Note: This blog post was authored by Aaron Baughman with Stephen Hammer, Eythan Holladay, Eduardo Morales and Gary Reiss. Wimbledon is one of the most prestigious major events in the world. With over 675 matches played and over 147,000 tennis points played, its size and scale are substantial. In fact, even if fans diligently watch their favorite players, they will miss a high proportion of the played points. Wimbledon uses IBM digital and AI capabilities to provide rapid access to match highlights to serve up the best content to fans.

Artificial intelligence (AI) programs have bested humans in checkers, chess, go and two-player poker, but multiplayer poker was always believed to be a bigger ask. Researchers at Carnegie Mellon University, working with Facebook's AI initiative, on Thursday announced that their program defeated a group of top professionals in six-player no-limit Texas Hold'em. The program, Pluribus, and its big wins were described in the US journal Science. "Pluribus achieved superhuman performance at multiplayer poker, which is a recognized milestone in artificial intelligence and in game theory," Carnegie Mellon computer science professor Tuomas Sandholm said. Sandholm worked with Noam Brown, who is working at Facebook AI while completing his doctorate at the Pittsburgh-based university.

When it comes to longevity, it's helpful to think of your personal expiration date as something more than just the day you'll die. Religion and spirituality have a variety of takes on mortality, as does the rapidly progressing world of science. A common medical view is that there are two types of age: your chronological age and your biological age. Chronologically, the number of years you lived is considered your age, and when you pass away, that total becomes your age of death. It's simple math, and the averages are used to determine a generalized sense of life expectancy for massive populations.

UNESCO has published the Beijing Consensus on Artificial Intelligence (AI) and Education, the first ever document to offer guidance and recommendations on how best to harness AI technologies for achieving the Education 2030 Agenda. It was adopted during the International Conference on Artificial Intelligence and Education, held in Beijing from 16 – 18 May 2019, by over 50 government ministers, international representatives from over 105 Member States and almost 100 representatives from UN agencies, academic institutions, civil society and the private sector. The Beijing Consensus comes after the Qingdao Declaration of 2015, in which UNESCO Member States committed to efficiently harness emerging technologies for the achievement of SDG 4. Ms Stefania Giannini, Assistant Director-General for Education at UNESCO, stated that ''we need to renew this commitment as we move towards an era in which artificial intelligence – a convergence of emerging technologies – is transforming every aspect of our lives (…) we need to steer this revolution in the right direction, to improve livelihoods, to reduce inequalities and promote a fair and inclusive globalization.'' The Consensus affirms that the deployment of AI technologies in education should be purposed to enhance human capacities and to protect human rights for effective human-machine collaboration in life, learning and work, and for sustainable development. The Consensus states that the systematic integration of AI in education has the potential to address some of the biggest challenges in education today, innovate teaching and learning practices, and ultimately accelerate the progress towards SDG 4. In summary, the Beijing Consensus recommends governments and other stakeholders in UNESCO's Member States to: The Consensus also details its ambitions for UNESCO to act as a support system for the capacity building of education policy-makers to implement the recommended measures, and to act as a convener for financing, partnership and international cooperation together with other international organizations and partners active in the field of AI in education.

Apple Park, the corporate HQ of Apple Inc., located in California. Digitalization is evolving from an economic challenge to a governance and political problem. Some studies suggest that by 2030, Artificial Intelligence (AI) might contribute up to EUR 13.33 trillion to the global economy (more than the current output of China and India combined). The essence of the political conflict that raises the issue of global governance is what type of actor (a state or a digital corporation) will lead this process, creating global asymmetry in terms of trade, information flows, social structures and political power. This means challenging the international system as we know it. AI is generating new large-scale systems based on (1) services (such as traffic management and smart vehicles, international banking systems, and new healthcare ecosystems); (2) global value chains, the Internet of things (IoT) and robotics (Industry 4.0); and (3) electronics with a new generation of microprocessors and highly specialized chips.

Mixtures-of-Experts (MoE) are conditional mixture models that have shown their performance in modeling heterogeneity in data in many statistical learning approaches for prediction, including regression and classification, as well as for clustering. Their estimation in high-dimensional problems is still however challenging. We consider the problem of parameter estimation and feature selection in MoE models with different generalized linear experts models, and propose a regularized maximum likelihood estimation that efficiently encourages sparse solutions for heterogeneous data with high-dimensional predictors. The developed proximal-Newton EM algorithm includes proximal Newton-type procedures to update the model parameter by monotonically maximizing the objective function and allows to perform efficient estimation and feature selection. An experimental study shows the good performance of the algorithms in terms of recovering the actual sparse solutions, parameter estimation, and clustering of heterogeneous regression data, compared to the main state-of-the art competitors.

As a type of pseudoinverse learning, extreme learning machine (ELM) is able to achieve high performances in a rapid pace on benchmark datasets. However, when it is applied to real life large data, decline related to low-convergence of singular value decomposition (SVD) method occurs. Our study aims to resolve this issue via replacing SVD with theoretically and empirically much efficient 5 number of methods: lower upper triangularization, Hessenberg decomposition, Schur decomposition, modified Gram Schmidt algorithm and Householder reflection. Comparisons were made on electroencephalography based brain-computer interface classification problem to decide which method is the most useful. Results of subject-based classifications suggested that if priority was given to training pace, Hessenberg decomposition method, whereas if priority was given to performances Householder reflection method should be preferred.

The use of machine learning systems to support decision making in healthcare raises questions as to what extent these systems may introduce or exacerbate disparities in care for historically underrepresented and mistreated groups, due to biases implicitly embedded in observational data in electronic health records. To address this problem in the context of clinical risk prediction models, we develop an augmented counterfactual fairness criteria to extend the group fairness criteria of equalized odds to an individual level. We do so by requiring that the same prediction be made for a patient, and a counterfactual patient resulting from changing a sensitive attribute, if the factual and counterfactual outcomes do not differ. We investigate the extent to which the augmented counterfactual fairness criteria may be applied to develop fair models for prolonged inpatient length of stay and mortality with observational electronic health records data. As the fairness criteria is ill-defined without knowledge of the data generating process, we use a variational autoencoder to perform counterfactual inference in the context of an assumed causal graph. While our technique provides a means to trade off maintenance of fairness with reduction in predictive performance in the context of a learned generative model, further work is needed to assess the generality of this approach.

Improving the performance of classifiers is the realm of prototype selection and kernel transformations. Prototype selection has been used to reduce the space complexity of k-Nearest Neighbors classifiers and to improve its accuracy, and kernel transformations enhanced the performance of linear classifiers by converting a non-linear separable problem into a linear one in the transformed space. Our proposal combines, in a model selection scheme, these transformations with classic algorithms such as Na\"ive Bayes and k-Nearest Neighbors to produce a competitive classifier. We analyzed our approach on different classification problems and compared it to state-of-the-art classifiers. The results show that the methodology proposed is competitive, obtaining the lowest rank among the classifiers being compared.

One of the questions that arises when designing models that learn to solve multiple tasks simultaneously is how much of the available training budget should be devoted to each individual task. We refer to any formalized approach to addressing this problem (learned or otherwise) as a task selection policy. In this work we provide an empirical evaluation of the performance of some common task selection policies in a synthetic bandit-style setting, as well as on the GLUE benchmark for natural language understanding. We connect task selection policy learning to existing work on automated curriculum learning and off-policy evaluation, and suggest a method based on counterfactual estimation that leads to improved model performance in our experimental settings.