How can we effectively regularize BERT? Although BERT proves its effectiveness in various downstream natural language processing tasks, it often overfits when there are only a small number of training instances. A promising direction to regularize BERT is based on pruning its attention heads based on a proxy score for head importance. However, heuristic-based methods are usually suboptimal since they predetermine the order by which attention heads are pruned. In order to overcome such a limitation, we propose AUBER, an effective regularization method that leverages reinforcement learning to automatically prune attention heads from BERT. Instead of depending on heuristics or rule-based policies, AUBER learns a pruning policy that determines which attention heads should or should not be pruned for regularization. Experimental results show that AUBER outperforms existing pruning methods by achieving up to 10% better accuracy. In addition, our ablation study empirically demonstrates the effectiveness of our design choices for AUBER.

Looked at on a world scale, the Covid-19 pandemic will continue to deliver shocks to global supply chains for some time to come. Even if the public health crisis abates in the UK, our economy is part of a global economy, and UK corporate IT will have its work cut out in supporting companies as they are forced to re-forge supply chains, perhaps over and over again, and at short notice. The crisis has provoked some rethinking of how the world economy ought to work, with an emphasis on the desirability of a shift from efficiency – doing things "just in time" – to resilience – building in more slack. The FT's Rana Faroohar provides an account of such rethinking in an article entitled From'just in time' to'just in case' published earlier this year. In the discussions which lie behind this article there are different emphases on a spectrum of opinion: some say we can have both efficiency and resilience equally, others that there is a choice to be made for one or the other, and yet others say it's a matter of balance, of trading off. Tony Harris, global vice-president of business network solutions at SAP, says it has to be a combination. "You wouldn't want to move to a resilient network or supply chain that wasn't also efficient," he says.

Artificial intelligence and machine learning are fast moving out of the hype status and into the reality status. South African industry recently launched the AI Institute of South Africa, with the objective of consulting with government on AI policy, while also representing South Africa in global AI forums and initiatives. The AI world is faced with many questions. How can we ensure that AI is for good and will not destroy itself and in the process, the world? Do we need global regulations?

In recent years there has been a cascade of research in attempting to make AI systems more interpretable by providing explanations; so-called Explainable AI (XAI). Most of this research has dealt with the challenges that arise in explaining black-box deep learning systems in classification and regression tasks, with a focus on tabular and image data; for example, there is a rich seam of work on post-hoc counterfactual explanations for a variety of black-box classifiers (e.g., when a user is refused a loan, the counterfactual explanation tells the user about the conditions under which they would get the loan). However, less attention has been paid to the parallel interpretability challenges arising in AI systems dealing with time series data. This paper advances a novel technique, called Native-Guide, for the generation of proximal and plausible counterfactual explanations for instance-based time series classification tasks (e.g., where users are provided with alternative time series to explain how a classification might change). The Native-Guide method retrieves and uses native in-sample counterfactuals that already exist in the training data as "guides" for perturbation in time series counterfactual generation. This method can be coupled with both Euclidean and Dynamic Time Warping (DTW) distance measures. After illustrating the technique on a case study involving a climate classification task, we reported on a comprehensive series of experiments on both real-world and synthetic data sets from the UCR archive. These experiments provide computational evidence of the quality of the counterfactual explanations generated.

There are now many meta-learning methods, each focusing on different modeling aspects of base and meta learners, but all can be (re)formulated as specific bilevel optimization problems. This work presents BOML, a modularized optimization library that unifies several meta-learning algorithms into a common bilevel optimization framework. It provides a hierarchical optimization pipeline together with a variety of iteration modules, which can be used to solve the mainstream categories of meta-learning methods, such as meta-feature-based and meta-initialization-based formulations.

Undertaking causal inference with observational data is extremely useful across a wide range of domains including the development of medical treatments, advertisements and marketing, and policy making. There are two main challenges associated with undertaking causal inference using observational data: treatment assignment heterogeneity (i.e., differences between the treated and untreated groups), and an absence of counterfactual data (i.e. not knowing what would have happened if an individual who did get treatment, were instead to have not been treated). We address these two challenges by combining structured inference and targeted learning. To our knowledge, Targeted Variational AutoEncoder (TVAE) is the first method to incorporate targeted learning into deep latent variable models. Results demonstrate competitive and state of the art performance.

The Artificial Intelligence for Edge Devices market study now available with Market Study Report, LLC, is a collation of valuable insights related to market size, market share, profitability margin, growth dynamics and regional proliferation of this business vertical. The study further includes a detailed analysis pertaining to key challenges, growth opportunities and application segments of the Artificial Intelligence for Edge Devices market. The Artificial Intelligence for Edge Devices market report delivers an exhaustive analysis of this industry vertical and comprises of insights pertaining to the market tendencies including profits estimations, periodic deliverables, current revenue, industry share and remuneration estimations over the forecast period. A summary of the performance evaluation of the Artificial Intelligence for Edge Devices market is offered in the report. It also includes crucial information concerning to the key industry trends and projected growth rate of the said market.

A fresh report titled "Artificial Intelligence In IoT Market" conveying key insights and providing a competitive advantage to clients through a comprehensive report. The report contains 123 pages which highly exhibit on up-to-date market analysis scenario, upcoming as well as future opportunities, revenue growth, pricing and profitability. An exclusive data and facts offered in this report is collected by research and industry experts' team. Research Trades proclaims the addition of new analytical data which helps to make informed business decisions. It has been abridged with a exhaustive description of the global Artificial Intelligence In IoT Market including overview, Types, Segments, Applications and Features of the market.

The report on the AI (Artificial Intelligence) Chip industry provides an in-depth assessment of the AI (Artificial Intelligence) Chip market including technological advancements, market drivers, challenges, current and emerging trends, opportunities, threats, risks, strategic developments, product advancements, and other key features. The report covers market size estimation, share, growth rate, global position, and regional analysis of the market. The report also covers forecast estimations for investments in the AI (Artificial Intelligence) Chip industry from 2020 to 2027. The report is furnished with the latest market dynamics and economic scenario in regards to the COVID-19 pandemic. The pandemic has brought about drastic changes in the economy of the world and has affected several key segments and growth opportunities.

The Vatican's Pontifical Academy for Life, which began the year by urging the ethical development and application of artificial intelligence (AI), has announced an effort to use technology to fight world hunger, which has worsened during the pandemic. The Vatican institution, in collaboration with IBM, Microsoft and the UN Food and Agriculture Organization, or FAO, is encouraging governments, nonprofits and corporations to assure that technology is used to feed everyone, and to make farmers' lives more efficient and productive. In its quest to assure the transparent, responsible and inclusive use of AI, the Vatican and FAO are pushing for solutions in agriculture that will benefit not just the well off, but also the poor. "We need to face the biggest challenges on the planet," said John E. Kelly III, executive vice president of IBM. Kelly, who participated in the FAO and Pontifical Academy's Sept. 24 virtual conference announcing the effort against hunger, was one of the signers of the Vatican's call for AI ethics in February. The Vatican's effort to promote ethical AI for social good includes a new program to use digital technology to ensure a more sustainable and efficient global food supply.