IoT: From concept to reality and risk

By TheWAY - 1월 31, 2018

IoT: From concept to reality and risk

The way we reconcile our security concerns with IoT’s inevitable integration will determine whether the revolution upgrades our lives or creates chaos

Remember when the Internet of Things was more concept than reality? Those days are long gone. Demand for IoT technologies has skyrocketed in the last year, with IHS estimating 20 billion connected devices globally in 2017.

And it’s not just the smart refrigerators and Fitbits and Bluetooth speakers we see at home. IoT technologies have permeated the enterprise as well, with sensors, analytic measurement tools, and VoIP phones appearing, sometimes without the blessing or involvement of IT and security leadership. The infiltration is not unlike what we experienced more than a decade ago when the smartphone initiated a Bring Your Own Device (BYOD) revolution.

Even as Business Insider predicts business spending on IoT solutions to hit $6 trillion by 2021, the rapid adoption of IoT has come, unfortunately, at the expense of security standards. In the first half of 2017, the number of IoT attacks increased by a staggering 280%. It’s clear that swift action is needed to address the gaping holes in IoT security that could devastate organizations and consumers alike. But with such new and rapidly evolving technology, where do we even begin?

An uphill battle

With billions of IoT devices projected to hit our networks over the next few years, we can’t stop every attack. After all, you can’t be perfect, and you can’t be everywhere. We’ll never be able to stop everything at the first layer, and that’s ok.

So, what do we do? We get even more proactive. We get even more diligent. We move as fast as circumstances allow. Remember the old NSA mantra: defense in depth.

As dry as it sounds, consistently reliable security procedures are a surefire way to prevent the majority of IoT attacks. Since IoT devices often look and feel like ordinary household objects, we may forget or even ignore the reality that they’re connected to our network. But they are, and they demand the same amount of scrutiny as traditional endpoints. It’s cliché, but it bears repeating: an ounce of prevention is worth a pound of cure.

Never take device security as a given. Manufacturers and legislators are not reliable defenders of your data. So, when an organization incorporates smart devices into its network, the same organization bears the responsibility of rolling them out with standardized policies and procedures. This means rigorous tests to ensure security and safety along with investing in scans that reveal code vulnerabilities. This type of procedure, with a well-executed patching process, will secure devices well before they hit the network.

Separation and visibility

If the first layer is process and procedure, the second is separation and visibility. Think of it as a closed-circuit TV: self-contained and accessible only to those with proper permissions.

We can imitate this for IoT via air gap, effectively isolating IoT systems from public networks. This can be a crucial part of a security solution for networks whose IoT systems serve critical roles. Hospitals, for example, may use network-based technologies to monitor patients and even administer medicine. In this case, an air gap would go a long way to ensure these devices are accessible by proper personnel only.

Another powerful way to increase IoT protection is to invest in visibility tools: software solutions that establish behavior baselines and then provide anomaly detection and an automated response. Firewall and antivirus technologies are highly over-leveraged in the current landscape. While they can help thwart brute force malware attacks, they often stop short of collecting information beyond the signatures of attacks they’re built to defend against.

To effectively keep pace with threats, we need more agile and responsive solutions that allow us to remain well-armed on the subtle fronts of user behavior and file movements. The visibility and insight this provides will allow for early anomaly detection. Moreover, it can provide IT teams with power and leverage over their virtual environments, helping them overcome the pestering fear of the unknown. With this knowledge and awareness, IoT can be more safely and effectively implemented.

Domestic awareness

Oddly enough, when evaluating IoT security in the enterprise, a good place to start is at home—not just your home, but every employee’s. Whether it’s a smart refrigerator, internet-connected toys or a tablet, you cannot throw a rock down a modern street today without hitting a home with some sort of smart-home network.

Knowing that, do you trust that every employee within your enterprise has secured their smart-home devices using the same level of scrutiny and vigilance that you expect of devices connected to your network? It’s an important question, considering that many of our smart devices connect not only in the office, but to our home networks as well. That being true, how will you ensure that employees take appropriate measures to secure IoT devices not only at work, but within the confines of their homes?

Every device added to your network is another potential access point for the ill-intentioned. As with all IT, risk is part of the package. But we need to be judicious about our allowance of that risk, and we need to understand how to mitigate unintended consequences.

Miracle or disaster?

IoT arrived in full force much earlier than anticipated. Today, we’ve suddenly been surrounded by devices that make the physical world more connected, more responsive and less secure. The way we reconcile our security concerns with IoT’s inevitable integration will determine whether the revolution upgrades our lives or creates chaos. How will you prevent the latter?

source: https://www.csoonline.com/article/3251426/internet-of-things/iot-from-concept-to-reality-and-risk.html

0 Comments

Merging AI and human minds could make the workforce smarter

By TheWAY - 1월 31, 2018

Merging AI and human minds could make the workforce smarter

Image Credit: Laurent T / Shutterstock

Given the promise of AI technology, it’s possible our world could one day mimic the storyline from Blade Runner. Last year, Elon Musk, founder and CEO of Tesla, stated that “robots will be able to do everything better than us,” implying even CEOs like himself may be in danger of job loss.
However, robot laws and regulations can address oversight and help both entrepreneurs and business associates reach their full potential. In fact, the rise of AI and bots could offer opportunities to adapt and improve human skills in various industries.

Estimates for the automation of most jobs vary, but the World Economic Forum’s Future of Jobs report suggests that automation will replace 7.1 million jobs and create 2 million new jobs by 2020. As we stand on the cusp of these tech disruptions, many people are asking if their professional, human skills are useless. The question begs to be computed: In a world of machine learning, will AI help manufacture a smarter, more skilled workforce?

The skills assessment phase

Smart machines can store, process, and remember information faster than any human ever could. IBM’s Watson can now strategize steps ahead in chess or research medical conditions to solve common problems. In the business world, automated machines are researching, organizing, and completing tasks within seconds. The AI of today helps us communicate and streamline operations more efficiently than ever before. Robots are able to solve quantitative, logically complex problems, but their speed does not necessarily equate to intelligence.
Since we’re now using deep learning robots and machines to complete repetitive tasks, we are beginning to see a shift in the way we learn and assess our own skills in the office. While businesses across the board will reshape their practices due to faster, more personable operations via robots and automation, these innovations will also force entrepreneurs and workers to reshape and adjust their talents.

The smart shift

As automated, skilled machines complete repetitive tasks, AI can reshape our skills. More free time from reallocating tedious tasks will allow us to polish our talents in areas that matter, like relationship-building with business associates and customers.
According to a 2016 McKinsey Quarterly article, the jobs that are least threatened by AI are those considered “knowledge work.” Activities like management, developing and training new talent, decision making, planning, or creative work are at lower risk for automation.
According to the PwC, 39 percent of CEOs are preparing for automation’s impact in their industries. Entrepreneurs and workers will have to keep up with their industry, and that means shifting their skills. Far from rendering human intelligence obsolete, AI advancements should make human emotions and cognitive skills more valuable.

As software companies and research institutions conceive more cognitive devices, humans may become smarter as they utilize faster approaches to learning and building skills. From a more scientific standpoint, psychologists have identified that as the volume of data and information balloons together with more sophisticated automated technologies, our brains need to work faster to keep up with more information, making us more intelligent. This theory is called the Flynn effect, and, along with better nutrition and public health, it explains why human intelligence increases with each new generation.
The Harvard Business Review points out that our definition of what it means to be “smart” will change as robots automate more jobs in the future. Instead, our capabilities for open-minded communication, self-awareness, and adaptability to new data will help us become more productive and successful workers. Emotional intelligence will be imperative in the differentiation between man and robot, helping companies excel as they connect with leaders, coworkers, and customers alike.

Merging of the minds

If it’s true that AI will become a major workplace player, as Musk and most experts predict, humans will want to harness the power of AI in a beneficial way. But how? As it turns out, there’s potential for merging the human and AI minds.

One way this could happen is if man and machine were to merge biological and digital intelligence in strategic ways. Computers can communicate at trillions of bits per second, compared to the 10 bits per second at which humans process information. To speed up human performance, communication, and problem-solving, Musk believes there is a need to merge with machines. This theory is already in practice: In March 2017, a company called Neuralink announced the development of high-bandwidth brain-machine interfaces.
The end product of this process would offer a new layer that would help humans access information more quickly and tap into artificially intelligent systems. As a result, people would become more educated and efficient in intelligent tasks, benefiting entrepreneurs, various workforce cohorts, and ultimately the whole world.

If you can’t beat AI, join it

The question isn’t whether computers will make us smarter, but in what ways we will become smarter and how machines will motivate us to improve. Of course, political, educational, and economic institutions will have to help reframe business opportunities for workers in the future. The business world will transform and combine both the best of humans and the best of AI.

But just because the future of the workplace is transforming quickly doesn’t mean we’re facing an ultimatum between innovation and unemployment. In the future, the best business mergers will be those made between man and machine.
Jake Croman is an entrepreneur, philanthropist, and college student who currently attends the University of Michigan.

Source: https://venturebeat.com/2018/01/29/merging-ai-and-human-minds-could-make-the-workforce-smarter/

0 Comments

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.

0 Comments

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

0 Comments

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.

0 Comments

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.

3:25

“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

0 Comments

6 digital health predictions for 2018

By TheWAY - 1월 22, 2018

6 digital health predictions for 2018

The IPO market won't open up in 2018, but expect plenty of M&A.

Artificial intelligence will be used in unsexy ways before it disrupts clinical workflows.

Tech companies will make big moves in the space.

Steve Kraus, Bessemer Venture Partners
Published 2:16 PM ET Sun, 14 Jan 2018CNBC.com

Sam Edwards | Caiaimage | Getty Images

Steve Kraus, one of the most active health-tech investors in the industry, gave his predictions for 2018, following the J.P. Morgan Healthcare Conference this week in San Francisco.

Don't expect IPOs

I think the public markets will remain closed in 2018 for health-tech companies. However, I do expect 2019 and the years beyond that to start to look more fruitful for initial public offerings, as well-funded start-ups like Health Catalyst, Flatiron Health, Welltok, Grand Rounds Health and others start to scale beyond $100 million in revenue and some of them start to reach positive EBITDA levels.

Healthy Returns

Investing In Health Care Innovation

March 28, 2018 | New York City, NY

LEARN MORE

Healthcare mergers and acquisitions will remain robust but will focus more around health-care service businesses — many of which may be tech-enabled — rather than pure health-care IT companies.

Start-ups need to do more

I'm bullish on so-called "full-stack" or vertically-integrated health-care companies continuing to thrive. And by that I mean, companies that provide all of the necessary services and support around conditions like diabetes or the palliative care experience.

These services are the ones that are getting real engagement from patients and they are able to go to risk-bearing entities with a pitch to take a population off their hands -- and provide better care more affordably. These solutions will also work better for the patient as they take care of their totality of their medical issue.

The truth about artificial intelligence in medicine

Few could have missed the hype around artificial intelligence and machine learning technologies in health care in 2017, specifically related to fields like radiology and pathology.

A vast array of companies are developing algorithms to improve clinical care, but these will take time to develop commercially. In my view, 2018 will be the year that these technologies are instead applied to the "guts" of healthcare, meaning such unsexy applications as improving workflow, allowing doctors to spend more time on clinical care and less time on paperwork or operations, improving the operational efficiency of hospitals, and so on.

Biology isn't just a tech problem

2017 was the year that Silicon Valley started to invest in computational biology. A lot of these venture dollars were poured into companies focused on improving the early discovery process, such as identifying targets and "hits" of those targets.

I'm betting that 2018 will be the year where these companies and investors realize that biology is not "programmable" or "codeable." The problem isn't that pharma and biotech are short of "hits" or drug candidates. What these companies really need is clinical-stage programs. If these start-ups really want to scale and actually get traction among the traditional industry, they will need to become real biotech and drug development companies. And this will be a different, much more capital-intensive bet than their original investors probably realized.

Big tech will make moves

Apple, Alphabet and Amazon will continue to push forward in health care in 2018. If these companies make a real big splash with some kind of acquisition, it could crack open the IPO markets sooner than any of us think.

The year of humble pie

I have seen far too many companies in recent months that have raised behemoth rounds at mammoth valuations return to the market with their tail between their legs -- finally open to valuation adjustments. These could still be potentially successful companies, but their investors were potentially mistaken that such companies could reach hyper-scale.

Steve Kraus is a partner at Bessemer Venture Partners.

source: https://www.cnbc.com/2018/01/14/bessemers-steve-kraus-makes-his-digital-health-predictions-for-2018.html?utm_source=ROA+Daily+%EA%B5%AC%EB%8F%85&utm_campaign=b5fe62ef62-EMAIL_CAMPAIGN_2018_01_17&utm_medium=email&utm_term=0_c04a15ea58-b5fe62ef62-77251531

0 Comments