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KDD - GWS Joint Session Schedule - AI in Astronomy and Impact on IVOA Standards - IVOA Nov 2024 Interoperability Meeting

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Draft Schedule

Time: 14:00-15:30

Location: Aula Magna

Speaker Title Time Material Abstract
Andre Schaaff NLP-chatbot R&D at CDS 10'+5'   Over the past years the CDS has undertaken a long term R&D work on Natural Language Processing applied to the querying of astronomical data services. The motivation was to enable new ways of interaction, especially a chatbot, as an alternative to the traditional forms with the aim to reach query results satisfying professional astronomers.

The Virtual Observatory (VO) brought us standards like TAP, UCDs, ..., implemented in the CDS services, helping us to query our services and opening the door to query the whole VO.

We will give a quick reminder and status of this work around a chatbot. We started in 2023 to explore how to improve it with the OpenAI API. We are now forking from this initial work to study how to apply it to the improving of our services, in a wider AI use. We will give a first overview of this new R&D study.
Sebastien Trujillo ‘Spherinator + HiPSter: from the known unknowns to the unknown unknowns’ 10'+5'   Current applications of machine learning to astrophysics focus on teaching machines to perform domain-expert tasks accurately and efficiently across enormous datasets. Although essential in the big data era, this approach is limited by our own intuitions and expectations, and provides at most only answers to the ‘known unknowns’. To address this, we are developing a new conceptual framework and software tools to help astronomers maximize scientific breakthroughs by letting the machine learn unbiased interpretable representations of complex data ranging from observational surveys to simulations. Our tools automatically learn low-dimensional representations of complex objects such as galaxies in multimodal data (e.g. images, spectra, datacubes, simulated point clouds, etc.), and provide interactive explorative access to arbitrarily large datasets using a simple graphical interface. Our framework is designed to be interpretable, work seamlessly across datasets regardless of their origin, and provide a path towards discovering the ‘unknown unknowns’.
Giuseppe Riccio   10'+5'    
John Abela The Computational Evolution of Human Intelligence in AI 10'+5'    

Panel:

Andre Schaaff, Sebastien Trujillo, Giuseppe Riccio, John Abela, Chenzhou Cui

Moderators:

Yihan Tao, Sara Bertocco, Jesus Salgado

 		Discussion on the use of AI in astronomy and its impact on IVOA standards      
Notes: TBD

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Topic revision: r3 - 2024-10-23 - SaraBertocco
 
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