UK Health Secretary Sajid Javid has greenlit a series of AI-based projects that aim to tackle racial inequalities in the NHS. Racial inequality continues to be rampant in healthcare. Examining the fallout of COVID-19 serves as yet another example of the disparity between ethnicities. In England and Wales, males of Black African ethnic background had the highest rate of death involving COVID-19, 2.7 times higher than males of a White ethnic background. Females of Black Caribbean ethnic background had the highest rate, 2.0 times higher than females of White ethnic background.

'Gutfeld!' panel weighs in on China's response to the statement This is a rush transcript of "Gutfeld" on October 22, 2021. This copy may not be in its final form and may be updated. Bad things are happening, but it's OK because we're all in this together. What did we get from Joe? An incoherent jumble of memories and confused looks. What the hell was that? JOE BIDEN, PRESIDENT OF THE UNITED STATES: Forty percent of all products coming into the United States of America on the West Coast go through Los Angeles and -- what am I doing here? COOPER: Do you have plans to visit the southern border? BIDEN: I've been there before and I haven't -- I mean, I know it well. I guess I should go down. But what you see is wages are actually up. I have the freedom to kill you. My guess is you'll start to see gas prices come down as we get by -- and going into the winter. I mean, excuse me, and then next year in 2022. I must tell you, I don't have a near-term answer. Well, that was the opposite of comforting. It seems his only strategy is to deflect from our current misery to promising more misery. Angelo Negri was from memory ranch. And she came up to me one day when I was -- when they just had announced that I had flown one million some X number of miles on Air Force aircraft. And asked, she comes up and I'm getting in the car and he goes, Joey baby, what do you do?

The Council of Europe and the Hungarian presidency of its Committee of Ministers are holding an online international conference on 26 October to discuss the challenges governments face to regulate artificial intelligence (AI) in a coordinated manner. Under the theme "Current and Future Challenges of Coordinated Policies on AI Regulation", the event will showcase various AI governance models and examine the interplay between national policies and the work of the Council of Europe and other organisations. One of the main contributions of the Council of Europe in this field is the work of the intergovernmental AI expert body CAHAI, which is examining the development of an international legal framework for the development, design and application of artificial intelligence based on the Council of Europe's standards on human rights, democracy and the rule. Representatives of international organisations, national policy experts, IT companies, civil society and academia will discuss the way to improve AI policymaking at the global, regional and national level. They will also examine case studies on best practices of AI governance and discuss issues such as the possible long-term societal effects of AI and the sustainable development of AI applications.

This article is an on-site version of the #techAsia newsletter.sign up here Send newsletter directly to your inbox every Wednesday Hello, Kenji from Tokyo this week is currently undergoing home quarantine for Covid-19. For our big story, there is another scoop about Apple from Nikkei Asia. China's state-owned enterprise has become a supplier of the latest flagship iPhone displays. This shows how advanced China's technology, including artificial intelligence, has advanced, as warned by a former Pentagon chief software officer (Mercedes Top 10). Meanwhile, China is building and diversifying its sources of strategic mineral resources, including lithium, a key component of the world's leading electric vehicle industry (our views, smart data and spotlights).

With new opportunities, risks, and threats to prosperity and security at stake, the promise and peril associated with this foundational technology are too vast for any single actor to manage alone. As a result, cooperation is inherently needed to equally mitigate international security risks, as well as to capitalise on the technology's potential to transform enterprise functions, mission support, and operations. The continued ability of the Alliance to deter and defend against any potential adversary and to respond effectively to emerging crises will hinge on its ability to maintain its technological edge. Militarily, futureproofing the comparative advantage of Allied forces will depend on a common policy basis and digital backbone to ensure interoperability and accordance with international law. With the fusion of human, information, and physical elements increasingly determining decisive advantage in the battlespace, interoperability becomes all the more essential.

On Thursday (21 October 2021), NATO Defence Ministers agreed to NATO's first-ever strategy for Artificial Intelligence (AI). The strategy outlines how AI can be applied to defence and security in a protected and ethical way. As such, it sets standards of responsible use of AI technologies, in accordance with international law and NATO's values. It also addresses the threats posed by the use of AI by adversaries and how to establish trusted cooperation with the innovation community on AI. Artificial Intelligence is one of the seven technological areas which NATO Allies have prioritized for their relevance to defence and security.

THE NEW HORIZONS SPACE PROBE carried 30 grams of Clyde Tombaugh's ashes to commemorate Tombaugh's discovery of Pluto in 1930. The package is no different in its information content than 30 grams of cigarette ashes, since Tombaugh's unique genetic information was burnt up and never loaded on board. A proper celebration of Tombaugh's historic contribution could have taken the form of an electronic record of his DNA. The Principal Investigator of the mission, Alan Stern, told me that sending a stem cell with Tombaugh's genetic making would have triggered a bureaucratic nightmare at NASA. The challenge is even bigger for launching a complete human being in the form of an astronaut to a journey in space that lasts more than a few years.

The symptoms and side effects of Covid-19 are scattered across a diagnostic spectrum. Some patients are asymptomatic or experience a mild immune response, while others report significant long-term illnesses, lasting complications, or suffer fatal outcomes. Three researchers from the Georgia Institute of Technology and one from Emory University are trying to help clinicians sort through these factors and spectrum of patient outcomes by equipping healthcare professionals with a new "decision prioritization tool." The team's new artificial intelligence-based tool helps clinicians understand and better predict which adverse effects their Covid-19 patients could experience, based on comorbidities and current side effects -; and, in turn, also helps suggest specific Food and Drug Administration-approved (FDA) drugs that could help treat the disease and improve patient health outcomes. The researcher's latest findings are the focus of a new study published October 21 in Scientific Reports. The team's new methodology, or tool, is called MOATAI-VIR (Mode Of Action proteins & Targeted therapeutic discovery driven by Artificial Intelligence for VIRuses.

Can artificially intelligent systems follow rules? The answer might seem an obvious `yes', in the sense that all (current) AI strictly acts in accordance with programming code constructed from highly formalized and well-defined rulesets. But here I refer to the kinds of rules expressed in human language that are the basis of laws, regulations, codes of conduct, ethical guidelines, and so on. The ability to follow such rules, and to reason about them, is not nearly as clear-cut as it seems on first analysis. Real-world rules are unavoidably rife with open-textured terms, which imbue rules with a possibly infinite set of possible interpretations. Narrowing down this set requires a complex reasoning process that is not yet within the scope of contemporary AI. This poses a serious problem for autonomous AI: If one cannot reason about open-textured terms, then one cannot reason about (or in accordance with) real-world rules. And if one cannot reason about real-world rules, then one cannot: follow human laws, comply with regulations, act in accordance with written agreements, or even obey mission-specific commands that are anything more than trivial. But before tackling these problems, we must first answer a more fundamental question: Given an open-textured rule, what is its correct interpretation? Or more precisely: How should our artificially intelligent systems determine which interpretation to consider correct? In this essay, I defend the following answer: Rule-following AI should act in accordance with the interpretation best supported by minimally defeasible interpretive arguments (MDIA).

Recent advances in machine learning have made it possible to train artificially intelligent agents that perform with super-human accuracy on a great diversity of complex tasks. However, the process of training these capabilities often necessitates millions of annotated examples -- far more than humans typically need in order to achieve a passing level of mastery on similar tasks. Thus, while contemporary methods in machine learning can produce agents that exhibit super-human performance, their rate of learning per opportunity in many domains is decidedly lower than human-learning. In this work we formalize a theory of Decomposed Inductive Procedure Learning (DIPL) that outlines how different forms of inductive symbolic learning can be used in combination to build agents that learn educationally relevant tasks such as mathematical, and scientific procedures, at a rate similar to human learners. We motivate the construction of this theory along Marr's concepts of the computational, algorithmic, and implementation levels of cognitive modeling, and outline at the computational-level six learning capacities that must be achieved to accurately model human learning. We demonstrate that agents built along the DIPL theory are amenable to satisfying these capacities, and demonstrate, both empirically and theoretically, that DIPL enables the creation of agents that exhibit human-like learning performance.