Chinese firms 'among first' to be told of Intel CPU flaws

By TheWAY - 1월 29, 2018

The US Government was not on the priority list of entities which Intel chose to notify when it became aware of flaws in its processors that were finally divulged to the world earlier this month.

The Wall Street Journal reported that a small group of customers, including Chinese technology firms, were among those who were notified about the flaws, dubbed Meltdown and Spectre, first.

Among the first to be told were Lenovo, Microsoft, Amazon, Alibaba and the UK branch of Arm.

Earlier this month, the head of the OpenBSD project, Theo de Raadt, told iTWire that the disclosure of the bug was handled "in an incredibly bad way".

"Only Tier-1 companies received advance information, and that is not responsible disclosure – it is selective disclosure," De Raadt said in response to queries. "Everyone below Tier-1 has just gotten screwed."

The two flaws were revealed in the first week of January and affect Intel processors made since 1995.

Meltdown removes the barrier between user applications and sensitive parts of the operating system. Spectre, which is also reportedly found in some AMD and ARM processors, can trick vulnerable applications into leaking the contents of their memory.

The flaw was initially discovered by a security researcher from Google's Project Zero team and reported to Intel which planned to make an announcement on 9 January — the day on which Microsoft was scheduled to announce its monthly updates — but advanced the date when news of the bugs leaked to the media.

The WSJ quoted Jake Williams, head of Rendition Infosec, as claiming that the Chinese Government was probably aware of the flaws as well. Williams, a former member of the NSA's elite Tailored Access Operations hacking group, said that such flaws would be of great interest to an agency which was collecting intelligence as they could be used to capture data from the cloud.

At the time of the disclosure, it also emerged that Intel chief executive Brian Krzanich had sold stock and options and netted US$25 million in late November 2017, well after Google had informed the company of flaws in its processors.

Krzanich avoided making any mention of the controversial sale in his keynote at the Consumer Electronics Show in Las Vegas later in the month.

The US Department of Homeland Security learned of the flaws from news reports after it became public knowledge, an official told the WSJ. The NSA also claimed it had not had advance knowledge of the flaws.

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Universal flu vaccine targets backbone of the virus

By TheWAY - 1월 25, 2018

Researchers at Georgia State University have developed a promising universal flu vaccine

etting an annual flu shot is never fun, and to add insult to injury, sometimes you'll get sick anyway. That's because it's hard to fight a virus that's always mutating, but new research out of Georgia State University is closing in on a universal vaccine. The study uses nanoparticles containing proteins that are common to all influenza A viruses.

If you look at a virus under a microscope, you'll see a ball with lots of tiny "stalks" poking out of it. These are proteins on its surface, and in the influenza virus it's known as hemagglutinin (HA). The proteins help the virus cling to the host's cells, particularly in the nose, throat and lungs, where it grows and spreads.

Flu vaccines contain inactivated virus particles made with the heads of these proteins. That alerts the immune system to what it should be watching out for, helping it to quickly fight off the virus if it ever detects those proteins again. And most of the time, that's an effective strategy.

Unfortunately, it's not foolproof. These protein heads are the parts of the virus that mutate the fastest, meaning there's a lot of variation between different species of influenza and even within a species year-to-year. The World Health Organization does its best to determine which strains are the most likely to take off in a given year, then pharmaceutical companies and research institutes around the world develop vaccines to target those. Of course, they don't always get it right.

"Vaccination is the most effective way to prevent deaths from influenza virus, but the virus changes very fast and you have to receive a new vaccination each year," says Bao-Zhong Wang, co-author of the study. "We're developing a universal influenza vaccine. You wouldn't need to change the vaccine type every year because it's universal and can protect against any influenza virus."

To provide wider protection, the researchers skipped the head of the proteins and aimed for the stalks instead. These sections don't mutate as drastically nor as often, making them a more effective target for the immune system to home in on. The team manufactured nanoparticles containing almost the entire protein that induces immune responses, and enclosed them in two layers to protect their function.

"This way you're protected against different viruses because all influenza viruses share this stalk domain," says Wang. "However, this stalk domain itself isn't stable, so we used a very special way to make this vaccine construct with the stalk domain and had success. We assembled this stalk domain into a protein nanoparticle as a vaccine. Once inside, the nanoparticle can protect this antigenic protein so it won't be degraded. Our immune cells have a good ability to take in this nanoparticle, so this nanoparticle is much, much better than a soluble protein to induce immune responses."

In mice tests, the researchers gave each animal two shots then exposed them to several flu strains, including H1N1, H3N2, H5N1 and H7N9. The vaccine worked to protect the mice from all strains, even at a lethal level of the virus, and significantly reduced the virus numbers found in the animals' lungs.

This study is just one of many universal flu vaccines currently in progress. Other strategies include DNA vaccines that target the virus on a genetic level, studying how the human immune system remembers which strains it's encountered before, and others that also target the protein stalks, including one that's currently undergoing human trials.

The research was published in the journal Nature Communications.

Source: Georgia State University

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Is Your Company’s Data Actually Valuable in the AI Era?

By TheWAY - 1월 23, 2018

Is Your Company’s Data Actually Valuable in the AI Era?

AI is coming. That is what we heard throughout 2017 and will likely continue to hear throughout this year. For established businesses that are not Google or Facebook, a natural question to ask is: What have we got that is going to allow us to survive this transition?

In our experience, when business leaders ask this with respect to AI, the answer they are given is “data.” This view is confirmed by the business press. There are hundreds of articles claiming that “data is the new oil” — by which they mean it is a fuel that will drive the AI economy.

If that is the case, then your company can consider itself lucky. You collected all this data, and then it turned out you were sitting on an oil reserve when AI happened to show up. But when you have that sort of luck, it is probably a good idea to ask “Are we really that lucky?”

The “data is oil” analogy does have some truth to it. Like internal combustion engines with oil, AI needs data to run. AI takes in raw data and converts it into something useful for decision making. Want to know the weather tomorrow? Let’s use data on past weather. Want to know yogurt sales next week? Let’s use data on past yogurt sales. AIs are prediction machines driven by data.

But does AI need your data? There is a tendency these days to see all data as potentially valuable for AI, but that isn’t really the case. Yes, data, like oil, is used day-to-day to operate your prediction machine. But the data you are sitting on now is likely not that data. Instead, the data you have now, which your company accumulated over time, is the type of data used to build the prediction machine — not operate it.

The data you have now is training data. You use that data as input to train an algorithm. And you use that algorithm to generate predictions to inform actions.

So, yes, that does mean your data is valuable. But it does not mean your business can survive the storm. Once your data is used to train a prediction machine, it is devalued. It is not useful anymore for that sort of prediction. And there are only so many predictions your data will be useful for. To continue the oil analogy, data can be burned. It is somewhat lost after use. Scientists know this. They spend years collecting data, but once it has produced research findings, it sits unused in a file drawer or on back-up disk. Your business may be sitting on an oil well, but it’s finite. It doesn’t guarantee you more in the AI economy than perhaps a more favorable liquidation value.

Even to the extent that your data could be valuable, your ability to capture that value may be limited. How many other sources of comparable data exist? If you are one of many yogurt vendors, then your database containing the past 10 years of yogurt sales and related data (price, temperature, sales of related products like ice cream) will have less market value than if you are the only owner of that type of data. In other words, just as with oil, the greater the number of other suppliers of your type of data, the less value you can capture from your training data. The value of your training data is further influenced by the value generated through enhanced prediction accuracy. Your training data is more valuable if enhanced prediction accuracy can increase yogurt sales by $100 million rather than only $10 million.

Moreover, the ongoing value of data usually comes from the actions you take in your day-to-day business — the new data you accrue each day. New data allows you to operate your prediction machine after it is trained. It also enables you to improve your prediction machine through learning. While 10 years of data on past yogurt sales is valuable for training an AI model to predict future yogurt sales, the actual predictions used to manage the supply chain require operational data on an ongoing basis. And this is the important point for today’s incumbent companies.

An AI startup that acquires a trove of data on past yogurt sales can train an AI model to predict future sales. It can’t actually use its model to make decisions unless the startup obtains ongoing operational data to learn from. Unlike startups, large enterprises generate operational data every day. That’s an asset. The more operations, the more data. Furthermore, the owner of the operation can actually make use of the prediction. It can use the prediction to enhance its future operation.

In the AI economy, the value of your accumulated data is limited to a one-time benefit from training your AI model. And the value of training data is, like oil or any other input, influenced by the overall supply — it’s less valuable when more people have it. In contrast, the value of your ongoing operational data is not limited to a one-time benefit, but rather provides a perpetual benefit for operating and further enhancing your prediction machine. So, despite all the talk about data being the new oil, your accumulated historical data isn’t the thing. However, it may be the thing that gets you to the thing. Its value for your future business prospects is low. But if you can find ways to generate a new, ongoing data stream that delivers a performance advantage in terms of your AI’s predictive power, that will give you sustainable leverage when AI arrives.

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Big Brother on wheels: Why your car company may know more about you than your spouse.

By TheWAY - 1월 23, 2018

Big Brother on wheels: Why your car company may know more about you than your spouse.

Most new vehicles monitor where the driver goes and how he or she drives. They have become sophisticated computers on wheels that offer even more access to our personal habits and behaviors than smartphones do. (AP Photo/Elaine Thompson, File)

DETROIT — Daniel Dunn was about to sign a lease for a Honda Fit last year when a detail buried in the lengthy agreement caught his eye.

Honda wanted to track the location of his vehicle, the contract stated, according to Dunn — a stipulation that struck the 69-year-old Temecula, Calif., retiree as a bit odd. But Dunn was eager to drive away in his new car and, despite initial hesitation, he signed the document, a decision with which he has since made peace.

“I don’t care if they know where I go,” said Dunn, who makes regular trips to the grocery store and a local yoga studio in his vehicle. “They’re probably thinking, ‘What a boring life this guy’s got.’ ”

Dunn may consider his everyday driving habits mundane, but auto and privacy experts suspect that big automakers like Honda see them as anything but. By monitoring his everyday movements, an automaker can vacuum up a massive amount of personal information about someone like Dunn, everything from how fast he drives and how hard he brakes to how much fuel his car uses and the entertainment he prefers. The company can determine where he shops, the weather on his street, how often he wears his seat belt, what he was doing moments before a wreck — even where he likes to eat and how much he weighs.

Though drivers may not realize it, tens of millions of American cars are being monitored like Dunn’s, experts say, and the number increases with nearly every new vehicle that is leased or sold.

The result is that carmakers have turned on a powerful spigot of precious personal data, often without owners’ knowledge, transforming the automobile from a machine that helps us travel to a sophisticated computer on wheels that offers even more access to our personal habits and behaviors than smartphones do.

“The thing that car manufacturers realize now is that they’re not only hardware companies anymore — they’re software companies,” said Lisa Joy Rosner, chief marketing officer of Otonomo, a company that sells connected-car data, sharing the profits with automakers. “The first space shuttle contained 500,000 lines of software code, but compare that to Ford’s projection that by 2020 their vehicles will contain 100 million lines of code. These vehicles are becoming turbocharged spaceships if you think of them from a purely horsepower perspective.”

Automakers say they collect customer data only with explicit permission, though that permission is often buried in lengthy service agreements. They argue that data is used to improve performance and enhance vehicle safety. The information that is gathered, they add, will soon be able to reduce traffic accidents and fatalities, saving tens of thousands of lives.

There are 78 million cars on the road with an embedded cyber connection, a feature that makes monitoring customers easier, according to ABI Research. By 2021, according to the technology research firm Gartner, 98 percent of new cars sold in the United States and in Europe will be connected, a feature that is being highlighted this week here at the North American International Auto Show in Detroit.

After being asked on multiple occasions what the company does with collected data, Natalie Kumaratne, a Honda spokeswoman, said that the company “cannot provide specifics at this time.” Kumaratne instead sent a copy of an owner’s manual for a Honda Clarity that notes that the vehicle is equipped with multiple monitoring systems that transmit data at a rate determined by Honda.

Connecting cars to computers is nothing new. Vehicles have relied on computerized systems since the 1960s, mostly in the form of diagnostic systems that remind drivers to check their engines and “event data recorders,” which capture accident data and are considered the “black boxes” of automobiles.

What’s changed in recent years is not only the volume and precision of that data but how it’s being extracted and connected to the Internet, according to Lauren Smith, who studies big data and cars as the policy counsel at the Future of Privacy Forum.

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“Before, devices that generate data would stay on the car, but there are new ways for that information to be communicated off the vehicle,” Smith said, referring to diagnostic services such as Verizon Hum, Zubie and Autobrain that connect cars to the Internet using a “key” or dongle that plugs into a vehicle. These services provide drivers and companies with everything from trip histories to maintenance issues.

Though the automotive industry still collects less personal information than the financial, health-care or education industries, experts say, it doesn’t take much to jeopardize customers’ privacy.

Some privacy experts believe that with enough data points about driver behavior, profiles as unique as fingerprints could be developed. But it’s location data, experts say, that already has the greatest potential to put customers at risk.

“Most people don’t realize how deeply ingrained their habits are and how where we park our car on a regular basis can tell someone many things about us,” Pam Dixon, executive director of the World Privacy Forum, said, noting that research shows that even aggregate data can be reinterpreted to track an individual’s habits. “There’s a load of anti-fraud companies and law enforcement agencies that would love to purchase this data, which can reveal our most intimate habits.”

Trips to homes or businesses reveal buying habits and relationships that could be valuable to corporations, government agencies or law enforcement. For example, regular visits to an HIV clinic can offer information about someone’s health.

But unlike information gathered by a hospital or a clinic, health data collected by a non-health provider isn’t covered by the federal privacy rule known as HIPAA, according to the National Institutes of Health.

In a 2014 letter to the Federal Trade Commission, automakers pledged to abide by a set of privacy policies that included not sharing information with third parties without owners’ consent.

They’ve tucked their warnings about data collection into a few lines of text in owner’s manuals or enticing lease and purchase agreements, and on their websites.

General Motors, which became one of the first automakers to start collecting customer data in real time with its OnStar system in 1996, said in an email that the company’s system “does not collect or use any personally identifiable customer data without a customer’s consent.”

“Before a customer even gives consent, we describe what kind of data is to be collected and how it will be used (mobile app, proactive alerts, etc.),” Dan Pierce, a GM spokesman, said. “If a customer declines, we do not collect any data from the vehicle.”
Karen Hampton, a Ford spokeswoman, replied to The Washington Post with a similar statement.

On a page outlining its customers’ privacy rights, Toyota notes that vehicle data is collected to improve safety, manage maintenance and analyze vehicle trends. The site also notes that, with permission, customer data may be shared with “companies affiliated with Toyota.”

Though people might be wary of their data being outsourced, Rosner said companies like Otonomo are focused on using customer data for the greater good — such as improving transportation, reducing emissions and saving lives with automatic crash detection.

Otonomo, which began in 2015 and calls itself the “first connected car data marketplace,” partners with major automakers that give Otonomo access to their raw driver data, the company said. Otonomo takes that data, analyzes it, “cleans it up,” and then sells the information to third parties, helping automakers commercialize their data, Rosner said.

What sort of third parties use Otonomo data? A parking app developer, for example, that wants to better understand a city’s traffic patterns, or a company that wants to use those patterns to chose the location of its next billboard or business.

“The automaker gets a revenue share on every piece of data that is consumed,” Rosner explained.

Though the pledge restricts automakers from selling data to an outside company without customers’ consent, experts have noted that the voluntary self-regulatory standard doesn’t stop them from using that data for their own benefit.

The law has been unable to keep up with rapid advancements in auto technology, according to Ryan Calo, an associate professor of law at the University of Washington who teaches courses on robotics law and policy.

“Ultimately, there’s no car privacy statute that car companies have to abide by,” he said. “Not only are automakers collecting a lot of data, they don’t have a particular regime that is regulating how they do it.”

Though the possibility of abuse exists, Calo and other experts say automakers have so far been “responsive” to concerns about data collection and privacy. While privacy scandals periodically erupt in Silicon Valley, automakers have sought to differentiate their business models by ensuring privacy, according to James Hodgson, a senior analyst at ABI Research.

“They want to sell cars and maintain a competitive advantage over the Googles and Apples of the world,” he said.

And yet, Calo said, by collecting massive amounts of data, car companies could be setting themselves up for the 21st century’s ultimate Faustian bargain. The more data a company collects, the more incentive the company has to monetize that data.

“Any company that has tons of data about consumers and can control the interaction with them is going to have the capability and incentive to try to use that information to the company’s advantage — and possibly to the detriment of consumers,” Calo said.

“It’s almost unavoidable,” he added.

source: https://www.washingtonpost.com/news/innovations/wp/2018/01/15/big-brother-on-wheels-why-your-car-company-may-know-more-about-you-than-your-spouse/?utm_term=.b88bb19af0ac

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