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ARTIFICIAL INTELLIGENCE WILL IMPROVE MEDICAL TREATMENTS

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It will not imminently put medical experts out of work

FOUR years ago a woman in her early 30s was hit by a car in London. She needed emergency surgery to reduce the pressure on her brain. Her surgeon, Chris Mansi, remembers the operation going well. But she died, and Mr Mansi wanted to know why. He discovered that the problem had been a four-hour delay in getting her from the accident and emergency unit of the hospital where she was first brought, to the operating theatre in his own hospital. That, in turn, was the result of a delay in identifying, from medical scans of her head, that she had a large blood clot in her brain and was in need of immediate treatment. It is to try to avoid repetitions of this sort of delay that Mr Mansi has helped set up a firm called Viz.ai. The firm’s purpose is to use machine learning, a form of artificial intelligence (AI), to tell those patients who need urgent attention from those who may safely wait, by analysing scans of their brains made on admission.

That idea is one among myriad projects now under way with the aim of using machine learning to transform how doctors deal with patients. Though diverse in detail, these projects have a common aim. This is to get the right patient to the right doctor at the right time.

In Viz.ai’s case that is now happening. In February the firm received approval from regulators in the United States to sell its software for the detection, from brain scans, of strokes caused by a blockage in a large blood vessel. The technology is being introduced into hospitals in America’s “stroke belt”—the south-eastern part, in which strokes are unusually common. Erlanger Health System, in Tennessee, will turn on its Viz.ai system next week.

The potential benefits are great. As Tom Devlin, a stroke neurologist at Erlanger, observes, “We know we lose 2m brain cells every minute the clot is there.” Yet the two therapies that can transform outcomes—clot-busting drugs and an operation called a thrombectomy—are rarely used because, by the time a stroke is diagnosed and a surgical team assembled, too much of a patient’s brain has died. Viz.ai’s technology should improve outcomes by identifying urgent cases, alerting on-call specialists and sending them the scans directly.

The AIs have it

Another area ripe for AI’s assistance is oncology. In February 2017 Andre Esteva of Stanford University and his colleagues used a set of almost 130,000 images to train some artificial-intelligence software to classify skin lesions. So trained, and tested against the opinions of 21 qualified dermatologists, the software could identify both the most common type of skin cancer (keratinocyte carcinoma), and the deadliest type (malignant melanoma), as successfully as the professionals. That was impressive. But now, as described last month in a paper in the Annals of Oncology, there is an AI skin-cancer-detection system that can do better than most dermatologists. Holger Haenssle of the University of Heidelberg, in Germany, pitted an AI system against 58 dermatologists. The humans were able to identify 86.6% of skin cancers. The computer found 95%. It also misdiagnosed fewer benign moles as malignancies.

There has been progress in the detection of breast cancer, too. Last month Kheiron Medical Technologies, a firm in London, received news that a study it had commissioned had concluded that its software exceeded the officially required performance standard for radiologists screening for the disease. The firm says it will submit this study for publication when it has received European approval to use the AI—which it expects to happen soon.

This development looks important. Breast screening has saved many lives, but it leaves much to be desired. Overdiagnosis and overtreatment are common. Conversely, tumours are sometimes missed. In many countries such problems have led to scans being checked routinely by a second radiologist, which improves accuracy but adds to workloads. At a minimum Kheiron’s system looks useful for a second opinion. As it improves, it may be able to grade women according to their risks of breast cancer and decide the best time for their next mammogram.

Efforts to use AI to improve diagnosis are under way in other parts of medicine, too. In eye disease, DeepMind, a London-based subsidiary of Alphabet, Google’s parent company, has an AI that screens retinal scans for conditions such as glaucoma, diabetic retinopathy and age-related macular degeneration. The firm is also working on mammography.

Heart disease is yet another field of interest. Researchers at Oxford University have been developing AIs intended to interpret echocardiograms, which are ultrasonic scans of the heart. Cardiologists looking at these scans are searching for signs of heart disease, but can miss them 20% of the time. That means patients will be sent home and may then go on to have a heart attack. The AI, however, can detect changes invisible to the eye and improve the accuracy of diagnosis. Ultromics, a firm in Oxford, is trying to commercialise the technology and it could be rolled out later this year in Britain.

There are also efforts to detect cardiac arrhythmias, particularly atrial fibrillation, which increase the risk of heart failure and strokes. Researchers at Stanford University, led by Andrew Ng, have shown that AI software can identify arrhythmias from an electrocardiogram (ECG) better than an expert. The group has joined forces with a firm that makes portable ECG devices and is helping Apple with a study looking at whether arrhythmias can be detected in the heart-rate data picked up by its smart watches. Meanwhile, in Paris, a firm called Cardiologs is also trying to design an AI intended to read ECGs.

Seeing ahead

Eric Topol, a cardiologist and digital-medicine researcher at the Scripps Research Institute, in San Diego, says that doctors and algorithms are comparable in accuracy in some areas, but computers have the advantage of speed. This combination of traits, he reckons, will lead to higher accuracy and productivity in health care.

Artificial intelligence might also make medicine more specific, by being able to draw distinctions that elude human observers. It may be able to grade cancers or instances of cardiac disease according to their risks—thus, for example, distinguishing those prostate cancers that will kill quickly, and therefore need treatment, from those that will not, and can probably be left untreated.

What medical AI will not do—at least not for a long time—is make human experts redundant in the fields it invades. Machine-learning systems work on a narrow range of tasks and will need close supervision for years to come. They are “black boxes”, in that doctors do not know exactly how they reach their decisions. And they are inclined to become biased if insufficient care is paid to what they are learning from. They will, though, take much of the drudgery and error out of diagnosis. And they will also help make sure that patients, whether being screened for cancer or taken from the scene of a car accident, are treated in time to be saved.

 

 

 

 

 

Source:  The Economist

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INDUSTRY GROUPS SUE TO STOP CALIFORNIA NET NEUTRALITY LAW

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The broadband industry’s four main lobbying groups are joining forces to stop California’s state net neutrality law from taking effect.

On Wednesday, mobile-industry lobbyist the CTIA; USTelecom, which lobbies for the telecommunications industry; and the two lobbying organizations for the cable industry, NCTA and the American Cable Association, jointly filed a lawsuit in federal court against the state of California to block its new law.

Collectively, the groups represent almost every broadband provider in the country, including mobile operators like T-Mobile and Sprint as well as cable and telecom companies such as AT&T, Comcast, Charter, Verizon, Frontier and CenturyLink.

The suit, filed in the US District Court for the Eastern District of California, asserts that California’s net neutrality protections are illegal because they’re pre-empted by the Federal Communications Commission, which rolled back federal net neutrality rules earlier this year.

The trade groups’ suit calls California’s law a “classic example of unconstitutional state regulation,” and it’s asking the court to block the rules from taking effect on Jan. 1.

This is the second lawsuit filed against California since Gov. Jerry Brown signed the bill into law on Sunday. The Trump administration’s Department of Justice is also suing California and seeking a preliminary injunction to stop the law from going into effect.

At issue is whether California and other states have the right to pass net neutrality laws, which they claim are necessary to protect their citizens. As part of its roll-back of federal net neutrality rules in June, the FCC included a provision in its order that pre-empted states from creating their own regulations. The DOJ and the broadband industry argue it would be too complicated for internet service providers to follow different net neutrality rules in 50 states.

US Attorney General Jeff Sessions said California’s law violated the Commerce Clause of the US Constitution.

“Under the Constitution, states do not regulate interstate commerce — the federal government does,” he said in a statement.

But net neutrality supporters argue that since the FCC has refused to regulate these services and because the agency actually abdicated its authority for such regulation to the Federal Trade Commission, states can impose their own rules for services delivered in their states.

California style

California’s law, which prohibits internet service providers from slowing down or blocking access to websites or charging companies like Netflix extra to deliver their services faster, is based on Obama-era net neutrality protections that the Republican-led FCC rolled back earlier this year. But California’s law goes further, also outlawing some zero-rating offers, such as AT&T’s offer, which exempts its own streaming services from its wireless customers’ data caps. The law also applies the net neutrality rules to so-called “interconnection” deals between network operators, something the FCC’s 2015 rules didn’t explicitly do.

The legislation has been opposed by the broadband industry, which considers it too restrictive.

California is just one of several states looking to enact its own rules governing an open internet, after the FCC, under Pai, rolled back the Obama-era net neutrality rules in June. States such as Washington have pushed through a net neutrality law, while others are considering doing so.

Meanwhile, attorneys general of 22 states and the District of Columbia have already filed a brief to a US Appeals Court to reverse the FCC’s move. Companies like Firefox publisher Mozilla and trade groups also filed arguments.

Net neutrality, the principle that all internet traffic is treated fairly, has been one of the hottest topics of debate over the past several years. Consumers, tech companies and Democrats have pushed for stricter regulations prohibiting the prioritization of traffic, which resulted in the rules put in place by the previous FCC. But the Trump-era FCC has agreed with ISPs and Republicans who fear the regulations are onerous and hurt capital investment.

Taking It to Extremes: Mix insane situations — erupting volcanoes, nuclear meltdowns, 30-foot waves — with everyday tech. Here’s what happens.

 

 

 

Source: https://www.cnet.com/news/industry-groups-sue-to-stop-california-net-neutrality-law/

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WITH WATSON, TECHNICIANS ARE EMPOWERED TO MAKE THE RIGHT REPAIRS. THE FIRST TIME. ANYWHERE.

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Since 2014 Apple and IBM have been working with clients to usher in a new era of smart enterprise. The latest collaboration offers companies interested in artificial intelligence (AI) and machine learning (ML) a chance to be a part of the next big shift in enterprise mobile intelligence — by bringing the power of IBM’s Watson AI services and Apple’s machine learning framework, Core ML, to native iOS apps. IBM Watson Services for Core ML delivers native iOS apps that give developers access to vast amounts of data, both on their device and through the cloud. This means that users can access information and deep insights directly on their iPhone or iPad, even when it’s not connected to a network.

AI Everywhere with IBM Watson and Apple Core ML →

The Coca-Cola Company is always innovating across their technology landscape, and AI is a key focus area. When presented with the opportunity to explore the value of IBM Watson services and machine learning, they quickly engaged. With the Coca-Cola emphasis on quality, they are currently partnering with IBM, working on prototypes for how IBM Watson Services for Core ML may transform in-field capabilities. Initial functionalities being analyzed are visual recognition problem identification, cognitive diagnosis and augmented repair. Early exploration is promising, and Coca-Cola and IBM continue to determine next steps.

Challenges in the field

Field technicians are deployed to service and repair beverage dispensing machines at restaurants and venues around the world. Once on site, the tech must be able to diagnose and correct an enormous array of problems, relying ultimately on their personal expertise and experience. If the system is not one the technician is familiar with – an uncommon water filter, for example – then routine repairs can become frustrating and time-consuming. Adding to the challenges, many sites are in remote or rural locations with no data connectivity, meaning no access to support, and therefore limited ability to make repairs. In these cases, the tech would need to spend time searching through informational databases, product manuals, and might even need to call in or consult with a colleague or specialist – resulting in lost productivity and prolonged system downtime.

Enter Watson Services for Core ML

With the AI capabilities of IBM Watson and Core ML, relevant information is put directly into a tech’s hands the moment she needs it, allowing her to resolve the issue quickly. Coca-Cola used Watson Services for Core ML to build an app that leverages visual recognition and augmented reality to identify equipment issues, diagnose problems, and troubleshoot repairs.

Through the app, the tech can use their iPhone or iPad camera to diagnose system problems via a virtual overlay and guided instructions pulled from the cloud, with zero latency, and even in areas with no network connectivity. Watson Visual Recognition on the device helps the technician identify older or poorly differentiated systems, or unfamiliar parts, and pinpoint the problem right away. Then, Watson Discovery Service helps identify possible solutions for the specific systems and type of malfunction.

Using ARKit, an iOS framework with resources to help create augmented reality experiences for the iPhone and iPad, developers are able to integrate apps with augmented reality models that help the technician solve complex problems on less-familiar systems.

As the technician is working on the job, data is captured. That data is then sent to the cloud once the device is back on the network, so Watson can learn from the interaction and make the learning available to other technicians in near real-time. Using the guided repair system, the technician is empowered to solve the problem the first time, increasing productivity, and elevating customer service – all without needing to call for assistance or reschedule the repair.

With Watson, the technician can identify the problem and determine a solution in less time, no matter their location. Watson Services for Core ML provides developers with the tools to build apps that can give technicians in the field the right data, knowledge, and capabilities to do their best work. Coca-Cola is piloting the app with its field technicians now.

Beth Smith announces IBM Watson Services for Core ML at Think 2018

02:47

Watch the IBM Watson Services for Core ML announcement at Think 2018

Watson Technology being used:

  • Watson Visual Recognition
  • Watson Studio

With the help of Watson, field technicians can now:

  • Leverage the power of Apple Core ML to diagnose and correct an enormous array of problems on-site, with little or no network connectivity
  • Save time and increase productivity
  • Use cutting-edge augmented-reality from ARKit merged with Watson’s advanced visual recognition and detection technology to accurately find possible solutions and avoid lengthy delays
  • Learn from other technicians’ experience in near real time

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E. & J. GALLO WINERY IS WORKING WITH WATSON TO DEVELOP AN INTELLIGENT IRRIGATION SYSTEM THAT INCREASES THE QUALITY OF ITS GRAPES.

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Overview: The problem

Having farmed in California for more than 80 years, E. & J. Gallo Winery knows that no resource is more important than water, which is why water management has been a top priority for the company for decades.

Enter Watson

E. & J. Gallo Winery and Watson are now using weather reports and remote sensor data to deliver precise amounts of water to each vine, optimizing growth. The secret is located above the clouds, in a satellite looking down on the vineyard.

This allows the exact needed amount of water – based on highly targeted irrigation requirements – to be dispensed to each grapevine. As the weather changes, the irrigation methods react to ensure vines only receive water when needed.

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IBM Cloud is built to handle cognitive workloads, such as the immense amount of data from satellites and IoT sensors. It also uses The Weather Company data from 2.2 Billion locations, which is combined with E. & J. Gallo Winery’s other data sources to help perfect individualized irrigation plans.

A competitive advantage

Because of this tailored watering, E. & J. Gallo Winery reduced its water use by 25%, while also improving the quality of its wine.

Wineries, hospitals, businesses, educators and governments are now working with Watson. In 45 countries and 20 industries, Watson is helping people make sense of data so they can make better decisions while uncovering new ideas.

How a vineyard is working with Watson

  • Watson ingests data from weather, satellite, and sensor data on the IBM Cloud.
  • The data helps identify conditions in the vines and atmosphere.
  • After determining specific needs of the vines, given situational data, the system adapts irrigation levels.
  • The watering is tailored for precise areas to ripen grapes in sync and with improved quality.
  • With improved water efficiency, the largest family- owned winery in the world creates a superior product and reduces its water use by 25% in the process.

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