Lifeboat Foundation

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.

‘Thinking at a distance’ with large language models sparks human-AI cognitive capacity transcending biological limits, but it risks existential ‘entangled mind’ miscalibration.

Amazon’s @Zoox is on the verge of launching a robotaxi ride-hailing service in Las Vegas. @EdLudlow went for a first ride.

Its fully autonomous vehicle is capable of smoothly traversing a 5-mile stretch just off the Vegas strip.

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.

Explore the significance of deep neural networks in artificial intelligence and how they simulate human brains for complex tasks through deep learning.

In Superintelligence, Nick Bostrom explored the question, ‘What might happen if AI development goes wrong?’ In this conversation, we ask the opposite question: ‘What happens if everything goes right?’

Scientists studying Large Language Models (LLMs) have found that LLMs perform similarly to humans in cognitive tasks, often making judgments and decisions that deviate from rational norms, such as risk and loss aversion. LLMs also exhibit human-like biases and errors, particularly in probability judgments and arithmetic operations tasks. These similarities suggest the potential for using LLMs as models of human cognition. However, significant challenges remain, including the extensive data LLMs are trained on and the unclear origins of these behavioural similarities.

The suitability of LLMs as models of human cognition is debated due to several issues. LLMs are trained on much larger datasets than humans and may have been exposed to test questions, leading to artificial enhancements in human-like behaviors through value alignment processes. Despite these challenges, fine-tuning LLMs, such as the LLaMA-1-65B model, on human choice datasets has improved accuracy in predicting human behavior. Prior research has also highlighted the importance of synthetic datasets in enhancing LLM capabilities, particularly in problem-solving tasks like arithmetic. Pretraining on such datasets can significantly improve performance in predicting human decisions.

Researchers from Princeton University and Warwick University propose enhancing the utility of LLMs as cognitive models by (i) utilizing computationally equivalent tasks that both LLMs and rational agents must master for cognitive problem-solving and (ii) examining task distributions required for LLMs to exhibit human-like behaviors. Applied to decision-making, specifically risky and intertemporal choice, Arithmetic-GPT, an LLM pretrained on an ecologically valid arithmetic dataset, predicts human behavior better than many traditional cognitive models. This pretraining suffices to align LLMs closely with human decision-making.