Charles Mok discusses the potentials and limitations of artificial intelligence (AI). While AI shows promise in areas like diagnosing diseases, fighting climate change, and translating languages, it also faces limitations. AI may struggle with tasks requiring "System 2" thinking like abstract reasoning, planning, and understanding cause and effect. It can also replicate and even amplify the biases of its human creators. To realize AI's full potentials, Mok argues we must know its limitations, impose proper oversight and controls, ensure ethics and equality are incorporated, and take a realistic approach focused on specific tasks rather than assuming it can solve all problems.
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Have you AI'ed today? A Reality Check
1. "Have You AI'ed Today?
A Reality Check"
The Potentials, Limits and Pitfalls of AI
Charles Mok
IET ICT Conference 2020 20112020
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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