Yoshua Bengio is recognized as one of the world's leading experts in artificial intelligence and a pioneer in deep learning. Since 1993, he has been a professor in the Department of Computer Science and Operational Research at the Université de Montréal. He is the founder and scientific director of Mila, the Quebec Institute of Artificial Intelligence, the world's largest university-based research group in deep learning. He is a member of the NeurIPS board and co-founder and general chair for the ICLR conference, as well as program director of the CIFAR program on Learning in Machines and Brains and is Fellow of the same institution. In 2018, Yoshua Bengio ranked as the computer scientist with the most new citations, worldwide, thanks to his many publications.
The 2020 Breakthrough Days event aims to generate and fuel meaningful projects in each of this year's three AI for Good Global Summit domains – Gender, Food, and Pandemics – that will advance progress on the UN Sustainable Development Goals (SDGs). Hear from keynote speakers and participate in interactive workshops designed to launch solutions to some of the world's greatest challenges. "Beneficial AI to advance SDGs" Keynote Speaker: Stuart Russell, Professor of Computer Science at UC Berkeley Moderator: Amir Banifatemi, Chief Innovation Officer, XPRIZE; Chair of the AI for Good Programme Committee In an effort to allow teams to prepare for main stage presentations on Monday and Tuesday, we have designated Friday 25 September as a time for teams and attendees to converse individually. Please use the AI for Good workspace on Slack to continue the conversation. Join us on Monday 28 September as we hear from teams in each of this year's AI for Good Breakthrough Track "What is AI for Good Anyway?" Keynote Speaker: Sasha Luccioni, Postdoctoral Researcher – AI for Humanity, Université de Montréal, Mila – Quebec AI Institute Moderator: Amir Banifatemi, Chief Innovation Officer, XPRIZE; Chair of the AI for Good Programme Committee Keynote Address Keynote Speaker: Peter H. Diamandis, entrepreneur, founder and executive chairman of the XPRIZE Foundation, Bestselling author of "Abundance – The Future Is Better Than You Think" Moderator: Amir Banifatemi, Chief Innovation Officer, XPRIZE; Chair of the AI for Good Programme Committee Interested individuals and teams from around the world have submitted project ideas to the Gender, Food and Pandemics Breakthrough Tracks. After being mentored by world-renowned experts and Brain Trusts, the top three finalists in each domain have been selected to present their project proposals in a series of interactive workshops during the Breakthrough Days event.
Almost every group on earth is working on'deep learning' in some form. In Canada there are the big three research units: MILA at Montreal, Vector at Toronto, AMII at Edmonton. Both MILA and Vector have several research groups/universities affiliated to them in Quebec and Ontario respectively. Weirdly folks at UBC are also affiliated with Vector. AMII is mostly University of Alberta.
On Archie's 30th anniversary, we salute the world's first search engine, a pioneer that paved the way for giants to come. Archie was first released to the general public on Sept. 10, 1990. It was developed as a school project by Alan Emtage at McGill University in Montreal. According to an interview with Digital Archaeology, Emtage had been working as a grad student in 1989 in the university's information technology department. His job required him to find software for other students and faculty.
The original game is a milestone in platformer adventures, going on to inspire "Another World," "Flashback," and other similar platformer adventures. The original "Tomb Raider" from 1996, one of the pioneers of 3D technology, drew heavy inspiration from Mechner's work. And after the critical and commercial success of the "Sands of Time" trilogy, Ubisoft Montreal attempted to evolve the combat and parkour formula of the series into an open-world formula. Through the team's research, they became fascinated with the history of assassins. The project eventually morphed into a new intellectual property, "Assassin's Creed," a series that has since become the centerpiece of Ubisoft's catalogue.
Article contentIrina Rish, now a renowned expert in the field of artificial intelligence, first became drawn to the topic as a teenager in the former Soviet republic of Uzbekistan. At 14, she was fascinated by the notion that machines might have their own thought processes."I "I didn't know the word yet (algorithm) but that's essentially what it was. How do you solve tough problems?"She But the other interesting part of that is that you hope that by doing so, you can also better understand the human mind and hopefully achieve better human intelligence.
Irina Rish, now a renowned expert in the field of artificial intelligence, first became drawn to the topic as a teenager in the former Soviet republic of Uzbekistan. At 14, she was fascinated by the notion that machines might have their own thought processes. "I was interested in math in school and I was looking at how you improve problem solving and how you come up with algorithms," Rish said in a phone interview Friday afternoon. "I didn't know the word yet (algorithm) but that's essentially what it was. How do you solve tough problems?"
Recently, researchers from DeepMind and McGill University proposed new approaches to speed up the solution of complex reinforcement learning problems. They mainly introduced a divide and conquer approach to reinforcement learning (RL), which is combined with deep learning to scale up the potentials of the agents. For a few years now, reinforcement learning has been providing a conceptual framework in order to address several fundamental problems. This algorithm has been utilised in several applications, such as to model robots, simulate artificial limbs, developing self-driving cars, play games like poker, Go, and more. Also, the recent combination of reinforcement learning with deep learning added several impressive achievements and is found to be a promising approach to tackle important sequential decision-making problems that are currently intractable.
TL;DR: As of Aug. 31, you can preorder select editions of Marvel's Avengers (out Sept. 4) at Amazon, Best Buy, GameStop, the Playstation Store, the Microsoft Store, and Steam. We've held out for a hero long enough: After several years in the making and a delayed release, Marvel's Avengers is finally coming to Xbox One, Playstation 4, and PC on Sept. 4. Developed by Crystal Dynamics in collaboration with Eidos-Montréal (the teams behind the latest Tomb Raider games), Marvel's Avengers is a third-person action-adventure game set five years after an A-Day tragedy that results in the Avengers' disbandment. Polygon reports that the game's story mode will have players assuming the roles of iconic characters like the Hunk, Kamala Khan (a.k.a. Ms. Marvel), Thor, Black Widow, Iron Man, and Captain America to get the gang back together and defeat evil entities like Advanced Idea Mechanics (AIM) and the supervillain MODOK. You'll also get the chance to link up with up to three other players for online multiplayer missions.
Successful behavior in an uncertain, changing, and open-ended environment critically relies on the ability to decide between continuing with the ongoing strategy or exploring new options. Neuroimaging studies have shown that the human medial prefrontal cortex (mPFC) is the part of the brain that primarily deals with this dilemma. However, the contribution of the different mPFC regions remains largely unknown. Domenech et al. recorded neuronal activity in six epileptic patients with depth electrodes in this brain area (see the Perspective by Steixner-Kumar and Gläscher). The ventral mPFC inferred the reliability of the ongoing action plan according to action outcomes. It proactively flagged outcomes either as learning signals to better exploit this plan or as potential triggers to explore new ones. The dorsal mPFC then evaluated action outcomes and generated an adaptive behavioral strategy. Science , this issue p. [eabb0184]; see also p.  ### INTRODUCTION Everyday life often requires arbitrating between pursuing an ongoing action plan by possibly adjusting it versus exploring new action plans instead. Resolving this so-called exploitation-exploration dilemma is critical to gradually build a repertoire of action plans for efficient adaptive behavior in uncertain, changing, and open-ended everyday environments. Previous studies have shown that its resolution primarily involves the medial prefrontal cortex (mPFC). Human functional magnetic resonance imaging shows that activations in the ventromedial PFC (vmPFC) reflect the subjective value of the ongoing plan according to action outcomes, whereas the dorsomedial PFC (dmPFC) exhibits activations when this value drops and the plan is abandoned for exploring new ones. However, the neural mechanisms that resolve the dilemma and make the decision to exploit versus explore remain largely unknown. ### RATIONALE We addressed this issue by recording neuronal activity in participants using intracranial electroencephalography while they were performing a task that induced systematic exploitation-exploration dilemmas in an uncertain, changing, and open-ended environment. Participants were six epileptic patients with electrodes implanted in the vmPFC and dmPFC (see the figure), who were eventually diagnosed with temporal or parietal lobe epilepsy with no impacts in the PFC. Using computational modeling, we identified from participants’ behavior the so-called stay trials, when participants adjusted and exploited their ongoing action plan through reinforcement learning, and the switch trials, when action outcomes instead led participants to covertly switch away from this plan and explore new ones in the following trials. We then analyzed vmPFC and dmPFC neural activity in both stay and switch trials. ### RESULTS vmPFC neural activity in the high-gamma frequency band (>50 Hz) that reflects local processing was found to encode outcome expectations after action selection. This vmPFC high-gamma activity further encoded the prior and posterior reliability of the ongoing action plan relative to action outcomes, which, according to the computational model, subserved the arbitration between exploiting and exploring. Notably, this reliability encoding yielded vmPFC activity to proactively flag forthcoming action outcomes as potential triggers to explore rather than as learning signals to exploit. Preceding the occurrence of action outcomes, switch trials—unlike stay trials—witnessed an increased neural activity in the beta frequency band (13 to 30 Hz) that reflects top-down neural processing (see the figure). Following action outcomes in switch compared with stay trials, dmPFC neural activity then decreased in the theta frequency band (4 to 8 Hz), which indicates that the dmPFC was then configured to respond to action outcomes according to this vmPFC proactive construct. In stay trials, outcome expectations encoded in the vmPFC were transmitted to the dmPFC, so that from 300 ms after action outcomes, dmPFC neural activity in the high-gamma frequency band encoded the reward prediction error (i.e., the discrepancy between expected and actual outcomes that scales reinforcement learning). In switch trials, by contrast, this encoding was disrupted through reconfiguring dmPFC activity in the alpha frequency band (8 to 12 Hz) to release the inhibition bearing upon alternative action plans from 250 ms after action outcomes. ### CONCLUSION The medial PFC resolves exploitation-exploration dilemmas through a predictive coding mechanism that was originally proposed for perception. The vmPFC monitors the reliability of the ongoing action plan to proactively set the functional signification of forthcoming action outcomes as either learning signals to exploit or potential triggers to explore. The dmPFC responds to action outcomes according to this functional construct, yielding to either stay and adjust the ongoing plan through reinforcement learning or switch away from this plan to explore new ones. This predictive coding mechanism has the advantage of speeding up the abandonment of ongoing action plans and preventing action outcomes that trigger exploration from inappropriately acting as learning signals. These findings support the idea that predictive coding also operates within the prefrontal executive system and constitutes a general mechanism that underlies information processing across the cerebral cortex. In perceptual neural systems, predictive coding operates so that observers’ prior beliefs about a scene alter how they perceive the scene. Our findings suggest that within the prefrontal executive system, predictive coding operates by proactively altering the functional signification of behavioral events according to the agents’ beliefs about their own behavior. ![Figure] Action outcomes triggering exploration. Neural activity around outcome onsets in switch compared with stay trials recorded in ventromedial (orange, vmPFC) and dorsomedial (blue, dmPFC) prefrontal electrodes implanted in the six patients. Electrode localizations are shown on a canonical sagittal brain slice [Montreal Neurological Institute (MNI) coordinate: x = −10], and neural activity is shown against time according to its spectral decomposition. vmPFC activity reflecting top-down neural processing increased and proactively flagged action outcomes as potential triggers to explore rather than as learning signals to exploit. dmPFC activity followed action outcomes triggering exploration through reconfiguring neural processing. Stim, stimulus. Everyday life often requires arbitrating between pursuing an ongoing action plan by possibly adjusting it versus exploring a new action plan instead. Resolving this so-called exploitation-exploration dilemma involves the medial prefrontal cortex (mPFC). Using human intracranial electrophysiological recordings, we discovered that neural activity in the ventral mPFC infers and tracks the reliability of the ongoing plan to proactively encode upcoming action outcomes as either learning signals or potential triggers to explore new plans. By contrast, the dorsal mPFC exhibits neural responses to action outcomes, which results in either improving or abandoning the ongoing plan. Thus, the mPFC resolves the exploitation-exploration dilemma through a two-stage, predictive coding process: a proactive ventromedial stage that constructs the functional signification of upcoming action outcomes and a reactive dorsomedial stage that guides behavior in response to action outcomes. : /lookup/doi/10.1126/science.abb0184 : /lookup/doi/10.1126/science.abd7258 : pending:yes