Google's DeepMind creates an AI with 'imagination'
Google's DeepMind creates an AI with 'imagination'
The AI firm is developing algorithms that simulate the
human ability to construct plans
By LIBBY PLUMMER Wednesday 26 July 2017
Google's DeepMind is developing an AI capable of
'imagination', enabling machines to see the consequences of their actions
before they make them.
In two new research papers, the British AI firm, which
was acquired by Google in 2014, describes new developments for
"imagination-based planning" to AI.
Its attempt to create algorithms that simulate the
distinctly human ability to construct a plan could eventually help to produce
software and hardware capable of solving complex tasks more efficiently.
DeepMind's previous research in this area has been
incredibly successful, with its AlphaGo AI managing to beat a series of human
champions at the notoriously tricky board game Go. However, AlphaGo relies on a
clearly defined set of rules to provide likely outcomes, with relatively few
factors to consider.
"The real world is complex, rules are not so clearly
defined and unpredictable problems often arise," explain the DeepMind
researchers in a blog post. "Even for the most intelligent agents,
imagining in these complex environments is a long and costly process."
The researchers have developed
"imagination-augmented agents" (I2As) – a neural network that learns
to extract information that might be useful for future decisions, while
ignoring anything irrelevant. These I2As can learn different strategies to
construct plans, choosing from a broad spectrum of strategies.
"This work complements other model-based AI systems,
like AlphaGo, which can also evaluate the consequences of their actions before
they take them," the DeepMind research team told WIRED.
"What differentiates these agents is that they learn
a model of the world from noisy sensory data, rather than rely on privileged
information such as a pre-specified, accurate simulator. Imagination-based
approaches are particularly helpful in situations where the agent is in a new
situation and has little direct experience to rely on, or when its actions have
irreversible consequences and thinking carefully is desirable over spontaneous
action."
DeepMind tested these agents using puzzle game Sokoban
and a spaceship navigation game, both of which require forward planning and
reasoning. "For both tasks, the imagination-augmented agents outperform
the imagination-less baselines considerably: they learn with less experience
and are able to deal with the imperfections in modelling the environment,"
explains the blog post.
A video shows an AI agent playing Sokoban, without
knowing the rules of the game. It shows the agent's five imagined outcomes for
each move, with the chosen route highlighted.
"This is initial research, but as AI systems become
more sophisticated and are required to operate in more complex environments,
this ability to imagine could enable our systems to learn the rules governing
their environment and thus solve tasks more efficiently," the researchers
told WIRED.
Earlier this year, researchers from DeepMind and Imperial
College London added memory to its AI so that it could learn to play multiple
Atari computer games. Previous iterations of the technology had only been able
to learn to play one game at a time, and while it could beat human players, it
could not 'remember' how it had done so.
Just last month, research from DeepMind and OpenAI
revealed developments that could help an AI to learn about the world around it
based on minimal, non-technical feedback – mimicking the human trait of
inference.
Comments
Post a Comment