This story was originally published by Slate and is reproduced here as part of the Climate Desk collaboration. It also contains spoilers for the film Don't Look Up. Streaming just in time for Christmas, Adam McKay's decidedly uncheery Netflix comedy, Don't Look Up, finds Jennifer Lawrence and Leonardo DiCaprio playing a pair of intrepid astronomers as they try (and mostly fail) to warn the world about a planet-killing comet that's hurtling toward Earth. From the beginning, the scientists' efforts are marked by futility, encapsulated in an early scene in which Kate Dibiasky (Lawrence) and Randall Mindy (DiCaprio) are brought to the White House to debrief President Janie Orlean (Meryl Streep) on the impending extinction-level event. Predictably, the meeting goes disastrously.

At the end of last month, a set of principles and advice on ethics in the use of Artificial Intelligence (AI) was known, adopted for the first time jointly and unanimously by the 193 member states of the General Council of the UNESCO. Beyond the uniqueness of its universal character, it is about Unesco launched a guide to improve the relationship between humans and robots and combines ethical issues to a warning voice that has been heard for a long time. There are already several international political organizations that have been warning about the need to provide an ethical component to what is undoubtedly the most notable advance in applied science of our time. In fact, in November but from '19 the European Union (EU) had published its Ethical Guidelines for a reliable artificial intelligence whose proposal revolves around the collateral effects, or unforeseen risks, that the implementation of disruptive technologies like this can generate. Likewise, in April of this year we learned about the European Commission regulation regarding the use of algorithms able to learn and make decisions.

What a ride it has been in the Transportation and Logistics (T&L)sector regarding the B2C eCommerce growth boom world-wide, much of this driven by the global retail sales growth during COVID-19. This accelerated growth and now with global trade rapidly rebounding, the timing is right for the transportation and logistics industry to advance smarter digital transformations. According to McKinsey, this industry must be completely digital to secure its future – but what will it take? The future although seems rosy, is complex and challenging due to rapid industry consolidations, new technology acceleration, ever constant regulatory changes such as GDBR, and of course, Brexit impacting European markets. The World Trade Organization (WTO) has been most vocal reinforcing the importance of the T&L Industry to take heed on the importance of customer experiences – how courier drivers ship, route and deliver parcels and products with agile speed has become the new normal.

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In a nondescript hospital laboratory in Denbighshire, a quiet revolution is taking place. It involves nothing more glamorous than a digital scanning machine and a computer terminal. But the ramifications for medicine could be profound. Here, it could be argued, virtual consultants are being generated. READ MORE: Wales' worst ever shipwreck disaster was off Anglesey - and no one's ever heard of it These are "experts" who, every day, are getting better and better at their jobs.

The fact that governments are serious about AI adoption is also reflected in the increasing share of AI investments--84% of agencies believe their AI investments will increase by 6% or more in the next fiscal year.4 With budget analysis showing that US Federal funding for AI research and development alone is expected to have already grown by nearly 50% to more than US$6 billion in FY 2021, government leaders are clearly bullish on AI.5As a result, they are making significant investments and exploring new AI projects. With enthusiasm and a growing pool of resources, many government organizations have launched pilots to explore how AI can help their organizations. Government organizations are exploring a range of AI use cases from speech recognition to predictive maintenance. Government sectors such as defense and health that have a long history of AI experimentation are among the leaders in fields such as responsible AI and data-sharing.

This article is part of our reviews of AI research papers, a series of posts that explore the latest findings in artificial intelligence. There's growing concern about new security threats that arise from machine learning models becoming an important component of many critical applications. At the top of the list of threats are adversarial attacks, data samples that have been inconspicuously modified to manipulate the behavior of the targeted machine learning model. Adversarial machine learning has become a hot area of research and the topic of talks and workshops at artificial intelligence conferences. Scientists are regularly finding new ways to attack and defend machine learning models.

Wind power is a powerful tool for reducing carbon emissions that cause climate change. The turbines, however, can be a threat to birds and bats, which is why experts are looking for--and finding--ways to eliminate the danger. The US government has allocated $13.5 million to look for solutions. But, already a Boulder, Colorado company has produced a camera- and AI-based technology that can recognize eagles, hawks and other raptors as they approach in enough time to pause turbines in their flight path. Their tool, called IdentiFlight, can detect 5.62 times more bird flights than human observers alone, and with an accuracy rate of 94 percent.

OpenAI is committed to developing general-purpose artificial intelligence that benefits all humanity, and we believe that achieving our goal requires expertise in public policy as well as technology. Will served three terms in the U.S. House of Representatives, has been a leading voice on technology policy, and coauthored bipartisan legislation outlining a national strategy for artificial intelligence. "Will brings a rare combination of expertise--he deeply understands both artificial intelligence as well as public policy, both of which are critical to a successful future for AI," said Sam Altman, OpenAI's CEO. Greg Brockman, OpenAI's chairman and Chief Technology Officer, added, "'AI public policy expert' isn't exactly a common title, and Will is squarely one of the leading ones. "I've been blown away by the scientific advances made by the team at OpenAI, and I've been inspired by their commitment to developing AI responsibly," said Will Hurd. OpenAI is committed to developing general-purpose artificial intelligence that benefits all humanity, and we believe that achieving our goal requires expertise in public policy as well as technology. Will served three terms in the U.S. House of Representatives, has been a leading voice on technology policy, and coauthored bipartisan legislation outlining a national strategy for artificial intelligence. "Will brings a rare combination of expertise--he deeply understands both artificial intelligence as well as public policy, both of which are critical to a successful future for AI," said Sam Altman, OpenAI's CEO. Greg Brockman, OpenAI's chairman and Chief Technology Officer, added, "'AI public policy expert' isn't exactly a common title, and Will is squarely one of the leading ones.

Many online platforms today (such as Amazon, Netflix, Spotify, LinkedIn, and AirBnB) can be thought of as two-sided markets with producers and customers of goods and services. Traditionally, recommendation services in these platforms have focused on maximizing customer satisfaction by tailoring the results according to the personalized preferences of individual customers. However, our investigation reinforces the fact that such customer-centric design of these services may lead to unfair distribution of exposure to the producers, which may adversely impact their well-being. On the other hand, a pure producer-centric design might become unfair to the customers. As more and more people are depending on such platforms to earn a living, it is important to ensure fairness to both producers and customers. In this work, by mapping a fair personalized recommendation problem to a constrained version of the problem of fairly allocating indivisible goods, we propose to provide fairness guarantees for both sides. Formally, our proposed {\em FairRec} algorithm guarantees Maxi-Min Share ($\alpha$-MMS) of exposure for the producers, and Envy-Free up to One Item (EF1) fairness for the customers. Extensive evaluations over multiple real-world datasets show the effectiveness of {\em FairRec} in ensuring two-sided fairness while incurring a marginal loss in overall recommendation quality. Finally, we present a modification of FairRec (named as FairRecPlus) that at the cost of additional computation time, improves the recommendation performance for the customers, while maintaining the same fairness guarantees.