Artificial Intelligence seminars

The emergence of brain-inspired neuromorphic computing as a paradigm for edge AI is motivating the search for high-performance and efficient spiking neural networks to run on this hardware. However, compared to classical neural networks in deep learning, current spiking neural networks lack competitive performance in compelling areas. Here, for sequential and streaming tasks, we demonstrate how spiking recurrent neural networks (SRNN) using adaptive spiking neurons are able to achieve state-of-the-art performance compared to other spiking neural networks and almost reach or exceed the performance of classical recurrent neural networks (RNNs) while exhibiting sparse activity. From this, we calculate a 100x energy improvement for our SRNNs over classical RNNs on the harder tasks. We find in particular that adapting the timescales of spiking neurons is crucial for achieving such performance, and we demonstrate the performance for SRNNs for different spiking neuron models.

Across evolution and, in particular, in brain evolutionary development we can observe how diverse adaptive biological mechanisms are displayed as a solution to environmental demands. In this talk, I will discuss some examples of emerging evolutionary developmental strategies allowing to increase brain computational capacities and how neurodevelopmental conservation, divergence, and convergence would inspire AI systems optimization.

In his bestseller 'Thinking, Fast and Slow', Daniel Kahneman popularized the idea that there are two fundamentally different process of thought: a 'System 1' process that is unconscious and instinctive, and a 'System 2' process that is deliberative and requires conscious attention. There is a growing recognition that machine learning is mostly stuck at the 'System 1' level of cognition, and that moving to 'System 2' methods are key to solving long-standing challenges such as out-of-distribution generalization. In this talk, AlphaZero will be used as a case-study of the power of combining 'System 1' and 'System 2' processes. The similarities and differences between AlphaZero and human learning will be explored, along with drawing lessons for the future of machine learning.

Model-based approaches to control and decision making have long held the promise of being more powerful and data efficient than model-free counterparts. However, success with model-based methods has been limited to those cases where a perfect model can be queried. The game of Go was mastered by AlphaGo using a combination of neural networks and the MCTS planning algorithm. But planning required a perfect representation of the game rules. I will describe new algorithms that instead leverage deep neural networks to learn models of the environment which are then used to plan, and update policy and value functions. These new algorithms offer hints about how brains might approach planning and acting in complex environments.

Cognitive architectures are attempts to build larger-scale models of minds. This talk will explore how structure-mapping models of analogical matching, retrieval, and generalization are used in the Companion cognitive architecture. Examples will include modeling conceptual change, learning by reading, and analogical Q/A training.

There has been a surge of publications on using machine learning (ML) on experimental data from physical systems: social, biological, statistical, and quantum. However, can these methods discover fundamentally new physics? It can be that their biggest impact is in better data preprocessing, while inferring new physics is unrealistic without specifically adapting the learning machine to find what we are looking for — that is, without the “intuition” — and hence without having a good a priori guess about what we will find. Is machine learning a useful tool for physics discovery? Which minimal knowledge should we endow the machines with to make them useful in such tasks? How do we do this? Eight speakers below will anchor the workshop, exploring these questions in contexts of diverse systems (from quantum to biological), and from general theoretical advances to specific applications. Each speaker will deliver a 10 min talk with another 10 minutes set aside for moderated questions/discussion. We expect the talks to be broad, bold, and provocative, discussing where the field is heading, and what is needed to get us there.

Scientists have worked to recreate human vision in computers for the past 50 years. But how much about human vision do we actually know? And can the brain be useful in furthering computer vision? This talk will take a look at the similarities and differences between (modern) computer vision and human vision, as well as the important crossovers, collaborations, and applications that define the interface between computational neuroscience and computer vision. If you want to know more about how the brain sees (really sees), how computer vision developments are inspired by the brain, or how to apply AI to neuroscience, this talk is for you.

Maximum inner-product search ranks candidate vectors by their unnormalized dot product with a query. This similarity creates difficulties for conventional locality-sensitive hashing. The talk introduces an asymmetric hashing framework in which queries and stored vectors undergo different transformations, turning approximate MIPS into a standard approximate nearest-neighbor problem. An explicit construction provides provably sublinear search and a simple implementation. Experiments on recommendation tasks using Netflix and MovieLens data compare the method with sign random projection and p-stable hashing for Euclidean distance, demonstrating substantial computational savings.