While effective, the backpropagation (BP) algorithm exhibits limitations in terms of biological plausibility, computational cost, and suitability for online learning. As a result, there has been a growing interest in developing alternative biologically plausible learning approaches that rely on local learning rules. This study focuses on the primarily unsupervised similarity matching (SM) framework, which aligns with observed mechanisms in biological systems and offers online, localized, and biologically plausible algorithms. i) To scale SM to large datasets, we propose an implementation of Convolutional Nonnegative SM using PyTorch. ii) We introduce a localized supervised SM objective reminiscent of canonical correlation analysis, facilitating stacking SM layers. iii) We leverage the PyTorch implementation for pre-training architectures such as LeNet and compare the evaluation of features against BP-trained models. This work combines biologically plausible algorithms with computational efficiency opening multiple avenues for further explorations.

WASHINGTON, DC, October 4, 2021 (ENS) – A new international project is creating advanced artificial intelligence, AI, programs that will enable machines to learn progressively over a lifetime and share those experiences with each other. Uses of this new technology could include co-operating self-learning autonomous vehicles such as self-driving cars, robotic rescue and exploration systems, distributed monitoring systems to detect emergencies, or cybersecurity systems of agents that monitor large networks. Researchers hope the technology will allow machines to reuse information, adapt quickly to new conditions and collaborate by sharing information. The project is part of the initiative Shared-Experience Lifelong Learning, or ShELL, a program funded by the Defense Advanced Research Projects Agency, DARPA. This U.S. government military agency is credited with some of the biggest technological advances in recent history such as the Internet, the miniaturization of GPS, Siri, and the computer mouse.

The aim behind a new international project is to develop advanced AI programs that will allow machines to learn gradually over a lifetime and share that input with each other. Scientists are optimistic that the technology will enable machines to reuse data, adapt rapidly to new conditions and work in partnership by sharing data. The project comes under the initiative known as Shared-Experience Lifelong Learning (ShELL), a program financially supported by the Defense Advanced Research Projects Agency (DARPA) -- a U.S. government agency known for some major technological developments in recent history such as the Internet, Siri, the miniaturization of GPS and the computer mouse. It began this month and is being headed by Dr. Andrea Soltoggio of Loughborough's Computer Science department, in partnership with Dr. Soheil Kolouri at Vanderbilt University and Dr. Cong Liu at the University of Texas at Dallas, both in the United States. The idea behind this project is to gain a deep understanding of how and what an AI system learns when dealing with a new task, so that we can exploit task similarities and share information to create fast, reliable, and collaborating learning agents.

Andrea Soltoggio received a combined BSc and MSc degree in Computer Science in 2004 from the Norwegian University of Science and Technology, Norway, and from Politecnico di Milano, Italy. He was awarded a Ph.D. in Computer Science in 2009 from the University of Birmingham, UK. He was with the Laboratory of Intelligent Systems at EPFL, Lausanne, CH, in 2006 and 2008-2009. He was a visiting researcher at the University of Central Florida, US, in 2009. From 2010 to 2014 he was Technical Coordinator of the FP7 European large-scale integration project AMARSi with the Research Institute for Cognition and Robotics, Bielefeld University, Germany.

Researchers at a British university are working on an artificial intelligence (AI) engine that can diagnose illness simply by smelling the breath of a person. Andrea Soltoggio, a member of the data science team at Loughborough University, said the engine is being taught how to identify a range of illness-revealing substances that humans might exhale. "Compared to that of animals, the human sense of smell is far less developed and certainly not used to carry out daily activities. For this reason, humans aren't particularly aware of the richness of information that can be transmitted through the air, and can be perceived by a highly sensitive olfactory system. AI may be about to change that," Soltoggio wrote in an article for online publication Smithsonian.com.