A chilling video featuring the faces of five Israeli women who were murdered by their husbands has gone viral in an eerie social media campaign that has brought them back to life after death. With artificial intelligence and animation capabilities from Israeli "creative reality" startup D-ID, the videos use the voice of each victim -- as well as realistic facial features and gestures -- to convey the message that someone living in the reality of domestic abuse can and should get out before its too late. The project, dubbed Listen To Our Voices, was created in response to a global and local surge in domestic violence since the start of the pandemic, and in honor of International Day for the Elimination of Violence Against Women on November 25. With deep learning technology, AI startup, D-ID captured the faces, voices, and gestures of the late Michal Sela, the late Esther Aharonovitch, the late Marin Haj Yechieh, the late Esther Barhani, and the late Sagit Ozeri, as they described their own marital difficulties which led to verbal and physical abuse from their spouses. The five victims also encouraged other women who experience similar relationships to talk to experts who know how to deal with these situations.

President & CEO of BBB National Programs, a non-profit organization dedicated to fostering a more accountable, trustworthy marketplace. It is one of the biggest conundrums of our time: businesses posting record numbers of available jobs and not being able to fill them. As with most intractable problems, there are multiple forces at play, with one involving the role of technology. Kathryn Dill at the Wall Street Journal recently wrote (paywall): "Companies are desperate to hire, and yet some workers still can't seem to find jobs. Here may be one reason why: The software that sorts through applicants deletes millions of people from consideration."

While 2021 was an exciting year for AI regarding innovations and new inventions, it was immune to controversies and scandals. In this article, we take a look at some of the most prominent ones that grabbed headlines. From the range of announcements made at the Tesla AI Day 2021, one that caught the fancy of a lot of people was the humanoid robot. Introduced in a unique manner, a human dressed in a white bodysuit and shiny mask did the news reveal during the event. Called Optimus, this humanoid robot, standing five feet eight inches and weighing 125 pounds, would be capable of performing repetitive tasks; the first prototype is likely to be released next year.

Philadelphia, PA, Nov. 29, 2021 (GLOBE NEWSWIRE) -- VSBLTY Groupe Technologies Corp. (OTCQB: VSBGF) (CSE: VSBY) (Frankfurt 5VS) ("VSBLTY"), a leading software provider of security and retail analytics technology, today issued its CEO Update to highlight Q3 financial performance as well as recent corporate milestones. VSBLTY CEO & Co-founder Jay Hutton said, "VSBLTY is pleased to report that the third quarter bookings were a strong $4.5 million USD, resulting in a booked to revenue ratio of over 8 to 1 and providing a leading indicator of the Company's revenue growth potential. Q3 revenue reached $522,683 USD, reflecting continued strong sales of both retail and security solutions." Hutton added, "Further encouraging is that Q4 revenue is already considerably ahead of Q3 results and we are anticipating a strong finish to this calendar year." Since the end of Q3 the Company has seen a significant increase in the exercise of its previously issued Warrants, resulting in cash inflows of over $2.8 million USD.

AI biases are common, persistent, and hard to address. We wish people see what AI can do but not its flaws. But this is like driving a Lamborghini with the check engine light on. It may run fine for the next few weeks but accidents are waiting to happen. To address the problem, we need to know what is fairness. Can it be judged or evaluated? In the previous article, we look at the complexity of AI bias. All AI designs need to follow the laws if applicable. In this section, we will discuss these issues. Sensitive characters are bias factors that are practically or morally irrelevant to a decision.

The planning domain has experienced increased interest in the formal synthesis of decision-making policies. This formal synthesis typically entails finding a policy which satisfies formal specifications in the form of some well-defined logic. While many such logics have been proposed with varying degrees of expressiveness and complexity in their capacity to capture desirable agent behavior, their value is limited when deriving decision-making policies which satisfy certain types of asymptotic behavior in general system models. In particular, we are interested in specifying constraints on the steady-state behavior of an agent, which captures the proportion of time an agent spends in each state as it interacts for an indefinite period of time with its environment. This is sometimes called the average or expected behavior of the agent and the associated planning problem is faced with significant challenges unless strong restrictions are imposed on the underlying model in terms of the connectivity of its graph structure. In this paper, we explore this steady-state planning problem that consists of deriving a decision-making policy for an agent such that constraints on its steady-state behavior are satisfied. A linear programming solution for the general case of multichain Markov Decision Processes (MDPs) is proposed and we prove that optimal solutions to the proposed programs yield stationary policies with rigorous guarantees of behavior.

Artificial Intelligence (AI) is not likely to make humans redundant. Nor will it create superintelligence anytime soon. But like it or not, AI technologies and intelligent systems will make huge advances in the next two decades--revolutionizing medicine, entertainment, and transport; transforming jobs and markets; enabling many new products and tools; and vastly increasing the amount of information that governments and companies have about individuals. Should we cherish and look forward to these developments, or fear them? There are reasons to be concerned. Current AI research is too narrowly focused on making advances in a limited set of domains and pays insufficient attention to its disruptive effects on the very fabric of society. If AI technology continues to develop along its current path, it is likely to create social upheaval for at least two reasons. For one, AI will affect the future of jobs. Our current trajectory automates work to an excessive degree while refusing to invest in human productivity; further advances will displace workers and fail to create new opportunities (and, in the process, miss out on AI's full potential to enhance productivity). For another, AI may undermine democracy and individual freedoms. Each of these directions is alarming, and the two together are ominous. Shared prosperity and democratic political participation do not just critically reinforce each other: they are the two backbones of our modern society. Worse still, the weakening of democracy makes formulating solutions to the adverse labor market and distributional effects of AI much more difficult. These dangers have only multiplied during the COVID-19 crisis. Lockdowns, social distancing, and workers' vulnerability to the virus have given an additional boost to the drive for automation, with the majority of U.S. businesses reporting plans for more automation.

In light of the April 2021 announcement of the world's first legislative framework for regulating Artificial Intelligence (AI), the European Artificial Intelligence Act (EU AIA), now is an opportune time for developers to revisit their strategies for testing AI applications. Incoming regulations mean that the group of stakeholders who care about your testing results just got bigger and more involved. The stakes are high, not least because companies that violate the terms of the legislation could face fines higher than those levied under the General Data Protection Act (GDPR). For the purpose of transparency, certain types of AI also have to make their accuracy metrics available to users, which adds to the pressure to get functional testing right. Following on from Applause's step-by-step guide to training and testing your AI algorithm, this article summarizes how developers should be testing AI applications in anticipation of the new era of AI regulations.

The U.N.'s Educational, Scientific, and Cultural Organization (UNESCO) this week approved a series of recommendations for AI ethics, which aim to recognize that AI can "be of great service" but also raise "fundamental … concerns." UNESCO's 193 member countries, including Russia and China, agreed to conduct AI impact assessments and place "strong enforcement mechanisms and remedial actions" to protect human rights. "The world needs rules for artificial intelligence to benefit humanity. The recommendation[s] on the ethics of AI is a major answer," UNESCO chief Audrey Azoulay said in a press release. "It sets the first global normative framework while giving States the responsibility to apply it at their level. UNESCO will support its … member states in its implementation and ask them to report regularly on their progress and practices."

Artificial intelligence through machine learning is increasingly used in the digital society. Solutions based on machine learning bring both great opportunities, thus coined "Software 2.0," but also great challenges for the engineering community to tackle. Due to the experimental approach used by data scientists when developing machine learning models, agility is an essential characteristic. In this keynote address, we discuss two contemporary development phenomena that are fundamental in machine learning development, i.e., notebook interfaces and MLOps. First, we present a solution that can remedy some of the intrinsic weaknesses of working in notebooks by supporting easy transitions to integrated development environments. Second, we propose reinforced engineering of AI systems by introducing metaphorical buttresses and rebars in the MLOps context. Machine learning-based solutions are dynamic in nature, and we argue that reinforced continuous engineering is required to quality assure the trustworthy AI systems of tomorrow.