Artificial intelligence is an umbrella term for technologies that simulate human intelligence. It is one of the greatest sources of systemic risk that insurers now face. It acts as a multiplier of existing exposures and a source of new liabilities, with the potential to cause catastrophic mass loss events.

In this webinar, we delve into the systemic risks of artificial intelligence, including privacy, security, and legal challenges that insurers must navigate.

Our speakers were joined by Dr. Matthew Bonner, Senior Fire Engineer and Research Lead at Trigon Fire Safety, and Rishi Baviskar, Cyber Risk Consultant at Allianz, for a discussion on the systemic risks of artificial intelligence – including privacy, security, and legal challenges that insurers must navigate.

Key topics include:

• Privacy violations
• Security threats, weaponisation and adversarial manipulation
• The threat of ‘uncontrollable AI’
• Sentient AI and the concept of legal personality
• And more!

Watch the recording

Australian scientists have successfully developed a research system that uses ‘biological artificial intelligence’ to design and evolve molecules with new or improved functions directly in mammal cells. The researchers said this system provides a powerful new tool that will help scientists develop more specific and effective research tools or gene therapies.

Named PROTEUS (PROTein Evolution Using Selection) the system harnesses ‘directed evolution’, a lab technique that mimics the natural power of evolution. However, rather than taking years or decades, this method accelerates cycles of evolution and natural selection, allowing them to create molecules with new functions in weeks.

This could have a direct impact on finding new, more effective medicines. For example, this system can be applied to improve gene editing technology like CRISPR to improve its effectiveness.

“This means PROTEUS can be used to generate new molecules that are highly tuned to function in our bodies, and we can use it to make new medicine that would be otherwise difficult or impossible to make with current technologies.” says co-senior author Professor Greg Neely, Head of the Dr. John and Anne Chong Lab for Functional Genomics at the University of Sydney.

“What is new about our work is that directed evolution primarily work in bacterial cells, whereas PROTEUS can evolve molecules in mammal cells.”

PROTEUS can be given a problem with uncertain solution like when a user feeds in prompts for an artificial intelligence platform. For example the problem can be how to efficiently turn off a human disease gene inside our body.

PROTEUS then uses directed evolution to explore millions of possible sequences that have yet to exist naturally and finds molecules with properties that are highly adapted to solve the problem. This means PROTEUS can help find a solution that would normally take a human researcher years to solve if at all.

The researchers reported they used PROTEUS to develop improved versions of proteins that can be more easily regulated by drugs, and nanobodies (mini versions of antibodies) that can detect DNA damage, an important process that drives cancer. However, they said PROTEUS isn’t limited to this and can be used to enhance the function of most proteins and molecules.

The findings were reported in Nature Communications, with the research performed at the Charles Perkins Centre, the University of Sydney with collaborators from the Centenary Institute.

Unlocking molecular machine learning

The original development of directed evolution, performed first in bacteria, was recognized by the 2018 Noble Prize in Chemistry.

“The invention of directed evolution changed the trajectory of biochemistry. Now, with PROTEUS, we can program a mammalian cell with a genetic problem we aren’t sure how to solve. Letting our system run continuously means we can check in regularly to understand just how the system is solving our genetic challenge,” said lead researcher Dr. Christopher Denes from the Charles Perkins Centre and School of Life and Environmental Sciences

The biggest challenge Dr. Denes and the team faced was how to make sure the mammalian cell could withstand the multiple cycles of evolution and mutations and remain stable, without the system “cheating” and coming up with a trivial solution that doesn’t answer the intended question.

They found the key was using chimeric virus-like particles, a design consisting of taking the outside shell of one virus and combining it with the genes of another virus, which blocked the system from cheating.

The design used parts of two significantly different virus families creating the best of both worlds. The resulting system allowed the cells to process many different possible solutions in parallel, with improved solutions winning and becoming more dominant while incorrect solutions instead disappear.

“PROTEUS is stable, robust and has been validated by independent labs. We welcome other labs to adopt this technique. By applying PROTEUS, we hope to empower the development of a new generation of enzymes, molecular tools and therapeutics,” Dr. Denes said.

“We made this system open source for the research community, and we are excited to see what people use it for, our goals will be to enhance gene-editing technologies, or to fine tune mRNA medicines for more potent and specific effects,” Professor Neely said.

Research aims to guide decision-makers on real-world effects of artificial intelligence on American workers

Case Western Reserve University economics professor Mark Schweitzer has joined a new, multi-university research collaboration examining the impact of artificial intelligence (AI) on workers and the labor market—an urgent area of inquiry as AI adoption accelerates across industries.

The $1.6 million project is supported by the Alfred P. Sloan Foundation and led by Carnegie Mellon University’s Block Center for Technology and Society and MIT’s FutureTech. Researchers from eight academic institutions—including the University of Pittsburgh, Northeastern University, the University of Virginia and the California Policy Lab—are contributing their expertise, along with collaborators from the U.S. Chamber of Commerce Foundation.

“This is an important opportunity to bring rigorous, data-driven insights to some of the most pressing economic questions of our time,” said Schweitzer, whose research at Case Western Reserve and the Federal Reserve Bank of Cleveland focuses on labor markets and regional economics. “By pooling knowledge across institutions, we can better understand where AI is helping workers—and where it’s leaving them behind.”

During the next two years, the team will work to improve labor-market data and produce both academic research and policy-relevant reports, he said. The goal is to support research-driven decision-making by employers, labor organizations and government.

More information on the Block Center’s AI and Work initiative.

For more information, contact Colin McEwen at colin.mcewen@case.edu.