“This talk is an exploratory tour through this brave new world, and its consequences to our field and to Computer Science (CS) education”, antecipa a oradora de uma apresentação num tópico que, fruto dos avanços da Inteligência Artificial (IA) em “Large Language Models (LLMs)”, mediaticamente expostos pelo ChatGPT, promete revolucionar o desenvolvimento de software.

“The End of Programming (as we know it)” será apresentada pela Prof. Cristina Videira Lopes, com moderação do Prof. João Paulo Fernandes, no dia 23 de março, 18:00, na sala B035, com transmissão online via Youtube.

Pela oradora:

“For the past 80 years, “programming” meant translating a high-level, semi-formal specification of a desired effect from natural language into computer instructions, using an artificial programming language. Mastering these translations requires domain knowledge of algorithms and data structures, talent, and years of practice. Large Language Models (LLMs) are disrupting the very notion of “programming.” The disruption is profound, and at two levels: (1) LLMs are capable of doing those translations automatically, and (2) many of the desired effects can be obtained without the use of algorithms or data structures. This talk is an exploratory tour through this brave new world, and its consequences to our field and to CS education.”

Short-Bio:

Cristina (Crista) Lopes is a Professor in the School of Information and Computer Sciences at University of California, Irvine, with research interests in Programming Languages, Software Engineering, and Distributed Virtual Environments. She is an IEEE Fellow and an ACM Distinguished Scientist. She is the recipient of the 2016 Pizzigati Prize for Software in the Public Interest for her work in the OpenSimulator virtual world platform. Her book “Exercises in Programming Style” has gained rave reviews, including being chosen as “Notable Book” by the ACM Best of Computing reviews.

“Computational Inaesthetics: Expanding the Boundaries of Artistic Research and Computational Aesthetics” será apresentada sexta feira, 24 de março, às 14:30, sala I-105, com a moderação do Prof. António Coelho, docente do DEI.

Pelo autor:

“The increasing use of digital technologies in artistic practice, coupled with the recent emergence of AI, has led to a growing intersection and mutual influence between two related fields: artistic research and computational aesthetics. Artistic research involves using artistic practices to generate new insights and understandings about the world and reflect critically on the process of creating art. In contrast, computational aesthetics involves the theory, practice, and implementation of aesthetics within the domain of computing, and in its most formal version relies on mathematical and computational methods to generate and evaluate art.

However, there has been criticism of computational aesthetics for failing to account for the subjective and non-algorithmic nature of aesthetic experience. Nonetheless, this presentation proposes a practical framework that seeks to resolve this critique by highlighting an expanded view of computational aesthetics, which the presenter terms Computational Inaesthetics.

Through the discussion of basic concepts and principles of artistic research and computational aesthetics, and through the analysis of a selection of artworks by the presenter, the presentation explores the ways in which these fields can inform and enhance each other. Furthermore, the presentation provides an overview of the artistic contents and theoretical underpinnings of the presenter’s artistic research PhD project.

Overall, this presentation showcases the exciting potential of bringing together artistic research and computational aesthetics to expand our understanding of aesthetics, art, creativity, and the role of technology and computation in society.”

Martinus Suijkerbuijk is an artist, designer and engineer that currently is working towards completing his artistic research PhD at the Trondheim Academy of Fine Art, Norway. His artistic research is focused on the concept of Computational Aesthetics, which he explores through the use of AI empowered Artificial Aesthetic Agents (AAA) in virtual environments. His diverse background has enabled him to present his research and work at cultural institutions such as ZKM and MetaMorf, as well as technology conferences like CHI 2018 and Philips Trend Event.

“Driving Simulators for the Study of Road Users’ Behaviour” será apresentado terça-feira, 7 de março, às 17:15, na sala B012.

“To be efficient and accepted, road safety counter-measures need to be defined thanks to scientific studies. The question is not only to imagine an optimal solution in the absolute, but to understand the real practices and, based on this knowledge, to design the measures (sensitivity campaigns, changes in Highway Code, changes in initial training curriculum or licence tests, infrastructure (re)design, vehicles homologations, etc.). In our talk we will describe the tools and methods we promote and refined for decades to improve road safety, and their use in research projects. Our approach is systemic and is based on three pillars: instrumentation of vehicles for in-depth naturalistic studies, traffic modelling and simulation using a multi-agents system, and design of driving simulators to study driving behaviors. We will illustrate our approach using research projects we have conducted over these last years.”

Stéphane ESPIÉ is a Research Director at the Gustave Eiffel University. He holds an Accreditation to Direct Research in Computer Science (HdR, Pierre et Marie Curie University, 2004). His main research areas are behavioral traffic simulation (MAS based), and the design of tools to study road user behaviors (driving/riding simulators and instrumented vehicles). He currently conducts his research in SATIE laboratory (Paris Saclay university) where he leads the MOSS (Methods and Tools for Signals and Systems) research group.

“Jan N. van Rijn holds a tenured position as assistant professor at Leiden University (ada.liacs.nl), where he works in the computer science department (LIACS) and Automated Design of Algorithms cluster (ADA).

His research interests include artificial intelligence, automated machine learning (AutoML) and meta-learning.

He obtained his PhD in Computer Science in 2016 at Leiden Institute of Advanced Computer Science (LIACS), Leiden University (the Netherlands).

During his PhD, he developed OpenML.org, an open science platform for machine learning, enabling sharing of machine learning results.

He made several funded research visits to the University of Waikato (New Zealand) and the University of Porto (Portugal).

After obtaining his PhD, he worked as a postdoctoral researcher in the Machine Learning lab at the University of Freiburg (Germany), headed by Prof. Dr. Frank Hutter, after which he moved to work as a postdoctoral researcher at Columbia University in the City of New York (USA). His research aim is to democratize access to machine learning and artificial intelligence across societal institutions, by developing knowledge and tools that support domain experts.

He is one of the authors of the book `Metalearning: Applications to Automated Machine Learning and Data Mining’ (published by Springer).”

”AutoML and Meta-learning for Neural Network Robustness Verification” será apresentada dia 25 de janeiro, às 14:45, na sala B006 – a entrada é livre, são todos bem vindos.

Abstract: Artificial intelligence is being increasingly integrated in modern society, with applications ranging from self-driving cars to medicine development. However, artificial intelligence models (in particular neural networks) have been notoriously known for being susceptible for various forms of attacks, including adversarial attacks. In a bid to make these models more trustworthy, the field of neural network robustness verification aims to determine to which degree a given network is susceptible to such an attack. This is a very time consuming task, that can greatly benefit from the various advances that the Automated Machine Learning and meta-learning community have made.

In this talk, it will be explained the basis of automated machine learning and meta-learning, and the speaker will talk about their research on applying this to robustness verification. He will also explain how the community can further engage in this endevour towards trustworthy artificial intelligence.

Pasi Fränti fez o seu Mestrado e Doutoramento na University of Turku, Finlândia, em 1991 e 1994. Desde 2000 é Professor em Ciências da Computação na University of Eastern Finland. Publicou 175 artigos em conferências e 99 em journals. Pasi Fränti é Diretor do grupo de investigação de Machine Learning. Atualmente os seus interesses de investigação incluem clustering algorithms, location-based services, machine learning, web and text mining e optimization of health care services. Orientou 30 estudantes de doutoramento e atualmente está a orientar mais nove.

 “Clustering Healthcare Data” será apresentada no dia 7 de novembro, às 11:00, na sala I125 – a entrada é livre, são todos bem vindos.

Abstract: Clustering can a powerful tool in analyzing healthcare data. We show how clustering based on k-means and its variants can be used to extract new insight from various data with the aim to better optimize the health care system. We first show that simple variants of k-means and random swap algorithms can provide highly accurate clustering results. We demonstrate how k-means can be applied to categorical data, sets, and graphs. We model health care records of individual patients as a set of diagnoses. These can be used to cluster patients, and also create co-occurence graph of diagnoses depending on how often the same pair of diseases are diagnosed in the record of the same patient. Taking into account the order of the diagnoses, we can construct a predictor for the likely forthcoming diseases. We also provide a clustering algorithm to optimize the location of health care systems based on patient locations. As a case study, we consider coronary heart disease patients and analyze in what way the optimization of the locations can affect the expected time to reach the hospital within the given time. All the results can provide additional statistical information to healthcare planners and also medical doctors at the operational level to guide their efforts to provide better healthcare services.