Coronavirus contact tracing apps were meant to save us. They won’t – Wired.co.uk

When youre in the supermarket queue in January 2021 socially distanced from those around you by two metres and the phone in your pocket buzzes with a notification from the contact tracing app you installed six months ago, the routine will be familiar. After all, you have been through the process multiple times already.

Someone you crossed paths with last week the app doesnt tell you who has tested positive for coronavirus. It tells you to go home straight away. You must self-isolate until a test has been completed. The test, as with those before it, was automatically ordered from a public health centre as soon as notification was sent to your phone.

This is our new normal. Contact tracing apps arent here for the short-term. After the first waves of coronavirus have passed and the public inquiries into government responses have started, the apps will still be watching over us. On their current trajectory they will become essential parts of our daily lives. And it will continue to be this way until a vaccine for coronavirus arrives.

The technology, officials seem to believe, will save us. Contact tracing apps have caught the imagination of politicians looking for ways to ease lockdowns and restart failing economies. They offer hope to world leaders looking for an answer to the tricky question of when the lockdown will end. They promise a return to normality, of sorts.

From Iceland to Israel, more than 30 systems are being developed by governments and health authorities. They promise to automate the laborious process of tracking down the contacts of infected individuals, helping to slow the spread of coronavirus through the population and save lives.

Inspired by China, Singapore, Taiwan and South Korea, all of which have used elements of digital tracing technology, huge faith is being placed in contact tracing apps. But there is little concrete evidence that they have any measurable effect. At best, tracing apps could aid the far more effective and complex sleuthing carried out by human contact tracers. At worst, the technology could prove useless, erode fundamental human rights and usher in unprecedented mass surveillance. Much of the hype around contact tracing apps, it seems, comes from anecdotal reporting rather than hard science.

This is absolutely new ground, explains Carly Kind, the director of the Ada Lovelace Institute, which has conducted a review of how technology can be used to ease coronavirus lockdowns. This is the first major epidemic or pandemic where these kinds of contract tracing apps have been under consideration. Theres not very much evidence at all to support the sustained benefit.

But we do know that manual contact tracing itself can be effective. Singapore, a technologically advanced but authoritarian state, was one of the first countries to introduce a contact tracing app. It was initially able to contain the spread of the virus. The conclusion many have come to is wrong: the contact tracing app had little to no effect.

Far more important was the role of human-led contact tracing. Teams of people, including police officers drafted in to help with the effort, conducted interviews with people who had contracted coronavirus. They asked where they had been for the last 14 days, who they had interacted with, and trawled through CCTV footage to track movements. Once an investigation had been completed and individuals identified, they checked whether those who had been in close contact with the infected person were unwell or showing any symptoms of the coronavirus.

Everything was going pretty well when it was manual and labour intensive, but the app is not replacing conventional tracing, this is just a supplement, says Dale Fisher, a professor of infectious diseases at the National University of Singapore who has been involved in the countrys response to coronavirus. He adds that contact tracing was one technique used in a wider package by Singaporean authorities. The country has also isolated its positive cases something not done by many others around the world and strictly enforced quarantines.

Taiwan has taken a similar approach, with the authorities working with telecoms companies to access phone location data. In South Korea, where manual contact tracing has also played a large part in its response, the Infectious Disease Control and Prevention Act gives authorities access to GPS, credit card, travel and health data.

In Asia, the data used by authorities to conduct contact tracing falls well outside of the limited remit of contact tracing apps being developed in the West. While the West focuses on using Bluetooth to track coronavirus, the success elsewhere has been based on analysing CCTV footage and phone location tracking. It doesnt necessarily follow that automated tracing, via phones, will be successful. The concern: will contact tracing apps being lauded by governments around the world end up doing more harm than good?

Getty Images / PAUL ELLIS / Contributor

Coronavirus has created a new type of surveillance. The most common type of contact tracing being developed shuns regular data collection methods a phones GPS location data or microphone to listen to surroundings for a more granular approach. Apps will largely use Bluetooth to gather details about people who are close to each other, and use these signals to create vast databases of close encounters.

This type of Bluetooth data collection, while not perfect, doesnt amass information on where people are in the world or monitor their precise movements. GPS data collection acts like a spy in your pocket, using satellite data to pinpoint your location. Bluetooth, which is a connection between nearby devices, cant tell whether youre at home or flouting lockdown regulations by gathering with friends. It merely communicates with the devices around it. Used in this way, Bluetooth gathers far less personal information than most of the apps on your phone.

For the tracking apps to work, the Bluetooth chip in any phone with it installed is essentially sending out pings while also listening for pings coming back. When one phone detects another it will record its unique identifying number against a database. Repeat this many millions of times and you can build up a fairly accurate picture of whos been near who. Get a confirmed coronavirus case and you can set in motion a chain reaction quarantines and tests.

Analysis from University of Oxford academics say manual contact tracing is too slow and cant be scaled up once an epidemic gets too big. We conclude that viral spread is too fast to be contained by manual contact tracing, but could be controlled if this process was faster, more efficient and happened at scale, the academics said.

This relies on high testing capacity being available but phone-based contact tracing could make it possible to notify tens of thousands of potentially infected people a day. If effective this would vastly reduce the amount of people interacting with others when they may be asymptomatic. Delaying contact tracing by even half a day from onset of symptoms can make the difference between epidemic control and resurgence, the researchers add.

Then there is the thorny and hugely contentious issues of where data is stored. On April 10, Apple and Google proposed a decentralised system where records of devices interacting with one another are stored on users phones. For this to work, each phone regularly downloads updated lists, allowing the system to send out alerts based on new movements and confirmed infections.

This approach stops one large database being created by health authorities or governments. Apple and Google havent committed to making their own apps, but rather have created a system that a myriad of apps can be built upon. Their system will be rolled out in mid-May. A European open-source project, DP3T, uses a similar system.

Some governments, including France and the UK, have opted to use centralised systems that dont follow the strict privacy guidelines set out by Apple and Google. (A centralised version of DP3T, called PEPP-PT also exists). This suggests that in the future officials may want their apps to collect more data than the random identifiers generated through Apple and Googles system. NHS documents show officials were considering adding the ability to send out notifications when people had been outside for too long. The NHS denies such a feature is being developed.

As a result, officials in France have called for tech firms to relax their privacy protections. Ministers have said they want to build an app that is tied to the countrys healthcare system. Apps that dont use the system developed by Apple and Google also face technical difficulties: this type of Bluetooth signal broadcasting will not work on iPhones when the app is open in the background or when the screen is locked.

Such calls for weaker privacy protections are likely to be rebuffed. If the companies were to change tact for one country, they would have to do so for all. Apple doesnt budge on its privacy red lines just ask the FBI. Meanwhile, German officials have backtracked from a centralised approach after facing a surveillance backlash from civil liberty groups and the public.

At the heart of the clash between centralised versus decentralised systems is a fundamental question: can you make contact tracing apps useful? In this nascent development community, there is a tension: do these apps produce the necessary results? Does the claim that they will help us return to some form of normality stand up? According to researchers at the KU Leuven Institute for the Future in Belgium, evidence for their effectiveness in managing disease outbreaks is limited.

Just ask Singapore. If you ask me whether any Bluetooth contact tracing system deployed or under development, anywhere in the world, is ready to replace manual contact tracing, I will say without qualification that the answer is: no, Jason Bay, a lead developer on Singapores TraceTogether app wrote in a blog post. He declined a request for an interview for this story. In the blog post, Bay argues its essential for humans to be involved in the contact tracing process due to the intensive sequence of difficult and anxiety-laden conversations" required and it would be technology triumphalism" to place too much hope in apps.

Others agree. One researcher working on the coronavirus response says its unlikely that any studies will ever be able to prove that a contact tracing app by itself has made any difference. The apps are intertwined with other response methods and it can be difficult to untangle the importance each contribution makes, they say. Similarly, officials from the Council of Europe have asked: Considering the absence of evidence of their efficacy, are the promises worth the predictable societal and legal risks?

And a team of experts from the non-profit Brookings Institution have cast doubt over how effective such apps are for individuals. Ultimately, contact tracing is a public health intervention, not an individual health one. It can reduce the spread of disease through the population, but does not confer direct protection on any individual, they argue. Officials in Belgium have ruled out using an app, preferring to focus their efforts on human contact tracers.

Another major issue for contact tracing apps is persuading people to actually use them. Academics at the University of Oxford involved in the development of the UKs contact tracing app have said 60 per cent of people would need to be using the app for it to work. These numbers are not yet being reached anywhere in the world.

Singapores TraceTogether has been downloaded by 20 per cent of the population, around 1.1 million people, and Australias has garnered more than two million downloads. Both are significant figures but not high enough to necessarily make a difference. In the US, three in five people have said they cant or wont use contact tracing apps. Oxfords analysis contradicts this: it say more than 75 per cent of people would be willing to use the apps in some countries.

None of the privacy measures or app efficacy matters if people dont download and use the apps. There is the chance that some people, including those who dont own smartphones, will be left behind. Theres a real risk that groups with low levels of trust in government are less likely to use the app, says Kind. Those same groups are the ones that are more likely to suffer from the ill effects of the illness.

Whats promoted as a panacea now could come back to bite us hard in the future. This is a slippery slope that leads to you being colour coded, says Alex Gladstein, chief strategy officer at Human Rights Foundation. In China thats already happened with people only being allowed to travel if their health status is listed as green, rather than amber of red. Gladstein worries that the apps could be co-opted, with officials adding more invasive features as the world goes through subsequent waves of coronavirus outbreaks.

The trade-off of using such apps is that we would, potentially, be allowed greater freedom: the freedom to go outside, to visit friends and family, to go out for dinner, to return to some form of normality. This is the issue that many tens of millions of people will grapple with in the coming months: what, really, is the price of freedom?

There are many things that could go wrong with contact tracing apps and plenty of them already have. The accuracy of Bluetooth may result in people being warned they have come into contact with people infected with coronavirus when in reality they were separated by a wall. A human contact tracer will similarly make mistakes, Bay wrote in his blog post. But the difference with humans conducting contact tracing is that they can use wider context, beyond a persons physical proximity, to determine whether there has been an exposure to coronavirus. Although as they do this, they are still recording data in some form.

Concerns have also been raised about the quality of the data collected by apps where users self-diagnose their conditions. Ultimately, people could lie in attempts to troll the system and force all of the people they have passed recently to go into quarantine. Australia has already seen one hoax related to its COVIDsafe app that has been shared hundreds of times on social media. The COVIDsafe app has detected you are now +20km from your nominated home address, the false message warned, before encouraging people to call the government to explain why they are so far from home. The scam forced the government to issue an official rebuttal.

And then theres the issue of data breaches. One proposed app that was presented to officials in the Netherlands leaked user data that belonged to another service created by the developers. Despite all these pitfalls, people are still downloading contact tracing apps in their millions. And as more are released, many million more people will follow.

But it is the long term consequences that could have a bigger impact than the short-term relief. Theres a chance that tracing apps will have health data built into them, or immunity certificates, and act as default definers of a persons status. Covid-19 is another sea change moment like 9/11 was, Gladstein says. Youre seeing a normalisation of surveillance. The NSA whistleblower Edward Snowden has said countries are building the architecture of oppression in response to the virus.

At the moment, coronavirus contact tracing apps are planned to be voluntary. But if theyre successful, it is not unthinkable that this could change. Each country developing a contact tracing app needs to decide what role the technology can play in their track and trace efforts.

Some apps may require people to check in with authorities to prove they are self-isolating. During his quarantine, Fisher says he was required to click on a link sent to him by the Singaporean authorities a couple of times a day. This would send back his location and confirm that people under quarantine were staying put.

An open letter from around 200 information security professionals in the UK called for safeguards to be placed on contact tracing apps. It is vital that, when we come out of the current crisis, we have not created a tool that enables data collection on the population, or on targeted sections of society, for surveillance, they wrote. In Taiwan, reports have emerged of people getting visits from the police when they have failed to report their location to authorities.

These approaches place greater importance on mass surveillance technologies. A Reuters report has found multiple surveillance technology companies, which produce the tools to hack into phones and monitor locations, have been touting their tools to governments as ways to fight the virus.

Israels spy agency had been tackling the location of the countrys infected until its Supreme Court banned the practice. The states choice to use its preventative security service for monitoring those who wish it no harm, without their consent, raises great difficulties and a suitable alternative must be found, the court ruled. The danger, privacy experts warn, is that authorities will be unwilling to give up the additional surveillance powers given to them by contact tracing apps especially if they are able to argue that the use of the technology helps keep people safe.

The mission creep has already started: politicians in Australia have been forced to bat away requests from police officials asking for any data created by its COVIDSafe app. In Canada, some coronavirus test results have been handed to the police.

Built-in legal protections are one way to avoid contact tracing apps being used to erode civil liberties. Alongside its app, Australia has published legislation that aims to protect the rights of individuals using the app. Elements of this legislation resemble a draft coronavirus safeguards bill published by Lilian Edwards, a Newcastle University Law School academic. Edwards legal protections state people should not be penalised for not having a phone, forgetting it when they go out, if the battery dies and people have the right to refuse to install tracing apps at any point in the future.

The decisions we make now, at a time of unprecedented political, economic and public health pressure, will have profound long term impacts. As with so much of our fight against coronavirus, when it comes to contact tracing apps we are flying blind. Until their use is widespread, we wont know how effective they are. And by then, it may be too late. If we allow the normalisation of mass surveillance in the name of public health it will be abused and we will regret it later, Gladstein says.

Matt Burgess is WIRED's deputy digital editor. He tweets from @mattburgess1

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Coronavirus contact tracing apps were meant to save us. They won't - Wired.co.uk

Chelsea Manning ordered released from prison, fined …

A judge on Thursday ordered that former Army intelligence analyst Chelsea Manning be released from prison, where she was being held in contempt of court for refusing to testify in front of a grand jury. Manning was also ordered to pay $256,000 in fines accrued during her detention. The order comes just a day after Manning's legal team said she attempted suicide at the Virginia detention center where she was incarcerated.Judge Anthony Trenga ruled that Manning's "appearance before the Grand Jury is no longer needed, in light of which her detention no longer serves any coercive purpose." Manning's release is not dependent on her paying the $256,000.Manning's legal team did not immediately respond to CBS News' request for comment. The U.S. Attorney's Office in the Eastern District of Virginia declined to comment.

Manning, who worked as an intelligence analyst in Iraq, was convicted in 2013 for leaking classified government and military documents to WikiLeaks and given a 35-year military sentence. President Obama commuted her sentence in 2017 before he left office.Two years later, Manning was jailed again in March 2019 for refusing to testify in front of a grand jury investigating WikiLeaks. She was released approximately two months later when the grand jury's term expired but then was jailed again a week later for refusing to comply with a second subpoena from the new grand jury.At the time, the judge said she could be incarcerated for up to 18 months, and that she'd be fined $500 per day for 30 days, and $1,000 per day after 60 days.Manning has repeatedly objected to the grand juries and said she was not willing to testify. In 2019, she told Judge Trenga in a letter: "I object to this grand jury ... as an effort to frighten journalists and publishers, who serve a crucial public good. I have had these values since I was a child, and I've had years of confinement to reflect on them. For much of that time, I depended for survival on my values, my decisions, and my conscience. I will not abandon them now."

Clare Hymes contributed reporting.

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Chelsea Manning ordered released from prison, fined ...

Chelsea Manning Tries to Kill Herself in Jail, Lawyers Say …

Chelsea Manning, the former Army intelligence analyst who was jailed last year for refusing to testify before a grand jury that is investigating WikiLeaks, has been hospitalized after she attempted suicide on Wednesday, according to her lawyers.

Ms. Manning, 32, is currently recovering, according to her lawyers, who did not say how Ms. Manning tried to kill herself while at a detention center in Alexandria, Va., where she has been held since May.

The Alexandria Sheriffs Office confirmed only that there was an incident involving Ms. Manning at 12:11 p.m. and said, It was handled appropriately by our professional staff and Ms. Manning is safe.

A statement from Ms. Mannings legal team said she was still scheduled to appear on Friday at a hearing before Judge Anthony Trenga of the United States District Court for the Eastern District of Virginia.

At the hearing, the judge is expected to rule on whether to end the civil contempt sanctions imposed on Ms. Manning after she refused to testify before a grand jury investigating the publication of thousands of American military and diplomatic files that she had provided to WikiLeaks in 2010.

Ms. Manning was also detained for two months starting in March 2019 for refusing to testify, then briefly released when that grand jurys term ended taking advantage of the window to announce that she had a book deal that she said would focus on her personal life. But prosecutors subpoenaed her again for testimony before a new grand jury, and she again refused to testify and was locked up again.

In spite of those sanctions which have so far included over a year of so-called coercive incarceration and nearly half a million dollars in threatened fines she remains unwavering in her refusal to participate in a secret grand jury process that she sees as highly susceptible to abuse, said the statement from Ms. Mannings legal team.

Ms. Manning has previously indicated that she will not betray her principles, even at risk of grave harm to herself, the statement said.

Joshua Stueve, a spokesman for the office of the United States Attorney in the Eastern District of Virginia, declined to comment.

A federal prosecutor had previously said that the Justice Department did not want to have Ms. Manning detained, but she had a legal obligation to testify before a grand jury when subpoenaed.

Ms. Manning has attempted suicide at least two previous times, both in 2016 once while in solitary confinement at Fort Leavenworth, Kan., which was itself a punishment for an earlier attempt to end her life that year.

Her actions today evidence the strength of her convictions, as well as the profound harm she continues to suffer as a result of her civil confinement, Ms. Mannings lawyers said in their statement on Wednesday.

The grand jury investigation is part of a long-running inquiry into WikiLeaks and its founder, Julian Assange, that dates to the Obama administration and which the Trump administration revived.

Ms. Manning said that when she appeared before the grand jury, prosecutors had asked her questions about WikiLeaks, but she refused to answer every question, saying it violated her constitutional rights.

In a letter last year to Judge Trenga, Ms. Manning described the investigation as an effort to frighten journalists and publishers, who serve a crucial public good.

Before her current incarceration, Ms. Manning served seven years in a military prison, including 11 months of solitary confinement, the statement said.

She was originally convicted in 2013 of providing more than 700,000 government files to WikiLeaks, exposing American military and diplomatic affairs around the world.

President Barack Obama intervened in her case in 2017, commuting all but four months of her 35-year sentence.

Last year, the Justice Department unsealed criminal charges against Mr. Assange, who had been holed up in the Ecuadorean embassy in London but was arrested. Prosecutors initially charged him with a narrow hacking conspiracy offense, for purportedly agreeing to try to help Ms. Manning crack a password that would have let her log onto a military computer system under a different user name, and cover her tracks.

But prosecutors later drastically expanded the case against Mr. Assange by bringing charges against him under the Espionage Act for soliciting, receiving and publishing classified information an unprecedented effort to deem such journalistic activities (a separate issue from the debate over whether Mr. Assange himself counts as a journalist) as crimes that raise novel First Amendment issues. Mr. Assange has been fighting extradition in a London court.

The criminal case against Mr. Assange does not involve his later actions in publishing Democratic emails, stolen by Russian hackers, during the 2016 presidential campaign.

Sandra E. Garcia and John Ismay contributed reporting.

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Assange should be furloughed from Belmarsh prison, says human rights org. Here’s a thought: He could stay with friends! – The Register

The son of British fashion designer Vivienne Westwood wants accused US government hacker Julian Assange "furloughed" from Belmarsh prison in southeast London, UK.

The apparently serious suggestion was made by human rights charity Humanade, of which Joseph Corr is a trustee.

Corr, Westwood's son by the late Sex Pistols manager Malcolm McLaren, told the British press that he, along with lawyer Clive Stafford-Smith, is "set to liaise with the UK government to 'furlough Julian Assange' from Belmarsh prison due to the serious threat on his life imposed by COVID-19."

"If Assange contracts COVID-19 and dies, the UK government will be accused of deliberately and methodically killing Julian Assange," Corr added in a canned statement.

A furlough which can mean a temporary leave of absence or a temporary layoff to cut costs is not new to the tech world, but many Brits have found themselves quickly swotting up after the UK government used it in the treasury's Coronavirus Job Retention Scheme. In the taxpayer-funded scheme, if staff can't work because of the nationwide coronavirus shutdown, businesses are given the option of sending them home and receiving a grant to cover 80 per cent of their salary up to a 2,500 gross monthly wage.

Furlough is also used to describe a situation where US prisoners are released for compassionate or medical reasons; in the UK eligibility for temporary release schemes are governed by the Ministry of Justice and such inmates who qualify (see guidance here) and do not have a tariff need "ministerial permission".

Earlier this week, Her Majesty's Prison and Probation Service introduced the End of Custody Temporary Release scheme (ECTR), although that's only for "risk-assessed prisoners, who are within two months of their release date". The MoJ said "pregnant or extremely medically vulnerable" types would be considered for Release on Temporary Licence on a case-by-case basis.

Humanade "believes Assange should be 'furloughed' somewhere outside of London, a hotbed for COVID-19, in one of the safe places that one of Julian's many friends would be happy to accommodate him, well away from London".

Lest anyone needs reminding, the last time old Jules was paroled to a mate's house, he promptly scarpered straight into the Ecuadorian Embassy in the UK capital, where he remained for most of the 2010s. Police, with the consent of the embassy, eventually dragged him out in 2019. He was later sentenced to a year in prison for jumping bail.

Moreover, Assange's previous attempts to get out of jail by using coronavirus as an excuse have already been dismissed by a judge and a legal system alike that are determined to treat the feisty WikiLeaker just like any other accused who's been remanded in custody.

Assange faces charges in the US of conspiracy to commit computer intrusion along with one-time US Army intelligence analyst Chelsea Manning. American authorities are seeking to have the Aussie extradited from London, though the COVID-19 pandemic shutdown has seemingly thrown the planned trial into chaos.

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Microsoft: This is how to protect your machine-learning applications – TechRepublic

Understanding failures and attacks can help us build safer AI applications.

Modern machine learning (ML) has become an important tool in a very short time. We're using ML models across our organisations, either rolling our own in R and Python, using tools like TensorFlow to learn and explore our data, or building on cloud- and container-hosted services like Azure's Cognitive Services. It's a technology that helps predict maintenance schedules, spots fraud and damaged parts, and parses our speech, responding in a flexible way.

SEE:Prescriptive analytics: An insider's guide (free PDF)(TechRepublic)

The models that drive our ML applications are incredibly complex, training neural networks on large data sets. But there's a big problem: they're hard to explain or understand. Why does a model parse a red blob with white text as a stop sign and not a soft drink advert? It's that complexity which hides the underlying risks that are baked into our models, and the possible attacks that can severely disrupt the business processes and services we're building using those very models.

It's easy to imagine an attack on a self-driving car that could make it ignore stop signs, simply by changing a few details on the sign, or a facial recognition system that would detect a pixelated bandanna as Brad Pitt. These adversarial attacks take advantage of the ML models, guiding them to respond in a way that's not how they're intended to operate, distorting the input data by changing the physical inputs.

Microsoft is thinking a lot about how to protect machine learning systems. They're key to its future -- from tools being built into Office, to its Azure cloud-scale services, and managing its own and your networks, even delivering security services through ML-powered tools like Azure Sentinel. With so much investment riding on its machine-learning services, it's no wonder that many of Microsoft's presentations at the RSA security conference focused on understanding the security issues with ML and on how to protect machine-learning systems.

Attacks on machine-learning systems need access to the models used, so you need to keep your models private. That goes for small models that might be helping run your production lines as much as the massive models that drive the likes of Google, Bing and Facebook. If I get access to your model, I can work out how to affect it, either looking for the right data to feed it that will poison the results, or finding a way past the model to get the results I want.

Much of this work has been published in a paper in conjunction with the Berkman Klein Center, on failure modes in machine learning. As the paper points out, a lot of work has been done in finding ways to attack machine learning, but not much on how to defend it. We need to build a credible set of defences around machine learning's neural networks, in much the same way as we protect our physical and virtual network infrastructures.

Attacks on ML systems are failures of the underlying models. They are responding in unexpected, and possibly detrimental ways. We need to understand what the failure modes of machine-learning systems are, and then understand how we can respond to those failures. The paper talks about two failure modes: intentional failures, where an attacker deliberately subverts a system, and unintentional failures, where there's an unsafe element in the ML model being used that appears correct but delivers bad outcomes.

By understanding the failure modes we can build threat models and apply them to our ML-based applications and services, and then respond to those threats and defend our new applications.

The paper suggests 11 different attack classifications, many of which get around our standard defence models. It's possible to compromise a machine-learning system without needing access to the underlying software and hardware, so standard authorisation techniques can't protect ML-based systems and we need to consider alternative approaches.

What are these attacks? The first, perturbation attacks, modify queries to change the response to one the attackers desire. That's matched by poisoning attacks, which achieve the same result by contaminating the training data. Machine-learning models often include important intellectual property, and some attacks like model inversion aim to extract that data. Similarly, a membership inference attack will try to determine whether specific data was in the initial training set. Closely related is the concept of model stealing, using queries to extract the model.

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Other attacks include reprogramming the system around the ML model, so that either results or inputs are changed. Closely related are adversarial attacks that change physical objects, adding duct tape to signs to confuse navigation or using specially printed bandanas to disrupt facial-recognition systems. Some attacks depend on the provider: a malicious provider can extract training data from customer systems. They can add backdoors to systems, or compromise models as they're downloaded.

While many of these attacks are new and targeted specifically at machine-learning systems, they are still computer systems and applications, and are vulnerable to existing exploits and techniques, allowing attackers to use familiar approaches to disrupt ML applications.

It's a long list of attack types, but understanding what's possible allows us to think about the threats our applications face. More importantly they provide an opportunity to think about defences and how we protect machine-learning systems: building better, more secure training sets, locking down ML platforms, and controlling access to inputs and outputs, working with trusted applications and services.

Attacks are not the only risk: we must be aware of unintended failures -- problems that come from the algorithms we use or from how we've designed and tested our ML systems. We need to understand how reinforcement learning systems behave, how systems respond in different environments, if there are natural adversarial effects, or how changing inputs can change results.

If we're to defend machine-learning applications, we need to ensure that they have been tested as fully as possible, in as many conditions as possible. The apocryphal stories of early machine-learning systems that identified trees instead of tanks, because all the training images were of tanks under trees, are a sign that these aren't new problems, and that we need to be careful about how we train, test, and deploy machine learning. We can only defend against intentional attacks if we know that we've protected ourselves and our systems from mistakes we've made. The old adage "test, test, and test again" is key to building secure and safe machine learning -- even when we're using pre-built models and service APIs.

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Machine-learning is a boon, but it still needs a human hand – Business Day

Advances in computer power, machine-learning and predictive algorithms are creating paradigm shifts in many industries. For example, when analgorithm outperformed six radiologistsin reading mammograms and accurately diagnosing breast cancer, this raised questions around the role of machine-learning in medicine and whether it will replace, or enhance, the work being done by doctors.

Similarly, when Googles AI software AlphaGo beat the worlds top Go master in what is described as humankinds most complicated board game, The New York Timesdeclared it isnt looking good for humanity when an algorithm can outperform a human in a highly complex task.

Both these examples point to narrow uses of artificial intelligence, specific types of machine-learning that are hugely effective. The medical example illustrates supervised learning, where a computer is programmed to solve a particular problem by looking for patterns. It is given labelled data sets, in this case X-rays with the diagnosis of presence or absence of breast cancer. When given a new X-ray, the computer applies an algorithm based on what it has learnt from all the previous X-rays to make a diagnosis. Unsupervised learning is a sort of self-optimisation where a computer has a set of rules, such as how to play Go, and through playing millions of games learns how to apply these rules and improve.

What is machine-learning?

Machine-learning is a phenomenal tool. To fully harness its potential it is essential to understand what machine-learning is (and isnt) and to demystify some of the hype and the fear around what it can and cant be used for. We have anthropomorphised computers; we speak about them in terms of intelligence and learning. But in essence, a machine computes it does not learn. Its algorithms are designed to mimic learning. In essence, these algorithms minimise the errors of a complicated function that maps inputs to outcomes and we interpret that as solving a problem, but the machine doesnt know what problem it is solving or that it is playing a game. The intelligence rests with the humans who design the algorithms and configure them for specific tasks.

Now, more than ever, we need intelligent and well-educated people who can apply these techniques in the correct context and interpret the results. When an algorithm fails, the consequences can be catastrophic. An obvious example is a fatal accident caused by aself-driving car. We need to build in fault tolerance. Data integrity is also an important issue what we put in is going to affect what we get out. Education is critical in making sure we get these elements right. And, of course, there are broader ethical issues to consider surrounding data collection, such as what data can be used, where it is sourced, and whether different data sets can be combined.

Machine-learning is particularly valuable in the financial sector. Many applications are already in use in banking, insurance and asset management. Financial institutions use pattern recognition successfully for fraud detection. It is also valuable for looking at trends in data sets and finding patterns that humans may not be able to identify directly, for example in profiling people who apply for credit. There are even robo-advisory applications for individual asset allocation. In financial modelling, machine-learning can be applied to pricing, calibration and hedging.

For example, valuing derivatives contracts depends on many complex factors and variables such as interest rates, exchange rates, equity values all of which fluctuate all the time. Financial mathematicians use models for this, but they are complicated and not easy to solve in a closed form. We may be able to build and apply a model to one contract, but banks have hundreds of contracts, and risk management and regulatory frameworks need to be updated all the time. Machine-learning, specifically deep learning and neural nets, provides a powerful shortcut. We can use classical numerical methods to produce financial models and then use them as labelled data sets as in the X-ray example. An algorithm can take this input to generate the output for multiple contracts.

Industries and organisations that are pulling ahead are figuring out where to replace standard methods and complex, time-consuming computations with machine-learning. They are also using it for more complex modelling approaches, adding further variables that cannot usually be factored into standard methodologies. The most obvious benefit is that it is faster machines can compute millions of times faster than humans. These techniques also have the potential to be far more accurate and allow us to make better-informed decisions.

But the human element is critical. The accuracy of potentially life-changing outcomes will depend on how we identify where we use these techniques, how we build the algorithms, how we choose and manage data and, finally, in how we interpret and act upon the results.

Prof McWalter is an applied mathematician who lectures computational finance at UCTs African Institute of Financial Markets and Risk Management. Prof Kienitz lectures at the University of Wuppertal and is an adjunct associate professor at UCT. His research interests include numerical methods in finance and machine-learning applied to financial problems and derivative instruments.

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Machine-learning is a boon, but it still needs a human hand - Business Day

Global Machine Learning As A Service (Mlaas) Market : Industry Analysis And Forecast (2020-2027) – MR Invasion

Global Machine Learning as a Service (MLaaS) Marketwas valued about US$ XX Bn in 2019 and is expected to grow at a CAGR of 41.7% over the forecast period, to reach US$ 11.3 Bn in 2027.

The report study has analyzed revenue impact of covid-19 pandemic on the sales revenue of market leaders, market followers and disrupters in the report and same is reflected in our analysis.

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Market Definition:

Machine learning as a service (MLaaS) is an array of services that offer ML tools as part of cloud computing services. MLaaS helps clients profit from machine learning without the cognate cost, time and risk of establishing an in-house internal machine learning team.The report study has analyzed revenue impact of covid-19 pandemic on the sales revenue of market leaders, market followers and disrupters in the report and same is reflected in our analysis.

Machine Learning Service Providers:

Global Machine Learning as a Service (MLaaS) Market

Market Dynamics:

The scope of the report includes a detailed study of global and regional markets for Global Machine Learning as a Service (MLaaS) Market with the analysis given with variations in the growth of the industry in each regions. Large and SMEs are focusing on customer experience management to keep a complete and robust relationship with their customers by using customer data. So, ML needs to be integrated into enterprise applications to control and make optimal use of this data. Retail enterprises are shifting their focus to customer buying patterns with the rising number of e-commerce websites and the digital revolution in the retail industry. This drives the need to track and manage the inventory movement of items, which can be done using MLaaS. The use of MLaaS by retail enterprises for inventory optimization and behavioral tracking is expected to have a positive impact on global market growth.Apart from this, the growing trend of digitization is driving the growth of the MLaaS market globally. Growth in adoption of cloud-based platforms is expected to positively impact the growth of the MLaaS market. However, a lack of qualified and skilled persons is believed to be the one of the challenges before the growth of the MLaaS market. Furthermore, increasing concern toward data privacy is anticipated to restrain the development of the global market.

Market Segmentation:

The report will provide an accurate prediction of the contribution of the various segments to the growth of the Machine Learning as a Service (MLaaS) Market size. Based on organization size, SMEs segment is expected to account for the largest XX% market share by 2027. SMEs businesses are also projected to adopt machine learning service. With the help of predictive analytics ML, algorithms not only give real-time data but also predict the future. Machine learning solutions are used by SME businesses for fine-tuning their supply chain by predicting the demand for a product and by suggesting the timing and quantity of supplies vital for satisfying the customers expectations.

Regional Analysis:

The report offers a brief analysis of the major regions in the MLaaS market, namely, Asia-Pacific, Europe, North America, South America, and the Middle East & Africa.North America play an important role in MLaaS market, with a market size of US$ XX Mn in 2019 and will be US$ XX Mn in 2027, with a CAGR of XX% followed by Europe. Most of the machine learning as service market companies are based in the U.S and are contributing significantly in the growth of the market. The Asia-Pacific has been growing with the highest growth rate because of rising investment, favorable government policies and growing awareness. In 2017, Google launched the Google Neural Machine Translation for 9 Indian languages which use ML and artificial neural network to upsurges the fluency as well as accuracy in their Google Translate.

Recent Development:

The MMR research study includes the profiles of leading companies operating in the Global Machine Learning as a Service (MLaas) Market. Companies in the global market are more focused on enhancing their product and service helps through various strategic approaches. The ML providers are competing by launching new product categories, with advanced subscription-based platforms. The companies have adopted the strategy of version up gradations, mergers and acquisitions, agreements, partnerships, and strategic collaborations with regional and global players to achieve high growth in the MLaaS market.

Such as, in April 2019, Microsoft developed a platform that uses machine teaching to help deep strengthening learning algorithms tackle real-world problems. Microsoft scientists and product inventors have pioneered a complementary approach called ML. This relies on people know how to break a problem into easier tasks and give ML models important clues about how to find a solution earlier.

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The objective of the report is to present a comprehensive analysis of the Global Machine Learning as a Service (MLaaS) Market including all the stakeholders of the industry. The past and current status of the industry with forecasted market size and trends are presented in the report with the analysis of complicated data in simple language. The report covers all the aspects of the industry with a dedicated study of key players that includes market leaders, followers and new entrants by region. PORTER, SVOR, PESTEL analysis with the potential impact of micro-economic factors by region on the market has been presented in the report. External as well as internal factors that are supposed to affect the business positively or negatively have been analyzed, which will give a clear futuristic view of the industry to the decision-makers.

The report also helps in understanding Global Machine Learning as a Service (MLaaS) Market dynamics, structure by analyzing the market segments and projects the Global Machine Learning as a Service (MLaaS) Market size. Clear representation of competitive analysis of key players by Application, price, financial position, Product portfolio, growth strategies, and regional presence in the Global Machine Learning as a Service (MLaaS) Market make the report investors guide.Scope of the Global Machine Learning as a Service (MLaaS) Market

Global Machine Learning as a Service (MLaaS) Market, By Component

Software ServicesGlobal Machine Learning as a Service (MLaaS) Market, By Organization Size

Large Enterprises SMEsGlobal Machine Learning as a Service (MLaaS) Market, By End-Use Industry

Aerospace & Defense IT & Telecom Energy & Utilities Public sector Manufacturing BFSI Healthcare Retail OthersGlobal Machine Learning as a Service (MLaaS) Market, By Application

Marketing & Advertising Fraud Detection & Risk Management Predictive analytics Augmented & Virtual reality Natural Language processing Computer vision Security & surveillance OthersGlobal Machine Learning as a Service (MLaaS) Market, By Region

Asia Pacific North America Europe Latin America Middle East AfricaKey players operating in Global Machine Learning as a Service (MLaaS) Market

Ersatz Labs, Inc. BigML Yottamine Analytics Hewlett Packard Amazon Web Services IBM Microsoft Sift Science, Inc. Google AT&T Fuzzy.ai SAS Institute Inc. FICO Predictron Labs Ltd.

MAJOR TOC OF THE REPORT

Chapter One: Machine Learning as a Service Market Overview

Chapter Two: Manufacturers Profiles

Chapter Three: Global Machine Learning as a Service Market Competition, by Players

Chapter Four: Global Machine Learning as a Service Market Size by Regions

Chapter Five: North America Machine Learning as a Service Revenue by Countries

Chapter Six: Europe Machine Learning as a Service Revenue by Countries

Chapter Seven: Asia-Pacific Machine Learning as a Service Revenue by Countries

Chapter Eight: South America Machine Learning as a Service Revenue by Countries

Chapter Nine: Middle East and Africa Revenue Machine Learning as a Service by Countries

Chapter Ten: Global Machine Learning as a Service Market Segment by Type

Chapter Eleven: Global Machine Learning as a Service Market Segment by Application

Chapter Twelve: Global Machine Learning as a Service Market Size Forecast (2019-2026)

Browse Full Report with Facts and Figures of Machine Learning as a Service Market Report at:https://www.maximizemarketresearch.com/market-report/global-machine-learning-as-a-service-mlaas-market/55511/

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Global Machine Learning As A Service (Mlaas) Market : Industry Analysis And Forecast (2020-2027) - MR Invasion

Beghou Consulting hires senior executive to expand advanced analytics and machine learning capabilities – The Trentonian

PRINCETON, N.J., April 29, 2020 /PRNewswire/ --Life sciences consulting firm Beghou Consulting recently hired industry veteran Janardhan Vellore to strengthen the firm's advanced analytics capabilities and technology solutions. Vellore will join as a vice president.

"Janardhan is an standout leader in the life sciences industry, especially in his innovative use of advanced analytics to get in front of emerging commercial challenges," said Beth Beghou, founder and managing director of Beghou Consulting. "As a result, he has become a trusted adviser to life sciences companies of all sizes. He will supplement our already strong advanced analytics team and play a key role in our growth efforts."

Vellore brings deep experience in end-to-end capabilities that shape and inform commercial strategy, including managed care and access, patient centricity, and marketing, digital and technology solutions. In addition, he'll bolster Beghou's offerings related to all aspects of commercial operations including forecasting, sales force design, segmentation, targeting and incentive compensation with particular expertise in launching new products.

Vellore previously held leadership roles at Bayer and Novartis Pharmaceuticals, where he led commercial analytics, market research and management science teams. He also served in a leadership role at Analytical Wizards, where he spearheaded growth of its advanced analytics practice area and commercialized cloud-based, big data platforms for the life sciences industry.

"Beghou consistently delivers premium value and top-notch results through subject matter expertise and seamless collaboration. Its superior client service and high-quality insights into commercial operations serve as key differentiators among its peers," said Vellore. "My experiences in-house at life sciences companies and working as a consultant have given me a unique perspective of a life sciences company's commercial challenges and opportunities. I'm excited to draw on those experiences to help more companies address their pressing commercial issues and drive greater success in the rapidly evolving marketplace."

Vellore earned an MBA from The Wharton School of the University of Pennsylvania, a master's degree in biomedical engineering from The University of Akron and a bachelor's degree from the Indian Institute of Technology, Madras. He is based in Princeton, New Jersey.

About Beghou ConsultingFounded in 1993, Beghou Consulting specializes in helping life sciences companies especially emerging pharma companies establish and manage commercial operations to better market and sell therapies. Deploying advanced analytics and proprietary technology, Beghou consultants have provided strategic counsel to the top pharmaceutical companies in the world, supporting some since infancy. Headquartered in Evanston, Ill., the firm has six offices and employs more than 150 professionals around the world. To learn more, visit http://www.beghouconsulting.com or follow us on Facebook and LinkedIn.

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Beghou Consulting hires senior executive to expand advanced analytics and machine learning capabilities - The Trentonian

Current research: Global Machine Learning Market is predicted to grow with demand and future opportunities – WhaTech Technology and Markets News

Machine learning the ability of computers to learn through experiences to improve their performance. Separate algorithms and human intervention are not required to train the computer. It merely learns from its past experiences and examples. In recent times, this market has gained utmost importance due to the increased availability of data and the need to process the data to obtain meaningful insights.

The Global Machine learning Market Report provides an extensive assessment of the global Machine learning market development, revenue, and profitability of the market. It also evaluates the scope, attractiveness, economy database, potential, and trends in the global Machine learning market.

The report mainly intends to assist market players, Machine learning business owners, researchers, students, and stakeholders with comprehensive market intelligence. The historic and current status of the global Machine learning market is deeply elucidated in the report.

The report offers authentic and reliable projections up to 2025 and predicts potential significant incidents to occur in the market in the near future. The report also describes changing market dynamics, contemporary trends, restraints, limitations, growth-boosting forces, technological advancements, uneven supply-demand proportions, unstable market structure, and uncertainties are analyzed in the market report as it could pose considerable impacts on the global Machine learning market structure and profitability.

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Rivalry scenario for the global Machine learning market, including business data of leading companies:

Google Inc., Microsoft, IBM Watson, Amazon, Baidu, Intel, Facebook, Apple Inc., and Uber.

The global Machine learning industry environment is heavily emphasized in the report as it holds the potential to impact the Machine learning market growth in a positive or negative manner.

The industry environment incorporates provincial trade regulations, international trade disputes, market entry barriers, as well as social, political, financial, and atmospheric circumstances, which could affect market growth momentum. The potential and current market opportunities, challenges, risks, obstacles, uncertainties, and threats are also highlighted in the report.

The report further elaborates on the key facets of the global Machine learning market which includes, competition, leading competitors, industry environment, and crucial segments in the market. An in-depth analysis of each facet has been exhaustively examined in the report to offer clients an inclusive conception of the global Machine learning market.

The report also provides thorough market analysis by investigating the market through adept analytical tools such as SWOT, Porter's Five Forces analysis that sheds light on the market threats, weaknesses, strengths, and various bargaining powers.

Expansive survey of Global Machine learning Market 2020

Insights into Machine learning market segments:

The global Machine learning market competition is also highlighted in the report with precise assessments based on the leading players in the global Machine learning market. The report analyses the manufacturing processes, production technologies, volume, plants, locations, effective production techniques, value chain, raw material, concentration rate, distribution networks, and global appearance.

Their strategic moves such as mergers, venture, amalgamations, partnerships, product launches, and brand promotions are also evaluated in the report.

Moreover, it offers profound analysis of efforts taken by leading market players to push their sales activities and capture maximum buyers. It explores their product research, innovations, and, technology adoptions, developments.

Their financial assessments are also illuminated in the global Machine learning market report with accurate evaluations of their revenue, gross margin, sales volume, production cost, cost structure, product prices, capital investments, market share, growth rates, and CAGR.

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Current research: Global Machine Learning Market is predicted to grow with demand and future opportunities - WhaTech Technology and Markets News

Is Machine Learning Model Management The Next Big Thing In 2020? – Analytics India Magazine

ML and its services are only going to extend their influence and push the boundaries to new realms of the technology revolution. However, deploying ML comes with great responsibility. Though efforts are being made to shed its black box reputation, it is crucial to establish trust in both in-house teams and stakeholders for a fairer deployment. Companies have started to take machine learning model management more seriously now. Recently, a machine learning company Comet.ml, based out of Seattle and founded in 2017, announced that they are making a $4.5 million investment to bring state-of-the-art meta-learning capabilities to the market.

The tools developed by Comet.ml enable data scientists to track, compare, monitor, and optimise model development. Their announcement of an additional $4.5 million investment from existing investors Trilogy Equity Partners and Two Sigma Ventures is aimed at boosting their plans to domesticate the use of machine learning model management techniques to more customers.

Since their product launch in 2018, Comet.ml has partnered with top companies like Google, General Electric, Boeing and Uber. This elite list of customers use comet.al services, which have enterprise-level toolkits, and are used to train models across multiple industries spanning autonomous vehicles, financial services, technology, bioinformatics, satellite imagery, fundamental physics research, and more.

Talking about this new announcement, one of the investors, Yuval Neeman of Trilogy Equity Partners, reminded that the professionals from the best companies in the world choose Comet and that the company is well-positioned to become the de-facto Machine Learning development platform.

This platform, says Neeman, allows customers to build ML models that bring significant business value.

According to a report presented by researchers at Google, there are several ML-specific risk factors to account for in system design, such as:

Debugging all these issues require round the clock monitoring of the models pipeline. For a company that implements ML solutions, it is challenging to manage in-house model mishaps.

If we take the example of Comet again, its platform provides a central place for the team to track their ML experiments and models, so that they can compare and share experiments, debug and take decisive actions on underperforming models with great ease.

Predictive early stopping is a meta-learning functionality not seen in any other experimentation platforms, and this can be achieved only by building on top of millions of public models. And this is where Comets enterprise products come in handy. The freedom of experimentation that these meta learning-based platforms offer is what any organisation would look up to. Almost all ML-based companies would love to have such tools in their arsenal.

Talking about saving the resources, Comet.ml in their press release, had stated that their platform led to the improvement of model training time by 30% irrespective of the underlying infrastructure, and stopped underperforming models automatically, which reduces cost and carbon footprint by 30%.

Irrespective of the underlying infrastructure, it stops underperforming models automatically, which reduces cost and carbon footprint by 30%.

The enterprise offering also includes Comets flagship visualisation engine, which allows users to visualise, explain, and debug model performance and predictions, and a state-of-the-art parameter optimisation engine.

When building any machine learning pipeline, data preparation requires operations like scraping, sampling, joining, and plenty of other approaches. These operations usually accumulate haphazardly and result in what the software engineers would like to call a pipeline jungle.

Now, add in the challenge of forgotten experimental code in the code archives. Things only get worse. The presence of such stale code can malfunction, and an algorithm that runs this malfunctioning code can crash stock markets and self-driving cars. The risks are just too high.

So far, we have seen the use of ML for data-driven solutions. Now the market is ripe for solutions that help those who have already deployed machine learning. It is only a matter of time before we see more companies setting up their meta-learning shops or partner with third-party vendors.

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Is Machine Learning Model Management The Next Big Thing In 2020? - Analytics India Magazine