Last year I wrote about how AI regulations will lead to the emergence of professional AI risk managers. This has already happened in the financial sector where regulations patterned after Basel rules have created a financial risk management profession to assess financial risks. Last week, the EU published a 108-page proposal to regulate AI systems. This will lead to the emergence of professional AI risk managers. The proposal doesn't cover all AI systems, just those deemed high-risk, and the regulation would vary depending on how risky the specific AI systems are: Since systems with unacceptable risks would be banned outright, most of the regulation is about high-risk AI systems.

The minister of state for communications and education also said the COVID-19 pandemic has shown people the importance of digital infrastructure. Survival during this difficult phase was possible due to the connectivity made available to citizens under the Digital India program envisioned by Prime Minister Narendra Modi, he added. "The modern technologies like AI, M2M (machine to machine) and data science are further going to change the world. The government is keen on utilising these technologies. Education is another priority area, specially for young citizens and women. "I call on the industry and all of you to work seriously on all these latest technologies for an Aatmanirbhar Bharat," Dhotre said at a virtual international conference organised by CMAI. Speaking at the conference, All India Council for Technical Education (AICTE) Chairman Anil Sahastrabudhe said a balance needs to be achieved between the virtual world and the physical mode of learning. "Teachers who have never used technology are now using technology more.

In 2019, the U.S. Postal Service had a need to identify and track items in its torrent of more than 100 million pieces of daily mail. A USPS AI architect had an idea. Ryan Simpson wanted to expand an image analysis system a postal team was developing into something much broader that could tackle this needle-in-a-haystack problem. With edge AI servers strategically located at its processing centers, he believed USPS could analyze the billions of images each center generated. The resulting insights, expressed in a few key data points, could be shared quickly over the network.

A huddle at the 2017 United Nations Climate Change Conference, where attendees cooperated on mutually beneficial joint actions on climate.Credit: Sean Gallup/Getty Artificial-intelligence assistants and recommendation algorithms interact with billions of people every day, influencing lives in myriad ways, yet they still have little understanding of humans. Self-driving vehicles controlled by artificial intelligence (AI) are gaining mastery of their interactions with the natural world, but they are still novices when it comes to coordinating with other cars and pedestrians or collaborating with their human operators. The state of AI applications reflects that of the research field. It has long been steeped in a kind of methodological individualism. As is evident from introductory textbooks, the canonical AI problem is that of a solitary machine confronting a non-social environment. Historically, this was a sensible starting point.

"As the name suggests, data scientists are responsible for assisting companies to adopt data-driven decision-making via discovery of valuable insights in data," Su said. A data scientist uses complex algorithms, manipulates data and leverages a host of technologies that require specialized math, technology and computer skills, and business acumen to generate insights. "They will lead the discovery process, work through extracting, cleaning and loading the data before conducting investigations and analysis of the data by applying statistical models or algorithms to the data set," added Jim Johnson, senior vice president for the technology division of Robert Half Technology, a human resources consulting firm. Meanwhile, the U.S. Bureau of Labor Statistics puts the salary range for data scientists and other mathematical science occupations at $53,000 to $158,000. The U.S. Bureau of Labor Statistics also identified data scientists among the 12 fastest-growing occupations for the upcoming decade.

It's a simple fact, says General John "Mike" Murray, we're going to have to learn to trust artificial intelligence in the battlefield. And that means, the rules governing human control over artificial intelligence might need to be relaxed. Speaking from Austin, Texas, at The Future Character of War and the Law of Armed Conflict online event, Murray provided a future battle scenario involving the rapid advance of artificial intelligence in the US military and the ethical challenges it presents. "If you think about things like a swarm of, let's say a hundred semi-autonomous or autonomous drones, some lethal, some sensing, some jamming, some in command and control -- think back to the closing ceremony of the Seoul Olympics. "Is it within a human's ability to pick out which ones have to be engaged and then make 100 individual engagement decisions against a drone swarm?" said Murray, Commander, Army Future Command (AFC). "And is it even necessary to have it a human in the loop, if you're talking about affects against an unmanned platform or against a machine.

The bipartisan "Endless Frontier" bill would authorize most of the money, $100 billion, over five years to invest in basic and advanced research, commercialization, and education and training programs in key technology areas, including artificial intelligence, semiconductors, quantum computing, advanced communications, biotechnology and advanced energy. The bill had been expected to be considered on April 28, but was delayed after more than 230 amendments were filed for consideration. Senate Democrats and Republicans are moving closer to reaching agreement. A congressional aide said "there has been very encouraging progress toward a deal." The measure, sponsored by Senate Democratic Leader Chuck Schumer, Republican Senator Todd Young and others, would also authorize another $10 billion to designate at least 10 regional technology hubs and create a supply chain crisis-response program to address issues like the shortfall in semiconductor chips harming auto production.

Understanding voluminous historical records provides clues on the past in various aspects, such as social and political issues and even natural science facts. However, it is generally difficult to fully utilize the historical records, since most of the documents are not written in a modern language and part of the contents are damaged over time. As a result, restoring the damaged or unrecognizable parts as well as translating the records into modern languages are crucial tasks. In response, we present a multi-task learning approach to restore and translate historical documents based on a self-attention mechanism, specifically utilizing two Korean historical records, ones of the most voluminous historical records in the world. Experimental results show that our approach significantly improves the accuracy of the translation task than baselines without multi-task learning. In addition, we present an in-depth exploratory analysis on our translated results via topic modeling, uncovering several significant historical events.

The advent of powerful prediction algorithms led to increased automation of high-stake decisions regarding the allocation of scarce resources such as government spending and welfare support. This automation bears the risk of perpetuating unwanted discrimination against vulnerable and historically disadvantaged groups. Research on algorithmic discrimination in computer science and other disciplines developed a plethora of fairness metrics to detect and correct discriminatory algorithms. Drawing on robust sociological and philosophical discourse on distributive justice, we identify the limitations and problematic implications of prominent fairness metrics. We show that metrics implementing equality of opportunity only apply when resource allocations are based on deservingness, but fail when allocations should reflect concerns about egalitarianism, sufficiency, and priority. We argue that by cleanly distinguishing between prediction tasks and decision tasks, research on fair machine learning could take better advantage of the rich literature on distributive justice.

Big gaps between the U.S. and Iran over the measures needed to roll back and limit the Iranian nuclear program are stalling the Vienna talks, European diplomats and former U.S. officials briefed on the issue tell me. What's happening: The Biden administration has said any deal to restore the 2015 nuclear accord must include a return by Iran to full compliance with its previous commitments. But that's complicated by the fact that Iran's nuclear program has advanced since 2015.