Normally we do not cover topics related to cosmetics, being a website focused on the more industrial, loud, and explosive applications of additive manufacturing. But we are interested in production-grade 3D printing, as well as AI, computer vision, and computer software. And this new story from cosmetic giant Neutrogena has all of those components, so we'll cover it. Read on to find out how we let an AI pass judgment on this writer's face skin! It's CES 2023 week which means we will be seeing plenty of stories of interesting new innovations saturating tech websites.

Despite recent advances in artificial intelligence (AI) research, human children are still by far the best learners we know of, learning impressive skills like language and high-level reasoning from very little data. Children's learning is supported by highly efficient, hypothesis-driven exploration: in fact, they explore so well that many machine learning researchers have been inspired to put videos like the one below in their talks to motivate research into exploration methods. However, because applying results from studies in developmental psychology can be difficult, this video is often the extent to which such research actually connects with human cognition. Why is directly applying research from developmental psychology to problems in AI so hard? For one, taking inspiration from developmental studies can be difficult because the environments that human children and artificial agents are typically studied in can be very different. Traditionally, reinforcement learning (RL) research takes place in grid-world-like settings or other 2D games, whereas children act in the real world which is rich and 3-dimensional.

Despite recent advances in artificial intelligence (AI) research, human children are still by far the best learners we know of, learning impressive skills like language and high-level reasoning from very little data. Children's learning is supported by highly efficient, hypothesis-driven exploration: in fact, they explore so well that many machine learning researchers have been inspired to put videos like the one below in their talks to motivate research into exploration methods. However, because applying results from studies in developmental psychology can be difficult, this video is often the extent to which such research actually connects with human cognition. Why is directly applying research from developmental psychology to problems in AI so hard? For one, taking inspiration from developmental studies can be difficult because the environments that human children and artificial agents are typically studied in can be very different.

In a preprint paper, researchers at Alphabet's DeepMind and the University of California, Berkeley propose a framework for comparing the ways children and AI learn about the world. The work, which was motivated by research suggesting children's learning supports behaviors later in life, could help close the gap between AI and humans when it comes to acquiring new abilities. For instance, it might lead to robots that can pick and pack millions of different kinds of products while avoiding various obstacles. Exploration is a key feature of human behavior, and recent evidence suggests children explore their surroundings more often than adults. This is thought to translate to more learning that enables powerful, abstract task generalization -- a type of generalization AI agents could tangibly benefit from.