Brain-inspired ULtra-fast & ULtra-sharp machines for AI-assisted health-care
MAECI-MOST funded projects 2021 (time window 2022-2023)
Israeli Partner: Prof. Ido Kanter (Head), Department of Physics, Bar-Ilan University
Italian Partner: Prof. Adriano Barra (Head), Dipartimento di Matematica & Fisica, Università del Salento
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Aims and purposes of this shared project between Israeli and Italian scientists: (argument: AI and its applications)
After the so-called “winter time”, following Minsky&Papert criticisms on Rosenblatt's perceptron, in the '70s and '80s the research focus in autonomous information processing systems drifted from models of single neuron to models of networks of interacting neurons. The new wave of Artificial Intelligence (AI) arouse in those decades as a discipline inspired by biological information processing in the brain, based on the neurophysiological knowledge available at that time. Next, the development of modern clouds (i.e., big-data repositories where these machines can be trained) as well as a shift in the paradigm from CPU-computing to GPU-computing, has brought to the so-called “computational spring time”, with modern-AI algorithms able of impressive results.
Indeed, nowadays the technology is ready to usher in a "AI revolution", which, much like earlier industrial revolutions, is sparking a great economic activity with an increase in the Gross Domestic Product of the Western world assessed around 18%. The ubiquity of related
applications has already changed our everyday life, yet, at present, AI is far from being optimized, especially as for health-care is concerned, where machine training requires massive datasets. In fact, the large-scale analysis which underlies a Personalized Medicine would imply
tremendous efforts in terms of time and energy consuming. A novel generation of learning machines is therefore imperative for large-scale healthcare applications (e.g., worldwide hospitals) and this project aims to contribute in this direction, by leveraging our recent understanding of information processing and storage in biological neural networks.
In this project we plan to extend the AI paradigm by including bio-inspired local improvement (at the single neuron level) such as implementing dendritic –i.e. beyond synaptic– learning that is expected to generate ultra-fast algorithms as well as bio-inspired global improvement (at the whole-network level). To this aim, we will implement sleeping mechanisms that allow neural networks to sensibly boost their storage capacity and precision in signal detection/pattern recognition, after proper rest. The natural outcome of this novel generation of ultra-fast & ultra-sharp AIs is expected to finally be its “scalability”, de facto allowing its broad usage in healthcare. We also plan to extensively investigate properties of this new AI at work with cancer-detection within our Laboratories in Italy (mainly pancreatic, brest and colon cancers with and without chemotherapies - for comparison), and at work with neural derangements within our Laboratory in Israel (in particular in cross-linking degeneracy and anisotropy at the single neuron level).
We acknowledge, from the Italy part, the MAECI -Ministero degli Affari Esteri e della Cooperazione Internazionale and, from the Israeli part, the MOST - Ministry of Science and Technology for for funding the project BULBUL.
After the so-called “winter time”, following Minsky&Papert criticisms on Rosenblatt's perceptron, in the '70s and '80s the research focus in autonomous information processing systems drifted from models of single neuron to models of networks of interacting neurons. The new wave of Artificial Intelligence (AI) arouse in those decades as a discipline inspired by biological information processing in the brain, based on the neurophysiological knowledge available at that time. Next, the development of modern clouds (i.e., big-data repositories where these machines can be trained) as well as a shift in the paradigm from CPU-computing to GPU-computing, has brought to the so-called “computational spring time”, with modern-AI algorithms able of impressive results.
Indeed, nowadays the technology is ready to usher in a "AI revolution", which, much like earlier industrial revolutions, is sparking a great economic activity with an increase in the Gross Domestic Product of the Western world assessed around 18%. The ubiquity of related
applications has already changed our everyday life, yet, at present, AI is far from being optimized, especially as for health-care is concerned, where machine training requires massive datasets. In fact, the large-scale analysis which underlies a Personalized Medicine would imply
tremendous efforts in terms of time and energy consuming. A novel generation of learning machines is therefore imperative for large-scale healthcare applications (e.g., worldwide hospitals) and this project aims to contribute in this direction, by leveraging our recent understanding of information processing and storage in biological neural networks.
In this project we plan to extend the AI paradigm by including bio-inspired local improvement (at the single neuron level) such as implementing dendritic –i.e. beyond synaptic– learning that is expected to generate ultra-fast algorithms as well as bio-inspired global improvement (at the whole-network level). To this aim, we will implement sleeping mechanisms that allow neural networks to sensibly boost their storage capacity and precision in signal detection/pattern recognition, after proper rest. The natural outcome of this novel generation of ultra-fast & ultra-sharp AIs is expected to finally be its “scalability”, de facto allowing its broad usage in healthcare. We also plan to extensively investigate properties of this new AI at work with cancer-detection within our Laboratories in Italy (mainly pancreatic, brest and colon cancers with and without chemotherapies - for comparison), and at work with neural derangements within our Laboratory in Israel (in particular in cross-linking degeneracy and anisotropy at the single neuron level).
We acknowledge, from the Italy part, the MAECI -Ministero degli Affari Esteri e della Cooperazione Internazionale and, from the Israeli part, the MOST - Ministry of Science and Technology for for funding the project BULBUL.
PUBLISHED PAPERS IN 2022
08. L. Albanese, A. Alessandrelli,
On Gaussian spin-glass with P-wise interactions,
Journal of Mathematical Physics webpage (2022).
This research is crucial for the project and BulBul was the principal sponsor.
07. V. Onesto, et al.,
Probing single cell fermentation flux and intercellular exchange networks via pH-microenvironment sensing and inverse modeling,
submitted to Nature Comm bioRxiv (2022).
This research is not pivotal for the project (it is a source of datasets for it) hence BulBul was a partner sponsor.
06. M. Aquaro, F. Alemanno, I. Kanter, F. Durante, E. Agliari, A. Barra,
Recurrent neural networks that generalize from examples and optimize by dreaming,
submitted to Neural Networks arXiv (22022).
This research is crucial for the project and BulBul was the principal sponsor.
05. A. Fachechi, E. Agliari, F. Alemanno, A. Barra,
Outperforming RBM feature-extraction capabilities by "dreaming" mechanisms,
IEEE Trans. on Neural Nets and Learn Machines in press webpage (2022).
This research is crucial for the project and BulBul was the principal sponsor.
04. A. Chandra, S. Pandija, F. Alemanno, R. Rizzo, R. Romano, G. Gigli, C. Bucci, A. Barra, L. Del Mercato,
A fully automatic computational approach for precisely measuring organelle acidification,
ACS Appl. Mat. & Interf. in press webpage (2022).
Note: Italian CNR (National Center for Scientific Research) press release available for this work: link to the website or download file
This research is not pivotal for the project (it is a source of datasets for it) hence BulBul was a partner sponsor.
03. F. Alemanno, M. Aquaro, I. Kanter, A. Barra, E. Agliari,
Supervised Hebbian learning: toward eXplainable AI,
submitted to Physical Review Letters arxiv (2022).
This research is crucial for the project and BulBul was the principal sponsor.
02. L. Albanese, F. Alemanno, A. Alessandrelli, A. Barra,
Replica symmetry breaking in dense neural networks,
submitted to Journal of Statistical Physics arxiv (2022).
This research is crucial for the project and BulBul was the principal sponsor.
01. E. Agliari, F. Alemanno, A. Barra, G. Di Marzio,
The emergence of a concept in shallow neural networks,
Neural Networks, 148, 232, arxiv webpage (2022).
This research is crucial for the project and BulBul was the principal sponsor.
Group Members (Italian side)**
Adriano Barra, Ross Rinaldi, Elena Agliari (staff members)
Alberto Fachechi, Francesco Alemanno (post-docs)
Chiara Marullo, Linda Albanese, Miriam Aquaro, Daniele Lotito (PhD students),
Andrea Alessandrelli (master student).
**the group is spread among the Salento Unit and the Rome Unit: for more info see our ResearchGate webapage
