A dozen students from Sweden and California switched places last week for summer research internships focused on digital transformation, kicking off a new exchange between KTH Royal Institute of Technology and University of California, Berkeley.

The addition of UC Berkeley to the annual Digital Futures Summer Research Internship (SRI) Program at KTH is one of the first steps toward closer collaboration between the two universities. Such exchanges are among the aims of an agreement KTH President Anders Söderholm and UC Berkeley Vice Provost for Academic Planning Lisa Alvarez-Cohen signed one year earlier at the UC Berkeley campus.

After a June 5 welcome event at the KTH Library, students from UC Berkeley got busy with their KTH supervisors on projects in energy, intelligent tutoring systems, robotics, transportation and artificial intelligence. They will be working under the supervision of KTH researchers who are affiliated with Digital Futures, a cross-disciplinary research centre based at KTH which is dedicated to shaping an economically, environmental and socially sustainable society through digital transformation.

In the Bay Area, KTH students will also be working on generative AI, marine energy and hybrid vehicles under the supervision of research leaders at UC Berkeley.

See the full KTH story, “KTH and UC Berkeley students switch places for eight-week research internship.”

From left, UC Berkeley students Wentinn Liao, Nicholas Jennings, Verona Teo, Samuel Bobick, Daisy Kerr and Giuseppe Perona – David Callahan photo for KTH

MIT Technology Review: Researchers are using Gen AI and other techniques to teach robots new skills — including tasks they could perform in homes.

While engineers have made great progress in getting robots to work in tightly controlled environments like labs and factories, the home has proved difficult to design for. Out in the real, messy world, furniture and floor plans differ wildly; children and pets can jump in a robot’s way; and clothes that need folding come in different shapes, colors, and sizes. Managing such unpredictable settings and varied conditions has been beyond the capabilities of even the most advanced robot prototypes.

That seems to finally be changing, in large part thanks to artificial intelligence…

Last year, Google DeepMind kick-started a new initiative, the Open X-Embodiment Collaboration. The company partnered with 34 research labs and around 150 researchers to collect data from 22 different robots. The resulting data set, published Oct 2023, consists of robots demonstrating 527 skills, for example, picking, pushing, and moving.

Sergey Levine, a computer scientist at UC Berkeley who participated in the project, says the goal was to create a “robot internet” by collecting data from labs around the world. “This would give researchers access to bigger, more scalable, and more diverse data sets. The deep-learning revolution that led to the generative AI of today started in 2012 with the rise of ImageNet, a vast online data set of images. The Open X-Embodiment Collaboration is an attempt by the robotics community to do something similar for robot data… Early signs show that more data is leading to smarter robots.

Sergey Levine is a cybersecurity researcher for the C3.ai Digital Transformation Institute.

Read the full story, “Is robotics about to have its own ChatGPT moment?“

Photo: Peter Adams for MIT Technology Review

A new method safely extracts valuable metals locked up in discarded electronics and low-grade ore using dramatically less energy and fewer chemical materials than current methods, according to a new paper published in the journal Nature Chemical Engineering by University of Illinois Urbana-Champaign researchers led by Chemical and Biomolecular Engineering Professor Xiao Su, a C3.ai DTI Principal Investigator.

Gold and platinum group metals such as palladium, platinum and iridium are in high demand for use in electronics. However, sourcing these metals from mining and current electronics recycling techniques is not sustainable and comes with a high carbon footprint. Gold used in electronics accounts for 8% of the metal’s overall demand, and 90 percent of the gold used in electronics ends up in U.S. landfills yearly, the study reports.

The study describes the first precious metal extraction and separation process fully powered by the inherent energy of electrochemical liquid-liquid extraction, or e-LLE. The method uses a reduction-oxidation reaction to selectively extract gold and platinum group metal ions from a liquid containing dissolved electronic waste.

Su said one of the many advantages of this new method is that it can run continuously in a green fashion and is highly selective in terms of how it extracts precious metals. “We can pull gold and platinum group metals out of the stream, but we can also separate them from other metals like silver, nickel, copper and other less valuable metals to increase purity greatly – something other methods struggle with.”

The team said that they are working to perfect this method by improving the engineering design and the solvent selection.

Read the full UIUC News article, “Electrochemistry helps clean up electronic waste recycling, precious metal mining.”

Read the study in Nature Chemical Engineering, “Redox-mediated electrochemical liquid-liquid Extraction (e-LLE) for selective metal recovery.” 

Photo by Fred Zwicky, UIUC

C3.ai DTI energy research led by Principal Investigator Qianwen Xu of the KTH Royal Institute of Technology in Stockholm, Sweden, has resulted in the development of AI algorithms to prevent power grid failure when electrification is increasingly supplied by variable sources like solar and wind.

“Wind power and solar radiation are not consistent from hour to hour,” says Xu. “And demand for charging EVs is based on people’s personal needs and habits. So, you have a high level of stochastics and uncertainties. Their integration will lead to voltage fluctuations, deviations and even voltage security violation challenges.” The new open-source deep reinforced learning (DRL) algorithms are designed to solve this challenge.

The open-source software package is published in GitHub.

Read the full TechXplore story: “Researchers design open-source AI algorithms to protect power grid from fluctuations caused by renewables and EVs.”

See the IEEE Transactions on Sustainable Energy research paper: “Data Driven Decentralized Control of Inverter based Renewable Energy Sources using Safe Guaranteed Multi-Agent Deep Reinforcement Learning.”

Photo by David Callahan, KTH Royal Institute of Technology

UC Berkeley CDSS News: On February 8,  Ziad Obermeyer, Blue Cross Distinguished Associate Professor of Health Policy and Management at Berkeley Public Health, warned the U.S. Senate Finance Committee about some of AI’s potential hazards within the healthcare field, and offered ways to ensure that AI systems are safe, unbiased and useful.

The hearing, “Artificial Intelligence and Health Care: Promise and Pitfalls,” explored the growing use of AI in medicine, and by federal health care agencies.

“Throughout my ten years of practicing medicine, I have agonized over missed diagnoses, futile treatments, unnecessary tests and more,” Obermeyer said. “The collective weight of these errors, in my view, is a major driver of the dual crisis in our healthcare system: suboptimal outcomes at very high cost. AI holds tremendous promise as a solution to both problems.”

C3.ai DTI Co-P.I. Ziad Obermeyer worked on the COVID-19 research project, “Using Data Science to Understand the Heterogeneity of SARS-COV-2 Transmission and COVID-19 Clinical Presentation in Mexico,” led by P.I. Stefano Bertozzi, Dean Emeritus and Professor of Health Policy and Management, at the University of California, Berkeley.

Read the full story, “Ziad Obermeyer testifies in U.S. Congress on how AI can help health care.”