Artificial intelligence (AI) currently plays a central role in the digitisation and modernisation strategies of public administrations and companies throughout Europe, the United States and China. The potential improvements and advances in efficiency that the incorporation of AI can offer strategic sectors in different countries have made it indispensable in a new era of technological transformation. And while no one wants to be left behind, the main players of this new digital era have from the very beginning approached these technologies in significantly different ways. While the United States and China have already embraced AI as one more component of their geopolitical strategies, the European Union (EU) is positioning itself as a global leader in its ethical use. According to the EU, in order to be considered ethical, any AI technology used in its territory must ensure respect for the fundamental rights of EU citizens.

As the world becomes increasingly dependent on technology to communicate, attend school, do our work, buy groceries and more, artificial intelligence (AI) and machine learning (ML) play a bigger role in our lives. Living through the second year of the COVID-19 pandemic has shown the value of technology and AI. It has also revealed a dangerous side and regulators have responded accordingly. In 2021, across the world, governing bodies have been working to regulate how AI and ML systems are used. From the UK to the EU to China, regulations on how industries should monitor their algorithms, best practices for auditing and frameworks for more transparent AI systems are on the rise.

The US Securities and Exchange Commission (SEC), which regulates public company securities, recently proposed its own climate impact reporting requirement. Many US public companies already voluntarily publish information on this topic for shareholders who have been asking for those details. But various state GOP attorneys general are already questioning the SEC's ability to impose the requirement, asserting that such a requirement lacks materiality. So there's a distinct possibility the SEC's proposal will get tied up in litigation, at least for now. Back in September 2021, Addisu Lashitew, nonresident fellow, global economy and development at The Brookings Institution, characterized the US stance on the climate impact reporting issue as laissez faire.

There is no doubt that healthcare is one of the areas where the use of Artificial Intelligence (AI) systems is highly growing. As well, there is full awareness that this growth offers high potential, but also high risks. In this respect, the EU Commission's recent proposal for a Regulation on AI (which is currently under discussion of the EU Council, and will be soon passed on first reading by the UE Parliament) is undoubtedly a legislative framework that could increase trust and security in the use of this technology in patient care. It is therefore important to highlight the current regulatory framework into which the new IA Regulation will fit. It is important to highlight that the current normative framework on medical software appears already quite complete, if correctly applied.

The EU needs to act as a'global standard-setter' in AI, according to a new report that also warned about the risks of mass surveillance. A new EU report says public debate on the use of artificial intelligence (AI) should focus on the technology's "enormous potential" to complement humans. The European Parliament's special committee on artificial intelligence in a digital age adopted its final recommendations yesterday (22 March) after 18 months of inquiries. The committee's draft text notes that the world is on the verge of "the fourth industrial revolution" from an abundance of data combined with powerful algorithms. But it adds that that the EU has "fallen behind" in the global race for tech leadership, which poses a risk that tech standards could be developed in the future by "non-democratic actors".

Founder and CEO at Fusemachines, Adjunct Associate Professor at Columbia University -- on a mission to democratize Artificial Intelligence. An organization is only as good as its people, and every business--big or small, local or global--is beginning to appreciate this reality, especially after the recent surge in post-pandemic employee turnovers. In 2021 alone, around 47 million people quit their jobs, causing the emergence of the phrase "Great Resignation," first coined by Anthony Klotz, a professor at Mays Business School, Texas A&M University. This trend of employees leaving their jobs, coupled with the digital disruption of the workforce, has forced organizations not equipped with the tools and resources needed to fulfill their HR and recruitment needs to turn to technology for help. We are already witnessing a rise in companies wanting to invest in HR technologies.

The right to be forgotten has been legislated in many countries, but its enforcement in the AI industry would cause unbearable costs. When single data deletion requests come, companies may need to delete the whole models learned with massive resources. Existing works propose methods to remove knowledge learned from data for explicitly parameterized models, which however are not appliable to the sampling-based Bayesian inference, i.e., Markov chain Monte Carlo (MCMC), as MCMC can only infer implicit distributions. In this paper, we propose the first machine unlearning algorithm for MCMC. We first convert the MCMC unlearning problem into an explicit optimization problem. Based on this problem conversion, an MCMC influence function is designed to provably characterize the learned knowledge from data, which then delivers the MCMC unlearning algorithm. Theoretical analysis shows that MCMC unlearning would not compromise the generalizability of the MCMC models. Experiments on Gaussian mixture models and Bayesian neural networks confirm the effectiveness of the proposed algorithm. "The right to be forgotten" refers to the right of individuals to request data controllers such as tech giants to delete the data collected from them. It has been recognized in many countries through legislation, including the European Union's General Data Protection Regulation (2016) and the California Consumer Privacy Act (2018).

The 2020s marked the start of a decade of artificial intelligence (AI) truly coming into its own. It is forecasted to grow by 33.2% annually as businesses across domains see clear evidence that AI delivers enormous business benefits. By 2030, AI will create an additional $15.7 trillion in global GDP, a whopping 26% increase. But despite its many advantages like maximizing efficiency, reducing cost, improving customer satisfaction, identifying new product and service opportunities and meeting regulatory compliance requirements, the topic still raises doubts among business leaders worldwide. Based on my experiences in championing AI as a core driver of business success, here are my top five suggestions for leaders to get the most out of AI in 2022.

NVIDIA's move into robotics took another major step. NVIDIA is also helping robot creators with a new Isaac Nova Orin platform that includes two Jetson AGX Orin modules and the sensors needed for a robot to detect the world around it. The platform can handle up to six cameras, three LiDAR units and eight ultrasonic sensors. You might have to wait for NVIDIA's robot innovations, though. Most of this will come before the end of 2022, with a $399 for a basic Orin NX kit set to debut in Q4.

As the world becomes increasingly dependent on technology to communicate, attend school, do our work, buy groceries and more, artificial intelligence (AI) and machine learning (ML) play a bigger role in our lives. Living through the second year of the COVID-19 pandemic has shown the value of technology and AI. It has also revealed a dangerous side and regulators have responded accordingly. In 2021, across the world, governing bodies have been working to regulate how AI and ML systems are used. From the UK to the EU to China, regulations on how industries should monitor their algorithms, best practices for auditing and frameworks for more transparent AI systems are on the rise.