Google’s DeepMind

Artificial Intelligence is Completely Transforming Modern Healthcare

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IN BRIEF

AI in medicine is changing healthcare as we know it. The introduction of deep learning systems is only possible by powerful computing capabilities; capabilities that Nvidia has made possible with their graphic processors.

A NEW AGE OF HEALTHCARE

Artificial intelligence is slowly making its way into the realm of modern healthcare. Google’s DeepMind is revolutionizing eye care in the United Kingdom, and IBM’s Watson is tackling cancer diagnostics on par with human physicians. Both AI systems use deep learning, a concept loosely mirroring how our own brains work by having AI software analyze exorbitant amounts of data and uncover patterns — which is particularly applicable in diagnostics.As medical imaging technology continues to take advantage of every new deep learningbreakthrough, the challenge is that the computing technology on which it relies must evolve just as quickly. A company called Nvidia is leading that charge under the guidance of Kimberley Powell, who is confident that Nvidia’s processors are not only meeting the deep learning standards of medical imagining, but also pushing the industry forward as a whole.

Nvidia’s hardware has established its silent but prominent role in deep learning’s marriage with medicine. Powell believes projects like their specialized computers, such as the DGX-1 a powerful deep-learning product, will become increasingly more common in hospitals and medical research centers. Strong computing power, like what the DGX-1 can provide, stands to increase the reliability of the diagnostic process; something that, in turn, would significantly boost the standard of care in developing countries.

DEEP LEARNING, MD

While AI won’t be replacing doctors anytime soon, it will provide physicians with tools to more efficiently — and reliably — assess patients. AI is already involved in mining medical data, diagnosing medical images, studying genomics-based data for personalized medicine, and improving the lives of the disabled.

Thanks to NVIDIA’s DGX-1, hospitals can efficiently compare a single patient’s tests and history with data from a vast population of other patients. Some medical research centers and startupsare automating the analysis of MRIs, CT scans, and X-rays to assist physicians in making a diagnosis. Others are utilizing deep learning to create genetic interpretation engines to identify cancer-causing mutations in patient genomes, bringing to life the concept of personalized medicine.

However, while AI will no doubt continue to revolutionize medicine for years to come, physicians often find themselves perplexed by how to incorporate the technology into their regular practice. Only once AI is accepted, and fully integrated, into medicine will we see the full potential for the technology in terms of lending itself to more efficient and accurate diagnostics — from routine checkups to more specialized fields.

 References: New on MIT Technology Review, Nvidia

New Processor Chips Promise Faster Neural Network Learning

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IN BRIEF

Scientists are proposing resistive processing units (RPUs) to speed up learning times drastically and cut horsepower requirements. These RPUs are theoretical chips that combine CPU and non-volatile memory.

ACCELERATING LEARNING

Deep neural networks (DNN), like Google’s DeepMind or the IBM Watson, are amazing machines. They can be taught to do many mental tasks like a human, and they represent our best shot to actual artificial intelligence.

The challenge has always been training and teaching these machines. For most of the tasks they have to do, the machines tie up big-ticket supercomputers or data centers for days at a time. But scientists from IBM’s T.J. Watson Research Center are poised to change all that.

They have proposed the use of resistive processing units (RPUs) to speed up learning times drastically and cut horsepower requirements. RPUs are theoretical chips that combine CPU and non-volatile memory. The RPUs make use of an existing new technology: resistive RAM. RPUs are slated to put large amounts of resistive RAM directly onto a CPU. In theory, these chips could fetch data as fast as it is being processed, thus speeding up learning times.

According to the paper documenting the study, “problems that currently require days of training on a datacenter-size cluster with thousands of machines can be addressed within hours on a single RPU accelerator.

“For large DNNs with about 1 billion weights this massively parallel RPU architecture can
achieve acceleration factors of 30,000 compared to state-of-the-art microprocessors while providing power efficiency.”

THE NEED FOR SPEED

Current DNNs will need this upgrade, as modern neural networks must perform billions of tasks simultaneously. That requires numerous CPU memory calls, which quickly adds up over billions of cycles. This gets more pronounced as harder tasks, such as natural voice recognition and true AI, are put on the table.

Currently, the chips are still undergoing research, but scientists believe they can be produced using regular CMOS technology. Once developed, they will be able to tackle Big Data problems such as natural speech recognition and translation between all world languages; real-time analytics on large streams of business and scientific data; and integration and analysis of multimodal sensory data flows from a massive number of IoT (Internet of Things) sensors.

Source:futurism.com

Google’s DeepMind plans bitcoin-style health record tracking for hospitals

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Tech company’s health subsidiary planning digital ledger based on blockchain to let hospitals, the NHS and eventually patients track personal data

 Patients at the A and E department of London’s Royal Free Hospital, which has partnered with Deepmind Health.
Patients at the A&E department of London’s Royal Free Hospital, which has partnered with DeepMind Health. 

Dubbed “Verifiable Data Audit”, the plan is to create a special digital ledger that automatically records every interaction with patient data in a cryptographically verifiable manner. This means any changes to, or access of, the data would be visible.

DeepMind has been working in partnership with London’s Royal Free Hospital to develop kidney monitoring software called Streams and has faced criticism from patient groups for what they claim are overly broad data sharing agreements. Critics fear that the data sharing has the potential to give DeepMind, and thus Google, too much power over the NHS.

Suleyman says that development on the data audit proposal began long before the launch of Streams, when Laurie, the co-creator of the widely-used Apache server software, was hired by DeepMind. “This project has been brewing since before we started DeepMind Health,” he told the Guardian, “but it does add another layer of transparency.

“Our mission is absolutely central, and a core part of that is figuring out how we can do a better job of building trust. Transparency and better control of data is what will build trust in the long term.” Suleyman pointed to a number of efforts DeepMind has already undertaken in an attempt to build that trust, from its founding membership of the industry group Partnership on AI to its creation of a board of independent reviewers for DeepMind Health, but argued the technical methods being proposed by the firm provide the “other half” of the equation.

Nicola Perrin, the head of the Wellcome Trust’s “Understanding Patient Data” taskforce, welcomed the verifiable data audit concept. “There are a lot of calls for a robust audit trail to be able to track exactly what happens to personal data, and particularly to be able to check how data is used once it leaves a hospital or NHS Digital. DeepMind are suggesting using technology to help deliver that audit trail, in a way that should be much more secure than anything we have seen before.”

Perrin said the approach could help address DeepMind’s challenge of winning over the public. “One of the main criticisms about DeepMind’s collaboration with the Royal Free was the difficulty of distinguishing between uses of data for care and for research. This type of approach could help address that challenge, and suggests they are trying to respond to the concerns.

“Technological solutions won’t be the only answer, but I think will form an important part of developing trustworthy systems that give people more confidence about how data is used.”

The systems at work are loosely related to the cryptocurrency bitcoin, and the blockchain technology that underpins it. DeepMind says: “Like blockchain, the ledger will be append-only, so once a record of data use is added, it can’t later be erased. And like blockchain, the ledger will make it possible for third parties to verify that nobody has tampered with any of the entries.”

Laurie downplays the similarities. “I can’t stop people from calling it blockchain related,” he said, but he described blockchains in general as “incredibly wasteful” in the way they go about ensuring data integrity: the technology involves blockchain participants burning astronomical amounts of energy – by some estimates as much as the nation of Cyprus – in an effort to ensure that a decentralised ledger can’t be monopolised by any one group.

DeepMind argues that health data, unlike a cryptocurrency, doesn’t need to be decentralised – Laurie says at most it needs to be “federated” between a small group of healthcare providers and data processors – so the wasteful elements of blockchain technology need not be imported over. Instead, the data audit system uses a mathematical function called a Merkle tree, which allows the entire history of the data to be represented by a relatively small record, yet one which instantly shows any attempt to rewrite history.

Although not technologically complete yet, DeepMind already has high hopes for the proposal, which it would like to see form the basis of a new model for data storage and logging in the NHS overall, and potentially even outside healthcare altogether. Right now, says Suleyman, “It’s really difficult for people to know where data has moved, when, and under which authorised policy. Introducing a light of transparency under this process I think will be very useful to data controllers, so they can verify where their processes have used or moved or accessed data.

“That’s going to add technical proof to the governance transparency that’s already in place. The point is to turn that regulation into a technical proof.”

In the long-run, Suleyman says, the audit system could be expanded so that patients can have direct oversight over how and where their data has been used. But such a system would come a long time in the future, once concerns over how to secure access have been solved.