Determining a causal DAG (directed acyclic graph) for a problem under consideration, is a major roadblock when doing Judea Pearl's Causal Inference (CI) in Statistics. The same problem arises when doing CI in Artificial Intelligence (AI) and Machine Learning (ML). As with many problems in Science, we think Nature has found an effective solution to this problem. We argue that human and animal brains contain an explicit engine for doing CI, and that such an engine uses as input an atlas (i.e., collection) of causal DAGs. We propose a simple algorithm for constructing such an atlas from a library of books or videos/movies. We illustrate our method by applying it to a database of randomly generated Tic-Tac-Toe games. The software used to generate this Tic-Tac-Toe example is open source and available at GitHub.

His case rests on a viral video he tweeted last month of a goldfish driving a water-tank-equipped robotic vehicle down the side of a street and inside his lab at Ben-Gurion University of the Negev in Israel. The roboride was part of a scientific study to test whether goldfish had the mental acuity to navigate a terrestrial environment toward a target using a machine. The six goldfish that took part in driver's training passed their test. They weren't the first to cross the finish line. Other neuroscientists have taught rats to drive cars as part of experiments testing how experience affects learning.

The Research Brief is a short take about interesting academic work. A unique material, nickel oxide demonstrates the ability to learn things about its environment in a way that emulates the most basic learning abilities of animals, as my colleagues and I describe in a new paper. For over half a century, neuroscientists have studied sea slugs to understand basic animal learning. Two fundamental concepts of learning are habituation and sensitization. Habituation occurs when an organism's response to a repeated stimulus continuously decreases.

Internal motivations can prompt spontaneous changes in animal behavior. A recent study strives to design an artificial intelligence that can mimic animal-like actions using chaotic dynamics. A woman walking to a bus stop realizes that she forgot her keys; she suddenly turns around and runs home. Such spontaneous activities are hallmarks of animal behavior. Eager to capture the essence of the human brain, roboticists have tried to imitate these sorts of actions.

Autonomous functions for robots, such as spontaneity, are highly sought after. Many control mechanisms for autonomous robots are inspired by the functions of animals, including humans. Roboticists often design robot behaviors using predefined modules and control methodologies, which makes them task-specific, limiting their flexibility. Researchers offer an alternative machine learning-based method for designing spontaneous behaviors by capitalizing on complex temporal patterns, like neural activities of animal brains. They hope to see their design implemented in robotic platforms to improve their autonomous capabilities.

Researchers and roboticists are continually trying to achieve autonomous functions in robots, and they often look toward the animal brain as a point of inspiration for control mechanisms. Because of the task-specific nature of robotic behavior, due to the reliance on predefined modules and control methodologies, they are often limited in flexibility.The newest development in this area is coming out of the University of Tokyo, where researchers have created an alternative machine learning-based method to give robotic AI spontaneous behaviors. The team did this by relying on intricate temporal patterns, such as an animal brain's neural activities.The research was published in Science Advances, titled "Designing spontaneous behavioral switching via chaotic itinerancy." High-Dimensional ChaosA dynamical system is a mathematical model of the ever-changing internal states of something, which describes robots and their control software. Researchers are especially focused on high-dimensional chaos, a class of dynamical systems, due to its impressive ability to model animal brains.

Elon Musk, the founder of the American company Tesla and SpaceX, demonstrated how to connect a computer chip made by his new company'Neuralink' to the animal brain. Elon Musk's company, Neuralink, has developed a chip in just four years, which is being hailed as the beginning of a new era in the world of medicine and technology. Musk, however, has expressed fears in the past that artificial intelligence will overtake the human mind in the future, and has apparently acknowledged the natural faculties of the human mind. Neuralink aims to implant wireless brain-computer interfaces that include thousands of electrodes in the most complex human organ to help cure neurological conditions like Alzheimer's, dementia and spinal cord injuries and ultimately fuse humankind with artificial intelligence. "An implantable device can actually solve these problems," Musk said on a webcast Friday, mentioning ailments such as memory loss, hearing loss, depression and insomnia.

What is the master algorithm that allows humans to be so efficient at learning things? That is a question that has perplexed artificial intelligence scientists and researchers who, for the past decades, have tried to replicate the thinking and problem-solving capabilities of the human brain. The dream of creating thinking machines has spurred many innovations in the field of AI, and has most recently contributed to the rise of deep learning, AI algorithms that roughly mimic the learning functions of the brain. But as some scientists argue, brute-force learning is not what gives humans and animals the ability to interact the world shortly after birth. The key is the structure and innate capabilities of the organic brain, an argument that is mostly dismissed in today's AI community, which is dominated by artificial neural networks. In a paper published in the peer-reviewed journal Nature, Anthony Zador, Professor of Neuroscience Cold Spring Harbor Laboratory, argues that it is a highly structured brain that allows animals to become very efficient learners.

This article is part of Demystifying AI, a series of posts that (try to) disambiguate the jargon and myths surrounding AI. What is the master algorithm that allows humans to be so efficient at learning things? That is a question that has perplexed artificial intelligence scientists and researchers who, for the past decades, have tried to replicate the thinking and problem-solving capabilities of the human brain. The dream of creating thinking machines has spurred many innovations in the field of AI, and has most recently contributed to the rise of deep learning, AI algorithms that roughly mimic the learning functions of the brain. But as some scientists argue, brute-force learning is not what gives humans and animals the ability to interact the world shortly after birth.

CSHL neuroscientist Anthony Zador shows how evolution and animal brains can be a rich source of inspiration for machine learning, especially to help AI tackle some enormously difficult problems, like... doing the dishes. Artificial intelligence (AI) still has a lot to learn from animal brains, says Cold Spring Harbor Laboratory (CSHL) neuroscientist Tony Zador. Now, he's hoping that lessons from neuroscience can help the next generation of artificial intelligence overcome some particularly difficult barriers. Anthony Zador, M.D., Ph.D., has spent his career working to describe, down to the individual neuron, the complex neural networks that make up a living brain. But he started his career studying artificial neural networks (ANNs).