Google wants to put its artificial intelligence technology to use in top hospitals. Earlier this week, the search giant announced it would work with the U.K.'s National Health Service, or NHS, to alert staff to patients at risk of serious complications due to kidney failure. Details about the technology are fairly thin on the ground at this stage. But it is known that Google DeepMind recently acquired an app called Hark, which is a task management app that aims to replace paper-based systems and pagers. Hark was developed over four years by a team at Imperial College London, which is one of the U.K.'s top medical schools.

Researchers will pit their A.I. algorithms against the game Doom, to showcase how computers can adapt to visual environments. Video games are a good way to train artificial intelligence algorithms to learn about a visual world--researchers can simulate any situation they want, and it's endlessly repeatable. Google DeepMind is famous for this approach, teaching its A.I. to play Atari. Now researchers are competing to make their algorithms play Doom, the iconic shooting game originally for PC. DeepMind has already trained its algorithm to walk around in a maze based on Doom, but this competition would have the A.I. play death match rounds with rocket launchers.

Despite significant progress in object categorization, in recent years, a number of important challenges remain, mainly, ability to learn from limited labeled data and ability to recognize object classes within large, potentially open, set of labels. Zero-shot learning is one way of addressing these challenges, but it has only been shown to work with limited sized class vocabularies and typically requires separation between supervised and unsupervised classes, allowing former to inform the latter but not vice versa. We propose the notion of semi-supervised vocabulary-informed learning to alleviate the above mentioned challenges and address problems of supervised, zero-shot and open set recognition using a unified framework. Specifically, we propose a maximum margin framework for semantic manifold-based recognition that incorporates distance constraints from (both supervised and unsupervised) vocabulary atoms, ensuring that labeled samples are projected closest to their correct prototypes, in the embedding space, than to others. We show that resulting model shows improvements in supervised, zero-shot, and large open set recognition, with up to 310K class vocabulary on AwA and ImageNet datasets.

What next for Google's DeepMind, now that the company has mastered the ancient board game of Go, beating the Korean champion Lee Se-Dol 4-1 this month? A paper from two UCL researchers suggests one future project: playing poker.

Google said on Monday that it had agreed to buy British artificial intelligence start-up company DeepMind for an undisclosed amount. "I can confirm that the acquisition has indeed gone ahead but unfortunately we are not commenting beyond that for now," a Google spokeswoman told AFP. Reports put the deal at between 400 million and 500 million (292-365 million euros). On its one-page website, DeepMind describes itself as "a cutting edge artificial intelligence company" which combines "the best techniques from machine learning and systems neuroscience to build powerful general-purpose learning algorithms". Artificial intelligence can help computers "think" in ways similar to the way humans think and help solve problems.

Zero-shot Recognition (ZSR) is to learn recognition models for novel classes without labeled data. It is a challenging task and has drawn considerable attention in recent years. The basic idea is to transfer knowledge from seen classes via the shared attributes. This paper focus on the transductive ZSR, i.e., we have unlabeled data for novel classes. Instead of learning models for seen and novel classes separately as in existing works, we put forward a novel joint learning approach which learns the shared model space (SMS) for models such that the knowledge can be effectively transferred between classes using the attributes. An effective algorithm is proposed for optimization. We conduct comprehensive experiments on three benchmark datasets for ZSR. The results demonstrates that the proposed SMS can significantly outperform the state-of-the-art related approaches which validates its efficacy for the ZSR task.

Viewpoint estimation, especially in case of multiple object classes, remains an important and challenging problem. First, objects under different views undergo extreme appearance variations, often making within-class variance larger than between-class variance. Second, obtaining precise ground truth for real-world images, necessary for training supervised viewpoint estimation models, is extremely difficult and time consuming. As a result, annotated data is often available only for a limited number of classes. Hence it is desirable to share viewpoint information across classes. Additional complexity arises from unaligned pose labels between classes, i.e. a side view of a car might look more like a frontal view of a toaster, than its side view. To address these problems, we propose a metric learning approach for joint class prediction and pose estimation. Our approach allows to circumvent the problem of viewpoint alignment across multiple classes, and does not require dense viewpoint labels. Moreover, we show, that the learned metric generalizes to new classes, for which the pose labels are not available, and therefore makes it possible to use only partially annotated training sets, relying on the intrinsic similarities in the viewpoint manifolds. We evaluate our approach on two challenging multi-class datasets, 3DObjects and PASCAL3D+.

We propose a new zero-shot Event-Detection method by Multi-modal Distributional Semantic embedding of videos. Our model embeds object and action concepts as well as other available modalities from videos into a distributional semantic space. To our knowledge, this is the first Zero-Shot event detection model that is built on top of distributional semantics and extends it in the following directions: (a) semantic embedding of multimodal information in videos (with focus on the visual modalities), (b) semantic embedding of concepts definitions, and (c) retrieve videos by free text event query (e.g., "changing a vehicle tire") based on their content. We first embed the video into the multi-modal semantic space and then measure the similarity between videos with the event query in free text form. We validated our method on the large TRECVID MED (Multimedia Event Detection) challenge. Using only the event title as a query, our method outperformed the state-the-art that uses big descriptions from 12.6\% to 13.5\% with MAP metric and from 0.73 to 0.83 with ROC-AUC metric. It is also an order of magnitude faster.

Microsoft is the world's most valuable company, with a 261 billion market cap. And an IBM computer named Deep Blue defeats Garry Kasparov, reigning world chess champion and, at the time, the highest-ranked chess player to have ever lived.

This talk was held on Wed, Feb 24 2016 Dr. Demis Hassabis is the Co-Founder and CEO of DeepMind, the world's leading General Artificial Intelligence (AI) company, which was acquired by Google in 2014 in their largest ever European acquisition. Demis draws on his eclectic experiences as an AI researcher, neuroscientist and videogames designer to discuss what is happening at the cutting edge of AI research, its future impact on fields such as science and healthcare, and how developing AI may help us better understand the human mind.