Orders in North America for robots are reaching record numbers as the U.S. economy continues to slog through a labor shortage fueled by the pandemic. According to data from the Association for Advancing Automation (A3) – a trade group representing organizations involved in robotics, AI and other tech – the total number or orders this year reached nearly 29,000, with a value of $1.48 billion. The orders are up 37% from a year ago, says the trade group. "With labor shortages throughout manufacturing, logistics and virtually every industry, companies of all sizes are increasingly turning to robotics and automation to stay productive and competitive," said Jeff Burnstein, president of A3, in a statement. Reinventing the (steering) wheel:Tesla's Elon Musk introduces new yoke-shaped wheel During the third quarter, North American companies order 9,928 robots valued at $513 million, the third highest quarter ever in orders and fifth highest ever for value, said A3.

Omnia's Trustworthy AI Module, Deloitte's unique artificial intelligence evaluation technology, has been recognized as'Digital Innovation of the Year' at the Digital Accountancy Forum and Awards 2021 in London earlier this week. This marks the second consecutive year Deloitte has garnered top honors for delivering innovative and disruptive technologies by The Accountant and International Accounting Bulletin. It also marks the fourth time Deloitte has won the award overall. Omnia DNAV, a digital cloud-based solution that revolutionizes the audit of securities and investments, was honored with the award in 2020. Deloitte won the 2018 'Audit Innovation of the Year' for its audit-transforming Cortex data platform and in 2015 for functionality using artificial intelligence that quickly identifies, extracts, and analyzes information across an entire population of documents.

Cybersecurity is the most vital part of any company. It helps make sure that their data is safe and secure. With an increasing demand for artificial intelligence and machine learning these technologies are also transforming the cybersecurity space. Machine learning has many applications in Cybersecurity such as identifying cyber threats, combating cybercrime, and improving available antivirus software using AI capabilities. So, let's see what are the applications of machine learning in cybersecurity.

Machine learning models based on temporal point processes are the state of the art in a wide variety of applications involving discrete events in continuous time. However, these models lack the ability to answer counterfactual questions, which are increasingly relevant as these models are being used to inform targeted interventions. In this work, our goal is to fill this gap. To this end, we first develop a causal model of thinning for temporal point processes that builds upon the Gumbel-Max structural causal model. This model satisfies a desirable counterfactual monotonicity condition, which is sufficient to identify counterfactual dynamics in the process of thinning. Then, given an observed realization of a temporal point process with a given intensity function, we develop a sampling algorithm that uses the above causal model of thinning and the superposition theorem to simulate counterfactual realizations of the temporal point process under a given alternative intensity function. Simulation experiments using synthetic and real epidemiological data show that the counterfactual realizations provided by our algorithm may give valuable insights to enhance targeted interventions.

As \emph{artificial intelligence} (AI) systems are increasingly involved in decisions affecting our lives, ensuring that automated decision-making is fair and ethical has become a top priority. Intuitively, we feel that akin to human decisions, judgments of artificial agents should necessarily be grounded in some moral principles. Yet a decision-maker (whether human or artificial) can only make truly ethical (based on any ethical theory) and fair (according to any notion of fairness) decisions if full information on all the relevant factors on which the decision is based are available at the time of decision-making. This raises two problems: (1) In settings, where we rely on AI systems that are using classifiers obtained with supervised learning, some induction/generalization is present and some relevant attributes may not be present even during learning. (2) Modeling such decisions as games reveals that any -- however ethical -- pure strategy is inevitably susceptible to exploitation. Moreover, in many games, a Nash Equilibrium can only be obtained by using mixed strategies, i.e., to achieve mathematically optimal outcomes, decisions must be randomized. In this paper, we argue that in supervised learning settings, there exist random classifiers that perform at least as well as deterministic classifiers, and may hence be the optimal choice in many circumstances. We support our theoretical results with an empirical study indicating a positive societal attitude towards randomized artificial decision-makers, and discuss some policy and implementation issues related to the use of random classifiers that relate to and are relevant for current AI policy and standardization initiatives.

The exponential development and application of artificial intelligence triggered an unprecedented global concern for potential social and ethical issues. Stakeholders from different industries, international foundations, governmental organisations and standards institutions quickly improvised and created various codes of ethics attempting to regulate AI. A major concern is the large homogeneity and presumed consensualism around these principles. While it is true that some ethical doctrines, such as the famous Kantian deontology, aspire to universalism, they are however not universal in practice. In fact, ethical pluralism is more about differences in which relevant questions to ask rather than different answers to a common question. When people abide by different moral doctrines, they tend to disagree on the very approach to an issue. Even when people from different cultures happen to agree on a set of common principles, it does not necessarily mean that they share the same understanding of these concepts and what they entail. In order to better understand the philosophical roots and cultural context underlying ethical principles in AI, we propose to analyse and compare the ethical principles endorsed by the Chinese National New Generation Artificial Intelligence Governance Professional Committee (CNNGAIGPC) and those elaborated by the European High-level Expert Group on AI (HLEGAI). China and the EU have very different political systems and diverge in their cultural heritages. In our analysis, we wish to highlight that principles that seem similar a priori may actually have different meanings, derived from different approaches and reflect distinct goals.

In gaming, as in the real world, a decisive strategic advantage can be used to consolidate a dominant position, forming a power singleton. The United States nearly achieved such a dominant position in global politics when it used the threat of nuclear war to attempt to persuade Russia to adopt the Baruch Plan. The Baruch Plan was rejected as Russia realized the plan would give the US a decisive strategic advantage of nuclear armament, or at the very least an unfair authority to police atomic weaponry with controls and inspections under the guise of the United Nations, where Stalin knew Russia would be easily out voted in the Security Council and General Assembly. Acquiring Bitcoin can give an individual, a company, or a country a decisive strategic advantage, because as we know (and can audit individually with our nodes), Bitcoin's issuance does not respond to demand, and there will only ever be 21,000,000 Bitcoin. We can presume Bitcoin adoption will continue to grow, and there will be far greater than 21,000,000 entities vying for even one whole coin.

Sber's international online conference "Artificial Intelligence Journey" culminated in a discussion titled "AI Technology to Address Social Issues", in which the President of the Russian Federation, Vladimir Putin, participated. The discussion was moderated by Sber CEO and Chairman of the Sberbank Executive Board, Herman Gref. The session was attended by the winners of the AI International Junior Contest, organized by Sber in partnership with the Artificial Intelligence Alliance. This year's conference hit all-time record, with 52,000 participants. Over 800 people presented their solutions to AI challenges, including innovative approaches to Strong AI and Artificial General Intelligence.

Artificial Intelligence (AI) 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. However, these rapid technological developments inevitably bring multiple risks and challenges, which have so far outpaced policy debates and regulatory frameworks. UNESCO is committed to supporting Member States to harness the potential of AI technologies for achieving the Education 2030 Agenda, while ensuring that the application of AI in educational contexts is guided by the core principles of inclusion and equity. UNESCO's mandate calls inherently for a human-centred approach to AI. It aims to shift the conversation to include AI's role in addressing current inequalities regarding access to knowledge, research and the diversity of cultural expressions and to ensure AI does not widen the technological divides within and between countries.

When it comes to national and international defense, drone warfare has placed itself firmly as one of the prime options these days. To understand the challenge at hand, let's first take a step back, and look at defense as a whole in general. The development of technologies such as artificial intelligence and advanced computing have made defense only more complicated. These complications have made military divisions more potent. But all this progress comes with a catch: this has largely evened-up the playing field as far as lower-mid-tier weaponry is concerned.