Yaron Singer climbed the tenure track ladder to a full professorship at Harvard in seven years, fueled by his work on adversarial machine learning, a way to fool artificial intelligence models using misleading data. Now, Singer’s startup, Robust Intelligence, which he formed with a former Ph.D. advisee and two former students, is emerging from stealth to take his research to market. (...)

Fraudsters and other bad actors can exploit the relative inflexibility of artificial intelligence models in processing unfamiliar data. For example, Singer says, a check for $401 can be manipulated by adding a few pixels that are imperceptible to the human eye yet cause the AI model to read the check erroneously as $701. “If fraudsters get their hands on checks, they can hack into these apps and start doing this at scale,” Singer says. Similar modifications to data inputs can lead to fraudulent financial transactions, as well as spoofed voice or facial recognition. (...)

Robust Intelligence is launching with two products, an AI firewall and a “red team” offering, in which Robust functions like an adversarial attacker. The firewall works by wrapping around an organization’s existing AI model to scan for contaminated data via Robust’s algorithms. The other product, called Rime (or “Robust Intelligence Machine Engine”), performs a stress test on a customer’s AI model by inputting basic mistakes and deliberately launching adversarial attacks on the model to see how it holds up. (...)

https://www.forbes.com/sites/kenrickcai/2020/10/21/robust-intelligence-adversarial-machine-learning-series-a-sequoia/?sh=421a1693457a