Michael Waltz (R-FL) and Don Beyer (D-VA) produced a deepfake video for the U.S. House Science subcommittee to demonstrate the threat such disinformation presents. Michael Waltz (R-FL) and Don Beyer (D-VA) produced an artificial intelligence-doctored political video, or deepfake, for the U.S. House Science subcommittee to demonstrate the threat such disinformation presents. Lawmakers are worried of malefactors using deepfakes to disrupt and divide U.S. voters in the run-up to the 2020 election, and Waltz and Beyer are urging investment in deepfake-detection solutions, especially as production tools become increasingly affordable and accessible. State University of New York at Albany's Siwei Lyu, who helped craft the deepfake demo, said his software could generate deepfakes of a minute-long YouTube video in eight hours. Meanwhile, the University of California, Berkeley's Hany Farid cited the sluggish progress of technology platforms like Facebook and Google to address deepfakes.

The DeepRacer league, developed by Amazon Web Services, is designed to teach a branch of artificial intelligence known as reinforcement learning. Amazon Web Services (AWS) has developed the DeepRacer League, a competition designed to teach a branch of artificial intelligence (AI) known as reinforcement learning, in which algorithms learn the correct way to perform an action based on trial and error, and observations. As part of the DeepRacer League, teams or individuals build and train AI algorithms using Amazon SageMaker software, then deploy them to self-driving model cars measuring about 10 inches long, which they race around a track roughly 17 feet by 26 feet. Morningstar is one of the companies participating in the DeepRacer League, and thanks to the training, the company expects to have dozens of projects based on reinforcement learning and other machine learning techniques in deployment by the end of next year. AWS developed the DeepRacer program in an effort to teach software developers about machine learning in a more engaging way than reading scientific articles.

ImageNet will remove 600,000 images of people stored on its database after an art project exposed racial bias in the program's artificial intelligence system. Created in 2009 by researchers at Princeton and Stanford, the online image database has been widely used by machine learning projects. The program has pulled more than 14 million images from across the web, which have been categorized by Amazon Mechanical Turk workers -- a crowdsourcing platform through which people can earn money performing small tasks for third parties. According to the results of an online project by AI researcher Kate Crawford and artist Trevor Paglen, prejudices in that labor pool appear to have biased the machine learning data. Training Humans -- an exhibition that opened last week at the Prada Foundation in Milan -- unveiled the duo's findings to the public, but part of their experiment also lives online at ImageNet Roulette, a website where users can upload their own photographs to see how the database might categorize them.

There's a reason why security experts picked 2019 as the year in which the first artificial intelligence hack takes place. Peter Bailey explains how hackers and defenders are arming themselves. With cybercrime as much a business as any other, albeit one on the wrong side of the law, hackers are already sizing up the potential for artificial intelligence (AI) to further their goals. It's the flip side of a coin: on the one side IT professionals are using AI to help identify and eliminate threats more effectively, and even anticipate attacks before they happen. On the other, intelligent malware offers the potential of adapting its payload and evading detection.

The U.S. Defense Department has made battlefield-ready artificial intelligence a priority in its planning, seeking a massive increase in related spending to counter China's rapid advances in the field. The department's Joint Artificial Intelligence Center requested $268 million under the draft federal budget for the fiscal year that began Oct. 1, roughly triple the figure from the previous year. "I am optimistic that 2020 will be a breakout year for the department when it comes to fielding AI-enabled capabilities," Lt. Gen. Jack Shanahan, the center's director, said in a recent press briefing. "For fiscal year '20, our biggest project will be what we are calling'AI for maneuver and fires,' with individual lines of effort or product lines oriented on warfighting operations," he told reporters. The U.S. military's AI development has focused on predictive maintenance for weapons systems, along with areas such as humanitarian missions and cybersecurity.

Insurers should consider self-regulating machine learning practices before regulations are imposed on them, a speaker suggested last week at the Connected Insurance Canada conference in Toronto. Moderator Stephen Applebaum, principal at Insurance Solutions Group, asked panelists if regulators understand machine learning and their perspectives on using machine learning in insurance, particularly since it's difficult to explain the concept. Koosha Golmohammadi, director of advanced analytics at Manulife, acknowledged that there are issues about the explanation of machine learning models. Machine learning, a subset of artificial intelligence, allows systems to automatically learn and improve without being explicitly programmed. "This is new in Canada," Golmohammadi said.

Hexwave, the AI-powered weapon imaging and detection technology, is now set to come to Toronto Pearson International Airport for testing. With its deployment, Pearson airport will become the first airport to sign up for testing following the completion of a collaboration agreement between the Greater Toronto Airports Authority and Liberty Defense Holdings Ltd. Hexwave uses 3D radar imaging and artificial intelligence to detect and identify weapons; thus enabling security forces to detect threats from outside the property with no obstruction to the crows that exist in these busy locations. Toronto Pearson International Airport is Canada's largest airport and saw 49.5 million passengers come through its doors in 2018. "The GTAA is committed to a proactive security philosophy that stays ahead of emerging threats across our aviation infrastructure to minimize risk for passengers, employees, and property. We track emerging technologies with the goal of balancing our operational security needs with overall customer service to make moving through Toronto Pearson a positive experience," said Dwayne Macintosh, the director of corporate safety and security for the GTAA, said in a press release.

In recent years, our fascination with the potential of AI has taken a more starry-eyed turn, as shown in the 2013 sci-fi drama "Her," where the main character falls in love with a virtual assistant. In reality, artificial intelligence (AI) technology is quickly permeating every aspect of our lives. From Amazon's voice-activated Alexa to writing technology that helps managers craft job postings, AI is in our hearts, homes and workplaces. And it's only going to become a bigger part of our lives: Experts call the rise of AI the driving force behind the fourth industrial revolution. On a recent afternoon at the NVIDIA robotics research lab in Seattle's University District, researchers use a simulated kitchen to test robots' ability to perform simple tasks such as grabbing objects.

Autonomous vehicles are on the rise to combat the country's motor vehicle fatalities. This article by Red Hat's Pete Brey takes a dive on how machine learning, artificial intelligence, and deep learning work together to achieve this goal. Houston, we have a problem. So does Los Angeles, Atlanta, New York, D.C, Boston, and all cities, towns, and counties throughout the United States. That problem is motor vehicle fatalities.

For those who are new to AI, Machine Learning, and Deep Learning, I recommend taking a look at the following article entitled "An Introduction to AI." I will refer to Machine Learning and Deep Learning as being subsets of AI. Furthermore, this article is non-exhaustive in relation to potential applications of AI to healthcare and Quantum Computing to various sectors of the economy. The reason for the focus on AI in healthcare is in light of recent articles by a few senior medical practitioners in the US expressing concern about the role of AI in healthcare. Some of the concerns expressed, such as the need for improved sharing of data by healthcare participants including hospitals and ensuring the highest quality in the preparation of data, are entirely valid and I take the view that the need for access to data and sharing of data by hospitals may need to become a matter of political and regulatory concern.