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

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.”

Nature: There’s a revolution brewing in batteries for electric cars, which will rely on alternative designs to the conventional lithium-ion batteries that have dominated EVs for decades. Although lithium-ion is hard to beat, researchers think that a range of options will soon fill different niches of the market: some very cheap, others providing much more power.

“We’re going to see the market diversify,” says Gerbrand Ceder, a materials scientist at the University of California, Berkeley and the Lawrence Berkeley National Laboratory.

Lithium-ion batteries have improved a lot since the first commercial product in 1991: cell energy densities have nearly tripled, while prices have dropped by an order of magnitude3. “Lithium-ion is a formidable competitor… The biggest challenges are resource-related,” says Ceder, who calculates that the projected 14 TWh needed for cars by 2050 will require 14 million tonnes of total metal.

C3.ai DTI P.I. Gerbrand Ceder developed the novel NLP-driven COVID Scholar literature review site in 2020 with a DTI grant.

Read the Nature story: “The new car batteries that could power the electric vehicle revolution.”

Photo by Chris Ratcliffe/Bloomberg/Getty

UChicago News: The Medical Imaging and Data Resource Center (MIDRC), hosted at the University of Chicago, has been selected to participate in a new pilot program from the NSF to democratize AI research.

The National AI Research Resource (NAIRR) pilot will gather 10 federal agencies and 25 private sector, nonprofit, and philanthropic organizations to build a shared research infrastructure that will strengthen access to critical resources necessary to power responsible AI discovery and innovation. The project will provide access to advanced computing, datasets, models, software, training, and user support to U.S.-based researchers and educators and, as it continues to grow, will inform the design of a national AI ecosystem.

“The overall goal of MIDRC is to support the medical imaging AI ecosystem. We’ve built the infrastructure to house, curate, and organize medical images, we’ve collected a huge amount of real-world imaging data, and we’ve put forth a concerted effort to educate users about the development of algorithms and potential sources of bias,” said Maryellen L. Giger, PhD, the A.N. Pritzker Distinguished Service Professor of Radiology at UChicago and MIDRC principal investigator. “The collaborations and infrastructure that have been established provide a solid foundation for the creation of more medical imaging datasets and the development of AI algorithms for all sorts of use cases through this new NAIRR pilot program.”

The launch of MIDRC was spurred by the 2020 C3.ai DTI COVID-19 research of Principal Investigator Maryellen Giger of UChicago, for the project, “Medical Imaging Domain-Expertise Machine Learning for Interrogation of COVID.”

Read the full story: “MIDRC selected for program to build national AI research infrastructure.”