Google's computer scientists have created an AI to tweak the company’s other AIs. The advanced machine learning
system, which goes by the faintly sinister name of Google Vizier,
automatically tunes algorithms right across Google’s parent company
Alphabet. But to test it, the researchers used an old-fashioned metric:
cookie quality in the canteen.
Modern machine learning systems are the
algorithmic equivalent of Formula 1 racing cars.
The systems have
tremendous power, but they are extremely sensitive: to function
effectively they need to be finely tuned, usually by hand.
In particular, the systems need carefully set
'hyperparameters': parameters set in advance that are adapted to the
problem at hand. This isn’t easy, because machine learning systems are
“black boxes”: even when you’ve made them, you can never be entirely
sure how they get their results. One common method for tuning is known
in the field as “grad student descent”: basically, you get a graduate student to twiddle the parameters until the algorithm works.
Google Vizier cuts short this tedious manual
task by automatically optimising hyperparameters of machine learning
models. According to Google’s researchers, the system is already in use
across the company.
"Our implementation scales to
service the entire hyperparameter tuning workload across Alphabet, which
is extensive," they write in a paper released this week,
citing an example where Google researchers “used Vizier to perform
hyperparameter tuning studies that collectively contained millions of
trials for a research project… That research project would not be
possible without effective black–box optimisation.”
One
process employed in Google Vizier is called 'transfer learning,'
essentially, learning from experience. Using data from previous studies
as a guide, the Vizier algorithm can suggest optimal hyperparameters for
new algorithms. The method is most effective when there have been lots
of studies in the area, but it also works when there is relatively
little crossover: for instance "when the observed metrics and the
sampling of the datasets are different".
As well as helping research, Google Vizier is being put
to use at Alphabet, where, its creators write, it "has made notable
improvements to production models underlying many Google products,
resulting in measurably better user experiences for over a billion
people".
These improvements include automated A/B testing
of features of Google's websites, including fonts, colours and the
format of search results. On Google Maps, for instance, the system is
being used to optimise the trade-off between the relevance of a
particular search and the distance of that result from the user (with
the inevitable aim of getting higher engagement for Google).
Google Vizier can also be used to solve black box
optimisation problems in the messy physical world. And this is where the
cookies come in.
To test their system, the
researchers gave cookie recipes to the contractors who make the puddings
in Google’s canteen. They taste-tested the result and tracked any
alterations the chefs made to improve the taste. Recipes are a kind of
algorithm in their own right, with similar black box properties (because
you never exactly know why your bake went wrong). And this test allowed
the researchers to try out their transfer learning approach:
“Before
starting to bake at large scale, we baked some recipes in a smaller
scale run-through,” they write. “This provided useful data that we could
transfer learn from when baking at scale.”
Even
when things went slightly wrong – as, for instance, when the dough was
allowed to sit longer, which "unexpectedly, and somewhat dramatically,
increased the subjective spiciness of the cookies for trials that
involved cayenne" – the schema was able to respond. After a number of
rounds, the cookies improved significantly, the researchers say:
“Later rounds were extremely well-rated and, in the authors’ opinions, delicious."
HT: The weekly Import AI newsletter from Jack Clark of Open AI. Essential reading for anyone interested in AI
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