A team of researchers from the NSF NHERI SimCenter, a computational modeling and simulation center for the natural hazards engineering community based at the University of California, Berkeley, have developed a suite of tools called BRAILS — Building Recognition using AI at Large-Scale — that can automatically identify characteristics of buildings in a city and even detect the risks that a city's structures would face in an earthquake, hurricane, or tsunami. ,b>Charles (Chaofeng) Wang, a postdoctoral researcher at the University of California, Berkeley, and the lead developer of BRAILS, says the project grew out of a need to quickly and reliably characterize the structures in a city.

UC Berkeley postdoctoral researcher Dr. Danyal Fer and his fellow researchers hope to advance the state of the art of robotic surgery. Automated robots in testing situations can meet or exceed a human in dexterity, precision and speed, according to a new research paper from the Berkeley team. The Berkeley team decided to build a new neural network that analyzed the robot's mistakes and learned how much precision it was losing with each passing day. "It learns how the robot's joints evolve over time," said Brijen Thananjeyan, a doctoral student on the team.

Researchers trying to improve health care with artificial intelligence usually subject their algorithms to a form of machine med school. Software learns from doctors by digesting thousands or millions of x-rays or other data labeled by expert humans until it can accurately flag suspect moles or lungs showing signs of COVID-19 by itself. A study published this month took a different approach—training algorithms to read knee x-rays for arthritis by using patients as the AI arbiters of truth instead of doctors. The results revealed that radiologists may have literal blind spots when it comes to reading Black patients' x-rays. Ziad Obermeyer, an author of the study and a professor at the University of California Berkeley's School of Public Health, was inspired to use AI to probe what radiologists weren't seeing by a medical puzzle.

In this video piece from the Washington Post, Ziad Obermeyer, professor of health policy and management at the UC Berkeley School of Public Health, said government regulation of artificial intelligence can have a positive impact, but it can't get ahead of the "many creative and potentially dangerous uses that people are going to put algorithms toward...In a lot of our work what we've found is there is a substantial amount of racial bias in algorithms that are fairly widespread...that's the kind of thing that certainly suggests a role for regulation."

Three University of California, Berkeley professors will lead the work to create new datasets to better understand poverty and mobility with the help of a $2 million grant. "By constructing these datasets and making them available to a wide array of researchers, we will unlock a generation's worth of new policy research," said professor Jesse Rothstein, co-lead researcher on the project, in a statement. Also leading the project are professors Hilary Hoynes and Steven Raphael. Researchers throughout the University of California system and others will take part in the work.

The chair of Germany's artificial intelligence committee has called for tougher measures to curb the spread of fake news. Her concern came as the CEP published a report called "The Creation, Weaponization and Detection of Deep Fakes". The report's author, Professor Hany Farid from the University of California, Berkeley, explained that deep fakes, which see famous people such as U.S. President Donald Trump having words placed over official video footage, are growing in sophistication. "I think everyone can see the damage that this type of content can create for misinformation campaigns," he said. "Now we have videos of someone saying whatever you want them to say to bolster that misinformation." For more on this, see our press release at Berkeley News.

Researchers at Google Brain, Intel AI Lab, and UC Berkeley have created Motion2Vec, an artificial intelligence model that can learn how to do tasks associated with robotic surgery, such as suturing and needle insertion.

A group of Berkeley researchers, including electrical engineering and computer sciences professor Pieter Abbeel, director of the Berkeley Robot Learning Lab and co-director of the Berkeley Artificial Intelligence Research (BAIR) lab, has released a state-of-the-art open-sourced Reinforcement Learning with Augmented Data (RAD) module. The reinforcement learning algorithm, used in machine learning for robotics, beats prior modules, including Google AI's PlaNet, DeepMind's Dreamer, and SLAC from Berkeley and DeepMind.

After a 1997 criminal case was largely decided by an unusual dark-and-light pattern in denim jeans captured by a security camera, a study of the case was used to set legal precedent for how patterns in photographs could be used as evidence. But now a study co-authored by postdoctoral information scholar Sophie Nightingale and information and electrical engineering and computer sciences professor Hany Farid, an expert on deepfake images, questions the reliability of such evidence. "Even under ideal conditions, trying to get an exact match is difficult," Professor Farid says. "This technique should be used with extreme caution, if at all." To conduct the study, they bought 100 pairs of jeans from local thrift stores, and they had 111 workers send in pictures of their own jeans. They then compared images of similar seams. According to the reporter: "The data showed that two images of the same seam often looked quite different -- so much so that it was often impossible to tell whether a pair of images were of the same seam or different ones. Much of the problem, the researchers concluded, comes down to the fact that cloth is flexible: it stretches, folds and drapes in complicated ways, which changes how it looks in photos. ... The lack of distinctiveness in images of seams significantly limits the accuracy of jeans identification, according to the study. The algorithm made a significant number of false matches between different pairs of jeans." Their study is available at PNAS.org.

Electrical engineering and computer sciences professor Costas Spanos is profiled in an article about his mission to use artificial intelligence to cut office energy use by half, as one way of addressing climate change. The story talks about how Singapore turned to him to help the heavily built tropical nation reduce its air conditioning use to ease climate risks. The author writes: "Recently, the Singaporean government offered Spanos a floor in an office building to renovate. After he finished in January, workers returned to an unassuming new interior evoking the aesthetics of a hip budget airline. The room had been packed with tiny sensors detecting humidity, light, temperature, and CO2 concentration; Spanos had also devised a way to use Wi-Fi to triangulate people's locations by detecting their phones as they move through space. The theory: Armed with that anonymized data, the system would learn the workers' movements, schedules, and preferences and tweak their environment to suit. ... If the workers got too hot or too cold, they could tap an app to say so. The AI would adapt, creating microclimates to reflect their feedback. But in time, Spanos expected, the workers wouldn't bother. His goal is to make the system forgettable."

AlphaGarden, a new project at Berkeley's AUTOLAB robotics laboratory, aims to develop AI systems for polyculture gardening. The AUTOLAB is best known for its DexNet system for robotic grasping. This project will see if humans can train robotic systems to fully automate the tending of a garden with multiple species of plants. Whether or not they can succeed is an "open question," says industrial engineering and operations research professor Ken Goldberg, AUTOLAB's director. "It certainly might be interesting to be able to have a fully automated garden. In my own view it's probably unlikely to be viable as a real functioning productive garden. I think that it's going to be very hard to learn, and that's the art side of the lesson, which is that nature is very complex, and that we can put some very complex machinery on it, but it's not going to necessarily open up and be controllable." Due to the COVID-19 pandemic, students working on the project will now use simulations and models, instead of a campus greenhouse. "For every real garden, we have 100,000 or millions of gardens that can be generated," Professor Goldberg says. "This runs at 100,000 times faster than nature so you can accelerate time dramatically, and for each one you can say, 'Well, if I tweak these parameters in my control policy, here's what the outcome will be in terms of how often you water, in what conditions you water, etc.'"

Electrical engineering and computer sciences professor S. Shankar Sastry is going to be co-directing a new $367 million public-private research consortium with the artificial intelligence firm C3.ai and other elite universities, to work on finding A.I. solutions to global problems. The first goal of the C3.ai Digital Transformation Institute, managed jointly by Berkeley and the University of Illinois, Urbana-Champaign, will be targeting ways to slow the COVID-19 coronavirus pandemic. "I cannot imagine a more important use of A.I.," says Thomas M. Siebel, the founder and chief executive of C3.ai, who is backing the project along with Microsoft Corp. According to this reporter: "The new institute will seek new ways of slowing the pathogen's spread, speeding the development of medical treatments, designing and repurposing drugs, planning clinical trials, predicting the disease's evolution, judging the value of interventions, improving public health strategies and finding better ways in the future to fight infectious outbreaks."