HumanE AI is driven by the fundamental Science & Technology challenges of modern AI and the need for fundamentally new interaction methods between AI systems and humans. It is important to note that these are not narrow, vertical problems. Indeed, they are Grand Challenges that cut across all areas of AI and will have a tremendous impact on how AI technology will be used and trusted. This impact will go way beyond AI and reach deeply into the cognitive sciences, social sciences and even philosophy. Bringing the HumanE AI vision to life via these interviews is to show the human side of the researchers behind the vision for a greater socio-economic impact in Europe an beyond.

VideoLectures.NET is part of the H2020 project MOVING, which has been working on developing new and more effective methods for lecture video fragmentation and fragment-level annotation, to allow for fine-grained access to lecture video collections. In the latest MOVING method, developed by CERTH (also a member, and coordinator of the MOVING consortium), automatically-generated speech transcripts of the lecture video are analysed with the help of word embeddings that are generated from pre-trained state-of-the-art neural networks. This lecture video fragmentation method is part of the MOVING platform, and its results are also being ingested in the VideoLectures.NET platform, making it possible for the users of both platforms to access and view specific fragments of lecture videos that cater to their information needs. For now, the fragments are accessible only for some lectures in VideoLectures.NET (testing phase); see for instance the lecture on deep learning. The fragments are presented as "chapters" to the right of the video player window, and can serve as a tool to find particular video parts easier and faster.

Rich Sutton is old school king of RL.

Machine learning approaches to natural language processing problems such as information retrieval, document classification, and information extraction have developed rapidly over recent years. Even more recently, the joint analysis of text and images has become a significant focus for machine learning. This autumn school will summarize the state of the art in machine learning for text analysis and for joint text/image analysis, as presented by researchers active in these fields. It is intended for students who already have a familiarity with machine learning, and is designed for software developers, graduate students, and advanced researchers with an interest in learning more about this area.

Every now and then I get asked for some help or for some pointers on a machine learning/data science topic. I tend respond with links to resources by folks that I consider to be experts in the topic area. Over time my list has gotten a little larger so I decided to put it all together in a blog post. Since it is based mostly on the questions I have received, it is by no means complete, or even close to a complete list, but hopefully it will be of some use. Perhaps I will keep it updated, or even better yet, feel free to comment with anything you think might be of help.

Deep neural networks that learn to represent data in multiple layers of increasing abstraction have dramatically improved the state-of-the-art for speech recognition, object recognition, object detection, predicting the activity of drug molecules, and many other tasks. Deep learning discovers intricate structure in large datasets by building distributed representations, either via supervised, unsupervised or reinforcement learning.

Rich Sutton is old school king of RL.

Rich Sutton is old school king of RL.

It is well understood that the performance of machine learning methods is heavily dependent on the choice of data representation (or features) on which they are applied. The rapidly developing field of representation learning is concerned with questions surrounding how we can best learn meaningful and useful representations of data. We take a broad view of the field, and include in it topics such as deep learning and feature learning, metric learning, kernel learning, compositional models, non-linear structured prediction, and issues regarding non-convex optimization.

A lot of people think that gophers go into hibernation during the winter months but that's not the case. Inference and learning on large graphical models, i.e. large systems of simple probabilistic units linked by a complex network of ... Following the Future Architecture platform's call for ideas that generated a full 291 ideas by 524 authors from 39 countries ... Future Architecture is the first pan-European platform of architecture museums, festivals and producers, bringing ideas on the future of cities and architecture closer to the wider public. From 18 - 20 February MAO organized Future Architecture Matchmaking Conference where candidates selected by the platform members and the public presented their projects. The workshop brings together researchers from the fields of machine learning and statistical physics in order to discuss the new challenges originating from dynamical data. It provides a forum for exploring possible synergies between the inference and learning approaches developed for the various models.