Microsoft squeezed AI onto a Raspberry Pi
Microsoft squeezed AI onto a Raspberry Pi
BY LANCE ULANOFF June 30, 2017
Artificial Intelligence and Machine Learning usually work
best with a lot of horsepower behind them to crunch the data, compute
possibilities and instantly come up with better solutions.
That's why most AI systems rely on local sensors to
gather input, while more powerful hardware in the cloud manages all the heavy
lifting of output. It's how Apple's Siri and Amazon Alexa work, and how IBM
Watson can tackle virtually any major task. It is, though, a limiting approach
when it comes to making smarter Internet of Things and applying intelligence
when there isn't Internet connectivity.
“The dominant paradigm is that these [sensor] devices are
dumb,” said senior researcher with Microsoft Research India, Manik Varma.
Now, Varma's team in India and Microsoft researchers in
Redmond, Washington, (the entire project is led by lead researcher Ofer Dekel)
have figured out how to compress neural networks, the synapses of Machine
Learning, down from 32 bits to, sometimes, a single bit and run them on a $10
Raspberry Pi, a low-powered, credit-card-sized computer with a handful of ports
and no screen. It's really just an open-source motherboard that can be deployed
anywhere. The company announced the research in a blog post on Thursday.
Microsoft's work is part of a growing trend of moving
Machine Learning closer to devices and end users.
Earlier this month at its annual World Wide Developer's
Conference, Apple announced new Machine Learning APIs (Vision and Natural
Language) that allow developers to add machine learning-based intelligence to
their apps with just a couple of lines of code. They also unveiled Core ML for
developers more well-versed in AI to take full advantage of all inference
capabilities available on the local hardware. Apple's model does have the
developers train their Machine Learning algorithms on libraries Apple provides.
The system then converts the code to run the AI locally.
Obviously, in Apple's case, that hardware is inside a
$700 iPhone and the CPU is much, much more powerful than anything found on a
Raspberry Pi. Still, the trend is clear. These companies are moving
intelligence closer to the local hardware and, where possible, relying less on
constant access to massive data and intelligence stores in the cloud.
“If you’re driving on a highway and there isn’t
connectivity there, you don’t want the [AI] implant to stop working,” said
Varma in the blog post. “In fact, that’s where you really need it the most.”
It's an approach that will make sense for smaller,
sensor-based tasks that can learn by location, intention, recent action and the
device data. In the near term, it won't be a solution for, say, coming up with
new cancer therapies (one of the areas of interest for IBM's Watson AI).
As for Microsoft, this Raspberry Pi breakthrough is
simply phase one in a quest to compress neural networks so much that they can
run on a breadcrumb-sized micro controller. To get there, the machine learning
models need to be, according to Microsoft, as much as 10,000 times smaller.
That's a problem the team is still working on.
In the meantime, Microsoft released previews of the
Raspberry Pi-sized machine learning and training algorithms on GitHub where
enterprising developers can try them out and, potentially deploy on Raspberry
Pi 3 and Raspberry Pi Zero.
Ultimately, this is another piece of Microsoft's growing
Intelligent Edge strategy, which Microsoft CEO Satya Nadella outlined earlier
this year a Microsoft Build developers conference. Microsoft hopes to see these
tiny AI-able microprocessors deployed in everything from our offices to the
clothes we wear.
For Varma, who is visually impaired, the research is a
little more personal. His team is already developing a prototype intelligent
walking stick to showcase their research.
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