AI News, Microsoft Research artificial intelligence

This is Microsoft’s AI pipeline, from research to reality

Microsoft’s move into designing its own hardware for optimal AI is hardly unique.

Project Brainwave sprang from Microsoft’s realization that embracing AI needed to start at the chip level [Photo: courtesy of Microsoft]Conventional chips know how to execute the computing instructions in their repertoire when they leave the factory, and can never be retrained for a different purpose-such as efficiently running a new machine-learning algorithm.

FPGA technology allows Microsoft to deliver highly efficient deep learning as a service in a way that addresses specific customer requests.

Doug’s team was able to turn that around and build these convolutional neural networks that ran super fast on the FPGA in just six months or so.”

When Burger had begun his personal investigation of FPGAs in 2010, it wasn’t clear–at least to people who aren’t prescient computer scientists–how quickly AI would go mainstream, let alone that delivering it as a service would become a strategic imperative for a company such as Microsoft.

Microsoft Research

It was formed in 1991, with the intent to advance state-of-the-art computing and solve difficult world problems through technological innovation in collaboration with academic, government, and industry researchers.

The Microsoft Research team employs more than 1,000 computer scientists, physicists, engineers, and mathematicians, including Turing Award winners, Fields Medal winners, MacArthur Fellows, and Dijkstra Prize winners.

Project Hanover

The advent of big data heralds a new era of precision medicine, where treatments become increasingly effective by tailoring to individual patients.

For example, rapid technical advances have reached the exciting disruption point of $1000 person genome, making it affordable to sequence genetic mutations in individual tumors.

Today, it takes hours for a molecular tumor board of many highly trained specialists to review a patient's genomics data and make treatment decisions.

We developed a general framework for incorporating diverse forms of indirect supervision to compensate for the lack of labeled examples, by combining deep learning with probabilistic logic.

Motivated by biomedical applications, we expanded the scope of machine reading from single sentences to cross-sentence and document-level, and proposed novel neural architectures such as graph LSTMs for incorporating and reasoning with linguistic constraints.

Indirect supervision bootstraps a machine reader for a specific domain with little labeled data, while expert curators quickly vet machine-read results in an assisted curation interface.

As long as the initial machine reader attains sufficiently high recall and reasonable precision, assisted curation will be more efficient than manual curation.

In the long run, we're also very interested in combining machine reading results with causal machine learning to facilitate cancer decision support and chronic disease management.

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