaningful attributes that could be used later on to classify unseen classes of data (e.g. visual data). In this paper, we propose to learn a visual feature dictionary that has semantically meaningful atoms. Such a dictionary is learned via joint dictionary learning for the visual domain and the attribute domain, while enforcing the same sparse coding for both dictionaries. Our novel attribute aware formulation provides an algorithmic solution to the domain shift/hubness problem in ZSL. Upon learning the joint dictionaries, images from unseen classes can be mapped into the attribute space by finding the attribute aware joint sparse representation using solely the visual data. We demonstrate that our approach provides superior or comparable performance to that of the state of the art on benchmark datasets.

The tasks are designed to be as varied as possible. They differ in the goals they target, from learning, to memory, to navigation. They vary visually, from brightly coloured, modern-styled texture, to the subtle brown and greens of a desert at dawn, midday, or by night. And they contain physically different settings, from open, mountainous terrain, to right-angled mazes, to open, circular rooms. In addition, some of the environments include'bots', with their own, internal, goal-oriented behaviours.

DeepMind's artificial intelligence programme AlphaZero became the most formidable chess player in the world in December after just four hours of playing the game. DeepMind has also scored off the charts at Go and Shogi, the Asian board games, as well as video games such as Pong and Space Invaders. Machines can now diagnose cancer from tissue slides better than human epidemiologists, translate text from one language to another almost instantly, drive cars, and -- in certain circumstances -- predict social unrest. Even so-called AI pessimists accept that machines will increasingly transform our economy, our lives and our planet. And yet there is one thing that machines cannot yet do, at least not very well: play most sports.…

Are you thinking what I'm thinking? It's a question researchers have been asking artificial intelligence from the start. Now, a team at Google's DeepMind has developed a virtual 3D laboratory called Psychlab in which both humans and machines can take a range of simple tests and compare their cognitive abilities.

Montreal-based Element AI has expanded to London, UK. Dr. Julien Cornebise, a former DeepMind scientist, will lead the lab as director of research. Cornebiese was an early employee of Deepmind before it was acquired by Google in 2012. He created and led the Health Applied Research Team, and has been working with Amnesty International since he left DeepMind in 2016. The company says that it's focusing on'AI for good' through this office, while also expanding its network of researchers, scientists, and the private and public sector.

The firm's DeepMind division says that it played 100 games against Stockfish 8, and won or drew all of them. With two grad students, Hinton showed that an unfashionable Dec 26, 2017 Google has introduced a new AI system that's trained to rate photos on whether or not they are good technically and … Read more: Google new ai

Imagine predicting the emotion of a tweet without providing any training examples of tweets with that emotion label.This research discusses the paradigm of Zero-shot learning for Text Classification and the paper is aptly titled as "Train Once, Test Anywhere: Zero-shot Learning For Text Classification". You can read the paper here or try a demo here.

Google's artificial intelligence unit DeepMind engaged in "highly questionable" practices when it struck a 2015 deal to access years' worth of UK hospital patient records held by the National Health Service, says a paper published March 16 in the journal "Health and Technology." The paper, written by Cambridge University law academic Julia Powles and Economist journalist Hal Hodson, is the first piece of scholarship to analyze the terms by which 1.6 million patient records from three London hospitals that are part of the NHS Royal Free London trust were shared with DeepMind. That agreement was replaced by a 2016 deal that the authors will analyze in future. The earlier agreement is currently being investigated by two UK regulatory bodies. One of those investigations, by the Information Commissioner's Office (ICO), is "close to conclusion," the ICO says. The paper argues that both DeepMind and the hospital administrations, in their eagerness to take advantage of national data-sets, were too lax in the way the data was shared.

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