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UCL News: DeepMind co-founder and UCL alumnus
webA 2016 UCL news piece featuring Demis Hassabis; useful background on early DeepMind philosophy and Hassabis's neuroscience-inspired approach to AGI, but limited in technical depth or direct AI safety analysis.
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Importance: 22/100news articlenews
Summary
A UCL news article covering a talk or profile of Demis Hassabis, DeepMind co-founder and UCL alumnus, discussing his vision for AGI, the role of neuroscience in inspiring AI development, and DeepMind's mission to solve intelligence. The piece highlights Hassabis's interdisciplinary approach connecting neuroscience and machine learning.
Key Points
- •Demis Hassabis draws on neuroscience principles to guide DeepMind's AI research and development approach.
- •Hassabis discusses the long-term goal of building artificial general intelligence (AGI) and its potential transformative applications.
- •The talk reflects on the intersection of human cognition, intuition, and machine learning capabilities.
- •DeepMind's philosophy emphasizes using AI as a scientific tool to accelerate discovery across domains.
- •Hassabis's background as a UCL alumnus underscores the academic-industry pipeline in AI development.
Cited by 1 page
| Page | Type | Quality |
|---|---|---|
| Demis Hassabis | Person | 45.0 |
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# Neuroscience, intuition and superhumans - how DeepMind co-founder and UCL alumnus Demis Hassabis is leading the Artificial Intelligence revolution
**16 November 2016**
Neuroscientist, computer game designer
and entrepreneur Demis Hassabis shares his passion for using Artificial
Intelligence to change the face of science and why he's so proud of his links with
UCL.
## How is Artificial Intelligence changing?
One of the reasons Artificial
Intelligence (AI) has become such a buzzword recently is because we are
starting to develop programming systems that have the ability to actually learn
how to solve a problem, rather than being pre-programmed with the solution.
Most of the AI that we interact with
today, including things like Siri on your phone, is ultimately pretty unhelpful
because as soon as you ask it something that it isn't pre-programmed for, it
has no idea what to do. It isn't really 'intelligent' and can't cope with the
unexpected.
Our approach from the beginning has been
to explore how to create systems that automatically learn for themselves, by
finding the structure in data and using it to figure out how to achieve goals.
A couple of years ago, we famously applied this approach to playing Atari
games. Instead of programming the solution to the games, we just gave the system
raw numbers and told it that its goal was to maximise the score. Everything
else it had to learn for itself, including the rules of the game. We would
leave it playing overnight on our servers and come back the next morning to
find it had mastered the game and could play it better than the best human
players.
## What are you working on at the moment?
This year we've been working on a system
called AlphaGo to take on the world's best player of the ancient game of Go.
The game is played in Asia, instead of chess, but is more complicated and
profound, with more board positions than there are atoms in the universe! The
way the best players in Japan, Korea and China get around this is by using
their intuition - it's too complicated to use just raw calculation.
It's a really interesting challenge for
AI as creativity and intuition are not something we usually associate with
computer systems. We built a system that played itself 30 million times over three
months, each time learning from its mistakes and incrementally getting better
at the game. Eventually we were ready to challenge the greatest player of the
past decade, Lee Sedol, earlier this year in a challenge match that we won 4-1.
More than 100 million people tuned in to
watch the match across the world, which was incredible!
## Why is AI exciting?
We use games as a training ground for
these AI systems, but the reason I've spent my whole career working in AI is
that I want to use these systems to help make breakth
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