The US government's mighty DARPA last year kicked off a research project designed to make systems controlled by artificial intelligence more accountable to their human users. The Defense Advanced Research Projects Agency, to use this $2.97bn agency its full name, is the Department of Defense's body responsible for emerging technology for use by the US armed forces. Significantly, it was DARPA's early funding of packet-switching network the Advanced Research Projects Agency Network (ARPANET) more than 40 years ago that helped bring about the internet. Coming bang up to date, the issue at the heart of the Explainable Artificial Intelligence (XAI) programme is that AI is starting to extend into many areas of everyday life yet the internal workings of such systems are often opaque, and could be concealing flaws in their decision-making processes. The field of AI has made great strides in the last several years, thanks to developments in machine learning algorithms and deep learning systems based on artificial neural networks (ANNs).

I'm standing in what is soon to be the center of the world, or is perhaps just a very large room on the seventh floor of a gleaming tower in downtown Toronto. Showing me around is Jordan Jacobs, who cofounded this place: the nascent Vector Institute, which opens its doors this fall and which is aiming to become the global epicenter of artificial intelligence. We're in Toronto because Geoffrey Hinton is in Toronto, and Geoffrey Hinton is the father of "deep learning," the technique behind the current excitement about AI. "In 30 years we're going to look back and say Geoff is Einstein--of AI, deep learning, the thing that we're calling AI," Jacobs says. Of the AI researchers at the top of the field, Hinton has more citations than the next three combined. His students and postdocs have gone on to run the AI labs at Apple, Facebook, and OpenAI; Hinton himself is a lead scientist on the Google Brain AI team. The Vector Institute, this monument to the ascent of Hinton's ideas, is a research center where companies from around the U.S. and Canada--like Google, and Uber, and Nvidia--will sponsor efforts to commercialize AI technologies. Money has poured in faster than Jacobs could ask for it; two of his cofounders surveyed companies in the Toronto area, and the demand for AI experts ended up being 10 times what Canada produces every year. Vector is in a sense ground zero for the now-worldwide attempt to mobilize around deep learning: to cash in on the technique, to teach it, to refine and apply it. Data centers are being built, towers are being filled with startups, a whole generation of students is going into the field.

Sequence modeling with neural networks has lead to powerful models of symbolic music data. We address the problem of exploiting these models to reach creative musical goals, by combining with human input. To this end we generalise previous work, which sampled Markovian sequence models under the constraint that the sequence belong to the language of a given finite state machine provided by the human. We consider more expressive non-Markov models, thereby requiring approximate sampling which we provide in the form of an efficient sequential Monte Carlo method. In addition we provide and compare with a beam search strategy for conditional probability maximisation. Our algorithms are capable of convincingly re-harmonising famous musical works. To demonstrate this we provide visualisations, quantitative experiments, a human listening test and audio examples. We find both the sampling and optimisation procedures to be effective, yet complementary in character. For the case of highly permissive constraint sets, we find that sampling is to be preferred due to the overly regular nature of the optimisation based results. The generality of our algorithms permits countless other creative applications.

We introduce the Hierarchically Interacting Particle Neural Network (HIP-NN) to model molecular properties from datasets of quantum calculations. Inspired by a many-body expansion, HIP-NN decomposes properties, such as energy, as a sum over hierarchical terms. These terms are generated from a neural network--a composition of many nonlinear transformations--acting on a representation of the molecule. HIP-NN achieves state-of-the-art performance on a dataset of 131k ground state organic molecules, and predicts energies with 0.26 kcal/mol mean absolute error. With minimal tuning, our model is also competitive on a dataset of molecular dynamics trajectories. In addition to enabling accurate energy predictions, the hierarchical structure of HIP-NN helps to identify regions of model uncertainty.

Person Re-Identification (person re-id) is a crucial task as its applications in visual surveillance and human-computer interaction. In this work, we present a novel joint Spatial and Temporal Attention Pooling Network (ASTPN) for video-based person re-identification, which enables the feature extractor to be aware of the current input video sequences, in a way that interdependency from the matching items can directly influence the computation of each other's representation. Specifically, the spatial pooling layer is able to select regions from each frame, while the attention temporal pooling performed can select informative frames over the sequence, both pooling guided by the information from distance matching. Experiments are conduced on the iLIDS-VID, PRID-2011 and MARS datasets and the results demonstrate that this approach outperforms existing state-of-art methods. We also analyze how the joint pooling in both dimensions can boost the person re-id performance more effectively than using either of them separately.

Latent features learned by deep learning approaches have proven to be a powerful tool for machine learning. They serve as a data abstraction that makes learning easier by capturing regularities in data explicitly. Their benefits motivated their adaptation to relational learning context. In our previous work, we introduce an approach that learns relational latent features by means of clustering instances and their relations. The major drawback of latent representations is that they are often black-box and difficult to interpret. This work addresses these issues and shows that (1) latent features created by clustering are interpretable and capture interesting properties of data; (2) they identify local regions of instances that match well with the label, which partially explains their benefit; and (3) although the number of latent features generated by this approach is large, often many of them are highly redundant and can be removed without hurting performance much.

According to industry estimates, only 21% of the available data is present in structured form. Data is being generated as we speak, as we tweet, as we send messages on Whatsapp and in various other activities. Majority of this data exists in the textual form, which is highly unstructured in nature. Few notorious examples include – tweets / posts on social media, user to user chat conversations, news, blogs and articles, product or services reviews and patient records in the healthcare sector. A few more recent ones includes chatbots and other voice driven bots. Despite having high dimension data, the information present in it is not directly accessible unless it is processed (read and understood) manually or analyzed by an automated system. In order to produce significant and actionable insights from text data, it is important to get acquainted with the techniques and principles of Natural Language Processing (NLP).

Are you a Machine Learning Scientist and do you know of the state-of-the-art tooling in capturing content and translating human annotations to machine models? Are you familiar with deep learning algorithms and solutions? We have the right opportunity for you! In line with the Elsevier corporate strategy of greater content volume, types and sophistication, the services that Elsevier provides are becoming increasingly dependent on Smart Content. We are therefore looking for a Machine Learning Scientist who can focus on designing and creating systems that enable machine learning in the context of article submission systems and other systems where authors or other human agents can enter metadata and other structured data to publications.

In my 24th year of MATLAB and toolbox development and design, I am excited to be tackling this new project. Deep learning refers to a collection of machine learning techniques that are based on neural networks that have a large number of layers (hence "deep"). By training these networks on labeled data sets, they can achieve state-of-the-art accuracy on classification tasks using images, text, and sound as inputs. Because of my background in image processing, I have followed the rapid progress in deep learning over the past several years with great interest. There is much that I would like to learn and share with you about the area, especially with respect to exploring deep learning ideas with MATLAB.

This free BTN event will be hosted by Anton Fishman (HR, OD, capability and culture specialist) and will look into the impact of AI and Deep Learning on organisations and the future of work, as part of our Fourth Industrial Revolution series. Anton Fishman, Director of Fishman & Partners, has 30 years' experience working with businesses to help clarify strategic direction, determine what leaders needs to do to deliver this, and ensure businesses have the talent and capability to make it happen. He has a particular interest in the enhancing the impact and reputation of HR functions and this has been the focus of his work in the last few years. In September 2016, Anton chaired an international conference on the impact of Social Robotics, AI and Deep learning on the future of work and chairs three more conferences on these themes this year. He now runs regular workshops and in-company briefing sessions on this topic in the UK and internationally.