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Introduction
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Hype
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Limitations
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Bias
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Adversarial attacks
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Impact on developing economics and jobs
A realistic view
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Goldilocks rule for AI:
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Too optimistic: Sentient/AGI, killer robots
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Too pessimistic: AI cannot do everything, so an AI winter is coming
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as opposed to the past, AI is creating value today.
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Just right: Can't do everything, but will transform industries
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Limitations of AI
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performance limitations. (limited data issues)
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Explainability is hard (instructible)
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Biased AI through biased data
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Adversarial attacks
Discrimination/Bias
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Biases
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Bias against women and minorities in hiring
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Bias against dark skinned people
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banks offering hiring interest rates to minorities
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reinforcing unhealthy stereotypes
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Technical solutions
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"Zero out" the bias in words
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Use more inclusive data
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More transparency and auditing processes
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More Diverse workforce
Adversarial attacks
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Minor perturbation to pixels can lead and AI to have a different B output.
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Adversarial defenses
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Defenses exist; incur some performance cost
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There are some applications that will remain in an arms race.
Adverse uses of AI
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DeepFakes, fakes can move faster than the truth can catch up
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Undermining of democracy and privacy, oppressive surveillance
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Generating fake comments
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spam vs. anti-spam, fraud vs. anti fraud
AI and developing economies
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AI will eliminate lower rung opportunities. The development of leapfrog opportunities will be required. Think how countries jumped to mobile phones, mobile payments, online education, etc.
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US and china leading, but still a very immature space.
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Use AI to strengthen country's vertical industries.
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More public-private partnerships
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invest in education
AI and Jobs
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AI is automation on steroids.
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Solutions
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Conditional basic income: provide a safety net but incentivize learning
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Lifelong learning society
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Political solutions
Conclusion
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What is AI?
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Building AI projects
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Building AI in your company
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AI and society
