Knowledge expands and diffuses like the branches of the tree...
Our Mission
The Knowledge Foundation , established in 2018 and with facilities in New York, NY, is a non-profit charitable organization. It is a private operating foundation under US Internal Revenue Code Section 501(c)(3), i.e. it primarily executes its own activities for the public good, as opposed to primarily dispensing funds to other charitable organizations. It is currently privately funded, but will also increasingly pursue charitable donations in the future. The mission of the Knowledge Foundation is to explore and develop new paradigms for the practical creation, searchability, and maintenance of knowledge. Its initial project focuses on expert knowledge, in particular about artificial intelligence (AI), aimed at AI researchers and practitioners both as knowledge authors and consumers. Called the Living Book Project, it can be seen as an alternative to standard textbooks, Wikipedia, or Google or other search engines. Goals include treating: 1) the weakness of textbooks regarding both scale of coverage and keeping up to date with rapid advances in the field, 2) the weakness of Wikipedia regarding the authoritativeness/trustworthiness and depth of its knowledge, and 3) the weakness of search engines regarding their reliance on words, causing the user to need to know the jargon relevant to the answer he/she is trying to find, and causing more esoteric topics to be swamped by more common topics which share the same words. The Living Book concept includes a new blend of curation and crowdsourcing from experts, as well as a new way of presenting knowledge so that it can be systematically and efficiently explored without knowing the relevant keywords.
About the Founder
Alexander Gray, Phd. serves as VP of Foundations of AI at IBM, leading IBM’s basic AI research globally. He previously served as CEO and CTO of Skytree, which he co-founded, then at Infosys as GM of Research and Fellow. Prior to that, he served as a tenured Associate Professor at the Georgia Institute of Technology. A theme of his research work, beginning at NASA in 1993, has been on the computational aspects of machine learning for handling massive datasets, long predating the movement of “big data” in industry. His work helped enable the Science journal’s Top Breakthrough of 2003, and have won a number of research awards. He served as a member of the 2010 National Academy of Sciences Committee on the Analysis of Massive Data, a National Academy of Sciences Kavli Scholar, and a frequent advisor and speaker on topics of large-scale machine learning and data science at top research conferences, government agencies, and leading corporations. He received AB degrees in Applied Mathematics and Computer Science from UC Berkeley and a PhD in Computer Science from Carnegie Mellon University. His current interests are in automated data science, automated programming, and in new formalisms for AI beyond today’s machine learning, toward achieving reading comprehension and strong AI.