Lifeboat Foundation

Collections of mobile, interacting objects—flocks of birds, colonies of bacterial, or teams of robots—can sometimes behave like solid materials, executing organized rotations or gliding coherently in one direction. But why such systems display one kind of collective organization rather than another has remained unclear. Now researchers have developed a theory that can predict the pattern most likely to emerge under specific conditions [1]. The theory, they hope, may be of use in designing living and artificial materials that can autonomously adapt to their environment.

An “active material” is any system made up of interacting objects able to move under their own power, such as animals, cells, or robots. In so-called active solids, a subset of active materials, strong cohesion between neighboring elements makes the collective act somewhat like a solid. Examples include clusters of certain cell types and networks of robots with rigid connections.

Active solids can display several kinds of collective, organized motion, says Claudio Hernández-López, a PhD student at the École Normale Supérieure and Sorbonne University in France. For example, researchers have observed both coherent rotations and coherent translations in collections of microbes from the phylum Placozoa. Existing theories, however, fail to explain pattern selection—why, if several patterns are possible, does one pattern of behavior emerge rather than another?

During her chemistry Nobel Prize lecture in 2018, Frances Arnold said, “Today we can for all practical purposes read, write and edit any sequence of DNA, but we cannot compose it.” That isn’t true anymore.

AI is shaking up the finance sector. Soon, it may be predicting financial crises before they happen. Alongside that potential, robust governance is key.

The big question is whether the wealth boom of the past decade, initially fueled by low interest rates and liquidity, and more recently by Covid-19 pandemic stimulus and artificial intelligence, can continue. Global conflicts, elections, interest rates and a potential economic slowdown could all slow the pace of wealth creation, said Elias Ghanem, global head of the Capgemini Research Institute for Financial Services.

“The last 10 years were exceptional,” Ghanem said. “We now have inflation, a potential recession and geopolitical problems and elections. The environment is completely different.”

Indeed, globally, the wealth picture looks more mixed than in the U.S. The number of millionaires worldwide grew 5.1% last year, to 22.8 million, according to the report. Their combined fortunes grew to a record $86.8 trillion.

The engineering challenges involved are fiendish, but worth tackling.

It would be interesting and scary if a superintelligent system already existed and was incontrol of all other lesser AIs.

“I’m scared. I’d be crazy not to be,” one former employee tells Vox.

Imagine if the world’s response to climate change relied solely on speculative predictions from pundits and CEOs, rather than the rigorous—though still imperfect—models of climate science. “Two degrees of warming will arrive soon-ish but will change the world less than we all think,” one might say. “Two degrees of warming is not just around the corner. This is going to take a long time,” another could counter.

This is more or less the world we’re in with artificial intelligence, with OpenAI CEO Sam Altman saying that AI systems that can do any task a human can will be developed in the “reasonably close-ish future,” while Yann LeCun, Chief AI Scientist at Facebook, argues that human-level AI systems are “going to take a long time.”

Jaime Sevilla, a 28-year-old Spanish researcher, is trying to change that. It is far from clear whether and how the capabilities of the most advanced AI systems will continue to rapidly progress, and what the effects of those systems will be on society. But given how important AI already is, it’s worth trying to bring a little of the rigor that characterizes climate science to predicting the future of AI, says Sevilla. “Even if AI innovation stopped, this is already a technology that’s going to affect many people’s lives,” he says. “That should be enough of an excuse for us to get serious about it.”

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What is the red line in the ability to imitate or simulate the commonly recognizable characteristics of a popular or non-popular person when we seek to assess the potential for unethical persuasion of technologies like GenAI?

What responsibility does the creator bear for the intrinsic consequences related to the unethical use of a person’s identity as a lever of persuasion through technology?

Persuasion has no dark side per se: only the intentions of those who wield it do, and GenAI is not inherently endowed with such intentions, neither for itself nor by itself.