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| Microsoft's Doug Burger holds a Project Brainwave electronics board. |
If a company wanted to tap into today's hot new artificial intelligence technology, not long ago it would've had to hire a stable of Ph.D.s to figure everything out. No more.
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| Microsoft's Project Brainwave using FPGA chips vastly outpaces conventional processors checking for errors in Jabil electronics manufacturing. |
At its Build conference this week, Microsoft is detailing how it's moved its own Project Brainwave AI technology out of its research lab and into its widely used Azure cloud-computing service, starting with an accelerated option for image recognition.
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| Microsoft's AI systems use fast and flexible FPGA chips. Microsoft's Project Brainwave AI service uses fast and flexible Intel FPGA chips. They plug into standard servers. |
You're not likely to tap into Project Brainwave yourself, but it's the
kind of thing that eventually could improve all kinds of services at
companies you do deal with anything from insurance to package
delivery.
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| Microsoft's Project Brainwave AI service uses FPGA chips for image processing. This diagram shows how a multilayer neural network analyzes the image, relying either on the Azure cloud-computing service or servers at a customer site. |
It can be updated often to accelerate AI chores with the latest algorithms, and it handles AI tasks rapidly enough to be used for real-time jobs where response time is crucial.
Second, customers eventually will be able to run the AI jobs with Microsoft hardware at their own sites, and not just by tapping into Microsoft's data centers, which speeds up operations another notch.
"This is a unique offering," said Forrester analyst Mike Gualtieri.
The project is a microcosm of the AI revolution sweeping the tech industry. On the one hand, it's maturing fast enough to become useful for countless tasks digesting legal contracts, finding empty parking spaces, looking for hiring biases and generating 3D models of people's bodies, limbs and heads from a video.
On the other hand, AI is moving fast enough that companies are racing for advantage by investing in new AI hardware and AI software.
"So far the requirements seem to be insatiable," with customers gobbling up any new speed boosts that arrive, said Doug Burger, a Microsoft distinguished engineer who's led Microsoft's work to adapt AI to FPGA chips. "There's a huge innovation arms race happening."
Today, there isn't a huge market for AI services, but eventually it's possible just about any job a computer performs could have AI smarts built in, and that's a lot of work when you consider that a company like Morgan Stanley runs more than 3,000 applications of its own to get business done.
As companies check their options, they're looking for leadership in AI services, and that's exactly what Project Brainwave gets for Microsoft, Gualtieri said.
AI services in the cloud
Services in the cloud, a market led by Amazon Web Services, have transformed how computing gets done.No longer do businesses need to buy and run their own servers. Instead, they tap into vast pools of computing power, paying as they go for resources like processor performance, storage space and network capacity. And now they can pay for AI processing, too.
Image processing is probably the best-established AI task: Already you can pump photos to Amazon's AWS, Microsoft's Azure, IBM's Watson, Google Cloud Platform and specialists like Clarifai. They'll send you back labels showing what their machines think are in the photos.
Image recognition is potentially useful all over the place: crop monitoring, self-driving cars, medical scan processing, security video analysis and particle accelerator science, to name a few.
But Microsoft plans to add other AI tools to Project Brainwave. "We'll be expanding the types of workloads," said Mark Russinovich, chief technology officer of Microsoft's Azure service.
Although, curiously, it turns out that image-recognition AI tools can be pretty versatile. "Internally at Microsoft, we use imaging deep neural networks to classify malware," he said.
Neural networks, technology loosely based on how brains work, are the foundation for what's commonly called AI, machine learning or deep learning.
A key advantage of the technology is that it works by training a system with real-world data. This requires careful labeling beforehand, but the neural network figures out the patterns on its own. That sidesteps all the complexities and rigidity of conventional programming.
Training requires immense computing resources, and these days usually runs on graphics chips that are well suited to the task. The task can take days, weeks or even months, and once an AI model is trained, it's time to start again with updated data and perhaps a tweaked model.
"This isn't a one-and-done," Gualtieri said. "You are retraining constantly. No wonder cloud-computing companies are eager for customers always hungry for more processing time.
FPGAs to the rescue?
Once an AI is trained, it's time for the second phase, called inference, which is actually getting use out of the AI. This is where Microsoft's Project Brainwave comes to play.Running an AI doesn't require the horsepower that training does, but it still benefits from acceleration. That's why the iPhone X comes with AI hardware, why Google is building its own custom AI chips and why startups like Wave Computing are entering the market.
But Microsoft thinks its FPGAs, manufactured by Intel, give it a particular edge since they combine flexibility with speed.
Google's chips, called tensor processing units (TPUs), are special-purpose models with a design baked in, but FPGAs can be reconfigured in a fraction of a second for different work. In Microsoft's own data centers, where there are thousands of FPGAs, the company gives them a personality transplant once or twice a month as algorithms improve.
"Our fleet continuously adapts to the latest advances in machine learning," Russinovich said.