For some, artificial intelligence represents nothing more than one tool among many aimed at increasing productivity and maximizing economic output. For others though, AI looks like more of a destination, a couple of words pointing to a tectonic shift in global society capable of ripping the ground out from under humanity's feet. Which camp do you think Henry Kissinger belongs in? Yes, the same Henry Kissinger who managed to whisper in presidents' ears long enough to fundamentally alter the course of events in the 20th century has some thoughts on what advances in AI could mean for the next hundred years. The Cold War veteran started prominently expressing his interest and concern over AI in a 2018 issue of The Atlantic titled "How the Enlightenment Ends."

Given the growing use of artificial intelligence (AI) and automated decision-making tools in consumer-facing decisions, we expect federal regulators in 2022 to continue their recent track record of interest in potential discrimination and unfairness, as well as data accuracy and transparency. Significant technological developments in these areas and the increasing use of data analytics to make automated decisions will likely result in further regulatory action this year in three key areas: (1) assessing whether AI and algorithms are excluding particular consumer groups in an unfair and discriminatory manner, whether intentionally or not; (2) evaluating whether collected data accurately reflects real-world facts and whether companies are giving consumers an opportunity to correct mistakes; and (3) assessing whether automated decisionmaking tools are being used in a transparent manner. Over the last year, federal regulators with enforcement authority in the consumer space--the Federal Trade Commission (FTC) and the Consumer Financial Protection Bureau (CFPB)--have expressed their intention to continue enforcement efforts. The FTC has identified "technology companies and digital platforms," "bias in algorithms and biometrics," and "deceptive and manipulative conduct on the Internet" as among its top enforcement priorities for the coming years, and directed staff to use compulsory processes to demand documents and testimony to investigate potential abuses in these areas. The FTC and the CFPB have each initiated or continued investigations into practices involving the collection of consumer data and the use of data analytics in consumer decisions, including the use of AI and algorithms by financial institutions, digital payment platforms, and social media, and video streaming firms.

Artificial intelligence (AI) features are increasingly being embedded in cars and are central to the operation of self-driving cars (SDC). There is little or no effort expended towards understanding and assessing the broad legal and regulatory impact of the decisions made by AI in cars. A comprehensive literature review was conducted to determine the perceived barriers, benefits and facilitating factors of SDC in order to help us understand the suitability and limitations of existing and proposed law and regulation. (1) existing and proposed laws are largely based on claimed benefits of SDV that are still mostly speculative and untested; (2) while publicly presented as issues of assigning blame and identifying who pays where the SDC is involved in an accident, the barriers broadly intersect with almost every area of society, laws and regulations; and (3) new law and regulation are most frequently identified as the primary factor for enabling SDC. Research on assessing the impact of AI in SDC needs to be broadened beyond negligence and liability to encompass barriers, benefits and facilitating factors identified in this paper. Results of this paper are significant in that they point to the need for deeper comprehension of the broad impact of all existing law and regulations on the introduction of SDC technology, with a focus on identifying only those areas truly requiring ongoing legislative attention.

The convenient access to copious multi-faceted data has encouraged machine learning researchers to reconsider correlation-based learning and embrace the opportunity of causality-based learning, i.e., causal machine learning (causal learning). Recent years have therefore witnessed great effort in developing causal learning algorithms aiming to help AI achieve human-level intelligence. Due to the lack-of ground-truth data, one of the biggest challenges in current causal learning research is algorithm evaluations. This largely impedes the cross-pollination of AI and causal inference, and hinders the two fields to benefit from the advances of the other. To bridge from conventional causal inference (i.e., based on statistical methods) to causal learning with big data (i.e., the intersection of causal inference and machine learning), in this survey, we review commonly-used datasets, evaluation methods, and measures for causal learning using an evaluation pipeline similar to conventional machine learning. We focus on the two fundamental causal-inference tasks and causality-aware machine learning tasks. Limitations of current evaluation procedures are also discussed. We then examine popular causal inference tools/packages and conclude with primary challenges and opportunities for benchmarking causal learning algorithms in the era of big data. The survey seeks to bring to the forefront the urgency of developing publicly available benchmarks and consensus-building standards for causal learning evaluation with observational data. In doing so, we hope to broaden the discussions and facilitate collaboration to advance the innovation and application of causal learning.

Following on from the publication of its Feasibility Study in December 2020, the Council of Europe's Ad Hoc Committee on Artificial Intelligence (CAHAI) and its subgroups initiated efforts to formulate and draft its Possible Elements of a Legal Framework on Artificial Intelligence, based on the Council of Europe's standards on human rights, democracy, and the rule of law. This document was ultimately adopted by the CAHAI plenary in December 2021. To support this effort, The Alan Turing Institute undertook a programme of research that explored the governance processes and practical tools needed to operationalise the integration of human right due diligence with the assurance of trustworthy AI innovation practices. The resulting framework was completed and submitted to the Council of Europe in September 2021. It presents an end-to-end approach to the assurance of AI project lifecycles that integrates context-based risk analysis and appropriate stakeholder engagement with comprehensive impact assessment, and transparent risk management, impact mitigation, and innovation assurance practices. Taken together, these interlocking processes constitute a Human Rights, Democracy and the Rule of Law Assurance Framework (HUDERAF). The HUDERAF combines the procedural requirements for principles-based human rights due diligence with the governance mechanisms needed to set up technical and socio-technical guardrails for responsible and trustworthy AI innovation practices. Its purpose is to provide an accessible and user-friendly set of mechanisms for facilitating compliance with a binding legal framework on artificial intelligence, based on the Council of Europe's standards on human rights, democracy, and the rule of law, and to ensure that AI innovation projects are carried out with appropriate levels of public accountability, transparency, and democratic governance.

Score-based generative models (SGMs) are a novel class of generative models demonstrating remarkable empirical performance. One uses a diffusion to add progressively Gaussian noise to the data, while the generative model is a "denoising" process obtained by approximating the time-reversal of this "noising" diffusion. However, current SGMs make the underlying assumption that the data is supported on a Euclidean manifold with flat geometry. This prevents the use of these models for applications in robotics, geoscience or protein modeling which rely on distributions defined on Riemannian manifolds. To overcome this issue, we introduce Riemannian Score-based Generative Models (RSGMs) which extend current SGMs to the setting of compact Riemannian manifolds. We illustrate our approach with earth and climate science data and show how RSGMs can be accelerated by solving a Schr\"odinger bridge problem on manifolds.

This article is brought to you thanks to the collaboration of The European Sting with the World Economic Forum. According to the World Economic Forum Global Cybersecurity Outlook 2022 report, 48% of executives believe that artificial intelligence (AI) will influence cyber transformation in the next two years. AI is a powerful tool for both cybercriminals and cybersecurity experts. Hackers are using AI to make their attacks more sophisticated and harder to detect, while cybersecurity specialists are finding ways of integrating AI into corporate cybersecurity systems to minimize financial and reputational losses. A survey of the CEOs of the world s 500 most influential companies across 11 industries found that cybersecurity issues will be the greatest risk to company growth in the next three years.

This story was contributed to TechCabal by Conrad Onyango/bird. African governments are ramping up national strategies on the adoption of Artificial Intelligence (AI) in a fresh hunt for crucial data that would help improve public service delivery and governance. AI is no longer a preserve of the private sector as Africa's public sector hops on a global trend where governments join the hunt for robust data to transform how they deliver services to an increasingly tech-savvy population. Oxford Insights in its'Government AI Readiness Index 2021,' shows governments across the continent are turning to AI to improve their public services and gain strategic economic advantages. More governments, the report says, are building up AI ecosystems-backed by national strategies to capitalize on a 10-year global boom that has seen private sector firms commercialize AI research and development.

New Delhi: The Indian Institute of Technology Hyderabad (IITH) has developed India s first Artificial Intelligence-based job platform for persons with Disabilities (PwDs). It will provide independence to persons with disabilities, through tech training and jobs. The platform is uniquely crafted using AI, for the benefit to the persons seeking employment having disabilities like visual impairment, hearing impairment, and locomotive disorders. The platform will analyze the available information and suggest the required training needed for the concerned jobseeker. It has been developed for both web and mobile versions for the maximum reach of the initiative.

Musculoskeletal care is a big problem for the U.S. healthcare system, digital health companies say. Disorders are common and expensive to treat, but care that could cut down those high costs is inaccessible to many who need it. That message is resonating with investors. Over the past year, they've poured hundreds of millions of dollars into the digital MSK space. Unicorns Hinge Health and SWORD Health both closed multiple rounds of funding in 2021, some worth nine figures.