A.I. IN THE NEWS
Waymo gets a big boost to stay afloat. The self-driving car company announced it has raised a whopping $2.5 billion in additional funding from investors that included its former parent company Alphabet, private equity firm Silver Lake, tech investment house Tiger Global, Silicon Valley venture capitalists Andreesen Horowitz, car-dealership chain AutoNation Inc., and Fidelity, among others, according to The Wall Street Journal. The size of the fundraise, coming just a year after the company raised an initial $3.5 billion from investors outside of Alphabet, may indicate just how much cash the company is burning as it struggles to make good on its vision of creating roving fleets of self-driving taxis throughout the U.S. The company is operating a fully-autonomous ride-hailing service in parts of Phoenix, and recently began a trial in San Francisco. But the company is also turning to freight delivery to make revenues, announcing a new partnership with J.B. Hunt Transport Services for long-haul self-driving trucks.
Zillow is using deep learning to improve its automatic home valuations. The Internet-based real estate listing company has been using software to provide automatic house valuations for some time. But the system was based on using lots of different algorithms, each trained on a data for a particular local area. Now the company, according to an article in Wired, has started using a single neural network to make the estimates. The result has been that Zillow, which buys some houses itself, has been able to make more cash offers for properties. The company says its new deep learning system reduced price errors by 11.5% for off-market homes in 30 regions across the U.S. and allowed the company to update its valuations more frequently.
Mayflower autonomous ship runs into trouble. Ocean research non-profit ProMare and IBM have been working since 2016 to create a solar-powered trimaran that is capable of sailing autonomously across The Atlantic from Plymouth, England, to Massachusetts to commemorate the 400th Anniversary of the original Mayflower's 1620 arrival with the Pilgrims. The project faced numerous delays even before the pandemic hit, and missed its initial window to make it across the Atlantic in time for the anniversary celebrations, which in any case were curtailed due to COVID-19. It finally set sail on its Atlantic crossing last week, only to run into serious technical difficulties after just three days at sea, according to the BBC. Now it has been instructed to motor slowly back to Plymouth for repairs.
EU data privacy regulators call for an outright ban on the use of facial recognition and other biometric-based identification systems in all "publicly-accessible" places. A group representing all the national data protection authorities from the 27-member states of the bloc as well as the bloc's own data privacy watchdog jointly called for a complete ban on the use of facial recognition and other real-time biometric-based systems designed to identify individuals in "publicly-accessible" spaces, including shops or stadiums. The call goes much further than even the strict limits and need to clearly inform people they were being monitored that the EU proposed in the recently introduced Artificial Intelligence Act. Privacy campaigners had faulted that proposed law allowing too much leeway for possible use of facial recognition by law enforcement and others. My Fortune colleague David Meyer has the story here.
A.I. drug discovery Insilico raised more money from Warburg Pincus, announces partnership with Israeli pharma company Teva. Hong Kong-based Insilico, which is one of many companies around the world trying to use A.I. for drug discovery, announced it has raised another $255 million in venture capital funding from a group of investors lead by Warburg Pincus, the company said Tuesday. The company also announced a new collaboration with Israel-based company Teva to give the pharmaceutical giant access to Insilico's software that helps identify new targets for possible drugs. Using that software, Insilico itself discovered a new target for pulmonary fibrosis and then designed a completely new compound to hit that target, which it has brought through to the verge of human clinical trials.
EYE ON A.I. TALENT
Frontdoor, a Tennessee-based company that provides service plans for home repairs and maintenance,
has hired Tony Bacos as its
senior vice president and
chief digital officer. Bacos had been a long-serving
Amazon technology executive, most recently serving as vice president and chief technology officer for Amazon Fashion.
Olea Edge Analytics, an Austin, Texas, company that builds intelligent computing systems for water utilities, has named Wariya Erez as its director of A.I. & analytics. Erez was formerly principal data scientist at Home Depot.
Abacai Group, a London-based insurance technology startup founded by Mark Wilson, the former CEO of insurance giant Aviva, has named
Pierre du Toit joins as
chief artificial intelligence officer, according to
a story in
Insurance Times. Toit previously served as chief analytics officer at health insurance firm
Vitality.
EYE ON A.I. RESEARCH
Facebook has unveiled new training libraries to develop A.I. systems that are more robust to manipulated data. One problem with A.I. systems is that they can sometimes be fooled into classifying two pieces of data that are, to a human, fundamentally the same or very similar, in different ways. This opens up a way for people to game these systems, trying various subtle manipulations—brightening an image slight, capitalizing random characters in a piece of text or adding a few nonsense words, or slightly distorting an audio track—that don't detract much from a humans ability to understand that image, passage or soundtrack, but which might mean that a system designed to do something like block copyrighted material from being uploaded on to a social media platform without permission or stop a piece of content that has already been identified as hate speech from being reposted, will fail to spot that content. These manipulations can be applied manually by people, or, in some cases, they can use A.I. itself to try to find the most minimal manipulations that will still fool the A.I. watchdog.
Facebook has now open sourced a Python data library that it calls AugLy that will help anyone creating an A.I. system train it to be less susceptible to these kinds of attacks. The library include different kinds of "data augmentations" (manipulations of the data) for different "modalities" of data: text, images, and video, for example. And it even includes many multimodal examples, where text might be overlaid on an image. In a blog announcing AuGly, Facebook says "Data augmentations are vital to ensure robustness of AI models. If we can teach our models to be robust to perturbations of unimportant attributes of data, models will learn to focus on the important attributes of data for a particular use case." The company also said it used AuGly internally to train its SimSearchNet, its system for detecting identical or near-identical pieces of content in order to prevent disinformation, hate speech or copyright infringing material from being slightly tweaked and reposted. It says it also used AuGly to help evaluate how robust the competitors were in its Deepfake Detection Challenge contest.
FORTUNE ON A.I.
How top CFOs are incorporating tech into their roles—by Anne Sraders
Europe’s privacy regulators call for a ban on facial recognition in publicly accessible spaces—by David Meyer
Why investors are backing this former Facebook manager’s ‘explainable A.I.’ startup—by Jonathan Vanian
A.I. insurance firm Tractable marks ‘unicorn’ status as it expands from cars into property claims—by Jeremy Kahn
Facebook says it’s made a big leap forward in detecting deepfakes—by Jeremy Kahn
BRAIN FOOD
Companies using A.I. need to have an "adversarial" mindset. If there's a common thread running through this week's Eye on A.I., it's that people designing A.I. systems need to think hard about how someone might try abuse, misuse, trick, or manipulate that system. That's true when talking about deepfakes, and also in talking about how to use augmented datasets to make A.I. systems more resilient to slightly tweaked examples. Now here's a third case courtesy of Alex Polyakov, the founder and CEO of Israeli A.I. security startup Adversa.Al. Polyakov and his team were, with some very subtle manipulations of a photograph of Polyakov's face, able to trick the popular (but controversial) facial recognition app Pimeyes into thinking Alex was Elon Musk. What's more, unlike many previous adversarial attacks on A.I. systems, Polyakov was able to pull this off even though he never had access to the data used to train Pimeyes or to the algorithm underpinning the app.
How could he do it? Well, Polyakov tells me that it turns out that if almost all facial recognition algorithms share certain commonalities, and there are fair number of facial recognition A.l. models that freely available through open source software or open source research papers. So if you take these open source models and train an A.I. system to reliably fool them, the system will actually be able to trick almost every other facial recognition system, even those it was never specifically trained to fool. Adversa calls this attack "the Adversarial Octopus" (you can read more about it in this company blog post.)
The problem, Polyakov tells me, is that machine learning engineers, for the most part, don't spend enough time thinking about how a malicious actor could attack their software. It's a mindset problem, more than anything else, he says. "I would say most of the engineers working on A.I., they don't understand the new attack vectors," he says. He says that the security vulnerabilities that plague neural network-based systems are much more akin to things like optical illusions and other tricks on biases in human perception and cognition, than they are to traditional cybersecurity attacks where some bad code gets injected into a piece of software or a network. And like those kinds of illusions or cognitive biases, they are difficult to fix. "It is a complex problem and a new type of challenge we need to solve," he says.
