Lifeboat Foundation: Safeguarding Humanity
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Category: robotics/AI – Page 387

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.

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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.

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Currently, computing technologies are rapidly evolving and reshaping how we imagine the future. Quantum computing is taking its first toddling steps toward delivering practical results that promise unprecedented abilities. Meanwhile, artificial intelligence remains in public conversation as it’s used for everything from writing business emails to generating bespoke images or songs from text prompts to producing deep fakes.

Some physicists are exploring the opportunities that arise when the power of machine learning — a widely used approach in AI research—is brought to bear on quantum physics. Machine learning may accelerate quantum research and provide insights into quantum technologies, and quantum phenomena present formidable challenges that researchers can use to test the bounds of machine learning.

When studying quantum physics or its applications (including the development of quantum computers), researchers often rely on a detailed description of many interacting quantum particles. But the very features that make quantum computing potentially powerful also make quantum systems difficult to describe using current computers. In some instances, machine learning has produced descriptions that capture the most significant features of quantum systems while ignoring less relevant details—efficiently providing useful approximations.

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