A.I. 2040 and Beyond 20221012

In the year 2031, or 2040 and beyond, there are outstanding questions to consider to explore significant advances in learning and reasoning.

Join host Dr. Benji Maruyama, AFRL Materials and Manufacturing Directorate, and co-host AFOSR program officer for the Science of Information, Computation, Learning, and Fusion, Doug Riecken, on October 12, 2022 from 2-4pm EDT for a lively discussion with A.I. leaders: X as they debate the next big question in the science of artificial intelligence.

This is an ongoing series of 2-hour sessions with thought leaders on the subject.

Agenda

INTRO SECTION
2:00-2:10 EDT	Welcome from AFOSR X Dr. Doug Riecken, AFOSR
THINKER/SPEAKER SECTION
2:10-3:25 EDT	Remarks and Panel Discussion Each speaker will present in ~7-8 min their question(s) and a couple of comments to communicate the key ideas – then at least two or more of the other four speakers will comment on the question(s) for ~7-8 min in order to explore more details. Speaking order: X
OPEN DISCUSSION BY SPEAKERS WITH ALL ATTENDING
3:25-4:00 EDT	Interactive Discussion We invite all attendees to pose questions/topics for the panel speakers

Panel Bios

	Speaker, Organization Question: Link to bio:
	Speaker, Organization Question: Link to bio:
	Speaker, Organization Question: Link to bio:
	Speaker, Organization Question: Link to bio:
	Vladimir Vapnik NEW MATHEMATICAL, ALGORITHMIC, AND PHILOSOPHICAL DEVELOPMENT OF LEARNING THEORY 1. The main content of learning theory constitute analysis of two selection problems which are appear due to two different modes of convergence of functions existing in Hilbert space: the weak mode and the strong mode of convergence. 2. Using mechanisms of weak convergence learning machine realizes the first selection problem: selection a set of admissible functions from functions of Hilbert space. 3. Second selection problem constitute the existing understanding of learning problem: selection of the desired function from the set of admissible functions. 4. To realize weak mode of convergence it is required to have some finite set of functions belonging to Hilbert space (the predicates). Formally any function of Hilbert space can serve as a predicate. Selection of the appropriate predicates is informal part of learning model which reflect prior knowledge of the problems of interest existing in the Nature. 5. Selection of predicates forms philosophical part of learning problem which have a direct connection to both: to concept what is intellect and to classical models of philosophy of the Nature. 6. In framework of this model there exist complete solution of learning problems in RKHS of a parametric families of kernels which allow effectively construct learning algorithms. Full Bio
	Dr. Benji Maruyama, AFRL Materials and Manufacturing Directorate Prof. Hiroaki Kitano has issued the “Nobel-Turing Challenge” for an AI Scientist to win a Nobel Prize by 2050. He notes that scientific research today is at the pre-industrial revolution level. Materials Scientists are pioneering the use of AI and autonomy to build research robots that perform cognitive and manual labor that are orders of magnitude faster than humans alone, leading to a Moore’s Law exponential increase in the speed of research. To enable this, closed-loop autonomous research systems need advanced AI methods to understand complex image and spectral data, and to make rapid, reasoned decisions in high-dimensional parameter space. Together, teams of human and AI scientists can revolutionize the research process. Stach, Eric, et al. "Autonomous experimentation systems for materials development: A community perspective." Matter(2021). Full Bio
	Doug Riecken, AFOSR program officer for the Science of Information, Computation, Learning, and Fusion Riecken is a trained concert pianist with a B.A. from the Manhattan School of Music and studies at the Juilliard School of Music. He spent many years performing classical, jazz, and rock styles on international concert tours with world-renowned artists before he switched to a career in cognitive and computing science. He received his PhD from Rutgers University under thesis advisor Dr. Marvin Minsky from MIT; a founding father of artificial intelligence. Riecken and Minsky spent 30+ years in friendship researching learning and the mind. Riecken is a thought leader in the areas of big data analytics and machine learning, human-computer interaction and design, knowledge discovery and data mining, global cloud enterprise architectures, and privacy management. He joined the Air Force Office of Scientific Research as a program officer in 2014 and is a senior member of the AFRL ACT3 team. Full Bio