Governments are paying growing attention to the potential of artificial intelligence – the simulation of human intelligence processes by machines – to enhance what they do. To explore how public authorities are approaching the use of AI for tasks related to public finances, Global Government Fintech – the sister title of Global Government Forum – convened an international panel on 4 October 2022 for a webinar titled'How can AI help public authorities save money and deliver better outcomes?'. The discussion, organised in partnership with SAS and Intel, highlighted how AI is already helping departments to deliver results. But also that AI remains very much an emerging and, to many, rather nebulous field with many hurdles to clear before widespread use. "Discussions of artificial intelligence often bring up connotations of an Orwellian nature, dystopian futures, Frankenstein…" said Peter Kerstens, advisor, technological innovation & cyber security at the European Commission's Financial Services Department.

Scholars explore regulatory approaches to artificial intelligence in the health care sector. Patient-centered care is a pillar of the American health care system. But, as the U.S. population grows and ages, providers need new ways to manage ever-increasing caseloads. Artificial intelligence (AI) offers an opportunity to improve the efficiency of health care administration, disease diagnosis and detection, drug development, and more. Although AI is poised to transform business operations across various sectors of the economy, experts agree that it holds heightened potential in the health care industry.

AI has extraordinary potential and developing countries must move forward quickly in this field to leverage their technological prowess, productivity, and competitiveness. Certainly, investing in R&D, developing capacities, and retaining AI talent is much easier said than done. Besides adopting a national AI strategy, if there is none, developing countries could put into practice a roadmap with clearly defined priorities and projects that bolster the economy. They can also build partnerships and reach out to other countries and organizations that are willing to cooperate in frontier technologies. A niche strategy might help to leapfrog in a few select sectors, as in the case of some small states that have become active players in the digital sphere. Interestingly enough, Kenya became last August the first African country to teach coding as a subject in schools. As stated in the UNCTAD 2021 Digital Economy report, developing countries risk becoming mere providers of data, while having to pay for digital intelligence produced with their data. Current international regulatory frameworks tend to be either too narrow in scope or too limited geographically, failing to enable cross-border data flows with an equitable sharing of economic gains. In a nutshell, developing countries need to find the optimal balance between promoting domestic economic development, protecting public policy interests, and integrating into the global digital ecosystem.

Large, diachronic datasets of political discourse are hard to come across, especially for resource-lean languages such as Greek. In this paper, we introduce a curated dataset of the Greek Parliament Proceedings that extends chronologically from 1989 up to 2020. It consists of more than 1 million speeches with extensive metadata, extracted from 5,355 parliamentary record files. We explain how it was constructed and the challenges that we had to overcome. The dataset can be used for both computational linguistics and political analysis-ideally, combining the two. We present such an application, showing (i) how the dataset can be used to study the change of word usage through time, (ii) between significant historical events and political parties, (iii) by evaluating and employing algorithms for detecting semantic shifts.

Robots that interact with humans in a physical space or application need to think about the person's posture, which typically comes from visual sensors like cameras and infra-red. Artificial intelligence and machine learning algorithms use information from these sensors either directly or after some level of symbolic abstraction, and the latter usually partitions the range of observed values to discretize the continuous signal data. Although these representations have been effective in a variety of algorithms with respect to accuracy and task completion, the underlying models are rarely interpretable, which also makes their outputs more difficult to explain to people who request them. Instead of focusing on the possible sensor values that are familiar to a machine, we introduce a qualitative spatial reasoning approach that describes the human posture in terms that are more familiar to people. This paper explores the derivation of our symbolic representation at two levels of detail and its preliminary use as features for interpretable activity recognition.

Recent advances in federated learning have demonstrated its promising capability to learn on decentralized datasets. However, a considerable amount of work has raised concerns due to the potential risks of adversaries participating in the framework to poison the global model for an adversarial purpose. This paper investigates the feasibility of model poisoning for backdoor attacks through rare word embeddings of NLP models. In text classification, less than 1% of adversary clients suffices to manipulate the model output without any drop in the performance on clean sentences. For a less complex dataset, a mere 0.1% of adversary clients is enough to poison the global model effectively. We also propose a technique specialized in the federated learning scheme called Gradient Ensemble, which enhances the backdoor performance in all our experimental settings.

Artificial Intelligence (AI) is a potentially transformational technology that will have broad social, economic, national security, and geopolitical implications for the United States and the world.1 AI is not one particular technology but a general-purpose technology combining software and hardware in systems that enable technologies (machine learning, knowledge representation, and other forms of computerized approximation of human intelligence). This general-purpose nature means that AI could have wide-ranging economic impacts across manufacturing, transportation, health, education, and many other sectors. In 2018, the McKinsey Global Institute estimated that AI could add around 16 percent, or $13 trillion, to global output by 2030.2 Since then COVID-19 has further accelerated the use of AI. While the United States is the world leader in AI, China is catching up fast (and may lead in some areas) and other governments are expanding their own AI capacity. Rather than a zero-sum game, many such efforts can be additive, benefiting global welfare. The U.S. can encourage and support AI efforts that seek to develop and compete on fair terms. Other national policies--China's above all--seek to erect barriers to free and open development of AI, appropriating the benefits for their national champions and applying AI as a geopolitical lever. Such policies could distort the development and benefits of AI for humanity, make the world less secure for the U.S. and allies, and markets less receptive to U.S. products and services. To foster AI policies that support development of beneficial, trustworthy, and robust artificial intelligence will require international engagement by the United States and cooperation among like-minded democracies that are leaders in artificial intelligence.

Cybersecurity awareness company NINJIO, a has acquired Israeli company DCOYA – a provider of behavior-centric cybersecurity solutions for organizations of all sizes. NINJIO says that the combination of NINJIO's cybersecurity content with DCOYA's powerful machine-learning-driven cybersecurity awareness platform will give CISOs and other company leaders the most effective cybersecurity awareness-training toolkit on the market. Like NINJIO, DCOYA focuses on behavior modification – an approach that is only becoming more crucial as cybercriminals continue to rely on social engineering to infiltrate companies and steal sensitive information. DCOYA's technology works backward from the psychological tactics of the most successful human-related hacks. The new solution will allow NINJIO to determine a person's area of greatest vulnerability (greed, fear, obedience, and others) and provide reinforcing education that specifically addresses that vulnerability.

But 2021 will likely be a big year for A.I., and with a new White House administration soon in place, there may be a clearer set of national A.I. policies that will trickle down to the business world. On New Year's Day, the U.S. Senate voted to overturn President Trump's veto of the National Defense Authorization Act and authorize $741 billion for defense spending, including the creation of a number of A.I.-related polices. Among the reasons Trump opposed the defense bill was the absence of a provision to repeal Section 230, which gives legal protections to Internet companies that host user-generated content. Although the defense bill was mostly geared toward military spending, it did contain a number of non-defense related A.I. initiatives, as Stanford University's Human-Centered Artificial Intelligence group outlined. For instance, the bill would create a "National AI Initiative" that would coordinate A.I. research and development between "civilian agencies," the Defense Department, and intelligence agencies.

But President Xi Jinping is engaged in a zero-sum game, similar to Soviet leaders. "For long, Xi's zero-sum instincts and his entourage, no different than that of Communist leaders from the Soviet era, were successful in navigating the Western liberal rules-based system of trade and international institutions," he told International Business Times in an email. "Recent events (e.g., the opaqueness regarding the pandemic's origins, the war in Ukraine, and China's non-commitment to greenhouse commitments) and repeated threats to Taiwan appear to have spooked even the most dovish U.S. administration in recent memory." Moreover, Professor Colares thinks Xi Jinping's Soviet-style leadership has not helped China overcome the middle-income trap. In this situation, an emerging market economy slows down, failing to transition to a high-income and become a developed economy.