Cristian Alonso is an economist in the IMF's Fiscal Affairs Department; Siddharth Kothari is an economist in the IMF's Asia and Pacific Department' Sidra Rehman is an economist in the IMF's Middle East and Central Asia Department. At a joint meeting of the UN's Economic and Social Council (ECOSOC) and its Economic and Social Committee, a robot named Sophia had an interactive session last year with Deputy Secretary-General Amina J. Mohammed. WASHINGTON DC, Dec 8 2020 (IPS) - New technologies like artificial intelligence (AI), machine learning, robotics, big data, and networks are expected to revolutionize production processes, but they could also have a major impact on developing economies. The opportunities and potential sources of growth that, for example, the United States and China enjoyed during their early stages of economic development are remarkably different from what Cambodia and Tanzania are facing in today's world. Our recent staff research finds that new technology risks widening the gap between rich and poor countries by shifting more investment to advanced economies where automation is already established.

Technology is everywhere in the 21st century, and America's food system is no exception. Scientists with the USDA Agricultural Research Service's (ARS) Western Human Nutrition Research Center (WHNRC), at the University of California (UC) – Davis, have joined forces with over 40 researchers from six organizations to form an institute that will use artificial intelligence (AI) to create the next-generation food system. The team, led by UC Davis, also includes UC Berkeley, Cornell University, and the University of Illinois at Urbana-Champaign. The project is funded by a $20 million grant from USDA's National Institute of Food and Agriculture. "The AI Institute for Next Generation Food Systems (AIFS) is dedicated to accelerating the use of artificial intelligence to optimally produce, process, and distribute safe and nutritious food," said Dr. Danielle Lemay, a USDA research molecular biologist at WHNRC.

This summer, in a leafy, wooded area near Utrecht, the Netherlands, scientists were testing out battlefield haute couture: adaptive camouflage. Researchers with the Netherlands Organisation for Applied Scientific Research mounted a swath of fabric on a stand and watched as its pattern shifted to match the greens and browns of the foliage. Cameras connected to the fabric picked up the scenery and hundreds of embedded light-emitting diodes mimicked it, like the skin of a chameleon. The team is testing other materials to weave into the futuristic camouflage, including polymers that absorb body heat and radio waves, making soldiers harder to detect with thermal imagers and radars. Just as striking as the fabrics is the project's funding source: the European Union, better known for trade rules and farm subsidies than military maneuvers. The camouflage work is part of a Swedish-led, six-country project that received a €2.6 million grant from the union's Preparatory Action on Defence Research (PADR). The 3-year fund, worth €90 million, also supports research in artificial intelligence (AI) for bomb detection, laser weapons, railguns, and drones. It is a sign of much bigger things to come: Next year, PADR will be rolled into the new European Defense Fund (EDF), with a budget of €7 billion over 7 years, split between early-stage research and late-stage development. That's tiny compared with the $80 billion per year the United States spends on defense R&D. And it's even small compared with the combined €5 billion or so spent on defense research each year by EU nations. But the European Union, which has no military resources of its own, hopes the EDF, by topping up joint national investments, will encourage its members to strengthen their modest defense capabilities. For European researchers, the spending is opening new opportunities—and stirring some qualms. ![Figure][1] CREDITS: (GRAPHIC) C. BICKEL/ SCIENCE ; (DATA) EUROPEAN DEFENCE AGENCY European governments slashed defense budgets in the 1990s, believing the danger of major conflict in Europe had ended with the Cold War. “It was a bit of a paradox, because of course we had the Balkan wars,” says Zdzisław Krasnodȩbski, a member of the European Parliament for Poland's governing Law and Justice Party who steered the EDF through Parliament. But recent events—particularly Russia's annexation of Crimea in 2014 and the subsequent war in Ukraine—have changed many minds, he says. At the same time, the United States is withdrawing from its role as guarantor of European security, says Julia Muravska, a researcher at the RAND Corporation, a global defense policy think tank. The Obama administration shifted U.S. defense resources to the Far East, and both it and the Trump administration have announced troop withdrawals from Europe. Successive U.S. presidents have urged the 21 EU countries that are also North Atlantic Treaty Organization members to honor commitments to spend at least 2% of their gross domestic product on defense. But only three actually do: Latvia, Estonia, and Greece. To address these gaps, European leaders are discussing ways to pool military resources, and French President Emmanuel Macron has even called for “a true European army.” The EDF aims to be another stimulant for collaboration. Every EDF project must include participants from at least three nations, and mandatory cofinancing for late-stage development projects will give the budget a “lever effect,” says Frederic Mauro, a Brussels-based lawyer specializing in defense. Even though the EDF is minuscule compared with U.S. spending, it “can make a huge difference at the European scale,” he says. One unanswered question is whether researchers in non-EU countries such as the United Kingdom and Switzerland can apply for EDF funding, and on what terms. If legislators allow their participation, the rules will likely be stricter than for Horizon 2020, the European Union's civilian research program, which already includes non-EU nations like Switzerland and Israel. Jean-François Ripoche, R&D chief for the European Defence Agency, which runs PADR, says the argument boils down to whether a foreign firm or institution can reliably contribute to EU military projects without interference from its home country. “In the defense business, it's hard to do without talking to your own government,” he says. PADR funding has gone to a mix of research institutes, including Germany's Fraunhofer Society; engineering companies such as French giant Thales; weaponsmakers like MBDA; and smaller businesses, such as Space Applications Services, a Belgian research firm. Universities also participate: For example, the University of Siena leads another camouflage project. Future EDF research topics will be specified in annual calls run by the European Commission, the EU executive branch, and approved by a committee of national delegates. AI will be a big topic, Ripoche says. He says EDF funding will also go to new materials, such as discreet metamaterial antennas that can be engineered into the surfaces of vehicles and weapons. Muravska says she expects “a healthy take-up” in the EDF by European academic researchers, “provided they are aware of it.” But some scientists are uneasy. “Military research can feed into technologies which are then exported to countries with poor human rights records,” says Stuart Parkinson, director of Scientists for Global Responsibility, a U.K. advocacy organization. He adds that nuclear arms races could be stoked by development of ostensibly nonnuclear technologies, such as hypersonic weapons. Reiner Braun, a board member of the Berlin-based International Network of Engineers and Scientists for Global Responsibility, says he wishes EDF money could be used for research into understanding and defending against poverty, climate change, and disease, which can also lead to conflict. “We need much more research for peaceful activities for supporting the sustainable development goals, for climate research, and for many other purposes, including the fight against the pandemic.” European security should come through dialogue with Russia and multilateral disarmament, Parkinson says. But he recognizes that a weapons-free world remains a far-off dream. “I'm not saying we should throw the doors open and throw our weapons on the floor.” [1]: pending:yes

The National Defense Authorization Act (NDAA) conference report includes provisions from the Artificial Intelligence for the Armed Forces Act, which looks to bolster the military's artificial intelligence (AI) capabilities. "The complexity of 21st-century warfare requires that our armed forces be both technically skilled and proficient with their weapons," U.S. Sen. Rob Portman (R-OH), co-chair of the Senate Artificial Intelligence Caucus, said. "I am glad to see that the FY 2021 NDAA conference report takes important steps towards that goal by implementing parts of my bipartisan Artificial Intelligence for the Armed Forces Act, which includes a number of key recommendations by the National Security Commission on Artificial Intelligence. These policy changes will help our military attract top AI talent and improve our military's effectiveness with respect to AI decision-making." Portman had introduced the Artificial Intelligence for the Armed Forces Act, from which the NDAA provisions originate.

Fox News contributor Karl Rove reacts to Trump blasting the media and Big Tech for being'massively corrupt.' WASHINGTON – Congressman-elect Jay Obernolte, a 50-year-old who is a video game developer by trade, will be a bit of an outlier in Congress. That's because members of Congress are not necessarily known as a technologically savvy bunch. This reputation has been earned by many awkward moments and stumbles by members when discussing tech, including in a 2018 hearing when Rep. Steve Cohen, D-Tenn., told Alphabet CEO Sundar Pichai, "I use your apparatus often," referring to Google, the search engine. But Obernolte – whose company FarSight Studios creates games for a variety of platforms ranging from PlayStation to iOS – said that, with the right approach, Congress can and should effectively address major tech issues ranging from net neutrality to Section 230. "I actually think that sometimes we get caught up in jargon from a technological standpoint, which is not helpful because I don't think the technology is unapproachable," he told Fox News in an interview.

Today, AI is used with increasing regularity across nearly every industry, with AI-based systems and technologies introducing new efficiencies, unlocking extraordinary opportunities and delivering powerful new insights and capabilities that were previously unattainable -- perhaps even unthinkable. Not only are health care and pharmaceuticals no exception to that rule, but life sciences actually represents one of the most innovative and exciting new frontiers for AI technology and machine learning. In recent years, AI usage has exploded in pharma, health care and biotech. Life sciences companies and institutions have used AI to develop and test new drugs, advance new therapeutics and treatment protocols, and, in some cases, completely transform the drug development and distribution process. The power and potential of AI-based technology in life sciences has arguably never been more important.

BEGIN ARTICLE PREVIEW: The work of data center management is changing quickly. There are hybrid environments and multi-cloud to deal with, edge computing, and a constant onslaught of rapidly evolving cybersecurity threats. AI promises to – any day now – come to the rescue of IT warriors, give them the silver bullet, the answer to all complexities they struggle against. Self-learning systems will adapt on their own to fast-evolving environments, protect against known and unknown threats, respond instantaneously with super-human accuracy, and do it all on the cheap. Related: Remote Data Center Management Tools Must Learn to Play TogetherIn theory, anyway; in practice, not so much. Not yet, and probably not for a long time, due to siloed systems and a lack of integrated management platforms. Data center complexity has been increasing exponentially, said Amr Ahmed, managing director at EY Consulting Services. I

This story appears in the December 2020 issue of Entrepreneur. How do you dress for the Pentagon? Most people hoping to secure a contract to send satellites into space would put on a suit. But Ben Lamm is not a fan of the expected. So on a visit to Washington, D.C., the night before his big meeting with Air Force generals, he was at a restaurant deliberating two important style questions: Which jean jacket would he wear? His dinner date that night knew the Pentagon well. It was Susan Penfield, a longtime executive VP at consulting giant Booz Allen Hamilton, which does a lot of work with the federal government (as well as with Lamm). "I don't know if it will fly at the Pentagon," she told him -- but if he insisted on a scarf, she suggested one with all-American red, white, and blue colors. The next morning, Lamm thought, Maybe not and threw on his Alexander McQueen -- black with white skulls.

The history of AI has included several "waves" of ideas. The first wave, from the mid-1950s to the 1980s, focused on logic and symbolic hand-encoded representations of knowledge, the foundations of so-called "expert systems". The second wave, starting in the 1990s, focused on statistics and machine learning, in which, instead of hand-programming rules for behavior, programmers constructed "statistical learning algorithms" that could be trained on large datasets. In the most recent wave research in AI has largely focused on deep (i.e., many-layered) neural networks, which are loosely inspired by the brain and trained by "deep learning" methods. However, while deep neural networks have led to many successes and new capabilities in computer vision, speech recognition, language processing, game-playing, and robotics, their potential for broad application remains limited by several factors. A concerning limitation is that even the most successful of today's AI systems suffer from brittleness-they can fail in unexpected ways when faced with situations that differ sufficiently from ones they have been trained on. This lack of robustness also appears in the vulnerability of AI systems to adversarial attacks, in which an adversary can subtly manipulate data in a way to guarantee a specific wrong answer or action from an AI system. AI systems also can absorb biases-based on gender, race, or other factors-from their training data and further magnify these biases in their subsequent decision-making. Taken together, these various limitations have prevented AI systems such as automatic medical diagnosis or autonomous vehicles from being sufficiently trustworthy for wide deployment. The massive proliferation of AI across society will require radically new ideas to yield technology that will not sacrifice our productivity, our quality of life, or our values.

Our title alludes to the three Christmas ghosts encountered by Ebenezer Scrooge in \textit{A Christmas Carol}, who guide Ebenezer through the past, present, and future of Christmas holiday events. Similarly, our article will take readers through a journey of the past, present, and future of medical AI. In doing so, we focus on the crux of modern machine learning: the reliance on powerful but intrinsically opaque models. When applied to the healthcare domain, these models fail to meet the needs for transparency that their clinician and patient end-users require. We review the implications of this failure, and argue that opaque models (1) lack quality assurance, (2) fail to elicit trust, and (3) restrict physician-patient dialogue. We then discuss how upholding transparency in all aspects of model design and model validation can help ensure the reliability of medical AI.