Learning about the causal structure of the world is a fundamental problem for human cognition. Causal models and especially causal learning have proved to be difficult for large pretrained models using standard techniques of deep learning. In contrast, cognitive scientists have applied advances in our formal understanding of causation in computer science, particularly within the Causal Bayes Net formalism, to understand human causal learning. In the very different tradition of reinforcement learning, researchers have described an intrinsic reward signal called "empowerment" which maximizes mutual information between actions and their outcomes. "Empowerment" may be an important bridge between classical Bayesian causal learning and reinforcement learning and may help to characterize causal learning in humans and enable it in machines. If an agent learns an accurate causal world model, they will necessarily increase their empowerment, and increasing empowerment will lead to a more accurate causal world model. Empowerment may also explain distinctive features of childrens causal learning, as well as providing a more tractable computational account of how that learning is possible. In an empirical study, we systematically test how children and adults use cues to empowerment to infer causal relations, and design effective causal interventions.

"First text, then images, now OpenAI has a model for generating videos," screamed Mashable the other day. The makers of ChatGPT and Dall-E had just announced Sora, a text-to-video diffusion model. Cue excited commentary all over the web about what will doubtless become known as T2V, covering the usual spectrum – from "Does this mark the end of [insert threatened activity here]?" to "meh" and everything in between. Sora (the name is Japanese for "sky") is not the first T2V tool, but it looks more sophisticated than earlier efforts like Meta's Make-a-Video AI. It can turn a brief text description into a detailed, high-definition film clip up to a minute long.

This paper offers a novel conceptual framework comprising four essential cognitive mechanisms that operate concurrently and collaboratively to enable metalearning (knowledge and regulation of learning) strategy implementation in young children. A roadmap incorporating the core mechanisms and the associated strategies is presented as an explanation of the developing brain's remarkable cross-context learning competence. The tetrad of fundamental complementary processes is chosen to collectively represent the bare-bones metalearning architecture that can be extended to artificial intelligence (AI) systems emulating brain-like learning and problem-solving skills. Utilizing the metalearning-enabled young mind as a model for brain-inspired computing, this work further discusses important implications for morally grounded AI.

Much discussion about large language models and language-and-vision models has focused on whether these models are intelligent agents. We present an alternative perspective. We argue that these artificial intelligence models are cultural technologies that enhance cultural transmission in the modern world, and are efficient imitation engines. We explore what AI models can tell us about imitation and innovation by evaluating their capacity to design new tools and discover novel causal structures, and contrast their responses with those of human children. Our work serves as a first step in determining which particular representations and competences, as well as which kinds of knowledge or skill, can be derived from particular learning techniques and data. Critically, our findings suggest that machines may need more than large scale language and images to achieve what a child can do.

A few days after Google and Microsoft announced they'd be delivering search results generated by chatbots -- artificially intelligent software capable of producing uncannily human-sounding prose -- I fretted that our new AI helpers are not to be trusted. After all, Google's own AI researchers had warned the company that chatbots would be "stochastic parrots" (likely to squawk things that are wrong, stupid, or offensive) and "prone to hallucinating" (liable to just make stuff up). The bots, drawing on what are known as large language models, "are trained to predict the likelihood of utterances," a team from DeepMind, the Alphabet-owned AI company, wrote last year in a presentation on the risks of LLMs. "Yet, whether or not a sentence is likely does not reliably indicate whether the sentence is also correct." These chatbots, in other words, are not actually intelligent.

The mathematician and computer science pioneer Alan Turing hit on a promising direction for artificial intelligence research way back in 1950. "Instead of trying to produce a program to simulate the adult mind," he wrote, "why not rather try to produce one which simulates the child's?" Now AI researchers are finally putting Turing's ideas into action. They're realizing that by paying attention to how children process information, they can pick up valuable lessons about how to create machines that learn. DARPA, the Defense Department's advanced research agency, is embracing this approach.

Human babies don't seem to make a good amount of sense, evolutionarily speaking. They're helpless for many years, and not particularly helpful either--they can't pitch in around the house or get a job. But in reality, these formative years are critical for training nature's most remarkable brain: With the simple act of play, children explore their world, adapting themselves to a universe of chaos. Kids can run circles around even the most advanced robots on Earth, which still only function well in strictly controlled environments like factories, where they perform regimented tasks. But as the machines slowly become more advanced and creep deeper into our daily lives, perhaps we'd do well to let them grow up in a way, argues UC Berkeley psychologist Alison Gopnik.

The mathematician and computer science pioneer Alan Turing hit on a promising direction for artificial intelligence research way back in 1950. "Instead of trying to produce a program to simulate the adult mind," he wrote, "why not rather try to produce one which simulates the child's?" Now AI researchers are finally putting Turing's ideas into action. They're realizing that by paying attention to how children process information, they can pick up valuable lessons about how to create machines that learn. DARPA, the Defense Department's advanced research agency, is embracing this approach.

Anyone who's had a frustrating interaction with Siri or Alexa knows that digital assistants just don't get humans. What they need is what psychologists call theory of mind, an awareness of others' beliefs and desires. Now, computer scientists have created an artificial intelligence (AI) that can probe the "minds" of other computers and predict their actions, the first step to fluid collaboration among machines--and between machines and people. "Theory of mind is clearly a crucial ability," for navigating a world full of other minds says Alison Gopnik, a developmental psychologist at the University of California, Berkeley, who was not involved in the work. By about the age of 4, human children understand that the beliefs of another person may diverge from reality, and that those beliefs can be used to predict the person's future behavior. Some of today's computers can label facial expressions such as "happy" or "angry"--a skill associated with theory of mind--but they have little understanding of human emotions or what motivates us.

Alison Gopnik's career began with a psychology experiment she now considers ridiculous. Aiming to understand how 15-month-olds connect words with abstract concepts (daddy caregiver), she decided to visit nine kids once a week for a year. The then Oxford graduate student would record everything they said as part of her dissertation. "It was absurd for a million reasons," says Gopnik, holed up on a winter Friday in her office at the University of California at Berkeley, where she is a professor of developmental psychology. "If a childhad moved away, if there weren't any take-aways after the year, or any number of things, all that work would have been gone," she says, before adding, "I would never allow a student of mine to do anything like that today."