5. Artificial intelligence (AI and
machine learning (ML changing our world
● Diagnosing diseases with greater accuracy
● Fight climate change and pollution
● Free humans from driving
● Detect online piracy
● Revolutionize art and creativity
● Get help 24/7 with virtual personal assistants
● Translate any language instantly
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6. The AI boom is growing, fast
● Ability to spot patterns in tranches of data with
‘superhuman accuracy’
● Algorithms that can train itself with little or no data
● Businesses rely on AI as catalyst for innovation
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7. What can’t AI do well (yet)?
Think truly like a human
● Planning
● abstract reasoning
● understanding cause and effect
● open-ended generalization
⟶“System 2” thinking
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11. Biased data, more prejudices?
● humans are the ones making the algorithms
● humans are the ones feeding those algorithms data
● Are we creating biased algorithms based on biased data?
● Privacy issues from gathering personal data?
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13. Is it fair? Algorithm assigning A-level
grades created controversies in the UK
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14. Gender and
racial bias:
the impact is real
● AI systems can unfairly
penalize women and
minorities
● Researchers find lower
accuracy in facial
recognition algorithms
when detecting non-white
facial images
● Training data deficiencies?
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15. Where do we go from here?
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Ethical AI, trusted AI, responsible AI?
16. AI may be powerful and clever,
but it is not immune to mistakes
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17. How can AI reach its full potentials?
Know what AI can’t
do
We still need to
impose checks and
control, and
incorporate
concepts/values such
as ethics, equality
Less ambitious,
more realistic?
Re-calibrating our AI
ambitions to achieve
specific tasks
More policies on AI
EU’s model: serve as
an example of having
policy framework and
tools for governments
and companies?
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