BEGIN:VCALENDAR VERSION:2.0 CALSCALE:GREGORIAN PRODID:adamgibbons/ics METHOD:PUBLISH X-PUBLISHED-TTL:PT1H BEGIN:VEVENT UID:1750@https://flypost.ie SUMMARY:Learning to Parrot – Workshop with Dennis McNulty – 30 September 6- 8pm DTSTAMP:20260926T232908Z DTSTART:20260930T170000Z DTEND:20260930T190000Z DESCRIPTION::::UPDATE::\n\n::This event is fully booked. You can join the w aitlist here\n[https://www.eventbrite.ie/e/learning-to-parrot-tickets-2001 069122613?aff=oddtdtcreator&keep_tld=true]::\n\nFSAS is delighted to host this workshop as the first event in a series\nconsidering questions about our institutional relationship to digital\ntechnologies and the corporatio ns they tie us to.\n\nLearning to Parrot Workshop with Dennis McNulty\, 6p m – 8pm\, Wednesday 30\nSeptember. This event is free but booking is essen tial. \n[https://www.eventbrite.ie/e/learning-to-parrot-tickets-2001069122 613?aff=oddtdtcreator&keep_tld=true]\n\nWe are living through an era where artificial intelligence (AI) is surfacing in\nall aspects of people’s liv es. These systems are presented as all-knowing and\nfar too complex for th e average person to understand. Learning to Parrot is a\ntwo-hour workshop that stages a hands-on encounter with the assumptions\nunderlying chatbot technologies such as ChatGPT. The aim is to demystify them\nand create a space for reflecting on the implications of their use. The title is\na ref erence to the term Stochastic Parrot and the work of Dr. Emily M. Bender.\ n\nPractical Details\n\nParticipants will need to bring a laptop that can connect to the internet and be\nfamiliar with using it. The workshop will last about two hours.\n\nBackground\n\nArtificial intelligence (AI) is wid ely understood to be a marketing term rather\nthan a description of a cohe rent set of technologies. That said\, when\nnon-experts refer to AI these days\, they are generally talking about\nchatbot-enabled text generation s ystems such as OpenAI’s ChatGPT or Anthropic’s\nClaude. These kinds of AI products are all based on a technology called Large\nLanguage Models (LLMs ). As the name might imply\, a language model pulls language\napart into f ragments so that computers can process it. In order to do that\, the\npeop le who created the language model need to make some assumptions about what \nlanguage is and how it “works”. As you might imagine\, these assumptions have a\nprofound effect on the language that LLM-powered chatbots tend to extrude.\n\nHuman writing emerged from a desire to capture knowledge and convey meaning.\nOver time\, the process of writing and reading text has e volved into a rich and\ncomplex support for communicating\, storing and ge nerating ideas. LLMs are\ndesigned to circumvent any requirement to interp ret the meaning of a text and\ninstead\, leverage the fact that writing ta kes the form of a sequence of graphic\nsymbols. Like old-school SMS autoco mplete\, the language models are constructed\nand fed data so that they ca n predict what the next word in any given sequence\nof words is likely be. Chatbot designers believe that meaning will just emerge\nas a byproduct o f generating a sequence of words in a plausible order.\n\nThis approach is described as statistical\, in that word prediction is based on\ncalculati ons of probability rather than any communicative intent. Consider\nwhether you would be more likely to believe in the predictive power of a\npolitic al poll based on the opinions of 10 or 10\,000 voters? This statistical\na pproach is the reason why the language models grew large. The engineers\nb elieved that analysing more text was the key to building models that could make\nbetter predictions about which word should come next.\n\nIn this ex ercise we will work through a very basic example with a short text to\ndev elop a sense of the limitations associated with modelling language as a\ns equence of words\, particularly with respect to the implications this appr oach\nmight have for the idea of meaning.\n\nThis event is free but bookin g is essential. Book here.\n[https://www.eventbrite.ie/e/learning-to-parro t-tickets-2001069122613?aff=oddtdtcreator&keep_tld=true]\n\nAbout Dennis M cNulty\n\nDennis McNulty is an artist\, researcher\, music-maker\, and ins trument designer\nwhose work grapples with know-ability and is often frame d with respect to\ntechnologies such as language\, diagrams or buildings. Learning to Parrot was\ndeveloped in his role as a research assistant in P rof. Dan Kilper’s group at\nCONNECT based in Trinity College Dublin\, wher e he explores the role of diagrams\nin cross-disciplinary collaboration\, particularly in quantum networking\nresearch.\n\nHis artwork has been pres ented at the São Paulo Bienal\, Liverpool Biennial\,\nPerforma Biennial\, IMMA\, Visual\, The Dock and Grazer Kunstverein among others.\nMcNulty is currently working on a collaborative Fingal County Council commission\nto mark EU Ireland’s presidency in 2026. www.dennismcnulty.com\n[http://www.d ennismcnulty.com] URL:https://flypost.ie/event/learning-to-parrot-workshop-with-dennis-mcnult y-30-september-6-8pm LOCATION:Fire Station Artists' Studios - 9-12 Buckingham Street Lower\, Dub lin 1\, D01 R6P3 STATUS:CONFIRMED CATEGORIES: X-ALT-DESC;FMTTYPE=text/html:

:::UPDATE::

::This event is fully bo oked. You can join the waitlist here::

FSAS is delighted to host this wor kshop as the first event in a series considering questions about our insti tutional relationship to digital technologies and the corporations they ti e us to.

Learning to Parrot Workshop with Dennis McNulty, 6pm – 8pm, Wednesday 30 September. This event is free but booking is essential. 

We are living through an er a where artificial intelligence (AI) is surfacing in all aspects of people ’s lives. These systems are presented as all-knowing and far too complex f or the average person to understand. Learning to Parrot is a two-hour work shop that stages a hands-on encounter with the assumptions underlying chat bot technologies such as ChatGPT. The aim is to demystify them and create a space for reflecting on the implications of their use. The title is a re ference to the term Stochastic Parrot and the work of Dr. Emily M. Bender.

Practical Details

Participants will need to bring a laptop that can connect to the internet and be familiar with using it. The workshop will last about two hours.

Background

Artificial intelligence (AI) is widely understood to be a marke ting term rather than a description of a coherent set of technologies. Tha t said, when non-experts refer to AI these days, they are generally talkin g about chatbot-enabled text generation systems such as OpenAI’s ChatGPT o r Anthropic’s Claude. These kinds of AI products are all based on a techno logy called Large Language Models (LLMs). As the name might imply, a langu age model pulls language apart into fragments so that computers can proces s it. In order to do that, the people who created the language model need to make some assumptions about what language is and how it “works”. As you might imagine, these assumptions have a profound effect on the language t hat LLM-powered chatbots tend to extrude.

Human writing emerged from a desire to capture knowledge and convey meaning. Over time, the process of writing and reading text has evolved into a rich and complex support fo r communicating, storing and generating ideas. LLMs are designed to circum vent any requirement to interpret the meaning of a text and instead, lever age the fact that writing takes the form of a sequence of graphic symbols. Like old-school SMS autocomplete, the language models are constructed and fed data so that they can predict what the next word in any given sequenc e of words is likely be. Chatbot designers believe that meaning will just emerge as a byproduct of generating a sequence of words in a plausible ord er.

This approach is described as statistical, in that word predicti on is based on calculations of probability rather than any communicative i ntent. Consider whether you would be more likely to believe in the predict ive power of a political poll based on the opinions of 10 or 10,000 voters ? This statistical approach is the reason why the language models grew lar ge. The engineers believed that analysing more text was the key to buildin g models that could make better predictions about which word should come n ext.

In this exercise we will work through a very basic example with a short text to develop a sense of the limitations associated with modell ing language as a sequence of words, particularly with respect to the impl ications this approach might have for the idea of meaning.

This even t is free but booking is essential. Book here.

About Den nis McNulty

Dennis McNulty is an artist, researcher, music- maker, and instrument designer whose work grapples with know-ability and i s often framed with respect to technologies such as language, diagrams or buildings. Learning to Parrot was developed in his role as a research assi stant in Prof. Dan Kilper’s group at CONNECT based in Trinity College Dubl in, where he explores the role of diagrams in cross-disciplinary collabora tion, particularly in quantum networking research.

His artwork has b een presented at the São Paulo Bienal, Liverpool Biennial, Performa Bienni al, IMMA, Visual, The Dock and Grazer Kunstverein among others. McNulty is currently working on a collaborative Fingal County Council commission to mark EU Ireland’s presidency in 2026. www.dennismcnulty.com

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