InterOpMay2023KD < IVOA

IVOA Web>WebPreferences>InterOpNov2021>InterOpMay2023KD (2024-05-09, RaffaeleDAbrusco) (raw view)

---++ Knowledge Discovery

Time: Tuesday May 09 11:00 CEST [[https://www.timeanddate.com/worldclock/fixedtime.html?msg=KD&iso=20230509T1100][<br /><br />]]
| *Speaker* | *Title* | *Time* | *Abstract* | *Material* |
| _Raffaele D'Abrusco_ | Greetings and Introduction | 10' | | [[%ATTACHURL%/slides_session_IVOAInterOp_Bologna_2023.pdf][pdf]] |
| _Sandor Kruk_ | Exploring astronomy data archives at large scales using deep learning and crowdsourcing | 15'+5' | The vast amount of data in astronomy archives presents an opportunity for new discoveries. Deep learning combined with crowdsourcing provides an efficient way to explore this data using. the intuition of the human brain and the processing power of machines. In the Hubble Asteroid Hunter project, we used a deep learning algorithm on Google Cloud, trained on volunteer classifications from the [[http://asteroidhunter.org/][asteroidhunter.org]] Zooniverse project to search two decades of Hubble Space Telescope (HST) observations from the ESA HST archives for objects not targeted by the Hubble observations. The project, which was set up as a collaboration between Zooniverse, ESAC Science Data Center and engineers at Google, led to the discovery of 1700 asteroids (Kruk et al. 2022), including 1031 previously unknown ones (Garcia Martinet al., in prep.), 198 new strong gravitational lenses (Garvin et al. 2022), and quantified the impact of artificial satellites on HST observations (Kruk et al. 2023). In this talk, we will present the results of this project and highlight the benefits of scientifically exploiting the vast amounts of data available in astronomy data archives using novel techniques. | [[%ATTACHURL%/20230509-Kruk-IVOABologna-Exploring_astronomy_data_archives_at_scale_using_deep_learning_and_crowdsourcing.pdf][pdf]] |
| _Raffaele/Yihan_ | Introduction to mini-session on generative AI and language models | 5' | | pdf |
| _Rafael Galarza-Martinez_ | Intro to Transformers | 10' | Transformers are the type of machine learning algorithm behind the highly successful generative pretrained transformer models that allow for tools such as ChatGPT. In this talk I give a very general introduction to transformers, present their architecture, describe their advantages over other algorithms used in language processing, such as recurrent neural networks (RNNs), and focus on how their self-attention module enables the most complicated language tasks. I will also present a small example of how Transformers are being used in the analysis of astrophysical data. | [[%ATTACHURL%/martinezgalarza_ivoa_interop_2023.pdf][.pdf]] |
| _Yihan Tao_ | Foundation models for Astronomy | 5' | While ChatGPT provides powerful conversational AI services utilizing large language models (LLMs), this talk focuses on how we can leverage the advances in AI technology to help astronomy research. "Foundation models" are models trained on large, broad data and can be adapted to a wide range of downstream tasks. LLMs are a special case of foundation models trained on language data for natural language processing tasks. This talk will introduce the concept of foundation models and some existing works that use astronomical data (such as light curves and images) to build foundation models for astronomy tasks. The potential of foundation models for advancing astronomy research will also be discussed. | [[%ATTACHURL%/Foundation_models_for_Astronomy-Yihan_Tao.pdf][pdf]] |
| _André Schaaff_ | AI in querying astronomical data services | 5' | This short presentation is mainly in the context of astronomical data services Querying as we have experienced through a Rasa-based Chatbot for several years. It is now possible to integrate ChatGPT into a Rasa chatbot, an interesting path to explore. In addition to that, another on going experiment is to evaluate how to add "astronomical" skills to Alexa, OK google, Siri, etc. | [[%ATTACHURL%/IVOA-Bologna-2023-KDIG-SchaaffA-final.pdf][pdf]] |
| _Ioana Ciuc&#x103;_ | Galactic ChitChat: Using Large Language Models to Engage with Astronomy Literature | 5' | We showcase the capacity of the OpenAI 's large language model GPT-4 for meaningful engagement with Astronomy papers using in-context prompting. We employ a distillation technique to optimise efficiency, reducing the input paper size by 50% while preserving paragraph structure and semantic integrity. The in-context model excels at providing detailed answers contextualised by related research findings by examining its responses within a multi-document context (ten distilled documents). Our investigation highlights the potential of foundation models for the astronomical community. For example, they can help researchers gain insights from astronomical literature, such as validating new scientific hypotheses or proposing novel ideas. | [[%ATTACHURL%/KDIG_IC_pdf.pdf][pdf]] |
| _Adrian Damian_ | Discover IVOA with ChatGPT | 5' | Discovering the International Virtual Observatory Alliance (IVOA) with ChatGPT is a seamless and insightful experience. Powered by its advanced natural language processing capabilities and extensive knowledge, ChatGPT can provide comprehensive guidance on exploring IVOA. By interacting with ChatGPT, users can ask questions, seek explanations, and receive step-by-step assistance in utilizing IVOA services. From understanding the standards and protocols to connecting with the Table Access Protocol (TAP) service, ChatGPT offers intuitive explanations and practical examples. With its ability to provide tailored responses and address individual queries, ChatGPT serves as a valuable companion in unraveling the world of IVOA. | [[%ATTACHURL%/ChatGPTIVOA.pdf][pdf]] |
| _Panel + audience_ | Discussion - panel comprising speakers | 25' | | pdf |
Moderator: [[Raffaele D'Abrusco]], Notetaker: [[IVOA.TBD][TBD]], [[https://yopad.eu/p/IVOA_Nov3_KD][Etherpad link]]

   * [[%ATTACHURL%/IVOA-Bologna-2023-KDIG-SchaaffA-final.pdf][IVOA-Bologna-2023-KDIG-SchaaffA-final.pdf]]: IVOA-Bologna-2023-KDIG-SchaaffA-final.pdf

Attachments

Topic attachments
I	Attachment	History	Action	Size	Date	Who
pdf	20230509-Kruk-IVOABologna-Exploring_astronomy_data_archives_at_scale_using_deep_learning_and_crowdsourcing.pdf	r1	manage	6512.6 K	2023-05-09 - 12:28	ChristopheArviset
pdf	ChatGPTIVOA.pdf	r1	manage	186.1 K	2023-05-09 - 06:25	AdrianDamian
pdf	Foundation_models_for_Astronomy-Yihan_Tao.pdf	r1	manage	369.5 K	2023-05-09 - 06:58	YihanTao
pdf	IVOA-Bologna-2023-KDIG-SchaaffA-final.pdf	r1	manage	8940.3 K	2023-05-09 - 08:08	AndreSchaaff
pdf	KD-IG_anomalies.pdf	r1	manage	34742.0 K	2021-11-03 - 13:21	RaffaeleDAbrusco
pdf	KDIG_IC_pdf.pdf	r2 r1	manage	2295.0 K	2023-05-09 - 07:50	RaffaeleDAbrusco
pdf	Mahabal_IVOA_20211103.pdf	r1	manage	59.2 K	2021-11-03 - 20:17	RaffaeleDAbrusco
pdf	martinezgalarza_ivoa_interop_2023.pdf	r1	manage	7945.3 K	2023-05-09 - 07:49	RaffaeleDAbrusco
pdf	skoda-bayesian-redshift.pdf	r1	manage	1812.3 K	2021-11-03 - 20:14	RaffaeleDAbrusco
pdf	slides_session_IVOAInterOp_Bologna_2023.pdf	r1	manage	111.9 K	2023-05-09 - 07:58	RaffaeleDAbrusco
pdf	slides_session_KDIG_IVOA_2021FallInterOp.pdf	r1	manage	193.3 K	2021-11-04 - 18:12	RaffaeleDAbrusco