Machine Learning (ML) provides important techniques for classification and predictions. Most of these are black-box models for users and do not provide decision-makers with an explanation. For the sake of transparency or more validity of decisions, the need to develop explainable/interpretable ML-methods is gaining more and more importance. Certain questions need to be addressed: How does an ML procedure derive the class for a particular entity? Why does a particular clustering emerge from a particular unsupervised ML procedure? What can we do if the number of attributes is very large? What are the possible reasons for the mistakes for concrete cases and models? For binary attributes, Formal Concept Analysis (FCA) offers techniques in terms of intents of formal concepts, and thus provides plausible reasons for model prediction. However, from the interpretable machine learning viewpoint, we still need to provide decision-makers with the importance of individual attributes to the classification of a particular object, which may facilitate explanations by experts in various domains with high-cost errors like medicine or finance. We discuss how notions from cooperative game theory can be used to assess the contribution of individual attributes in classification and clustering processes in concept-based machine learning. To address the 3rd question, we present some ideas on how to reduce the number of attributes using similarities in large contexts.

In a February 19 speech at the Munich Security Conference, delivered virtually from the White House, President Joe Biden declared, "We must shape the rules that will govern the advance of technologies and the norms of behavior in cyberspace, artificial intelligence, biotechnology, so they are used to lift people up, not used to pin them down." A few weeks earlier, during an address at the State Department's Truman Building, the president said, "Diplomacy is back at the center of our foreign policy." The Trump administration's undermining of years of work on internet diplomacy makes technology an ever more vital (and challenging) element of renewed US engagement abroad. Digital issues are no longer extricable from "traditional" foreign policy issues across trade, human rights, and security. And as the new White House starts to navigate these waters, one idea in particular has become a sort of bumper sticker for an overarching strategy: Unite democracies on technology. As the Chinese and Russian governments become more technologically assertive and undermine human rights, and as democracies grapple with how to appropriately implement rules and regulations for the likes of artificial intelligence systems, this work is essential.

Using artificial intelligence, Dutch photographer Bas Uterwijk released on Friday modern images of the Egyptian King Akhenaten (Amenhotep IV) and Queen Nefertiti. The pictures, which Uterwijk posted on his Twitter page, are based on old carvings and engravings that portrayed the two. The artist said in his post: "I don't claim to be a scientist. The historical portraits I make are based on artworks mostly made during the period of their subjects. With AI I filter out the sculpting styles of ancient portraiture and guide it to a credible outcome."

Every day, digital advertisement agencies serve billions of ads on news websites, search engines, social media networks, video streaming websites, and other platforms. And they all want to answer the same question: Which of the many ads they have in their catalog is more likely to appeal to a certain viewer? Finding the right answer to this question can have a huge impact on revenue when you are dealing with hundreds of websites, thousands of ads, and millions of visitors. Fortunately (for the ad agencies, at least), reinforcement learning (RL), the branch of artificial intelligence that has become renowned for mastering board and video games, provides a solution. Reinforcement learning models seek to maximize rewards.

Reduced-order dynamical models play a central role in developing our understanding of predictability of climate irrespective of whether we are dealing with the actual climate system or surrogate climate-models. In this context, the Linear-Inverse-Modeling (LIM) approach, by capturing a few essential interactions between dynamical components of the full system, has proven valuable in providing insights into predictability of the full system. We demonstrate that Reservoir Computing (RC), a form of learning suitable for systems with chaotic dynamics, provides an alternative nonlinear approach that improves on the predictive skill of the LIM approach. We do this in the example setting of predicting sea-surface-temperature in the North Atlantic in the pre-industrial control simulation of a popular earth system model, the Community-Earth-System-Model so that we can compare the performance of the new RC based approach with the traditional LIM approach both when learning data is plentiful and when such data is more limited. The improved predictive skill of the RC approach over a wide range of conditions -- larger number of retained EOF coefficients, extending well into the limited data regime, etc. -- suggests that this machine-learning technique may have a use in climate predictability studies. While the possibility of developing a climate emulator -- the ability to continue the evolution of the system on the attractor long after failing to be able to track the reference trajectory -- is demonstrated in the Lorenz-63 system, it is suggested that further development of the RC approach may permit such uses of the new approach in more realistic predictability studies.

Complex systems fail. I argue that failures can be a blueprint characterizing living organisms and biological intelligence, a control mechanism to increase complexity in evolutionary simulations, and an alternative to classical fitness optimization. Imitating biological successes in Artificial Life and Artificial Intelligence can be misleading; imitating failures offers a path towards understanding and emulating life it in artificial systems.

Speakers in tonight's Society, Robots and Us at 6pm PST Tuesday Feb 23 include Henry Evans, mute quadriplegic and founder of Robots4Humanity and Aaron Edsinger, founder of Hello Robot. We'll also being talking about robots for people with disabilities with Disability Advocate Adriana Mallozi, founder of Puffin Innovations and Daniel Seita, who is a deaf roboticist. The event is free and open to the public. As a result of a sudden stroke, Henry Evans turned from being a Silicon Valley tech builder into searching for technologies and robots that would improve his life, and the life of his family and caregivers, as the founder of Robots4Humanity. Since then Henry has shaved himself with the help of the PR2 robot, and spoken on the TED stage with Chad Jenkins in a Suitable Tech Beam.

AI researchers say data sharing is a key part of economic growth in Africa but that it faces a number of common obstacles, including the threat of data colonialism. The African data market is expected to grow steadily in the coming years, and the African Data Centre trade organization predicts the African data market will need hundreds of new datacenters to meet demand in the coming decade. In a paper titled "Narratives and Counternarratives on Data Sharing in Africa," the research team lays out structural problems including but limited to financial or infrastructure problems. Coauthors argue that failure to consider ethical concerns associated with those obstacles could cause irreparable harm. "Currently, a significant proportion of Africa's digital infrastructure is controlled by Western technology powers, such as Amazon, Google, Facebook, and Uber," the paper reads.

For all their simplicity, viruses are sneaky little life forces. Take SARS-Cov-2, the virus behind Covid-19. Challenged with the human immune system, the virus has gradually reshuffled parts of its genetic material, making it easier to spread among a human population. The new strain has already terrorized South Africa and shut down the UK, and recently popped up in the United States. The silver lining is that our existing vaccines and antibody therapies are still likely to be effective against the new strain. "Viral escape" is a nightmare scenario, in which the virus mutates just enough so that existing antibodies no longer recognize it.

Logic-based argumentation is a well-established formalism modelling nonmonotonic reasoning. It has been playing a major role in AI for decades, now. Informally, a set of formulas is the support for a given claim if it is consistent, subset-minimal, and implies the claim. In such a case, the pair of the support and the claim together is called an argument. In this paper, we study the propositional variants of the following three computational tasks studied in argumentation: ARG (exists a support for a given claim with respect to a given set of formulas), ARG-Check (is a given set a support for a given claim), and ARG-Rel (similarly as ARG plus requiring an additionally given formula to be contained in the support). ARG-Check is complete for the complexity class DP, and the other two problems are known to be complete for the second level of the polynomial hierarchy (Parson et al., J. Log. Comput., 2003) and, accordingly, are highly intractable. Analyzing the reason for this intractability, we perform a two-dimensional classification: first, we consider all possible propositional fragments of the problem within Schaefer's framework (STOC 1978), and then study different parameterizations for each of the fragment. We identify a list of reasonable structural parameters (size of the claim, support, knowledge-base) that are connected to the aforementioned decision problems. Eventually, we thoroughly draw a fine border of parameterized intractability for each of the problems showing where the problems are fixed-parameter tractable and when this exactly stops. Surprisingly, several cases are of very high intractability (paraNP and beyond).