The rise of artificial intelligence is entrenching more economic and political power in the hands of Big Tech companies, according to researchers at New York University (NYU) who argue AI must undergo more scrutiny and regulation. AI Now, a research institute at NYU, released a new report detailing how major tech companies wield significant control over AI, arguing such influence must be addressed now before the situation gets too out of hand. "This report is written with this task in mind: we are drawing from our experiences inside and outside government to outline an agenda for how we -- as a group of individuals, communities, and institutions deeply concerned about the impact of AI unfolding around us -- can meaningfully confront the core problem that AI presents, and one of the most difficult challenges of our time: the concentration of economic and political power in the hands of the tech industry -- Big Tech in particular," the document states. The authors add that AI development has been "foundationally reliant" on resources controlled by Big Tech, including data and computer power. Plus, they write, Big Tech companies have gained geopolitical importance by playing a central role in the U.S.-China race for AI supremacy, thereby conflating "the continued dominance of Big Tech as synonymous with U.S. economic prowess, and [ensuring] the continued accrual of resources and political capital to these companies."

Federal Trade Commission chair Lina Khan is hiring three artificial intelligence researchers from New York University to advise on emerging technology issues. Khan announced Friday that NYU's Meredith Whittaker, Amba Kak, and Sarah Myers West are joining an AI strategy group within the FTC's Office of Policy Planning. They join Olivier Sylvain, a law professor from Fordham University, who is serving as Khan's senior adviser on technology. The new hires likely signal an increased regulatory emphasis on AI technologies like facial recognition, which is also an area of expertise for Alvaro Bedoya, who's been nominated as an FTC commissioner.

Some software developers are now letting artificial intelligence help write their code. They're finding that AI is just as flawed as humans. Last June, GitHub, a subsidiary of Microsoft that provides tools for hosting and collaborating on code, released a beta version of a program that uses AI to assist programmers. Start typing a command, a database query, or a request to an API, and the program, called Copilot, will guess your intent and write the rest. Alex Naka, a data scientist at a biotech firm who signed up to test Copilot, says the program can be very helpful, and it has changed the way he works.

Magnetic resonance imaging is an invaluable tool in the medical field, but it's also a slow and cumbersome process. It may take fifteen minutes or an hour to complete a scan, during which time the patient, perhaps a child or someone in serious pain, must sit perfectly still. NYU has been working on a way to accelerate this process, and is now collaborating with Facebook with the goal of cutting down MRI durations by 90 percent by applying AI-based imaging tools. It's important at the outset to distinguish this effort from other common uses of AI in the medical imaging field. An X-ray, or indeed an MRI scan, once completed, could be inspected by an object recognition system watching for abnormalities, saving time for doctors and maybe even catching something they might have missed.

Early in August, NYU professor Siddharth Garg checked for traffic, and then put a yellow Post-it onto a stop sign outside the Brooklyn building in which he works. When he and two colleagues showed a photo of the scene to their road-sign detector software, it was 95 percent sure the stop sign in fact displayed a speed limit. The stunt demonstrated a potential security headache for engineers working with machine learning software. The researchers showed that it's possible to embed silent, nasty surprises into artificial neural networks, the type of learning software used for tasks such as recognizing speech or understanding photos. Malicious actors can design that behavior to emerge only in response to a very specific, secret signal, as in the case of Garg's Post-it.

Early in August, NYU professor Siddharth Garg checked for traffic, and put a yellow Post-it onto a stop sign outside the Brooklyn building in which he works. When he and two colleagues showed a photo of the scene to their road-sign detector software, it was 95 percent sure the stop sign in fact displayed a speed limit. The stunt demonstrated a potential security headache for engineers working with machine learning software. The researchers showed it's possible to embed silent, nasty surprises into artificial neural networks, the type of learning software used for tasks such as recognizing speech or understanding photos. Malicious actors can design that behavior to emerge only in response to a very specific, secret, signal, as in the case of Garg's Post-it.