Restrepo (2025) develops a framework for economic growth in which Artificial General Intelligence (AGI) can perform any human task given sufficient computational resources. In his model, all economically essential "bottleneck" work is eventually automated, wages converge to the computational cost of replicating human work, and labor's share of GDP approaches zero as computational resources expand. This note relaxes one of his assumptions: that all task types have uniform automation costs. Drawing on Moravec's Paradox [1]--the observation that tasks humans find effortless (perception, mobility, manipulation) often require enormous computational resources, while tasks humans find difficult (mathematics, logic) require relatively modest computation--we extend his model to allow for differential automation costs across cognitive and physical tasks.

This discrepancy between the relative ease of teaching a machine abstract thinking and the difficulty of teaching it basic sensory, social, and motor skills is what's known as Moravec's paradox. Named after an observation the roboticist Hans Moravec made back in the late 1980s, the paradox states that what's hard for humans (math, logic, scientific reasoning) is easy for machines, and what's hard for machines (tying shoelaces, reading emotions, having a conversation) is easy for humans. In her latest book, Robots and the People Who Love Them: Holding On to Our Humanity in an Age of Social Robots, science writer Eve Herold argues that thanks to new approaches in machine learning and continued advances in AI, we're finally starting to unravel this paradox. As a result, a new era of personal and social robots is about to unfold, she says--one that will force us to reimagine the nature of everything from friendship and love to work, health care, and home life. To give readers a sense of what this brave new world of social robots will look like, Herold points us toward Pepper, a doe-eyed humanoid robot that's made by the Japanese company SoftBank.

Despite excelling in high-level reasoning, current language models lack robustness in real-world scenarios and perform poorly on fundamental problem-solving tasks that are intuitive to humans. This paper argues that both challenges stem from a core discrepancy between human and machine cognitive development. While both systems rely on increasing representational power, the absence of core knowledge-foundational cognitive structures in humans-prevents language models from developing robust, generalizable abilities, where complex skills are grounded in simpler ones within their respective domains. It explores empirical evidence of core knowledge in humans, analyzes why language models fail to acquire it, and argues that this limitation is not an inherent architectural constraint. Finally, it outlines a workable proposal for systematically integrating core knowledge into future multi-modal language models through the large-scale generation of synthetic training data using a cognitive prototyping strategy.

Robots in 2024 are far more complex than their single-armed factory-working predecessors. Modern robots can run, jump, do the splits, and even hold down a basic conversation. At the same, despite decades of technical advancements and billions of dollars of investment, even the most advanced robot systems still struggle to do many everyday tasks humans take for granted like folding laundry or stacking blocks. Ironically, robots are quite bad at doing things we find easy. New advances in robot training take some inspiration from massively popular large language models like ChatGPT may change that… eventually.

This article was originally written for the June 2024 issue of the Bulletin of European Association for Theoretical Computer Science, in the framework of the "Logic in Computer Science" column administered by Yuri Gurevich. In the following pages, the article is reproduced as is. The ongoing AI revolution raises many foundational problems. For quite a while, I felt that the issue needs to be addressed in this column. Not being an AI expert, I was looking for volunteers. This didn't work, and so one day I took a deep breath and started to write an article myself. Andreas Blass, my long-time collaborator, was reluctant to join me, but eventually he agreed. A hundred years ago, logic was almost synonymous with foundational studies. I tried to rekindle that tradition in [5]. The goal of the following dialog is to provoke young logicians with a taste for foundations to notice the foundational problems raised by the ongoing AI revolution. I think the most beautiful thing about deep learning is that it actually works. Q: I just learned that Daniel Kahneman, Nobel laureate in economics and the author of "Thinking, fast and slow" [7], passed away on March 27, 2024. I heard a lot about this book but have never read it.

In his afterword to the short murder mystery Death of an Author, the writer Stephen Marche invokes a concept called Moravec's paradox. Hans Moravec, a robotics scientist, observed that tasks human beings find challenging, such as playing chess, are easy for computers, while many of the actions human beings effortlessly perform without conscious thought, such as perception or oriented movement through space, are extremely difficult for the machines. Moravec's paradox is a useful way to think about the surprising ways that Death of an Author, described by its publisher as a "groundbreaking experiment" in artificial intelligence, succeeds. Jacob Weisberg, the head of podcast production company Pushkin Industries (and a former Slate editor in chief), asked Marche, a journalist who writes about artificial intelligence, to make Death of an Author earlier this year. The goal was a novella whose text was to be 95 percent computer-generated.

When people imagine the AI apocalypse, they generally imagine robots. But the robot-takeover scenario most often envisioned by science fiction is not exactly looming. Recent and explosive progress in AI--along with recent and explosive hype surrounding it--has made the existential risks posed by the technology a topic of mainstream conversation. Yet progress in robotics--which is to say, machines capable of interacting with the physical world through motion and perception--has been lagging way behind. "I can't help but feel a little envious," said Eric Jang, the vice president of AI at the humanoid-robotics company 1X, in a talk at a robotics conference last year.

According to a survey of 65 AI experts in the UK and Japan, robots could perform around 39% of household tasks within the next decade, with grocery shopping being the most likely to be automated. However, caring for the young and old is less likely to be affected. The increased use of AI in homes could also result in privacy concerns. One of the report's authors, Ekaterina Hertog, suggested that society needs to have a public discussion about privacy in the age of smart technology, as per a report from the Guardian. Despite this, she believes that greater automation could lead to improved gender equality by reducing the burden of unpaid work, which is mainly borne by women.

When I first began 1SecondPainting, I knew AI had crossed a line we'd never be able to go back from. Machines have always grown more capable with time. Yet I (and most others) had always thought their abilities would be relegated to simple, monotonous tasks. Operating an assembly line, or putting together a car, for instance: these were the sorts of things AI was "for", and I had always been comfortable with that. But the events of 2020 changed everything.

An econometrics problem illustrates the difference between artificial and human intelligence. Understanding the tacit knowledge and limits of AI is crucial to implementing it effectively and fairly. One of the only lucid thought experiments ever conducted by econometricians, the "red bus-blue bus" problem illustrates a central drawback that comes with using statistical estimation to quantify the probability that a person will make a specific choice when faced with to various alternatives. As the thought experiment proceeds, imagine that you are indifferent about taking a car or a red bus to work. Due to his indifference, an estimate of his probability of choosing either option is to flip a coin.