"Cognitive science is the interdisciplinary study of mind and intelligence, embracing philosophy, psychology, artificial intelligence, neuroscience, linguistics, and anthropology. Its intellectual origins are in the mid-1950s when researchers in several fields began to develop theories of mind based on complex representations and computational procedures."
– Paul Thagard. Cognitive Science , in The Stanford Encyclopedia of Philosophy.
We are presently living in an age of "artificial intelligence" -- but not how the companies selling "AI" would have you believe. According to Silicon Valley, machines are rapidly surpassing human performance on a variety of tasks from mundane, but well-defined and useful ones like automatic transcription to much vaguer skills like "reading comprehension" and "visual understanding." According to some, these skills even represent rapid progress toward "Artificial General Intelligence," or systems which are capable of learning new skills on their own. Given these grand and ultimately false claims, we need media coverage that holds tech companies to account. Far too often, what we get instead is breathless "gee whiz" reporting, even in venerable publications like The New York Times.
On this podcast Jason Stoughton is joined by Alan Cowen, CEO and Chief Scientist, at Hume AI. As AI progresses, both in terms of what it can do and how widely it is deployed, the ability for AI to understand and empathize with our emotions is still a glaring hole in AI's capabilities. On this podcast Jason and Alan talk about the state of the technology, unpack the hopes and dreams and fears of an AI that understands, and can potentially manipulate, our emotions and how Hume is not only leading the way in advancing AI's capabilities in this area but is also leading the way in ensuring that AI should service human well being above all else.
Tenyx, an AI technology company, announced it has raised $15 million in seed funding to grow its research and development team and further product development. Investors include AME Cloud Ventures, Cota Capital, Morado Ventures, Pathbreaker Ventures, Point72 Ventures and StageOne Ventures, as well as notable angel investors John Lilly, Georges Harik and Jaan Tallinn. Tenyx is led by the founding team behind Apprente, which developed the world's first voice-based AI solutions to automate the order-taking process at drive-thru restaurants. Apprente was acquired by McDonald's Corporation and subsequently by IBM. Tenyx' seasoned leadership team includes Dr. Itamar Arel, a former professor of AI and CEO at Apprente, and Prof. Ron Christly, an established AI researcher and head of the Cognitive Science program at Sussex University.
Tenyx, an AI technology company, today announced it has raised $15 million in seed funding to grow its research and development team and further product development. Investors include AME Cloud Ventures, Cota Capital, Morado Ventures, Pathbreaker Ventures, Point72 Ventures, and StageOne Ventures, as well as notable angel investors John Lilly, Georges Harik, and Jaan Tallinn. Tenyx is led by the founding team behind Apprente, which developed the world's first voice-based AI solutions to automate the order-taking process at drive-thru restaurants. Apprente was acquired by McDonald's Corporation and subsequently by IBM. Tenyx' seasoned leadership team includes Dr. Itamar Arel, a former professor of AI and CEO at Apprente, and Prof. Ron Christly, an established AI researcher and head of the Cognitive Science program at Sussex University.
Controlled Natural Languages (CNLs) are effective languages for Knowledge Representation and Reasoning that look like the ones you use every day, so you can easily read and understand them. However, when they are based on Logical AI, meaning behind what is being said can be accurately processed not just by humans but also by machines. As logical CNLs can represent information about the real world in a way that machines can process, you will be able to ensure that meaning of what you write is accurately understood by creating definitions of words yourself or selecting the definitions from pre-defined vocabularies (ontologies). For the first time on any social platform, utility and information will not be lost or neglected, because, by writing in logical CNLs, you will be able to see the overlapping points of agreement, disagreements, and contradictions in the meaning map of all conversations and use logical reasoning to solve complex tasks such as diagnosing a medical condition. Other social platforms (such as Facebook, Twitter, etc.) do not really understand meaning of what you're saying.
Understanding and manipulating articulated objects such as doors and drawers is a key skill for robots in human environments. However, it is difficult to train systems that generalize to variations of those objects. The sensory signal comes from an Azure Kinect depth camera, and the agent is a Sawyer BLACK robot. A novel per-point representation of the articulation structure of an object is proposed, called 3D Articulation Flow. A newly-developed 3D vision neural network architecture takes as input a static 3D point cloud and predicts the 3D Articulation Flow of the input under articulation motion.
Popular Science: If cars are to become self-driving, machines must learn from the human brain. Time and space are fundamental to the existence of the universe, and human intelligence is our tool for navigating time and space in an appropriate manner. Our ability to see the future is critical. Through evolution, the human brain has evolved into a tool that perceives not only time, place, and things, but our neural network also predicts what will happen in the near future. What kind of path will the stone that you throw take?
In the second of our round-ups of the invited talks at the International Conference on Learning Representations (ICLR) we focus on the presentation by Been Kim. Been Kim's research focusses on interpretability and explanability of AI models. In this presentation she talked about work towards developing a language to communicate with AI systems. The ultimate goal is that we would be able to query an algorithm as to why a particular decision was made, and it would be able to provide us with an explanation. To illustrate this point, Been used the example of AlphaGo, and the famous match against world champion Lee Sedol. At move 37 in one of the games, AlphaGo produced what commentators described as a "very strange move" that turned the course of the game.
Educated at St Pauls School, London and Cambridge University, José Luis Bermúdez is Professor of Philosophy at Texas A&M University, where he has also served as Dean of Liberal Arts and Associate Provost for Strategic Planning. Since his first book, The Paradox of Self-Consciousness (MIT Press 1998) he has been working on interdiscipinary aspects of self-representation and self-consciousness, most recently in Understanding "I": Language and Thought (OUP, 2017) and The Bodily Self: Selected Essays (MIT Press, 2018). He also works on rationality and reasoning, where he has published Decision Theory and Rationality (OUP, 2009). He is currently writing a book of framing and rationality, and also preparing the third edition of his textbook Cognitive Science: An Introduction to the Science of the Mind, both for Cambridge University Press. His work has appeared in seven languages and he is one of the 100 most cited philosophers on Google scholar.