Though police have been using facial recognition technology for the last two decades to try to identify unknown people in their investigations, the practice of putting the majority of Americans into a perpetual photo lineup has gotten surprisingly little attention from lawmakers and regulators. Lawmakers, civil liberties advocates and police chiefs have debated whether and how to use the technology because of concerns about both privacy and accuracy. But figuring out how to regulate it is tricky. So far, that has meant an all-or-nothing approach. City Councils in Oakland, Portland, San Francisco, Minneapolis and elsewhere have banned police use of the technology, largely because of bias in how it works.

Systems designed to detect deepfakes--videos that manipulate real-life footage via artificial intelligence--can be deceived, computer scientists showed for the first time at the WACV 2021 conference which took place online Jan. 5 to 9, 2021. Researchers showed detectors can be defeated by inserting inputs called adversarial examples into every video frame. The adversarial examples are slightly manipulated inputs which cause artificial intelligence systems such as machine learning models to make a mistake. In addition, the team showed that the attack still works after videos are compressed. "Our work shows that attacks on deepfake detectors could be a real-world threat," said Shehzeen Hussain, a UC San Diego computer engineering Ph.D. student and first co-author on the WACV paper.

In an effort to create a standard set of tools that would help data science teams collaborate on AI development, an infrastructure initiative launched this week will promote a unified stack for developing and scaling machine learning models. The AI Infrastructure Alliance said this week it will initially focus on creating Canonical Stack for AI envisioned as a development platform for machine learning models destined for enterprise applications. As with previous hardware and software stacks, the machine learning initiative seeks to forge an AI development infrastructure that would free developers to address more complex problems. As machine learning models move to the edge, the alliance said it would create a single platform that integrates existing AI technologies into a common framework that would accelerate and improve MLOps and edge applications. Establishing a so-called canonical AI stack for machine learning and MLOps would include developing best practices and architectures used to scale machine learning models in edge and other applications.

On a recent afternoon, Mr. Luckey, dressed as if ready for the beach in a Hawaiian-like shirt, shorts and flip-flops, joined other Anduril employees at the company's testing site near Camp Pendleton, a Marine training facility. As the drone took off and swooped between the hills, Mr. Luckey said it could track an object and capture detailed images from seven football fields away. Using many of the artificial intelligence technologies that underpin self-driving cars, Anduril's drones can identify and track vehicles, people and other objects largely on their own. The drones are not armed, but could be useful for guarding bases or reconnaissance. The same sensor technologies that allow the drones to fly on their own could also be used to identify targets on a battlefield.

Most companies are struggling to develop working artificial intelligence strategies, according to a new survey by cloud services provider Rackspace Technology. The survey, which includes 1,870 organizations in a variety of industries, including manufacturing, finance, retail, government, and healthcare, shows that only 20 percent of companies have mature AI/machine learning initiatives. The rest are still trying to figure out how to make it work. Lower costs, improved precision, better customer experience, and new features are some of the benefits of applying machine learning models to real-world applications. But machine learning is not a magic wand.

Robotic dogs are helping prevent the spread of COVID-19 in Seville, Spain.

Montezuma's Revenge is one of the most challenging Atari games An artificial intelligence that can remember its previous successes and use them to create new strategies has achieved record high scores on some of the hardest video games on classic Atari consoles. Many AI systems use reinforcement learning, in which an algorithm is given positive or negative feedback on its progress towards a particular goal after each step it takes, encouraging it towards a particular solution. This technique was used by AI firm DeepMind to train AlphaGo, which beat a world champion Go player in 2016. Adrien Ecoffet at Uber AI Labs and OpenAI in California and his colleagues hypothesised that such algorithms often stumble upon encouraging avenues but then jump to another area in the hunt for something more promising, leaving better solutions overlooked. "What do you do when you don't know anything about your task?" says Ecoffet. "If you just wave your arms around, it's unlikely that you're ever going to make a coffee."

The following article by Zhang et al. is well-known for having highlighted that widespread success of deep learning in artificial intelligence brings with it a fundamental new theoretical challenge, specifically: Why don't today's deep nets overfit to training data? This question has come to animate the theory of deep learning. Let's understand this question in context of supervised learning, where the machine's goal is to learn to provide labels to inputs (for example, learn to label cat pictures with "1" and dog pictures with "0"). Deep learning solves this task by training a net on a suitably large training set of images that have been labeled correctly by humans. The parameters of the net are randomly initialized and thereafter adjusted in many stages via the simplest algorithm imaginable: gradient descent on the current difference between desired output and actual output.

In January 2020, Robert Williams of Farmington Hills, MI, was arrested at his home by the Detroit Police Department. He was photographed, fingerprinted, had his DNA taken, and was then locked up for 30 hours. He had not committed one; a facial recognition system operated by the Michigan State Police had wrongly identified him as the thief in a 2018 store robbery. However, Williams looked nothing like the perpetrator captured in the surveillance video, and the case was dropped. Rewind to May 2019, when Detroit resident Michael Oliver was arrested after being identified by the very same police facial recognition unit as the person who stole a smartphone from a vehicle.

Seeking to call into question the mental acuity of his opponent, Donald Trump looked across the presidential debate stage at Joseph Biden and said, "So you said you went to Delaware State, but you forgot the name of your college. Biden chuckled, but viewers may have been left wondering: did the former vice president misstate where he went to school? Those who viewed the debate live on an app from the London-based company Logically were quickly served an answer: the president's assertion was false. A brief write-up posted on the company's website the next morning provided links to other fact-checks from National Public Radio and the Delaware News Journal on the same claim, which explain that Biden actually said his first Senate campaign received a boost from students at the school. Logically is one of a number of efforts, both commercial and academic, to apply techniques of artificial intelligence (AI), including machine learning and natural language processing (NLP), to identify false ...