Continual learning (CL) aims to incrementally learn multiple tasks that are presented sequentially. The significance of CL lies not only in the practical importance but also in studying the learning mechanisms of humans who are excellent continual learners. While most research on CL has been done on structured data such as images, there is a lack of research on CL for abstract logical concepts such as counting, sorting, and arithmetic, which humans learn gradually over time in the real world. In this work, for the first time, we introduce novel algorithmic reasoning (AR) methodology for continual tasks of abstract concepts: CLeAR. Our methodology proposes a one-to-many mapping of input distribution to a shared mapping space, which allows the alignment of various tasks of different dimensions and shared semantics. Our tasks of abstract logical concepts, in the form of formal language, can be classified into Chomsky hierarchies based on their difficulty. In this study, we conducted extensive experiments consisting of 15 tasks with various levels of Chomsky hierarchy, ranging from in-hierarchy to inter-hierarchy scenarios. CLeAR not only achieved near zero forgetting but also improved accuracy during following tasks, a phenomenon known as backward transfer, while previous CL methods designed for image classification drastically failed.

Continual learning (CL) aims to incrementally learn multiple tasks that are presented sequentially. The significance of CL lies not only in the practical importance but also in studying the learning mechanisms of humans who are excellent continual learners. While most research on CL has been done on structured data such as images, there is a lack of research on CL for abstract logical concepts such as counting, sorting, and arithmetic, which humans learn gradually over time in the real world. In this work, for the first time, we introduce novel algorithmic reasoning (AR) methodology for continual tasks of abstract concepts: CLeAR. Our methodology proposes a one-to-many mapping of input distribution to a shared mapping space, which allows the alignment of various tasks of different dimensions and shared semantics.

The intersection of Artificial Intelligence (AI) and neuroscience in Explainable AI (XAI) is pivotal for enhancing transparency and interpretability in complex decision-making processes. This paper explores the evolution of XAI methodologies, ranging from feature-based to human-centric approaches, and delves into their applications in diverse domains, including healthcare and finance. The challenges in achieving explainability in generative models, ensuring responsible AI practices, and addressing ethical implications are discussed. The paper further investigates the potential convergence of XAI with cognitive sciences, the development of emotionally intelligent AI, and the quest for Human-Like Intelligence (HLI) in AI systems. As AI progresses towards Artificial General Intelligence (AGI), considerations of consciousness, ethics, and societal impact become paramount. The ongoing pursuit of deciphering the mysteries of the brain with AI and the quest for HLI represent transformative endeavors, bridging technical advancements with multidisciplinary explorations of human cognition.

A bipartisan panel of voters weighed in on the future of artificial intelligence and growing concerns surrounding the potential dangers of the emerging technology. A U.K.-based tech expert said he is not losing sleep at night over the recent growth of artificial intelligence but argued he does have concerns over AI potentially becoming a hellish boss that oversees an employee's every move. Michael Wooldridge is a professor of computer science at the University of Oxford who has been a leading expert on AI for at least 30 years. He spoke with The Guardian this month regarding upcoming lectures he will lead this winter to demystify artificial intelligence, while noting what concerns he does have with the tech. He told the outlet that he does not share the same worries as some AI experts who warn the powerful systems could one day lead to the downfall of humanity.

Summary: Examining the cognitive abilities of the AI language model, GPT-3, researchers found the algorithm can keep up and compete with humans in some areas but falls behind in others due to a lack of real-world experience and interactions. Researchers at the Max Planck Institute for Biological Cybernetics in Tübingen have examined the general intelligence of the language model GPT-3, a powerful AI tool. Using psychological tests, they studied competencies such as causal reasoning and deliberation, and compared the results with the abilities of humans. Their findings paint a heterogeneous picture: while GPT-3 can keep up with humans in some areas, it falls behind in others, probably due to a lack of interaction with the real world. Neural networks can learn to respond to input given in natural language and can themselves generate a wide variety of texts.

Even apart from the enduring dream of human-like intelligence, the future of AI is expected to play a deeply significant role in consumer and business markets. Artificial intelligence's impact on the world has already been felt in a variety of ways, from chess computers to search engine algorithms to a chatbot so convincing that a Google researcher thinks it's sentient. Obviously, the future of AI can't be predicted any more than tomorrow's lottery numbers can. But even as the research in the field drives the technology further and further, we can put our futurist caps on and speculate about what the world might look like in an AI-driven future. Fiction can have an impact on real-world scientific research.

Imagine asking a computer to make a digital painting, or a poem--and happily getting what you asked for. Or imagine chatting with it about various topics, and feeling it was a real interaction. What once was science fiction is becoming reality. In June, Google engineer Blake Lemoine told the Washington Post he was convinced Google's AI chatbot, LaMDA, was sentient. "I know a person when I talk to it," Lemoine said. Therein lies the rub: As algorithms are getting increasingly good at producing the kind of "outputs" we once thought were distinctly human, it's easy to be dazzled.

My grandpa recently discovered that I've been writing medium articles. After reading a few that I wrote about AI, he sent me a text asking for me to write an article explaining it to "seniors" like him. This is my best attempt at that. Artificial Intelligence (AI) is a phrase used by computer scientists to describe programs that can do things that require applying knowledge. But isn't any computer program'applying' the knowledge given to it in its own programming?

Vancouver's Sanctuary AI, a company focused on creating the world's first human-like intelligence in general-purpose robots, today announced the successful closing of an oversubscribed $75.5 million Series A funding. Investors in the massive financing round include Bell, Evok Innovations, Export Development Canada, Magna, SE Health, Verizon Ventures, and Workday Ventures. Using breakthrough technology in artificial intelligence (AI), cognition, and robotics, Sanctuary will improve the quality of the work experience, assist humans with difficult or dangerous tasks, create new jobs, bring new opportunities to those who might be less capable of physical work, and reduce the impact of labour shortages around the world. The strategic industry investors reflect applications for human-like intelligence in general-purpose robots across a wide range of industry verticals and tasks. Founded in 2018 by Geordie Rose, Suzanne Gildert, Olivia Norton, and Ajay Agrawal, Sanctuary is on a mission to create the world's first human-like intelligence in general-purpose robots that will help us work more safely, efficiently, and sustainably.

Artificial Intelligence is a program that can demonstrate human-like intelligence where it can sense, act, adapt, and reason. So we are studying the mental faculties using computational models. But why do we need smart programs, or why should we need to study the brain? The basic idea behind this thought is that the brain and computers work in the same way. Doing some multiplication is an easy task for our brain and the same we can do with computers.