Lectures

Lecture 1. “Multimodal Foundation Models” 1/4

Abstract TBA

Lecture 2. “Multimodal Foundation Models” 2/4

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Lecture 3. “Multimodal Foundation Models” 3/4

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Lecture 4. “Multimodal Foundation Models” 4/4

Abstract TBA

Gabriel Barth-Maron

Lecture 1. “Cluster-detection Methods in Network-based Data Analysis” 1/2

Cluster analysis is an important task arising in network-based data analysis. Perhaps the most natural model of a cluster in a network is given by a clique, which is a subset of pairwise-adjacent nodes. However, the clique model appears to be overly restrictive in practice, which has led to introduction of numerous models relaxing various properties of cliques, known as clique relaxations. This talk focuses on a systematic cluster analysis framework based on clique relaxation models.

Lecture 2. “Continuous Approaches to Cluster-Detection Problems in Networks ” 2/2

We discuss continuous formulations for several important cluster-detection problems in networks. More specifically, the problems of interested are formulated as quadratic, cubic, or higher-degree polynomial optimization problems subject to linear constraints. The proposed formulations are used to develop analytical bounds as well as effective algorithms for some of the problems. Moreover, a novel hierarchy of nonconvex continuous reformulations of optimization problems on networks is discussed.

Sergiy Butenko

Lecture 1. “Introduction on Foundation Models” 1/5

Abstract TBA

Lecture 2. “Introduction on Foundation Models” 2/5

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Lecture 3. “Introduction on Foundation Models” 3/5

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Lecture 4. “Introduction on Foundation Models” 4/5

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Lecture 5. “Introduction on Foundation Models” 5/5

Abstract TBA

Sven Giesselbach

Lecture 1. “Continuous Neural Networks / Liquid Neural Networks” 1/4

Abstract TBA

Lecture 2. “Continuous Neural Networks / Liquid Neural Networks” 2/4

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Lecture 3. “Continuous Neural Networks / Liquid Neural Networks” 3/4

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Lecture 4. “Continuous Neural Networks / Liquid Neural Networks” 4/4

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Ramin Hasani

Lecture 1. “Large Language Models: Life after Pre-training” 1/4

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Lecture 2. “Large Language Models: Evaluation” 2/4

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Lecture 3. “Large Language Models: Reasoning and Factuality” 3/4

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Lecture 4. “Large Language Models: Retrieval augmentation and Tool-use” 4/4

Abstract TBA

Nora Kassner

Lecture 1. “Machine Learning for Modeling and Control of Complex Systems” 1/2

Abstract TBA

Lecture 2. “Machine Learning for Modeling and Control of Complex Systems” 2/2

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Lecture 3. “Solving forward and inverse problems with and without neural networks”

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Lecture 4. “Bayesian Uncertainty Quantification”

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Petros Koumoutsakos

Lecture 1. “Longitudinal Language Processing with User Generated Content” 1/4

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Lecture 2. “Longitudinal Language Processing with User Generated Content” 2/4

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Lecture 3. “Longitudinal Language Processing with User Generated Content” 3/4

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Lecture 4. “Longitudinal Language Processing with User Generated Content” 4/4

Abstract TBA

Maria Liakata

Lecture 1. TBA

Abstract TBA

Panos Pardalos

Lecture 1. “Artificial Intelligence: Where We Are, Where We Are Going?” 1/3

At present, there is a great deal of confusion as to the final objective of AI. Some see Artificial General Intelligence as the ultimate and imminent goal suggesting that it can be achieved through machine learning and its further developments.

We argue that despite the spectacular rise of AI, we still have weak AI that only provides building blocks for intelligent systems, mainly intelligent assistants that interact with users in question-answer mode.
A bold step toward human-level intelligence would be the advent of autonomous systems resulting from the marriage between AI and ICT envisaged in particular by the IoT. In this evolution, the ability to guarantee the trustworthiness of AI systems – reputed to be “black boxes” very different from traditional digital systems – will determine their degree of acceptance and integration in critical applications.

We review the current state of the art in AI and its possible evolution, including:

Avenues for the development of future intelligent systems, in particular autonomous systems as the result of the convergence between AI and ICT;
The inherent limitations of the validation of AI systems due to their lack of explainability, and the case for new theoretical foundations to extend existing rigorous validation methods;
Complementarity between human and machine intelligence, which can lead to a multitude of intelligence concepts reflecting the ability to combine data-based and symbolic knowledge to varying degrees.

In light of this analysis, we conclude with a discussion of AI-induced risks, their assessment and regulation.