On 11 April 2019, Daniel Fiott was invited by the EU's Political and Security Committee (PSC) to participate in a lunch debate on Artificial intelligence (AI). The event was part of the PSC's initiative to enhance dialogue with think tanks, NGOS and academia on key challenges for EU foreign, security and defence policy. The event brought together PSC Ambassadors, as well as representatives from the European Commission and the European External Action Service. Daniel joined experts from the Centre for the Study of Existential Risk (CSAR) at the University of Cambridge and Tilburg University, and he outlined recent AI developments and implications for the defence sector, with a particular focus on the EU and AI developments in Russia, China and the United States. The legal challenges and ethical dilemmas of AI were also discussed.

The creative spark is one of humanity's defining features. But increasingly powerful artificial intelligence (AI) is bringing innovation and automation together to create new tools for invention. Eureka moments tend to come from the rare convergence of ideas from diverse fields, says Julian Nolan, chief executive of Iprova. However, growing specialisation among experts means this is becoming even rarer, he adds, often relying on chance meetings at conferences or a fortuitous conversation at the office coffee machine. "How crazy is it that something so key to corporations is based on human serendipity?" says Mr Nolan.

Artificial intelligence and machine learning (AI/ML) are increasingly transforming the healthcare sector. From spotting malignant tumours to reading CT scans and mammograms, AI/ML-based technology is faster and more accurate than traditional devices - or even the best doctors. But along with the benefits come new risks and regulatory challenges. In their latest article Algorithms on regulatory lockdown in medicine recently published in Science, Boris Babic, INSEAD Assistant Professor of Decision Sciences; Theodoros Evgeniou, INSEAD Professor of Decision Sciences and Technology Management; Sara Gerke, Research Fellow at Harvard Law School's Petrie-Flom Center for Health Law Policy, Biotechnology, and Bioethics; and I. Glenn Cohen, Professor at Harvard Law School and Faculty Director at the Petrie-Flom Center look at the new challenges facing regulators as they navigate the unfamiliar pathways of AI/ML. They consider the questions: What new risks do we face as AI/ML devices are developed and implemented?

The personnel changes at Alphabet continue, this time with Mustafa Suleyman -- one of the three co-founders of the company's influential AI lab DeepMind -- moving to Google. Suleyman announced the news on Twitter, saying that after a "wonderful decade" at DeepMind, he would be joining Google to work with the company's head of AI Jeff Dean and its chief legal officer Kent Walker. The exact details of Suleyman's new role are unclear but a representative for the company told The Verge it would involve work on AI policy. The move is notable, though, as it was reported earlier this year that Suleyman had been placed on leave from DeepMind. Some speculated that Suleyman's move was the fallout of reported tensions between DeepMind and Google, as the former struggled to commercialize its technology. Although DeepMind has achieved a number of research milestones in the AI world, most notably the success of its AlphaGo program in 2016, the lab has also recorded significant financial losses.

Serial cybersecurity entrepreneur Shlomo Kramer said in a 2005 interview that cybersecurity is "a bit like Alice in Wonderland" where you run as fast as you can only to stay in place. In 2020, to paraphrase the second part of the Red Queen's observation (actually from Through the Looking Glass), if you wish to stay ahead of cyber criminals, you must run twice--or ten times--as fast as that. The 141 predictions listed here reveal the state-of-mind of key participants in the cybersecurity defense industry and highlight all that's hot today. The future is murky, but we know for sure that on January 1, 2020, the California Consumer Privacy Act (CCPA) will go into effect; that the U.S. presidential election will take place on November 3, 2020; and that on October 1, 2020, if you "wish to fly on commercial aircrafts or access federal facilities" in the U.S., you must have a REAL ID compliant card. Other than these known events, the crystal balls of the participants in this survey warn us ...

With the incorporation of new data gathering methods in clinical research, it becomes fundamental for survival analysis techniques to deal with high-dimensional or/and non-standard covariates. In this paper we introduce a general non-parametric independence test between right-censored survival times and covariates taking values on a general (not necessarily Euclidean) space $\mathcal{X}$. We show that our test statistic has a dual interpretation, first in terms of the supremum of a potentially infinite collection of weight-indexed log-rank tests, with weight functions belonging to a reproducing kernel Hilbert space (RKHS) of functions; and second, as the norm of the difference of embeddings of certain finite measures into the RKHS, similar to the Hilbert-Schmidt Independence Criterion (HSIC) test-statistic. We study the asymptotic properties of the test, finding sufficient conditions to ensure that our test is omnibus. The test statistic can be computed straightforwardly, and the rejection threshold is obtained via an asymptotically consistent Wild-Bootstrap procedure. We perform extensive simulations demonstrating that our testing procedure generally performs better than competing approaches in detecting complex nonlinear dependence.

There have been numerous attempts in explaining the general learning behaviours using model-based and model-free methods. While the model-based control is flexible yet computationally expensive in planning, the model-free control is quick but inflexible. The model-based control is therefore immune from reward devaluation and contingency degradation. Multiple arbitration schemes have been suggested to achieve the data efficiency and computational efficiency of model-based and model-free control respectively. In this context, we propose a quantitative 'value of information' based arbitration between both the controllers in order to establish a general computational framework for skill learning. The interacting model-based and model-free reinforcement learning processes are arbitrated using an uncertainty-based value of information. We further show that our algorithm performs better than Q-learning as well as Q-learning with experience replay.

Once users have shared their data online, it is generally difficult for them to revoke access and ask for the data to be deleted. Machine learning (ML) exacerbates this problem because any model trained with said data may have memorized it, putting users at risk of a successful privacy attack exposing their information. Yet, having models unlearn is notoriously difficult. After a data point is removed from a training set, one often resorts to entirely retraining downstream models from scratch. We introduce SISA training, a framework that decreases the number of model parameters affected by an unlearning request and caches intermediate outputs of the training algorithm to limit the number of model updates that need to be computed to have these parameters unlearn. This framework reduces the computational overhead associated with unlearning, even in the worst-case setting where unlearning requests are made uniformly across the training set. In some cases, we may have a prior on the distribution of unlearning requests that will be issued by users. We may take this prior into account to partition and order data accordingly and further decrease overhead from unlearning. Our evaluation spans two datasets from different application domains, with corresponding motivations for unlearning. Under no distributional assumptions, we observe that SISA training improves unlearning for the Purchase dataset by 3.13x, and 1.658x for the SVHN dataset, over retraining from scratch. We also validate how knowledge of the unlearning distribution provides further improvements in retraining time by simulating a scenario where we model unlearning requests that come from users of a commercial product that is available in countries with varying sensitivity to privacy. Our work contributes to practical data governance in machine learning.

In this paper, we (1) argue that the international human rights framework provides the most promising set of standards for ensuring that AI systems are ethical in their design, development and deployment, and (2) sketch the basic contours of a comprehensive governance framework, which we call'human rights-centred design, deliberation and oversight', for ensuring that AI can be relied upon to operate in ways that will not violate human rights.

I group analytics, machine learning and other advanced, systematized technologies under the umbrella of artificial intelligence. To borrow an indelible Arthur C. Clarke quote and apply it here, "Any sufficiently advanced analytics system is indistinguishable from artificial intelligence." All the so-called AI tools we use in eDiscovery are simply advanced analytics, and generally lawyers tend to be comfortable with how analytics work. After all, email threading, concept searching, and clustering have been part of the legal industry for years, and the technology works well. With vast amounts of data, the eDiscovery industry, particularly in Canada, has lived at the forefront of the utilization of advanced technologies.