Google's data centers hold thousands of servers and they power everything from Google Search to Gmail to YouTube. But those data centers need to be kept cool in order for those servers to run reliably. A couple of years ago, Google began applying AI…
The inside of data centers is loud and hot — and keeping servers from overheating is a major factor in the cost of running them. It’s no surprise then that the big players in this space, including Facebook, Microsoft and Google, all look for different ways of saving cooling costs. Facebook uses cool outside air when possible, Microsoft is experimenting with underwater data centers and Google is being Google and looking to its AI models for some extra savings.
A few years ago, Google, through its DeepMind affiliate, started looking into how it could use machine learning to provide its operators some additional guidance on how to best cool its data centers. At the time, though, the system only made recommendations and the human operators decided whether to implement them. Those humans can now take longer naps during the afternoon, because the team has decided the models are now good enough to give the AI-powered system full control over the cooling system. Operators can still intervene, of course, but as long as the AI doesn’t decide to burn the place down, the system runs autonomously.
The new cooling system is now in place in a number of Google’s data centers. Every five minutes, the system polls thousands of sensors inside the data center and chooses the optimal actions based on this information. There are all kinds of checks and balances here, of course, so the chances of one of Google’s data centers going up in flames because of this is low.
Like most machine learning models, this one also became better as it gathered more data. It’s now delivering energy savings of 30 percent on average, compared to the data centers’ historical energy usage.
One thing that’s worth noting here is that Google is obviously trying to save a few bucks, but in many ways, the company is also looking at this as a way of promoting its own machine learning services. What works in a data center, after all, should also work in a large office building. “In the long term, we think there’s potential to apply this technology in other industrial settings and help tackle climate change on an even grander scale,” DeepMind writes in today’s announcement.
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DeepMind’s foray into digital health services continues to raise concerns. The latest worries are voiced by a panel of external reviewers appointed by the Google-owned AI company to report on its operations after its initial data-sharing arrangements with the U.K.’s National Health Service (NHS) ran into a major public controversy in 2016.
The DeepMind Health Independent Reviewers’ 2018 report flags a series of risks and concerns, as they see it, including the potential for DeepMind Health to be able to “exert excessive monopoly power” as a result of the data access and streaming infrastructure that’s bundled with provision of the Streams app — and which, contractually, positions DeepMind as the access-controlling intermediary between the structured health data and any other third parties that might, in the future, want to offer their own digital assistance solutions to the Trust.
While the underlying FHIR (aka, fast healthcare interoperability resource) deployed by DeepMind for Streams uses an open API, the contract between the company and the Royal Free Trust funnels connections via DeepMind’s own servers, and prohibits connections to other FHIR servers. A commercial structure that seemingly works against the openness and interoperability DeepMind’s co-founder Mustafa Suleyman has claimed to support.
“There are many examples in the IT arena where companies lock their customers into systems that are difficult to change or replace. Such arrangements are not in the interests of the public. And we do not want to see DeepMind Health putting itself in a position where clients, such as hospitals, find themselves forced to stay with DeepMind Health even if it is no longer financially or clinically sensible to do so; we want DeepMind Health to compete on quality and price, not by entrenching legacy position,” the reviewers write.
Though they point to DeepMind’s “stated commitment to interoperability of systems,” and “their adoption of the FHIR open API” as positive indications, writing: “This means that there is potential for many other SMEs to become involved, creating a diverse and innovative marketplace which works to the benefit of consumers, innovation and the economy.”
“We also note DeepMind Health’s intention to implement many of the features of Streams as modules which could be easily swapped, meaning that they will have to rely on being the best to stay in business,” they add.
However, stated intentions and future potentials are clearly not the same as on-the-ground reality. And, as it stands, a technically interoperable app-delivery infrastructure is being encumbered by prohibitive clauses in a commercial contract — and by a lack of regulatory pushback against such behavior.
The reviewers also raise concerns about an ongoing lack of clarity around DeepMind Health’s business model — writing: “Given the current environment, and with no clarity about DeepMind Health’s business model, people are likely to suspect that there must be an undisclosed profit motive or a hidden agenda. We do not believe this to be the case, but would urge DeepMind Health to be transparent about their business model, and their ability to stick to that without being overridden by Alphabet. For once an idea of hidden agendas is fixed in people’s mind, it is hard to shift, no matter how much a company is motivated by the public good.”
“We have had detailed conversations about DeepMind Health’s evolving thoughts in this area, and are aware that some of these questions have not yet been finalised. However, we would urge DeepMind Health to set out publicly what they are proposing,” they add.
DeepMind has suggested it wants to build healthcare AIs that are capable of charging by results. But Streams does not involve any AI. The service is also being provided to NHS Trusts for free, at least for the first five years — raising the question of how exactly the Google-owned company intends to recoup its investment.
Google of course monetizes a large suite of free-at-the-point-of-use consumer products — such as the Android mobile operating system; its cloud email service Gmail; and the YouTube video sharing platform, to name three — by harvesting people’s personal data and using that information to inform its ad targeting platforms.
Hence the reviewers’ recommendation for DeepMind to set out its thinking on its business model to avoid its intentions vis-a-vis people’s medical data being viewed with suspicion.
The company’s historical modus operandi also underlines the potential monopoly risks if DeepMind is allowed to carve out a dominant platform position in digital healthcare provision — given how effectively its parent has been able to turn a free-for-OEMs mobile OS (Android) into global smartphone market OS dominance, for example.
So, while DeepMind only has a handful of contracts with NHS Trusts for the Streams app and delivery infrastructure at this stage, the reviewers’ concerns over the risk of the company gaining “excessive monopoly power” do not seem overblown.
They are also worried about DeepMind’s ongoing vagueness about how exactly it works with its parent Alphabet, and what data could ever be transferred to the ad giant — an inevitably queasy combination when stacked against DeepMind’s handling of people’s medical records.
“To what extent can DeepMind Health insulate itself against Alphabet instructing them in the future to do something which it has promised not to do today? Or, if DeepMind Health’s current management were to leave DeepMind Health, how much could a new CEO alter what has been agreed today?” they write.
“We appreciate that DeepMind Health would continue to be bound by the legal and regulatory framework, but much of our attention is on the steps that DeepMind Health have taken to take a more ethical stance than the law requires; could this all be ended? We encourage DeepMind Health to look at ways of entrenching its separation from Alphabet and DeepMind more robustly, so that it can have enduring force to the commitments it makes.”
Responding to the report’s publication on its website, DeepMind writes that it’s “developing our longer-term business model and roadmap.”
“Rather than charging for the early stages of our work, our first priority has been to prove that our technologies can help improve patient care and reduce costs. We believe that our business model should flow from the positive impact we create, and will continue to explore outcomes-based elements so that costs are at least in part related to the benefits we deliver,” it continues.
So it has nothing to say to defuse the reviewers’ concerns about making its intentions for monetizing health data plain — beyond deploying a few choice PR soundbites.
On its links with Alphabet, DeepMind also has little to say, writing only that: “We will explore further ways to ensure there is clarity about the binding legal frameworks that govern all our NHS partnerships.”
“Trusts remain in full control of the data at all times,” it adds. “We are legally and contractually bound to only using patient data under the instructions of our partners. We will continue to make our legal agreements with Trusts publicly available to allow scrutiny of this important point.”
“There is nothing in our legal agreements with our partners that prevents them from working with any other data processor, should they wish to seek the services of another provider,” it also claims in response to additional questions we put to it.
“We hope that Streams can help unlock the next wave of innovation in the NHS. The infrastructure that powers Streams is built on state-of-the-art open and interoperable standards, known as FHIR. The FHIR standard is supported in the UK by NHS Digital, NHS England and the INTEROPen group. This should allow our partner trusts to work more easily with other developers, helping them bring many more new innovations to the clinical frontlines,” it adds in additional comments to us.
“Under our contractual agreements with relevant partner trusts, we have committed to building FHIR API infrastructure within the five year terms of the agreements.”
Asked about the progress it’s made on a technical audit infrastructure for verifying access to health data, which it announced last year, it reiterated the wording on its blog, saying: “We will remain vigilant about setting the highest possible standards of information governance. At the beginning of this year, we appointed a full time Information Governance Manager to oversee our use of data in all areas of our work. We are also continuing to build our Verifiable Data Audit and other tools to clearly show how we’re using data.”
So developments on that front look as slow as we expected.
The Google-owned U.K. AI company began its push into digital healthcare services in 2015, quietly signing an information-sharing arrangement with a London-based NHS Trust that gave it access to around 1.6 million people’s medical records for developing an alerts app for a condition called Acute Kidney Injury.
It also inked an MoU with the Trust where the pair set out their ambition to apply AI to NHS data sets. (They even went so far as to get ethical signs-off for an AI project — but have consistently claimed the Royal Free data was not fed to any AIs.)
However, the data-sharing collaboration ran into trouble in May 2016 when the scope of patient data being shared by the Royal Free with DeepMind was revealed (via investigative journalism, rather than by disclosures from the Trust or DeepMind).
None of the ~1.6 million people whose non-anonymized medical records had been passed to the Google-owned company had been informed or asked for their consent. And questions were raised about the legal basis for the data-sharing arrangement.
Last summer the U.K.’s privacy regulator concluded an investigation of the project — finding that the Royal Free NHS Trust had broken data protection rules during the app’s development.
Yet despite ethical questions and regulatory disquiet about the legality of the data sharing, the Streams project steamrollered on. And the Royal Free Trust went on to implement the app for use by clinicians in its hospitals, while DeepMind has also signed several additional contracts to deploy Streams to other NHS Trusts.
More recently, the law firm Linklaters completed an audit of the Royal Free Streams project, after being commissioned by the Trust as part of its settlement with the ICO. Though this audit only examined the current functioning of Streams. (There has been no historical audit of the lawfulness of people’s medical records being shared during the build and test phase of the project.)
Linklaters did recommend the Royal Free terminates its wider MoU with DeepMind — and the Trust has confirmed to us that it will be following the firm’s advice.
“The audit recommends we terminate the historic memorandum of understanding with DeepMind which was signed in January 2016. The MOU is no longer relevant to the partnership and we are in the process of terminating it,” a Royal Free spokesperson told us.
So DeepMind, probably the world’s most famous AI company, is in the curious position of being involved in providing digital healthcare services to U.K. hospitals that don’t actually involve any AI at all. (Though it does have some ongoing AI research projects with NHS Trusts too.)
In mid 2016, at the height of the Royal Free DeepMind data scandal — and in a bid to foster greater public trust — the company appointed the panel of external reviewers who have now produced their second report looking at how the division is operating.
And it’s fair to say that much has happened in the tech industry since the panel was appointed to further undermine public trust in tech platforms and algorithmic promises — including the ICO’s finding that the initial data-sharing arrangement between the Royal Free and DeepMind broke U.K. privacy laws.
The eight members of the panel for the 2018 report are: Martin Bromiley OBE; Elisabeth Buggins CBE; Eileen Burbidge MBE; Richard Horton; Dr. Julian Huppert; Professor Donal O’Donoghue; Matthew Taylor; and Professor Sir John Tooke.
In their latest report the external reviewers warn that the public’s view of tech giants has “shifted substantially” versus where it was even a year ago — asserting that “issues of privacy in a digital age are if anything, of greater concern.”
At the same time politicians are also gazing rather more critically on the works and social impacts of tech giants.
Although the U.K. government has also been keen to position itself as a supporter of AI, providing public funds for the sector and, in its Industrial Strategy white paper, identifying AI and data as one of four so-called “Grand Challenges” where it believes the U.K. can “lead the world for years to come” — including specifically name-checking DeepMind as one of a handful of leading-edge homegrown AI businesses for the country to be proud of.
Still, questions over how to manage and regulate public sector data and AI deployments — especially in highly sensitive areas such as healthcare — remain to be clearly addressed by the government.
Meanwhile, the encroaching ingress of digital technologies into the healthcare space — even when the techs don’t even involve any AI — are already presenting major challenges by putting pressure on existing information governance rules and structures, and raising the specter of monopolistic risk.
Asked whether it offers any guidance to NHS Trusts around digital assistance for clinicians, including specifically whether it requires multiple options be offered by different providers, the NHS’ digital services provider, NHS Digital, referred our question on to the Department of Health (DoH), saying it’s a matter of health policy.
The DoH in turn referred the question to NHS England, the executive non-departmental body which commissions contracts and sets priorities and directions for the health service in England.
And at the time of writing, we’re still waiting for a response from the steering body.
Ultimately it looks like it will be up to the health service to put in place a clear and robust structure for AI and digital decision services that fosters competition by design by baking in a requirement for Trusts to support multiple independent options when procuring apps and services.
Without that important check and balance, the risk is that platform dynamics will quickly dominate and control the emergent digital health assistance space — just as big tech has dominated consumer tech.
But publicly funded healthcare decisions and data sets should not simply be handed to the single market-dominating entity that’s willing and able to burn the most resource to own the space.
Nor should government stand by and do nothing when there’s a clear risk that a vital area of digital innovation is at risk of being closed down by a tech giant muscling in and positioning itself as a gatekeeper before others have had a chance to show what their ideas are made of, and before even a market has had the chance to form.
If I show you a single picture of a room, you can tell me right away that there’s a table with a chair in front of it, they’re probably about the same size, about this far from each other, with the walls this far away — enough to draw a rough map of the room. Computer vision systems don’t have this intuitive understanding of space, but the latest research from DeepMind brings them closer than ever before.
The new paper from the Google -owned research outfit was published today in the journal Science (complete with news item). It details a system whereby a neural network, knowing practically nothing, can look at one or two static 2D images of a scene and reconstruct a reasonably accurate 3D representation of it. We’re not talking about going from snapshots to full 3D images (Facebook’s working on that) but rather replicating the intuitive and space-conscious way that all humans view and analyze the world.
When I say it knows practically nothing, I don’t mean it’s just some standard machine learning system. But most computer vision algorithms work via what’s called supervised learning, in which they ingest a great deal of data that’s been labeled by humans with the correct answers — for example, images with everything in them outlined and named.
This new system, on the other hand, has no such knowledge to draw on. It works entirely independently of any ideas of how to see the world as we do, like how objects’ colors change toward their edges, how they get bigger and smaller as their distance changes and so on.
It works, roughly speaking, like this. One half of the system is its “representation” part, which can observe a given 3D scene from some angle, encoding it in a complex mathematical form called a vector. Then there’s the “generative” part, which, based only on the vectors created earlier, predicts what a different part of the scene would look like.
(A video showing a bit more of how this works is available here.)
Think of it like someone handing you a couple of pictures of a room, then asking you to draw what you’d see if you were standing in a specific spot in it. Again, this is simple enough for us, but computers have no natural ability to do it; their sense of sight, if we can call it that, is extremely rudimentary and literal, and of course machines lack imagination.
Yet there are few better words that describe the ability to say what’s behind something when you can’t see it.
“It was not at all clear that a neural network could ever learn to create images in such a precise and controlled manner,” said lead author of the paper, Ali Eslami, in a release accompanying the paper. “However we found that sufficiently deep networks can learn about perspective, occlusion and lighting, without any human engineering. This was a super surprising finding.”
It also allows the system to accurately recreate a 3D object from a single viewpoint, such as the blocks shown here:
Obviously there’s nothing in any single observation to tell the system that some part of the blocks extends forever away from the camera. But it creates a plausible version of the block structure regardless that is accurate in every way. Adding one or two more observations requires the system to rectify multiple views, but results in an even better representation.
This kind of ability is critical for robots, especially because they have to navigate the real world by sensing it and reacting to what they see. With limited information, such as some important clue that’s temporarily hidden from view, they can freeze up or make illogical choices. But with something like this in their robotic brains, they could make reasonable assumptions about, say, the layout of a room without having to ground-truth every inch.
“Although we need more data and faster hardware before we can deploy this new type of system in the real world,” Eslami said, “it takes us one step closer to understanding how we may build agents that learn by themselves.”
A third party audit of a controversial patient data-sharing arrangement between a London NHS Trust and Google DeepMind appears to have skirted over the core issues that generated the controversy in the first place.
The audit (full report here) — conducted by law firm Linklaters — of the Royal Free NHS Foundation Trust’s acute kidney injury detection app system, Streams, which was co-developed with Google-DeepMind (using an existing NHS algorithm for early detection of the condition), does not examine the problematic 2015 information-sharing agreement inked between the pair which allowed data to start flowing.
“This Report contains an assessment of the data protection and confidentiality issues associated with the data protection arrangements between the Royal Free and DeepMind . It is limited to the current use of Streams, and any further development, functional testing or clinical testing, that is either planned or in progress. It is not a historical review,” writes Linklaters, adding that: “It includes consideration as to whether the transparency, fair processing, proportionality and information sharing concerns outlined in the Undertakings are being met.”
Yet it was the original 2015 contract that triggered the controversy, after it was obtained and published by New Scientist, with the wide-ranging document raising questions over the broad scope of the data transfer; the legal bases for patients information to be shared; and leading to questions over whether regulatory processes intended to safeguard patients and patient data had been sidelined by the two main parties involved in the project.
In November 2016 the pair scrapped and replaced the initial five-year contract with a different one — which put in place additional information governance steps.
They also went on to roll out the Streams app for use on patients in multiple NHS hospitals — despite the UK’s data protection regulator, the ICO, having instigated an investigation into the original data-sharing arrangement.
And just over a year ago the ICO concluded that the Royal Free NHS Foundation Trust had failed to comply with Data Protection Law in its dealings with Google’s DeepMind.
The audit of the Streams project was a requirement of the ICO.
Though, notably, the regulator has not endorsed Linklaters report. On the contrary, it warns that it’s seeking legal advice and could take further action.
In a statement on its website, the ICO’s deputy commissioner for policy, Steve Wood, writes: “We cannot endorse a report from a third party audit but we have provided feedback to the Royal Free. We also reserve our position in relation to their position on medical confidentiality and the equitable duty of confidence. We are seeking legal advice on this issue and may require further action.”
In a section of the report listing exclusions, Linklaters confirms the audit does not consider: “The data protection and confidentiality issues associated with the processing of personal data about the clinicians at the Royal Free using the Streams App.”
So essentially the core controversy, related to the legal basis for the Royal Free to pass personally identifiable information on 1.6M patients to DeepMind when the app was being developed, and without people’s knowledge or consent, is going unaddressed here.
And Wood’s statement pointedly reiterates that the ICO’s investigation “found a number of shortcomings in the way patient records were shared for this trial”.
“[P]art of the undertaking committed Royal Free to commission a third party audit. They have now done this and shared the results with the ICO. What’s important now is that they use the findings to address the compliance issues addressed in the audit swiftly and robustly. We’ll be continuing to liaise with them in the coming months to ensure this is happening,” he adds.
“It’s important that other NHS Trusts considering using similar new technologies pay regard to the recommendations we gave to Royal Free, and ensure data protection risks are fully addressed using a Data Protection Impact Assessment before deployment.”
While the report is something of a frustration, given the glaring historical omissions, it does raise some points of interest — including suggesting that the Royal Free should probably scrap a Memorandum of Understanding it also inked with DeepMind, in which the pair set out their ambition to apply AI to NHS data.
This is recommended because the pair have apparently abandoned their AI research plans.
On this Linklaters writes: “DeepMind has informed us that they have abandoned their potential research project into the use of AI to develop better algorithms, and their processing is limited to execution of the NHS AKI algorithm… In addition, the majority of the provisions in the Memorandum of Understanding are non-binding. The limited provisions that are binding are superseded by the Services Agreement and the Information Processing Agreement discussed above, hence we think the Memorandum of Understanding has very limited relevance to Streams. We recommend that the Royal Free considers if the Memorandum of Understanding continues to be relevant to its relationship with DeepMind and, if it is not relevant, terminates that agreement.”
In another section, discussing the NHS algorithm that underpins the Streams app, the law firm also points out that DeepMind’s role in the project is little more than helping provide a glorified app wrapper (on the app design front the project also utilized UK app studio, ustwo, so DeepMind can’t claim app design credit either).
“Without intending any disrespect to DeepMind, we do not think the concepts underpinning Streams are particularly ground-breaking. It does not, by any measure, involve artificial intelligence or machine learning or other advanced technology. The benefits of the Streams App instead come from a very well-designed and user-friendly interface, backed up by solid infrastructure and data management that provides AKI alerts and contextual clinical information in a reliable, timely and secure manner,” Linklaters writes.
What DeepMind did bring to the project, and to its other NHS collaborations, is money and resources — providing its development resources free for the NHS at the point of use, and stating (when asked about its business model) that it would determine how much to charge the NHS for these app ‘innovations’ later.
Yet the commercial services the tech giant is providing to what are public sector organizations do not appear to have been put out to open tender.
Also notably excluded in the Linklaters’ audit: Any scrutiny of the project vis-a-vis competition law, public procurement law compliance with procurement rules, and any concerns relating to possible anticompetitive behavior.
The report does highlight one potentially problematic data retention issue for the current deployment of Streams, saying there is “currently no retention period for patient information on Streams” — meaning there is no process for deleting a patient’s medical history once it reaches a certain age.
“This means the information on Streams currently dates back eight years,” it notes, suggesting the Royal Free should probably set an upper age limit on the age of information contained in the system.
While Linklaters largely glosses over the chequered origins of the Streams project, the law firm does make a point of agreeing with the ICO that the original privacy impact assessment for the project “should have been completed in a more timely manner”.
It also describes it as “relatively thin given the scale of the project”.
Giving its response to the audit, health data privacy advocacy group MedConfidential — an early critic of the DeepMind data-sharing arrangement — is roundly unimpressed, writing: “The biggest question raised by the Information Commissioner and the National Data Guardian appears to be missing — instead, the report excludes a “historical review of issues arising prior to the date of our appointment”.
“The report claims the ‘vital interests’ (i.e. remaining alive) of patients is justification to protect against an “event [that] might only occur in the future or not occur at all”… The only ‘vital interest’ protected here is Google’s, and its desire to hoard medical records it was told were unlawfully collected. The vital interests of a hypothetical patient are not vital interests of an actual data subject (and the GDPR tests are demonstrably unmet).
“The ICO and NDG asked the Royal Free to justify the collection of 1.6 million patient records, and this legal opinion explicitly provides no answer to that question.”
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