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<p class="MsoNormal"><span style="font-size:18.0pt">Dear Antonio and colleagues,<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:18.0pt"><o:p> </o:p></span></p>
<p class="MsoNormal"><span style="font-size:18.0pt">I just saw the announcement below of your workshop on Human Aligned AI, notably your statement that:<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:18.0pt"><o:p> </o:p></span></p>
<p class="MsoNormal"><span style="font-size:18.0pt">“The rapid advancement of Artificial Intelligence (AI) brings forth critical considerations for its
<b>trustworthiness</b>, including safety and security, fairness, privacy, and <b>
explainability</b>, demanding a <b>human-aligned approach</b>” [<b>boldface </b>mine].<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:18.0pt"><o:p> </o:p></span></p>
<p class="MsoNormal"><span style="font-size:18.0pt">I am writing to call to the attention of your attendees that there is already a human-aligned solution to this problem that has been available for several decades.<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:18.0pt"><o:p> </o:p></span></p>
<p class="MsoNormal"><span style="font-size:18.0pt">The following article discusses this problem and a solution to it:<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:18.0pt"><o:p> </o:p></span></p>
<p class="MsoNormal"><span style="font-size:18.0pt">Grossberg, S. (2020). A path towards Explainable AI and autonomous adaptive intelligence: Deep Learning, Adaptive Resonance, and models of perception, emotion, and action.<br>
<i>Frontiers in Neurobotics</i>, June 25, 2020.<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:18.0pt"><a href="https://www.frontiersin.org/articles/10.3389/fnbot.2020.00036/full">https://www.frontiersin.org/articles/10.3389/fnbot.2020.00036/full</a><o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:18.0pt"><o:p> </o:p></span></p>
<p class="MsoNormal"><span style="font-size:18.0pt">The Abstract of the article explains the extent to which a trustworthy and explainable biological neural network solution of the problem exists:<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:18.0pt"><o:p> </o:p></span></p>
<p class="MsoNormal"><span style="font-size:18.0pt">“</span><span style="font-size:18.0pt">Biological neural network models whereby brains make minds help to understand autonomous adaptive intelligence. This article summarizes why the dynamics and emergent
properties of such models for perception, cognition, emotion, and action are explainable, and thus amenable to being confidently implemented in large-scale applications. Key to their explainability is how these models combine fast activations, or short-term
memory (STM) traces, and learned weights, or long-term memory (LTM) traces. Visual and auditory perceptual models have explainable conscious STM representations of visual surfaces and auditory streams in surface-shroud resonances and stream-shroud resonances,
respectively. Deep Learning is often used to classify data. However, Deep Learning can experience catastrophic forgetting: At any stage of learning, an unpredictable part of its memory can collapse. Even if it makes some accurate classifications, they are
not explainable and thus cannot be used with confidence. Deep Learning shares these problems with the back propagation algorithm, whose computational problems due to non-local weight transport during mismatch learning were described in the 1980s. Deep Learning
became popular after very fast computers and huge online databases became available that enabled new applications despite these problems. Adaptive Resonance Theory, or ART, algorithms overcome the computational problems of back propagation and Deep Learning.
ART is a self-organizing production system that incrementally learns, using arbitrary combinations of unsupervised and supervised learning and only locally computable quantities, to rapidly classify large non-stationary databases without experiencing catastrophic
forgetting. ART classifications and predictions are explainable using the attended critical feature patterns in STM on which they build. The LTM adaptive weights of the fuzzy ARTMAP algorithm induce fuzzy IF-THEN rules that explain what feature combinations
predict successful outcomes. ART has been successfully used in multiple large-scale real-world applications, including remote sensing, medical database prediction, and social media data clustering. Also explainable are the MOTIVATOR model of reinforcement
learning and cognitive-emotional interactions, and the VITE, DIRECT, DIVA, and SOVEREIGN models for reaching, speech production, spatial navigation, and autonomous adaptive intelligence. These biological models exemplify complementary computing and use local
laws for match learning and mismatch learning that avoid the problems of Deep Learning.”<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:18.0pt"><o:p> </o:p></span></p>
<p class="MsoNormal"><span style="font-size:18.0pt">Best,<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:18.0pt"><o:p> </o:p></span></p>
<p class="MsoNormal"><span style="font-size:18.0pt">Steve<o:p></o:p></span></p>
<div>
<p class="MsoNormal"><span style="font-size:18.0pt;color:#212121"><br>
Stephen Grossberg</span><span style="font-size:12.0pt;color:#212121"><o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:18.0pt;color:#212121">Wang Professor of Cognitive and Neural Systems</span><span style="font-size:11.0pt;color:#212121"><o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:18.0pt;color:#212121">Director, Center for Adaptive Systems</span><span style="font-size:11.0pt;color:#212121"><o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:18.0pt;color:#212121">Emeritus Professor of Mathematics & Statistics, Psychological & Brain Sciences, and Biomedical Engineering</span><span style="font-size:11.0pt;color:#212121"><o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:18.0pt;color:#212121">Boston University</span><span style="font-size:11.0pt;color:#212121"><o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:18.0pt;color:#212121">sites.bu.edu/steveg/</span><span style="font-size:11.0pt;color:#212121"><o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:18.0pt;color:#212121"><a href="mailto:steve@bu.edu" title="mailto:steve@bu.edu"><span style="color:#0078D7">steve@bu.edu</span></a></span><span style="font-size:11.0pt;color:#212121"><o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:18.0pt;color:#212121"><a href="http://en.wikipedia.org/wiki/Stephen_Grossberg" title="http://en.wikipedia.org/wiki/Stephen_Grossberg"><span style="color:#0078D7">http://en.wikipedia.org/wiki/Stephen_Grossberg</span></a></span><span style="font-size:11.0pt;color:#212121"><o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:18.0pt;color:#212121"><a href="http://scholar.google.com/citations?user=3BIV70wAAAAJ&hl=en" title="http://scholar.google.com/citations?user=3BIV70wAAAAJ&hl=en"><span style="color:#0078D7">http://scholar.google.com/citations?user=3BIV70wAAAAJ&hl=en</span></a></span><span style="font-size:11.0pt;color:#212121"><o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:18.0pt;color:#212121"><a href="https://sites.bu.edu/steveg/files/2021/08/Grossberg-CV-8-14-21.pdf" title="https://sites.bu.edu/steveg/files/2021/08/Grossberg-CV-8-14-21.pdf"><span style="color:#0078D7">https://sites.bu.edu/steveg/files/2021/08/Grossberg-CV-8-14-21.pdf</span></a></span><span style="font-size:11.0pt;color:#212121"><o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:18.0pt;color:#212121"><a href="https://youtu.be/9n5AnvFur7I" title="https://youtu.be/9n5AnvFur7I"><span style="color:#0078D7">https://youtu.be/9n5AnvFur7I</span></a></span><span style="font-size:11.0pt;color:#212121"><o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:18.0pt;color:#212121"><a href="https://www.youtube.com/watch?v=_hBye6JQCh4" title="https://www.youtube.com/watch?v=_hBye6JQCh4"><span style="color:#0078D7">https://www.youtube.com/watch?v=_hBye6JQCh4</span></a></span><span style="font-size:11.0pt;color:#212121"><o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:18.0pt;color:#212121"><a href="https://www.amazon.com/Conscious-Mind-Resonant-Brain-Makes/dp/0190070552" title="https://www.amazon.com/Conscious-Mind-Resonant-Brain-Makes/dp/0190070552"><span style="color:#0078D7">https://www.amazon.com/Conscious-Mind-Resonant-Brain-Makes/dp/0190070552</span></a></span><span style="font-size:11.0pt;color:#212121"><o:p></o:p></span></p>
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<p class="MsoNormal"><a name="h2"></a><span style="font-size:12.0pt"><o:p> </o:p></span></p>
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<p class="MsoNormal" style="margin-bottom:12.0pt"><b><span style="font-size:12.0pt;color:black">From:
</span></b><span style="font-size:12.0pt;color:black">Connectionists <connectionists-bounces@mailman.srv.cs.cmu.edu> on behalf of Antonio Cinà <antonio.cina@unige.it><br>
<b>Date: </b>Thursday, June 13, 2024 at 10:21</span><span style="font-size:12.0pt;font-family:"Arial",sans-serif;color:black"> </span><span style="font-size:12.0pt;color:black">AM<br>
<b>To: </b>connectionists@mailman.srv.cs.cmu.edu <connectionists@mailman.srv.cs.cmu.edu>, aixia@aixia.it <aixia@aixia.it>, Martina Mattioli <martina.mattioli@unive.it>, Teresa Scantamburlo <teresa.scantamburlo@unive.it><br>
<b>Subject: </b>Connectionists: Workshop CFP - Human Aligned AI: Towards Algorithms that Humans Can Trust<o:p></o:p></span></p>
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<p style="margin:0in"><span style="color:#0E101A">[Apologies if you receive multiple copies of this CFP]<o:p></o:p></span></p>
<p style="margin:0in"><span style="color:#0E101A"><o:p> </o:p></span></p>
<p style="margin:0in"><span style="color:#0E101A">Call for papers: Workshop on "Human Aligned AI: Towards Algorithms that Humans Can Trust" at ICMLA 2024 - <a href="https://urlsand.esvalabs.com/?u=https%3A%2F%2Fwww.icmla-conference.org%2Ficmla24%2Fworkshops.html&e=ed7a584b&h=c4edb686&f=y&p=y"><span style="color:#4A6EE0">https://www.icmla-conference.org/icmla24/workshops.html</span></a><o:p></o:p></span></p>
<p style="margin:0in"><span style="color:#0E101A">Accepted papers will be included in the main conference proceedings.<o:p></o:p></span></p>
<p style="margin:0in"><span style="color:#0E101A"><o:p> </o:p></span></p>
<p style="margin:0in"><span style="color:#0E101A">International Conference on Machine Learning and Applications (ICMLA 2024).<o:p></o:p></span></p>
<p style="margin:0in"><span style="color:#0E101A">18-20 December 2024, Miami, Florida, USA - <a href="https://urlsand.esvalabs.com/?u=https%3A%2F%2Fwww.icmla-conference.org%2Ficmla24%2F&e=ed7a584b&h=9bd78a16&f=y&p=y"><span style="color:#4A6EE0">https://www.icmla-conference.org/icmla24/</span></a><o:p></o:p></span></p>
<p style="margin:0in"><span style="color:#0E101A"><o:p> </o:p></span></p>
<p style="margin:0in"><span style="color:#0E101A">DESCRIPTION:<o:p></o:p></span></p>
<p style="margin:0in"><span style="color:#0E101A">The rapid advancement of Artificial Intelligence (AI) brings forth critical considerations for its trustworthiness, including safety and security, fairness, privacy, and explainability, demanding a human-aligned
approach. This workshop discusses the imperative for embedding these principles in the design and deployment of AI systems to ensure they align with human values and societal norms. We examine the challenges and strategies towards developing trustworthy AI,
preventing unintended consequences, and addressing potential security vulnerabilities. Furthermore, we argue the necessity for a framework for the responsible development of AI, advocating for interdisciplinary collaboration and regulatory oversight. The goal
is to foster AI technologies that enhance human well-being, uphold privacy and dignity, and contribute to a secure and equitable future.<o:p></o:p></span></p>
<p style="margin:0in"><span style="color:#0E101A"><o:p> </o:p></span></p>
<p style="margin:0in"><span style="color:#0E101A">TOPICS OF INTEREST:<o:p></o:p></span></p>
<p style="margin:0in"><span style="color:#0E101A">Trustworthy AI<o:p></o:p></span></p>
<p style="margin:0in"><span style="color:#0E101A">- Adversarial attacks and defenses on machine learning and deep learning<o:p></o:p></span></p>
<p style="margin:0in"><span style="color:#0E101A">- Formal verification of machine learning and deep learning models<o:p></o:p></span></p>
<p style="margin:0in"><span style="color:#0E101A">- Privacy-preserving machine learning and deep learning<o:p></o:p></span></p>
<p style="margin:0in"><span style="color:#0E101A">- Explainability and Fairness<o:p></o:p></span></p>
<p style="margin:0in"><span style="color:#0E101A">- Theoretical foundations of Human Aligned AI<o:p></o:p></span></p>
<p style="margin:0in"><span style="color:#0E101A">Applications of Trustworthy AI<o:p></o:p></span></p>
<p style="margin:0in"><span style="color:#0E101A">- Generative AI<o:p></o:p></span></p>
<p style="margin:0in"><span style="color:#0E101A">- Healthcare<o:p></o:p></span></p>
<p style="margin:0in"><span style="color:#0E101A">- Cybersecurity<o:p></o:p></span></p>
<p style="margin:0in"><span style="color:#0E101A">- Transportation<o:p></o:p></span></p>
<p style="margin:0in"><span style="color:#0E101A">- Robotics<o:p></o:p></span></p>
<p style="margin:0in"><span style="color:#0E101A">- Industry<o:p></o:p></span></p>
<p style="margin:0in"><span style="color:#0E101A"><o:p> </o:p></span></p>
<p style="margin:0in"><span style="color:#0E101A">SUBMISSION:<o:p></o:p></span></p>
<p style="margin:0in"><span style="color:#0E101A">Prospective authors must submit their paper through the ICMLA portal following the instructions provided in <a href="https://urlsand.esvalabs.com/?u=https%3A%2F%2Fwww.icmla-conference.org%2Ficmla24%2Fhowtosubmit.html&e=ed7a584b&h=5bb8a0a8&f=y&p=y"><span style="color:#4A6EE0">https://www.icmla-conference.org/icmla24/howtosubmit.html</span></a> selecting
"Workshop: Human Aligned AI: Towards Algorithms that Humans Can Trust".<o:p></o:p></span></p>
<p style="margin:0in"><span style="color:#0E101A">Each paper will undergo a peer-reviewing process for its acceptance.<o:p></o:p></span></p>
<p style="margin:0in"><span style="color:#0E101A"><o:p> </o:p></span></p>
<p style="margin:0in"><span style="color:#0E101A">IMPORTANT DATES:<o:p></o:p></span></p>
<p style="margin:0in"><span style="color:#0E101A">Submission of papers: 31 July 2024<o:p></o:p></span></p>
<p style="margin:0in"><span style="color:#0E101A">Notification of acceptance: 31 August 2024<o:p></o:p></span></p>
<p style="margin:0in"><span style="color:#0E101A">ICMLA conference: 18-20 December 2024<o:p></o:p></span></p>
<p style="margin:0in"><span style="color:#0E101A"><a href="https://urlsand.esvalabs.com/?u=https%3A%2F%2Fwww.icmla-conference.org%2Ficmla24%2Fkeydates.html&e=ed7a584b&h=c5550093&f=y&p=y"><span style="color:#4A6EE0">https://www.icmla-conference.org/icmla24/keydates.html</span></a><o:p></o:p></span></p>
<p style="margin:0in"><span style="color:#0E101A"><o:p> </o:p></span></p>
<p style="margin:0in"><span style="color:#0E101A">SPECIAL SESSION ORGANISERS:<o:p></o:p></span></p>
<p style="margin:0in"><span style="color:#0E101A">Luca Oneto, University of Genoa<o:p></o:p></span></p>
<p style="margin:0in"><span style="color:#0E101A">Battista Biggio, University of Cagliari<o:p></o:p></span></p>
<p style="margin:0in"><span style="color:#0E101A">Noemi Greco, Google<o:p></o:p></span></p>
<p style="margin:0in"><span style="color:#0E101A">Davide Anguita, University of Genoa<o:p></o:p></span></p>
<p style="margin:0in"><span style="color:#0E101A">Fabio Roli, University of Genoa<o:p></o:p></span></p>
<p style="margin:0in"><span style="color:#0E101A">Maura Pintor, University of Cagliari<o:p></o:p></span></p>
<p style="margin:0in"><span style="color:#0E101A">Luca Demetrio, University of Genoa<o:p></o:p></span></p>
<p style="margin:0in"><span style="color:#0E101A">Antonio Emanuele Cinà, University of Genoa<o:p></o:p></span></p>
<p style="margin:0in"><span style="color:#0E101A">Ambra Demontis, University of Cagliari<o:p></o:p></span></p>
<p style="margin:0in"><span style="color:#0E101A"><o:p> </o:p></span></p>
<p style="margin:0in"><span style="color:#0E101A">ACKNOWLEDGEMENTS:<o:p></o:p></span></p>
<p style="margin:0in"><span style="color:#0E101A">ELSA Project </span><span style="color:#4A6EE0"><a href="https://www.elsa-ai.eu/">https://www.elsa-ai.eu/</a></span><span style="color:#0E101A"><o:p></o:p></span></p>
<p style="margin:0in"><span style="color:#0E101A"><o:p> </o:p></span></p>
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<p class="MsoNormal"><span style="font-size:12.0pt;color:black">Antonio Emanuele Cinà<br>
Assistant Professor @ University of Genoa, DIBRIS<br>
Via All'Opera Pia, 13, 16145 Genova GE<o:p></o:p></span></p>
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<p class="MsoNormal"><span style="font-size:12.0pt"><o:p> </o:p></span></p>
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