Corporate leaders including Google CEO Sundar Pichai, Microsoft President Brad Smith, Tesla and SpaceX CEO Elon Musk, and IBM ex-CEO Ginni Rometty have called for increased regulation of artificial intelligence. So have politicians on the both sides of the aisle, as have respected scholars at academic research institutes and think tanks. At the root of the call to action is the understanding that, for all of its many benefits, AI also presents many risks. Concerns include biased algorithms, privacy violations, and the potential for injuries attributable to defective autonomous vehicle software. With the increasing adoption of AI-based solutions in areas such as criminal justice, health care, robotics, financial services, and education, there will be incentives that conflict corporate interests with societal benefits.

I reach out to you still contemplating the profundity of what Mark Zuckerberg told his congressional inquisitors on Wednesday: "The space of people connecting with other people is a very large space." So large, it even includes newsletters in your inbox. Three clear winners and one loser emerged from Wednesday's Big Tech hearing in Washington. The winners were Rep. Pramila Jayapal, our new "eviscerator in chief"; Alphabet CEO Sundar Pichai's future career as an anger-management therapist; and Tim Wu. When the going gets tough in coming weeks, I will close my eyes and picture the Google CEO soothingly saying "congressman" with infinite patience, as he did at the beginning of all his answers. The more irate the congressional questioner, the more patient, measured, and empathetic his "congressman" sounded.

Examples of the researchers' 3D adversarial logo attack using different 3D object meshes, with the aim of fooling a YOLOV2 detector. Over the past decade, researchers have developed a growing number of deep neural networks that can be trained to complete a variety of tasks, including recognizing people or objects in images. While many of these computational techniques have achieved remarkable results, they can sometimes be fooled into misclassifying data. An adversarial attack is a type of cyberattack that specifically targets deep neural networks, tricking them into misclassifying data. It does this by creating adversarial data that closely resembles and yet differs from the data typically analyzed by a deep neural network, prompting the network to make incorrect predictions, failing to recognize the slight differences between real and adversarial data.

Will Hurd and Robin Kelly are from opposite sides of the ever-widening aisle, but they share a concern that the United States may lose its grip on artificial intelligence, threatening the American economy and the balance of world power. They want to cut off China's access to AI-specific silicon chips and push Congress and federal agencies to devote more resources to advancing and safely deploying AI technology. Although Capitol Hill is increasingly divided, the bipartisan duo claims to see an emerging consensus that China poses a serious threat and that supporting US tech development is a vital remedy. Kelly, a member of the Congressional Black Caucus, says that she has found many Republicans, not just Hurd, the only Black Republican in the House, open to working together on tech issues. "I think people in Congress now understand that we need to do more than we have been doing," she says.

Las Vegas's search for an adaptive security solution led it to deploy Darktrace AI across its enterprise, cloud and industrial networks. Background In recent years, Las Vegas has become a prototypical Smart City. As riders glide down the Strip aboard the first completely autonomous shuttle ever deployed on a public roadway, they are unlikely to notice much trash on the sidewalk – the city's surveillance cameras stream to an AI service that directs clean-up crews towards concentrations of litter. And when rush hour approaches, its passengers can rest assured that an array of connected sensors are helping officials anticipate gridlock at busy intersections. But while smart infrastructure enables Las Vegas to achieve new heights of efficiency, conventional security tools are largely ill-equipped to defend the hybrid cloud and industrial networks that power this infrastructure.

This is where artificial intelligence comes in, canceling out the noise from the sensor … But then you could also cancel it out with machine learning".

This paper describes the SemEval-2020 shared task "Assessing Humor in Edited News Headlines." The task's dataset contains news headlines in which short edits were applied to make them funny, and the funniness of these edited headlines was rated using crowdsourcing. This task includes two subtasks, the first of which is to estimate the funniness of headlines on a humor scale in the interval 0-3. The second subtask is to predict, for a pair of edited versions of the same original headline, which is the funnier version. To date, this task is the most popular shared computational humor task, attracting 48 teams for the first subtask and 31 teams for the second.

Assistant HHS Secretary Admiral Brett Giroir weighs in on the coronavirus pandemic on'The Daily Briefing.' The leaders of the House Problem Solvers Caucus Friday expressed optimism that Republicans and Democrats will soon come together on a major coronavirus deal to continue supplemental unemployment benefits, help struggling small businesses and fund the reopening of schools. Tom Reed, R-N.Y., and Josh Gottheimer, D-N.J., predict an agreement will come within a matter of days. Negotiators are under pressure to act due to Friday's expiration of $600-per-week federal unemployment benefits, schools needing help to reopen this month and lawmakers wanting to preserve their August recess. "I think we're going to get this done this coming week," Gottheimer said in an interview with Fox News on Friday.

A PwC study in 2017 estimated the world would gain $15.7 trillion by 2030 if artificial intelligence (AI) was adopted across nations. The study said that AI would first lead to productivity enhancement, and a major portion of gains would accrue from consumer-side effects. China, it had said, could see its GDP rising by around a fourth as it was using AI more aggressively. Although the study did not estimate how much India would gain from using AI, new research by Icrier along with Nasscom and Google shows that even a marginal increase in artificial intelligence adoption may add 2.5% to GDP in the immediate term. Moreover, it highlights that if the government spends the Rs 7,000 crore it had envisaged for the national AI programme, GDP could get boosted by as much as $86 billion.

The experimental use of AI spread across sectors and moved beyond the internet into the physical world. Stores used AI perceptions of shoppers' moods and interest to display personalized public ads. Schools used AI to quantify student joy and engagement in the classroom. Employers used AI to evaluate job applicants' moods and emotional reactions in automated video interviews and to monitor employees' facial expressions in customer service positions. It was a year notable for increasing criticism and governance of AI related to emotion and affect.