We introduce a Bayesian nonparametric regression model for data with multiway (tensor) structure, motivated by an application to periodontal disease (PD) data. Our outcome is the number of diseased sites measured over four different tooth types for each subject, with subject-specific covariates available as predictors. The outcomes are not well-characterized by simple parametric models, so we use a nonparametric approach with a binomial likelihood wherein the latent probabilities are drawn from a mixture with an arbitrary number of components, analogous to a Dirichlet Process (DP). We use a flexible probit stick-breaking formulation for the component weights that allows for covariate dependence and clustering structure in the outcomes. The parameter space for this model is large and multiway: patients $\times$ tooth types $\times$ covariates $\times$ components. We reduce its effective dimensionality, and account for the multiway structure, via low-rank assumptions. We illustrate how this can improve performance, and simplify interpretation, while still providing sufficient flexibility. We describe a general and efficient Gibbs sampling algorithm for posterior computation. The resulting fit to the PD data outperforms competitors, and is interpretable and well-calibrated. An interactive visual of the predictive model is available at http://ericfrazerlock.com/toothdata/ToothDisplay.html , and the code is available at https://github.com/lockEF/NonparametricMultiway .

Recurrent Neural Networks (RNNs) are very successful at solving challenging problems with sequential data. However, this observed efficiency is not yet entirely explained by theory. It is known that a certain class of multiplicative RNNs enjoys the property of depth efficiency --- a shallow network of exponentially large width is necessary to realize the same score function as computed by such an RNN. Such networks, however, are not very often applied to real life tasks. In this work, we attempt to reduce the gap between theory and practice by extending the theoretical analysis to RNNs which employ various nonlinearities, such as Rectified Linear Unit (ReLU), and show that they also benefit from properties of universality and depth efficiency. Our theoretical results are verified by a series of extensive computational experiments.

This week I was invited to speak at the Oxford Union in England on the topic: AI: An inhumane future? In this panel there was Kenneth Cukier, a senior editor of The Economist, D Catherine Havasi, the Massachusetts Institute of Technology (MIT) scientist and Professor Sir Adrian Smith, the director of The Alan Turing Institute. As I was preparing for this talk, I was reminded of the famous British author Charles Dickens who in his classic novel, A Tale of Two Cities, famously characterised the French Revolution as follows: "It was the best of times, it was the worst of times, it was the age of wisdom, it was the age of foolishness, it was the epoch of belief, it was the epoch of incredulity, it was the season of Light, it was the season of Darkness, it was the spring of hope, it was the winter of despair." Artificial Intelligence (AI) is catalysing another revolution and not the French one…but the fourth industrial revolution (4IR). While the French revolution ushered new improved ways of human relations, it also ushered the cruelty of political factions.

Climate change is the most horrific threat our species has ever known: No matter how powerful you are or how much money you have, our transforming planet is a reckoning for every one of us. But there are degrees to this misery. If you're perched in a Manhattan penthouse, the effects might not be immediately apparent (because you don't care or aren't paying attention, or both). If you're a subsistence farmer in Kenya, the situation is already much more dire. There's an equalizer, though, that is helping small farmers adapt to a changing planet: smartphones.

On the occasion of Data Protection Day on 28 January, the Committee of the Council of Europe's data protection treaty "Convention 108" has published Guidelines on Artificial Intelligence and Data Protection. The guidelines aim to assist policy makers, artificial intelligence (AI) developers, manufacturers and service providers in ensuring that AI applications do not undermine the right to data protection. The Convention's Committee underlines that the protection of human rights, including the right to protection of personal data, should be an essential pre-requisite when developing or adopting AI applications, in particular when they are used in decision-making processes, and be based on the principles of the updated data protection convention, "Convention 108, opened for signature in October 2018. In addition, any innovation in the field of AI should pay close attention to avoiding and mitigating the potential risks of processing of personal data, and allow meaningful control by data subjects over the data processing and its effects. Minister for Foreign Affairs of Finland and Chair of the Committee of Ministers of the Council of Europe Timo Soini welcomed the adoption of the guidelines and said: "Artificial intelligence brings benefits to our daily lives.

TL;DR: I had a pleasure to be a part of the first AI conference in Somaliland, organised by Shaqodoon, HarHub and Elmi Academy, and featuring speakers from Google, MIT, major Somaliland telecoms, banks, University of Hargeisa and Ministry of Telecommunication & Technology of Somaliland. See slides (lectures workshops) and event program for details. Below are my personal notes and pictures from this trip. Whenever I tell this story people seem to be surprised with choice of spending vacation time in Somaliland and running an AI-related event there. So let me share some first-hand experience with you and explain why trips and events like this are useful, fun and safe.

Approval ballot based committee formation is concerned with aggregating individual approvals of voters. Voters submit their approvals of candidates and these approvals are aggregated to arrive at the optimal committee of specified size. There are several aggregation techniques proposed in the literature and these techniques differ among themselves on the criterion function they optimize. Voters preferences for a candidate is based on his/her opinion on candidate suitability. We note that candidates have attributes that make him/her suitable or otherwise. Hence, it is relevant to approve attributes and select candidates who have the approved attributes. This paper addresses the committee selection problem when voters submit their approvals on attributes. Though attribute based preference is addressed in several contexts, committee selection problem with attribute approval has not been attempted earlier. We note that extending the theory of candidate approval to attribute approval in committee selection problem is not trivial. In this paper, we study different aspects of this problem and show that none of the existing aggregation rules satisfies Unanimity and Justified Representation when attribute based approvals are considered. We propose a new aggregation rule that satisfies both the above properties. We also present other analysis of committee selection problem with attribute approval.

Knowledge graphs are structured representations of real world facts. However, they typically contain only a small subset of all possible facts. Link prediction is a task of inferring missing facts based on existing ones. We propose TuckER, a relatively simple but powerful linear model based on Tucker decomposition of the binary tensor representation of knowledge graph triples. TuckER outperforms all previous state-of-the-art models across standard link prediction datasets. We prove that TuckER is a fully expressive model, deriving the bound on its entity and relation embedding dimensionality for full expressiveness which is several orders of magnitude smaller than the bound of previous state-of-the-art models ComplEx and SimplE. We further show that several previously introduced linear models can be viewed as special cases of TuckER.

As machine learning models become more accurate, they typically become more complex and uninterpretable by humans. The black-box character of these models holds back its acceptance in practice, especially in high-risk domains where the consequences of failure could be catastrophic such as health-care or defense. Providing understandable and useful explanations behind ML models or predictions can increase the trust of the user. Example-based reasoning, which entails leveraging previous experience with analogous tasks to make a decision, is a well known strategy for problem solving and justification. This work presents a new explanation extraction method called LEAFAGE, for a prediction made by any black-box ML model. The explanation consists of the visualization of similar examples from the training set and the importance of each feature. Moreover, these explanations are contrastive which aims to take the expectations of the user into account. LEAFAGE is evaluated in terms of fidelity to the underlying black-box model and usefulness to the user. The results showed that LEAFAGE performs overall better than the current state-of-the-art method LIME in terms of fidelity, on ML models with non-linear decision boundary. A user-study was conducted which focused on revealing the differences between example-based and feature importance-based explanations. It showed that example-based explanations performed significantly better than feature importance-based explanation, in terms of perceived transparency, information sufficiency, competence and confidence. Counter-intuitively, when the gained knowledge of the participants was tested, it showed that they learned less about the black-box model after seeing a feature importance-based explanation than seeing no explanation at all. The participants found feature importance-based explanation vague and hard to generalize it to other instances.

The sequencing of ancient Neanderthal and Denisovan fossils supported introgression events into anatomically modern humans (AMH) (out of Africa). However, recent studies also support the presence of gene flow from AMH into Neanderthals, thus suggesting a complex hominin evolution. All modern humans are genetically related to each other at a time depth of up to 300 thousand years ago and share a common African root. The migratory routes used by AMH after the African diaspora and aspects of the interbreeding between AMH and presently extinct hominins living at the time in Eurasia (here referred as Eurasian Extinct Hominins, EEH) are still under debate. Recently, for the first time, deep learning has been successfully used to explain human history, paving the way for this technology to be applied in other questions in medicine, genomics, and evolution.