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.

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

16. digital breaks
market economics

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’

21. financial and
environmental cost

23. 2019
626,000 lbs
CO2 to train

24. 2020
warnings
from within

25. 2023
trillion
training
tokens

26. who can
afford it?

27. companies and countries?

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

32. citizens and communities?

33. a growing divide?

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