Story map
Follow the problem, early work, people reached and the moments that changed the outcome.
The problem they noticed
Can learning systems discover useful approaches to problems with enormous numbers of possibilities?
From first version to product
Hassabis co-founded DeepMind, bringing neuroscience, computer science, and large research teams together.
Reaching people
AlphaGo's 2016 match with Lee Sedol demonstrated unexpected and effective play in Go.
Impact
Work can create value and still involve limits or trade-offs. Look at both sides of the impact.
Positive
- +Advanced AI research and stimulated new approaches to scientific problems.
Trade-offs
- ±Capability gains raise questions about concentration of power, safety, and responsible use.
- ±Powerful systems can be wrong or misused and need human verification, governance, and accountability.
What to remember
If you had to explain this story to a friend, what would you want them to remember?
- Treat surprising output as something to investigate.
- Keep human judgement and verification.
- Technical progress and responsible use must be considered together.
Featured in these lessons
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Sources & further reading
- Google DeepMind, AlphaGo - https://deepmind.google/research/alphago/
- Google DeepMind, 10 years of AlphaGo - https://deepmind.google/blog/10-years-of-alphago/