Inclusivity and AI: opportunity or threat
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Keynote at 9th International Conference on Computing and Informatics (ICOCI 2023), "Nurturing an inclusive digital society for a sustainable nation", 13-14 September 2023, Kuala Lumpur, Malaysia https://alandix.com/academic/talks/ICOCI2023-keynote/ AI is transforming many sectors of the economy and our day-today lives. We hear of success stories including medical advances, but also worries that AI will destroy jobs or even be an existential threat to humanity. We also know that for previous waves of technology – mechanical, electronic and digital – the costs and benefits do not fall equally to everyone in society. There are clear dangers that AI will further entrench existing power and deepen the digital divide: both at an individual level and globally. For example, training foundation models, such as GPT-4, requires enormous computational power and massive data sets accessible only to the largest corporations. However, the ways in which these can be used generatively in more niche areas, offers potential for minority languages and individualised learning that was previously only accessible to the rich. Whether the threats of AI or its opportunities dominate is not simply an abstract question, but one that impacts the most disadvantaged around us, and one that, as researchers and practitioners in digital technology, we can affect. If we truly want an inclusive digital society, then we need to make it happen.
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Inclusivity and AI: opportunity or threat
- 1. Alan Dix https://alandix.com/ @alanjohndix Inclusivity and AI opportunity or threat
- 3. ACCESS DOING IT RIGHT POLICY AI for Social Justice
- 4. impact of AI what AI does how AI shapes society
- 5. c.f. cars what cars do +ve transport, independence –ve accidents, speeding, drunk drivers how cars shape society – suburban sprawl – car poverty – global warming
- 6. impact of AI what AI does – productivity – bias – policing – warfare
- 7. impact of AI what AI does – productivity, bias, policing, warfare how AI shapes society – economy, education, environment – politics, power – democracy, digital poverty
- 8. AI … why now? 1990 2000 2010 2020 1980 1970 1950 1960 AI winters
- 9. AI … why now? 1990 2000 2010 2020 Early NN web search recommender systems MapReduce deep NN AlphaGo LLM ChatGPT Big Data
- 10. AI and Computation deep NN were always known about … what changed? web (search, ecommerce) => big data => big data centres big data + big computation => DNN the new AI
- 11. new AI BIG data + BIG computation = BIG business
- 12. Datanomics 2000s – networks … 2 kinds internet –global reach => transnational markets –loss of national control (e.g. online gambling) human networks
- 13. network effects networks of connections within groups and between groups networks change value – if your colleagues use Word it is more of value to you teachers parents children
- 14. feedback loops negative – stability, balance, golden mean – smooth shapes, spherical water drop – universal controller positive – instability, dynamism, extremes – sharp edges, fractals, snowflake – tipping points and hysteresis https://commons.wikimedia.org/w/index.php?curid=44338386
- 15. markets and monopolies market economics assumes open markets monopolies – natural – single resource – engineered => anti-trust laws (national) network effects => winner takes all => emergent monopoly https://commons.wikimedia.org/w/index.php?curid=127909261
- 17. AI and peacock tails Jatin Sindhu, CC BY-SA 4.0, via Wikimedia Commons
- 18. why peacock tails? Darwinian explanation – runaway sexual selection economics often described in Darwinian terms – technological evolution – commercial survival of the fittest how does AI fit in?
- 19. the attention economy on the ‘free’ web … ultimately advertising pays web sites (e.g. social networks, search) best placement => best click-throughs advertisers want click-throughs => advertisers choose best placement winner takes all
- 20. add AI big data + best AI => best placement advertisers winner takes all => best AI takes most of the money small improvement in AI => lots more money + AI relatively small part of cost => runaway AI … well beyond ‘optimal’
- 23. 2019 626,000 lbs CO2 to train
- 28. Open Data “I don’t know if any public sector has necessarily cracked the nut on attracting the right skills and capabilities,” … “The commercial sector has, because they’ve got the dollars to spend.” Ian Bartram, Gartner
- 29. signs of hope
- 30. Google open LLaMa the model’s carbon footprint has been reduced to a seventh of GPT-3 the pretrained model will obviate the need to train the model … deploy the full model “using only 16 NVIDIA V100 GPUs.” ~ $400,000
- 31. LORA – open and (relatively) cheap reduce cost and carbon low dimension representation of inner layers
- 34. signs of hope
- 35. generative enables niche education – personalised tutors => choice of materials – changes what we need to learn … not necessarily digital! minority languages accessibility – no more alt tags? – neurodiversity too https://www.theguardian.com/world/2023/sep/11/sweden-says-back-to-basics-schooling-works-on-paper
- 36. summary AI and digital – changes society & undermines market economics – can entrench power and disenfranchise the poor signs of hope – lower cost lower carbon foot print AI – opportunities to redress inequalities
- 37. Inclusivity and AI opportunity or threat Alan Dix https://alandix.com/academic/talks/ ICOCI2023-keynote / the difference … is you
Hinweis der Redaktion
- By Remi Jouan - Photo taken by Remi Jouan, CC BY-SA 3.0, https://commons.wikimedia.org/w/index.php?curid=7245143
- https://en.m.wikipedia.org/wiki/File:Microsoft_Office_Word_%282019%E2%80%93present%29.svg
- By Charles Schmitt - Own work, CC BY-SA 4.0, https://commons.wikimedia.org/w/index.php?curid=44338386
- By Calistemon - Own work, CC BY-SA 4.0, https://commons.wikimedia.org/w/index.php?curid=127909261
- Jatin Sindhu, CC BY-SA 4.0, via Wikimedia Commons
- ROTFLOLEB, CC BY-SA 3.0 <https://creativecommons.org/licenses/by-sa/3.0>, via Wikimedia Commons https://commons.wikimedia.org/wiki/File:Human_eye_with_blood_vessels.jpg
- ROTFLOLEB, CC BY-SA 3.0 <https://creativecommons.org/licenses/by-sa/3.0>, via Wikimedia Commons https://commons.wikimedia.org/wiki/File:Human_eye_with_blood_vessels.jpg
- By NASA ICE - A close look at the shelf, Public Domain, https://commons.wikimedia.org/w/index.php?curid=24450299 Thwaites Ice Shelf https://www.computerworld.com/article/3431148/why-data-centres-are-the-new-frontier-in-the-fight-against-climate-change.html
- https://arxiv.org/abs/1906.02243
- https://www.unite.ai/can-you-build-large-language-models-like-chatgpt-at-half-cost/ https://theconversation.com/it-takes-a-lot-of-energy-for-machines-to-learn-heres-why-ai-is-so-power-hungry-151825
- in general can use flakey AI choose between good alternatives, search generate and test https://www.theguardian.com/world/2023/sep/11/sweden-says-back-to-basics-schooling-works-on-paper