Workshops, Conferences, & Symposia
As part of the Knowledge Transfer mission of CBMM, workshops provide opportunities for disseminating knowledge, shaping new areas of scientific interest and providing fora for discussing the latest research. CBMM workshops are focused in terms of topics, invited speakers and audience while being interdisciplinary in nature. Workshops are organized in conjunction with major conferences in artificial intelligence, machine learning and neuroscience, following formal calls or proposals. These provide to CBMM the chance to participate, propose, integrate and communicate research to academia and industry. Workshops are also initiated by CBMM or partner institutions, with specific CBMM areas or Turing++ questions in mind, in order to facilitate close collaborations and shape concrete research directions for the Center and outside participants.
AAAI Symposium - Science of Intelligence: Computational Principles of Natural and Artificial Intelligence
March 27 - 29, 2017 | Stanford University - Palo Alto, CA
The Science of Intelligence is a new emerging field dedicated to developing a computation-based understanding of intelligence -both natural and artificial- and to establishing an engineering practice based on that understanding. This symposium is designed to bring together experts in artificial intelligence, cognitive science, and computational neuroscience to share and discuss the advances and the challenges in the scientific study of natural and artificial intelligence.
February 2 - 3, 2017 | MIT - Cambridge, MA
The focus of the workshop is on the computations and learning involved in human speech understanding and that are required for speech-enabled machines, following CBMM’s mission to understand intelligence in brains and replicate it in machines. The workshop will bring together experts in the fields of neuroscience, perception, development, machine learning, automatic speech recognition and speech synthesis.
June 20-22, 2016 | Sestri Levante, Italy
PreFor three days -- one day for each of the Brains, Minds, and Machines areas -- we will bring together computer scientists/roboticists, cognitive scientists, and neuroscientists to share and discuss advances in integrated, multimodal approaches to the study of human intelligence.
June 10-12, 2016 | McGovern Institute for Brain Research, MIT
The workshop aims at bringing together leading scientists in deep learning and related areas within machine learning, artificial intelligence, mathematics, statistics, and neuroscience. No formal submission is required and participation is by invitation only. Participants are invited to present their recently published work as well as work in progress, and to share their vision and perspectives for the field.
December 10, 2015 | Montréal, CANADA
Today's science, tomorrow's engineering: We will be discussing current results in the scientific understanding of intelligence and how these results enable new approaches to replicate intelligence in engineered systems.
September 3rd - 5th, 2015 | McGovern Institute for Brain Research, MIT
The Center for Brains Minds and Machines (CBMM) is organizing a workshop on "Understanding Face Recognition: neuroscience, psychophysics and computation" from 3:30pm on September 3rd to 1pm on September 5th, 2015, at MIT in Cambridge. Attendance to workshop is by invitation only.
Thursday, 11 June 2015 - Boston, Massachusetts
July 18, 2014 | McGovern Institute for Brain Research, MIT
Psychologists and neuroscientists routinely borrow ideas from machine learning to understand and model reinforcement learning in humans and animals. Likewise, ideas from psychology and neuroscience filter into machine learning in a variety of ways. The goal of the workshop is to highlight some of the theoretical synergies that have arisen from this cross-pollination.
November 22-24, 2013 | McGovern Institute for Brain Research, MIT
The goal of the meeting is to investigate advances and challenges in learning "good representations" from data, in particular representations that can reduce the complexity of later supervised learning stages. The meeting will gather experts in the field to discuss current and future challenges for the theory and applications of learning representations.