Archiv der Kategorie: Data Analystics

Cybersecurity is one of the fastest-growing segments of the technology industry

Source: https://www.fool.com/investing/the-10-biggest-cybersecurity-stocks.aspx

The 10 Biggest Cybersecurity Stocks

When looking to invest in this high-growth tech industry, start with the biggest names on the cybersecurity block.

Cybersecurity is one of the fastest-growing segments of the technology industry. As more people around the globe connect to the internet and hundreds of millions of devices get connected to a network every year, the need to keep all of that data secure is on the rise.

In fact, according to research firm Global Market Insights, cybersecurity is expected to go from a $120 billion-a-year endeavor in 2017 to more than $300 billion in 2024, good for an average 12% annual growth rate. It’s no wonder, then, that so many businesses are getting in on the movement. Old tech titans like Microsoft (NASDAQ:MSFT), Cisco (NASDAQ:CSCO), and Oracle (NYSE:ORCL) all offer cybersecurity as part of their service suites. Other names are investing in the action, too. Old smartphone maker BlackBerry (NYSE:BB), for example, bought small cybersecurity outfit Cylance in early 2019 to further its transformation as a software company.

A silhouette of a person filled in with digital data, signifying artificial intelligence.

Image source: Getty Images.

As the world goes digital, managing new digital-first business operations and keeping information safe and secure will continue to evolve and grow in importance. For those wanting to invest in the cybersecurity industry, researching the biggest names in the business is a good place to get started (after brushing up on the basics here). Here are the 10 largest companies that make cybersecurity their primary concern based on market capitalization (the value of the company calculated by number of shares outstanding multiplied by price per share).

Company Market Capitalization as of July 2019 What the Company Does
1. Palo Alto Networks (NYSE:PANW) $21.3 billion A diversified provider of security solutions, with an increasing focus on cloud software
2. Splunk (NASDAQ:SPLK) $20.5 billion Big data analytics, including security orchestration and automated response
3. Check Point Software (NASDAQ:CHKP) $17.9 billion A diversified provider of security software and hardware
4. CrowdStrike (NASDAQ:CRWD) $17.5 billion Cloud-based endpoint security
5. Okta (NASDAQ:OKTA) $15.4 billion Cloud-based identity and privileged-access management software
6. Fortinet (NASDAQ:FTNT) $14.9 billion A diversified provider of security software and hardware
7. Symantec (NASDAQ:SYMC) $14.0 billion Largest security provider by revenue; owner of LifeLock and Norton Antivirus
8. Akamai Technologies (NASDAQ:AKAM) $13.6 billion Internet content delivery and security
9. Zscaler (NASDAQ:ZS) $10.4 billion Diversified provider of cloud-based security
10. F5 Networks (NASDAQ:FFIV) $8.7 billion Internet and application content delivery and security
Bonus: Proofpoint (NASDAQ:PFPT) $7.0 billion Employee communications and internet security

Data as of July 23, 2019. Data source: YCharts and company-specific investor relations.

Types of cybersecurity stocks

„Cybersecurity“ is the umbrella term, but there are different types of security firms tackling various problems in today’s connected age.

Broad-focus cybersecurity companies

For example, the larger outfits have been angling themselves to cover a wide range of needs, becoming one-stop security shops. Palo Alto Networks and Fortinet are two such companies, covering everything from firewalls (a network feature, sometimes a piece of hardware but more often software, that decides what data to let in and out) to artificial intelligence-based software that automates tasks and monitors an organization’s digital activity.

Endpoint security providers

These companies focus on securing remote devices connected to a network. The number of devices hooked up to the internet has been growing by the hundreds of millions every year, and that trend is expected to continue. Businesses are leading the charge, and everything from employee smartphones and tablets to assets in transit to connected machinery is in need of safekeeping. Endpoint protection software handles that specific need. Startup CrowdStrike, among others, is a specialist in this space.

Specialized security services

These niche companies include Okta, which provides privileged-access management — basically, only allowing users access to the sensitive data that they’re supposed to see. Then there’s security for the cloud, or computing and software that is offered remotely by way of a data center. Zscaler concerns itself with keeping cloud connections and data safe for businesses and organizations.

Regardless of the security need, digital-based operations and communications are on the rise across the board, which means all of the top cybersecurity companies are experiencing growth of some sort. That creates an opportunity for investors to cash in on the movement. Here is a breakdown of each of the top cybersecurity companies and how their stocks are valued.

The top 10 biggest cybersecurity stocks

1. Palo Alto Networks: The largest cybersecurity stock

Sitting atop the cybersecurity pure-play list is Palo Alto Networks. The company has built itself into the leader in the security space, offering a broad range of services for its customers from firewalls to automated threat response to cloud security. The largest player in the cybersecurity niche by market cap, Palo Alto has managed to outpace the industry’s average growth rate in spite of its size.

Part of the story behind Palo Alto’s growth is the company’s acquisition spree of smaller competitors. In May 2019, the company announced its intent to purchase two cloud-based cybersecurity outfits, one for $410 million and the other for a smaller undisclosed sum. Both were added to a new cloud security service segment called Prisma, aimed at continuously updating Palo Alto’s offerings as needs of customers evolve over time. CEO Nikesh Arora, a former executive at Alphabet’s (NASDAQ:GOOGL) (NASDAQ:GOOG) Google, has indicated that strategic acquisitions will continue to play an integral part in his company’s strategy to remain relevant.

The sums of money paid for acquisitions have been substantial (at least $1 billion spent since 2018), and they’re among the reasons Palo Alto is not yet a profitable business. However, when backing out one-time nonrecurring expenses and noncash items, the company still manages to post positive free cash flow (money left over after basic operating expenses and capital expenditures). In short, that means the company can afford its aggressive buying spree.

The free cash flow generation is important, because it gives the leader in pure-play network security the wiggle room it needs to invest heavily in cloud computing, AI, and other technology as customer needs change over time. Global cloud spending is expected to grow an average of about 16% a year through 2022, according to technology research group Gartner. Sitting at the intersection of two double-digit growth industries, that long-term trend should give Palo Alto Networks an enduring outlet to sustain double-digit sales growth and help it maintain its pole position within the world of cybersecurity.

2. Splunk: Big data and securing business operations

Splunk started out as a big data monitoring company. Its software suite allows organizations to analyze and make sense of information being generated from their digital systems, from websites to connected equipment to payment processing networks, among other things. If it’s an electronic system, it creates data; and if it creates data, Splunk can help monitor it and give customers the ability to make sense of trends and other behavior of digital systems. Incidentally, one of the primary use cases for the data parsing and analytics platform is cybersecurity.

To increase its capabilities in that department, Splunk has also embarked on an aggressive acquisition spree. As a result, the big data company is now a leader in the fast-growing security orchestration, automation, and response (SOAR) segment of the cybersecurity industry. SOAR utilizes artificial intelligence (a software system that mimics how the human brain works and learns and adapts to changing circumstances) to sift through information in real time, detect potential threats, and take action to keep things on lockdown. With data breaches a constant threat, the ability to automate aspects of the workload holds appeal for large organizations.

Despite its size, Splunk has still been growing quickly. The downside is that Splunk is spending lots of cash to foster further expansion, which keeps the company in the red. Specifically, research and development of new software capabilities and sales and marketing to acquire new customers are the biggest line items affecting the bottom line. However, much like Palo Alto Networks, Splunk is free cash flow positive; profits will be a bigger consideration later on as the company matures.

That’s because Splunk’s primary industry, big data analytics, should grow an average of 13% a year and surpass $274 billion in size by 2023 — according to researcher IDC. Along the way, Splunk will also benefit from the booming and fast-changing cybersecurity industry, making it one of the best plays on the trend. The company’s expertise in monitoring and making sense of large and complex sets of data particularly lends itself to keeping business information locked up, and its recent takeovers of smaller peers have helped bolster its position in network security. Splunk’s prospects and chances at continued industry leadership look especially good.

3. Check Point Software: Adjusting to a new technology

Check Point Software, as its name implies, offers software security along with hardware to keep business networks secure. Much like Palo Alto Networks, the company has a diversified mix of solutions covering on-premeses computer networks, cloud, and endpoint protection.

Though it’s one of the largest and oldest cybersecurity companies around (founded in 1993), Check Point has not been growing at the breakneck speed of some of its peers. Low-single-digit sales growth has been the norm for some time. The reason? New technologies like the cloud have made some of Check Point’s legacy services like hardware-based security less compelling. The company is trailing some of its competitors, so spending to update the business model for today’s security needs has been a top priority. It isn’t paying off yet, and Check Point’s sluggish pace could mean its younger peers will bypass it in the years ahead.

There is one thing that makes Check Point different from other companies on this list, though. As an older, well-established company, it does turn a profit. Thus, traditional valuation metrics (without the need to make adjustments for things like stock-based compensation, shares a company pays to employees as an extra perk) work for the stock. However, heavy spending to transform the business into a more relevant one for the times has the bottom line stuck in a rut. Until that changes, there’s little compelling reason to consider the stock.

Check Point has been working hard to update its offerings for more modern needs, but the sheer number of newer start-ups could mean this established cybersecurity business will continue to get disrupted. That’s not an enviable situation to be in, especially when the industry overall is growing by double digits.

4. CrowdStrike: The newest stock on the top-10 list

Endpoint security company CrowdStrike more than doubled in value after it had its IPO (sold shares to raise money, making it available to the general investing public for the first time) in June 2019. That easily puts the firm among the largest in the cybersecurity business by market cap.

The stock has years‘ worth of double-digit sales growth baked into it, but momentum could be on CrowdStrike’s side. Revenues more than doubled in 2018. The number of connected devices around the globe is increasing every year — by the hundreds of millions — which plays right into the hands of this security company and its endpoint-protection software suite. Since many of those devices are not tethered to an office or other physical location, CrowdStrike’s cloud computing-native system lends itself to this type of security particularly well.

Because it is cloud based, CrowdStrike also boasts the ability to make near-instant system updates when a threat is detected, and its software can learn and adapt from uploaded customer data. Paired with millions of new connections getting added to an internet-connected network every year, it adds up to lots of new customer sign-ups and expanding relationships with existing ones. Dollar-based net expansion (which measures how much money existing clients spend each year) has been over 100% for years, indicating customers spend more with CrowdStrike as time passes. It’s a powerful business model, one that CrowdStrike plans on putting to use in other security disciplines as it begins to expand beyond endpoint security. With the cloud and the number of endpoints increasing dramatically, it’s no wonder this stock is off to a hot start and looks like it has years‘ worth of growth left ahead of it.

5. Okta: Keeping data on a need-to-know basis

Another upstart security company, Okta has only been around since 2009, but the identity-protection specialist has been growing like a weed. The company ensures that employees and others with privileged access within an organization get connected to the apps and data they need — and keeps everyone else out. The number of digital systems and software being utilized by organizations continues to rise, increasing the complexity and difficulty in keeping systems secure from intruders. Thus, the need for Okta’s identity services has been booming.

In just a few years‘ time, Okta has become one of the largest cybersecurity pure plays around, with sales consistently growing north of 50% in the past. Management expects that trajectory will moderate to somewhere in the mid-30% range for the foreseeable future — still nothing to balk at. And that rate of expansion could be sustainable, too. According to the Global Market Insights cybersecurity report, identity, authentication, and access management services are expected to be an especially fast-growing subset of cybersecurity, with the potential for services to increase an average of 17% a year through 2024. At the forefront of the movement, Okta is primed to gobble up market share as identity and access management increases in importance.

Here’s the downside: Okta is not a profitable business as of this writing. The company is funneling cash into marketing and research to maximize its sales growth now. Profits will be a concern later. The good news, though, is that gross profit margin (the amount of money the company keeps after producing a service and then selling it but before paying other operating expenses) is on the rise as the company grows.

That bodes well for the future of this cybersecurity leader. Identity security/privileged data access rights is expected to be a high-growth segment of network security for the next few years, and Okta is a leader in the space.

6. Fortinet: Successfully bridging legacy security with the new

Another diversified provider of firewalls, cloud and endpoint security, and identity management, Fortinet took a hit amid worries that the trade war between the U.S. and China would dampen growth in the company’s important international markets — Asia and Europe specifically. Newer security upstarts have also disrupted some of Fortinet’s legacy offerings like hardware-based network security for on-premises protection. Economic and industry headwinds or not, though, this cybersecurity outfit is doing just fine.

Revenues and adjusted earnings were up 20% and 77%, respectively, in 2018. Fortinet has been adding dozens of new deals worth more than $1 million every quarter, winning customers over with its new and improved software suite aimed at keeping all parts of an organization safe. Although less aggressive in its acquisition strategy than Palo Alto Networks or Splunk, Fortinet continues to invest heavily in updating its offerings to keep its customers secure. The cloud has been an area of focus, as well as increasing the number of subscription-based software deals. The investments in new technology have been paying off and yielding results for shareholders, even as other legacy cybersecurity companies have been failing to make the cut.

As a result of its less aggressive nature, Fortinet also runs a profitable business where some of its competitors don’t — and the bottom line has been rising faster than sales as the company’s investments have started to yield results. Ample cash means this security business can continue to invest in its new high-octane segments like cloud, endpoint, and identity security, which bodes well for it being able to maintain its two-figure top-line growth rate for some time even as legacy lines of business fade. With a well-established presence in the industry and a successful business update strategy well underway and paying off, Fortinet is one of the best cybersecurity stocks around.

7. Symantec: The biggest cybersecurity company by revenue

Symantec is the world leader in cybersecurity services when using sales figures as the metric. With nearly $5 billion in revenue in the last year, it is nearly double the size of its younger peers like Palo Alto Networks. Yet despite Symantec’s leadership, its market cap lags. One of the oldest network security players around and owner of recognizable software names like LifeLock and Norton Antivirus, Symantec has had to deal with disruption and shifting technology that have left growth near nonexistent and profitability underwhelming.

Though Symantec has been updating its operations — it recently announced a new comprehensive cloud-based security suite covering everything from email to application login protection — results have been sluggish. Fiscal 2019 sales fell 2%. The company’s legacy operations are holding it back, and bloated operating expenses have meant paltry bottom-line earnings. Not exactly what investors should be looking for from the leader of a high-flying industry.

There could be hope of a rebound, though, as Symantec continues to work through its transition. Chipmaker Broadcom (NASDAQ:AVGO) thought there was value in Symantec and was reportedly interested in acquiring the old security company to add it to its growing software division. However, negotiations fell through, and Symantec will have to go it alone for now. Until the company can demonstrate a strategy that can gain some traction in the growing world of cybersecurity, Symantec will continue to struggle in the wake of younger and more nimble peers that started investing earlier in the shifting landscape.

8. Akamai: Guarding the security of the internet itself

The next security outfit on the list handles a different piece of the industry than any of the others covered thus far. Akamai (NASDAQ:AKAM) helps deliver and secure web content as it travels from its source to the end user, from live and streaming video to traditional web page text and pictures. The internet’s continual expansion has been a boon for Akamai, which has launched new services to cover new web applications (like video streaming) and new mobile device types to keep the internet connection to them secure.

Akamai’s traditional web business is a low- to mid-single-digit growth story, but its newer cloud security services have been growing well into the double digits. New services are still a small fraction of the whole, but they are a high-margin endeavor. Akamai’s bottom line has been getting a big double-digit boost as Akamai’s investment and spending on new web delivery applications subside and past spending starts to yield results.

Akamai has grown into one of the internet’s primary content delivery platforms, responsible for handling as much as a third of global web traffic. As such, this company will be slower moving than other security businesses, but Akamai still has growth prospects ahead of it. Internet infrastructure company Cisco expects web traffic — led by video content — to grow an average of 26% a year through 2022. That means Akamai’s newer business should continue to move the needle for some time; plus the overall operation is solidly in profitable territory. In short, the leading internet content delivery and security company should be a slow-and-steady play for the foreseeable future.

9. Zscaler: Another investment in the cloud

Back to small but up-and-coming cybersecurity. Zscaler has its sights set on securing cloud computing and thus built itself from the ground up as a cloud-only software suite. The world is going mobile, and so are business operations. With fewer centralized locations and more remotely connected devices popping up, Zscaler helps keep newer business networks safe for its customers and their employees.

With a business model similar to those of CrowdStrike and Okta, Zscaler plays in a new multibillion-dollar industry that will only continue to grow larger, and the company has been frank in saying it is all about maximizing growth right now. And no wonder, as Gartner says in its cloud research that annual spending will nearly double from 2018 to 2022 to more than $330 billion a year. Sales at Zscaler have been growing north of 60% year over year for some time, but what’s a few hundred million in annual sales when the whole market is worth hundreds of billions? The downside is that in spite of massive growth and a rosy outlook for the good times to continue, operating losses are still substantial. With Zscaler all about nurturing sales as fast as possible, the red ink is unlikely to disappear anytime soon.

Much like its start-up peers, though, Zscaler takes those losses by design as it keeps its foot on the gas. Gross profit margin was an enviable 81% at last report, one of the best in the industry. With profit potential like that in a fast-expanding cloud computing sandbox, it makes sense Zscaler is all about growth now and profit later. With the world going mobile, this security stock looks like an especially promising one in the years ahead as it takes advantage of its early cloud-based security lead.

10. F5 Networks: Lagging behind the cybersecurity growth average

F5 Networks provides hardware and software solutions that help companies keep their applications and app delivery secure. Similar to Akamai, the company’s legacy business isn’t exactly lighting the world on fire. However, newer services, particularly those aimed at cloud computing-based apps, are on a tear. To that end, F5 recently acquired app optimization and security peer NGINX for $670 million.

It’s a sizable sum but likely a prudent move for F5. The company has been reporting low-single-digit revenue growth the last few years — nearly all of which has been driven by big expansion in its software service segment. While the top line has been sluggish, the upside is that new software and security offerings are a much more profitable concern. As a result, earnings are up nearly 40% over the last trailing three-year stretch.

During its transition phase to more modern app security and delivery, F5’s stock has taken a beating. There’s worry that the transition will continue to be a bumpy one, thus making this stock among the cheapest in the cybersecurity industry. However, though the low valuation reflects the fact that F5 has fallen behind the curve in the digital age, F5 is an inexpensive play on digital security and delivery. With internet traffic and content delivery still a slow-and-steady endeavor, F5 can continue to thrive — albeit at a much slower rate than elsewhere in cybersecurity.

Bonus. Proofpoint: An up-and-coming communications security specialist

One of the smaller outfits in the security space, Proofpoint is worth a mention as a bonus number 11 on the top-10 list. The company specifically helps organizations keep their employees safe. Email attacks are a key pain point for many businesses, and securing communications in that department — as well as on social media, cloud applications, and mobile devices — is a specialty at Proofpoint.

Though a niche offering within the greater cybersecurity industry, Proofpoint is expanding fast. After the company grew 38% in 2018, management forecasted full-year 2019 revenue to be up at least another 22%. However, as with its high-powered sales-oriented peers, the company does run up big losses. As with many other cybersecurity plays we’ve been discussing, though, that’s due to Proofpoint reinvesting in itself to foster more growth.

Nevertheless, when we adjust the bottom line for one-time items and other noncash expenses, Proofpoint is free cash flow positive, a metric that has been steadily on the rise. That should help Proofpoint keep up its double-digit growth trajectory as employee access points via remote computers, smartphones, and other devices continue to boom in the States and especially overseas. It’s a much smaller business than the top 10 companies are, but this cybersecurity concern still offers a compelling growth story worth keeping an eye on as it keeps communications safe and secure.

Proofpoint will also likely see long-term benefit from the explosion in devices hooked up to a network in the years ahead. The workforce’s increasing mobility means keeping employee communications on lockdown will be an increasingly complex problem, one that this small security company can help solve.

An illustrated shield displayed on top of a wall of digital data.

Image source: Getty Images.

Choosing the right cybersecurity stock to invest in

Taking a high-level look at the biggest companies in the cybersecurity market is only the start to choosing an investment. Some of the stocks are buys, others not so much. As the industry is still in high-growth mode and adapting fast to technological developments, investors would be best off picking the companies posting the fastest revenue expansion rates and those that carry the highest gross profit margins. Click here for a discussion on the top cybersecurity stocks and an introduction on how to pick the best companies in the industry.

Before investing, though, it’s important to remember a few things. Though cybersecurity is one of the fastest-expanding industries around, with high growth expectations comes a high level of volatility. Stock prices can run higher very quickly — and reverse course just as fast. Only investors who have a long-term perspective (no less than a few years) and the ability to purchase a position over time (buying a few shares at a time on a set schedule, like monthly, quarterly, or whenever the stock dips in price by at least double digits) should consider buying.

For those with the time to wait, though, investing in cybersecurity should be a profitable endeavor. In a decade’s time, this top-10 list will no doubt look very different, but a few of these names will still be around and will likely be much larger than they are today.

 

Werbeanzeigen

Steve Rymell Head of Technology, Airbus CyberSecurity answers What Should Frighten us about AI-Based Malware?

Of all the cybersecurity industry’s problems, one of the most striking is the way attackers are often able to stay one step ahead of defenders without working terribly hard. It’s an issue whose root causes are mostly technical: the prime example are software vulnerabilities which cyber-criminals have a habit of finding out about before vendors and their customers, leading to the almost undefendable zero-day phenomenon which has propelled many famous cyber-attacks.

A second is that organizations struggling with the complexity of unfamiliar and new technologies make mistakes, inadvertently leaving vulnerable ports and services exposed. Starkest of all, perhaps, is the way techniques, tools, and infrastructure set up to help organizations defend themselves (Shodan, for example but also numerous pen-test tools) are now just as likely to be turned against businesses by attackers who tear into networks with the aggression of red teams gone rogue.

Add to this the polymorphic nature of modern malware, and attackers can appear so conceptually unstoppable that it’s no wonder security vendors increasingly emphasize the need not to block attacks but instead respond to them as quickly as possible.

The AI fightback
Some years back, a list of mostly US-based start-ups started a bit of a counter-attack against the doom and gloom with a brave new idea – AI machine learning (ML) security powered by algorithms. In an age of big data, this makes complete sense and the idea has since been taken up by all manner of systems used to for anti-spam, malware detection, threat analysis and intelligence, and Security Operations Centre (SoC) automation where it has been proposed to help patch skills shortages.

I’d rate these as useful advances, but there’s no getting away from the controversial nature of the theory, which has been branded by some as the ultimate example of technology as a ‘black box’ nobody really understands. How do we know that machine learning is able to detect new and unknown types of attack that conventional systems fail to spot? In some cases, it could be because the product brochure says so.

Then the even bigger gotcha hits you – what’s stopping attackers from outfoxing defensive ML with even better ML of their own? If this were possible, even some of the time, the industry would find itself back at square one.

This is pure speculation, of course, because to date nobody has detected AI being used in a cyber-attack, which is why our understanding of how it might work remains largely based around academic research such as IBM’s proof-of-concept DeepLocker malware project.

What might malicious ML look like?
It would be unwise to ignore the potential for trouble. One of the biggest hurdles faced by attackers is quickly understanding what works, for example when sending spam, phishing and, increasingly, political disinformation.

It’s not hard to imagine that big data techniques allied to ML could hugely improve the efficiency of these threats by analyzing how targets react to and share them in real time. This implies the possibility that such campaigns might one day evolve in a matter of hours or minutes; a timescale defender would struggle to counter using today’s technologies.

A second scenario is one that defenders would even see: that cyber-criminals might simulate the defenses of a target using their own ML to gauge the success of different attacks (a technique already routinely used to evade anti-virus). Once again, this exploits the advantage that attackers always have sight of the target, while defenders must rely on good guesses.

Or perhaps ML could simply be used to crank out vast quantities of new and unique malware than is possible today. Whichever of these approaches is taken – and this is only a sample of the possibilities – it jumps out at you how awkward it would be to defend against even relatively simple ML-based attacks. About the only consolation is that if ML-based AI really is a black box that nobody understands then, logically, the attackers won’t understand it either and will waste time experimenting.

Unintended consequences
If we should fear anything it’s precisely this black box effect. There are two parts to this, the biggest of which is the potential for ML-based malware to cause something unintended to happen, especially when targeting critical infrastructure.

This phenomenon has already come to pass with non-AI malware – Stuxnet in 2010 and NotPetya in 2017 are the obvious examples – both of which infected thousands of organizations not on their original target list after unexpectedly ‘escaping’ into the wild.

When it comes to powerful malware exploiting multiple zero days there’s no such thing as a reliably contained attack. Once released, this kind of malware remains pathogenically dangerous until every system it can infect is patched or taken offline, which might be years or decades down the line.

Another anxiety is that because the expertise to understand ML is still thin on the ground, there’s a danger that engineers could come to rely on it without fully understanding its limitations, both for defense and by over-estimating its usefulness in attack. The mistake, then, might be that too many over-invest in it based on marketing promises that end up consuming resources better deployed elsewhere.  Once a more realistic assessment takes hold, ML could end up as just another tool that is good at solving certain very specific problems.

Conclusion
My contradictory-sounding conclusion is that perhaps ML and AI makes no fundamental difference at all. It’s just another stop on a journey computer security has been making since the beginning of digital time. The problem is overcoming our preconceptions about what it is and what it means. Chiefly, we must overcome the tendency to think of ML and AI as mysteriously ‘other’ because we don’t understand it and therefore find it difficult to process the concept of machines making complex decisions.

It’s not as if attackers aren’t breaching networks already with today’s pre-ML technology or that well-prepared defenders aren’t regularly stopping them using the same technology. What AI reminds us is that the real difference is how organizations are defended, not whether they or their attackers use ML and AI or not. That has always been what separates survivors from victims. Cybersecurity remains a working demonstration of how the devil takes the hindmost.

Source: https://www.infosecurity-magazine.com/opinions/frighten-ai-malware-1/

45 Techniques Used by Data Scientists

These techniques cover most of what data scientists and related practitioners are using in their daily activities, whether they use solutions offered by a vendor, or whether they design proprietary tools. When you click on any of the 45 links below, you will find a selection of articles related to the entry in question. Most of these articles are hard to find with a Google search, so in some ways this gives you access to the hidden literature on data science, machine learning, and statistical science. Many of these articles are fundamental to understanding the technique in question, and come with further references and source code.

Starred techniques (marked with a *) belong to what I call deep data science, a branch of data science that has little if any overlap with closely related fields such as machine learning, computer science, operations research, mathematics, or statistics. Even classical machine learning and statistical techniques such as clustering, density estimation,  or tests of hypotheses, have model-free, data-driven, robust versions designed for automated processing (as in machine-to-machine communications), and thus also belong to deep data science. However, these techniques are not starred here, as the standard versions of these techniques are more well known (and unfortunately more used) than the deep data science equivalent.

To learn more about deep data science,  click here. Note that unlike deep learning, deep data science is not the intersection of data science and artificial intelligence; however, the analogy between deep data science and deep learning is not completely meaningless, in the sense that both deal with automation.

Also, to discover in which contexts and applications the 40 techniques below are used, I invite you to read the following articles:

Finally, when using a technique, you need to test its performance. Read this article about 11 Important Model Evaluation Techniques Everyone Should Know.

The 40 data science techniques

  1. Linear Regression
  2. Logistic Regression
  3. Jackknife Regression *
  4. Density Estimation
  5. Confidence Interval
  6. Test of Hypotheses
  7. Pattern Recognition
  8. Clustering – (aka Unsupervised Learning)
  9. Supervised Learning
  10. Time Series
  11. Decision Trees
  12. Random Numbers
  13. Monte-Carlo Simulation
  14. Bayesian Statistics
  15. Naive Bayes
  16. Principal Component Analysis – (PCA)
  17. Ensembles
  18. Neural Networks
  19. Support Vector Machine – (SVM)
  20. Nearest Neighbors – (k-NN)
  21. Feature Selection – (aka Variable Reduction)
  22. Indexation / Cataloguing *
  23. (Geo-) Spatial Modeling
  24. Recommendation Engine *
  25. Search Engine *
  26. Attribution Modeling *
  27. Collaborative Filtering *
  28. Rule System
  29. Linkage Analysis
  30. Association Rules
  31. Scoring Engine
  32. Segmentation
  33. Predictive Modeling
  34. Graphs
  35. Deep Learning
  36. Game Theory
  37. Imputation
  38. Survival Analysis
  39. Arbitrage
  40. Lift Modeling
  41. Yield Optimization
  42. Cross-Validation
  43. Model Fitting
  44. Relevancy Algorithm *
  45. Experimental Design

Source: https://www.datasciencecentral.com/profiles/blogs/40-techniques-used-by-data-scientists

Tim Cook: The Genius Who Took Apple to the Next Level

 

 

Excerpted from Tim Cook: The Genius Who Took Apple to the Next Level

 

They knew that they had to respond immediately. The writ would dominate the next day’s news, and Apple had to have a response. “Tim knew that this was a massive decision on his part,” Sewell said. It was a big moment, “a bet-the-company kind of decision.” Cook and the team stayed up all night—a straight 16 hours—working on their response. Cook already knew his position—Apple would refuse—but he wanted to know all the angles: What was Apple’s legal position? What was its legal obligation? Was this the right response? How should it sound? How should it read? What was the right tone?

iOS 8 added much stronger encryption than had been seen before in smartphones. It encrypted all the user’s data—phone call records, messages, photos, contacts, and so on—with the user’s passcode. The encryption was so strong, not even Apple could break it. Security on earlier devices was much weaker, and there were various ways to break into them, but Apple could no longer access locked devices running iOS 8, even if law enforcement had a valid warrant. “Unlike our competitors, Apple cannot bypass your passcode and therefore cannot access this data,” the company wrote on its website. “So it’s not technically feasible for us to respond to government warrants for the extraction of this data from devices in their possession running iOS 8.”

The War Room

For the next two months, the executive floor at One Infinite Loop turned into a 24/7 situation room, with staffers sending out messages and responding to journalists’ queries. One PR rep said that they were sometimes sending out multiple updates a day with up to 700 journalists cc’d on the emails. This is in stark contrast to Apple’s usual PR strategy, which consists of occasional press releases and routinely ignoring reporters’ calls and emails.

Cook also felt he had to rally the troops, to keep morale high at a time when the company was under attack. In an email to Apple employees, titled “Thank you for your support,” he wrote, “This case is about much more than a single phone or a single investigation.” He continued, “At stake is the data security of hundreds of millions of law-abiding people and setting a dangerous precedent that threatens everyone’s civil liberties.” It worked. Apple employees trusted their leader to make the decision that was right not only for them but also for the general public.

Cook was very concerned about how Apple would be perceived throughout this media firestorm. He wanted very much to use it as an opportunity to educate the public about personal security, privacy, and encryption. “I think a lot of reporters saw a new version, a new face of Apple,” said the PR person, who asked to remain anonymous. “And it was Tim’s decision to act in this fashion. Very different from what we have done in the past. We were sometimes sending out emails to reporters three times a day on keeping them updated.”

Outside Apple’s walls, Cook went on a charm offensive. Eight days after publishing his privacy letter, he sat down for a prime-time interview with ABC News. Sitting in his office at One Infinite Loop, he sincerely explained Apple’s position. It was the “most important [interview] he’s given as Apple’s CEO,” said the Washington Post. “Cook responded to questions with a raw conviction that was even more emphatic than usual,” wrote the paper. “He used sharp and soaring language, calling the request the ‘software equivalent of cancer’ and talking about ‘fundamental’ civil liberties.

https://www.wired.com/story/the-time-tim-cook-stood-his-ground-against-fbi/

Alexa do you work for the NSA ;-)

Tens of millions of people use smart speakers and their voice software to play games, find music or trawl for trivia. Millions more are reluctant to invite the devices and their powerful microphones into their homes out of concern that someone might be listening.

Sometimes, someone is.

Amazon.com Inc. employs thousands of people around the world to help improve the Alexa digital assistant powering its line of Echo speakers. The team listens to voice recordings captured in Echo owners’ homes and offices. The recordings are transcribed, annotated and then fed back into the software as part of an effort to eliminate gaps in Alexa’s understanding of human speech and help it better respond to commands.

The Alexa voice review process, described by seven people who have worked on the program, highlights the often-overlooked human role in training software algorithms. In marketing materials Amazon says Alexa “lives in the cloud and is always getting smarter.” But like many software tools built to learn from experience, humans are doing some of the teaching.

The team comprises a mix of contractors and full-time Amazon employees who work in outposts from Boston to Costa Rica, India and Romania, according to the people, who signed nondisclosure agreements barring them from speaking publicly about the program. They work nine hours a day, with each reviewer parsing as many as 1,000 audio clips per shift, according to two workers based at Amazon’s Bucharest office, which takes up the top three floors of the Globalworth building in the Romanian capital’s up-and-coming Pipera district. The modern facility stands out amid the crumbling infrastructure and bears no exterior sign advertising Amazon’s presence.

The work is mostly mundane. One worker in Boston said he mined accumulated voice data for specific utterances such as “Taylor Swift” and annotated them to indicate the searcher meant the musical artist. Occasionally the listeners pick up things Echo owners likely would rather stay private: a woman singing badly off key in the shower, say, or a child screaming for help. The teams use internal chat rooms to share files when they need help parsing a muddled word—or come across an amusing recording.

 Amazon in Bucharest
Amazon has offices in this Bucharest building.
Photographer: Irina Vilcu/Bloomberg

Sometimes they hear recordings they find upsetting, or possibly criminal. Two of the workers said they picked up what they believe was a sexual assault. When something like that happens, they may share the experience in the internal chat room as a way of relieving stress. Amazon says it has procedures in place for workers to follow when they hear something distressing, but two Romania-based employees said that, after requesting guidance for such cases, they were told it wasn’t Amazon’s job to interfere.

“We take the security and privacy of our customers’ personal information seriously,” an Amazon spokesman said in an emailed statement. “We only annotate an extremely small sample of Alexa voice recordings in order [to] improve the customer experience. For example, this information helps us train our speech recognition and natural language understanding systems, so Alexa can better understand your requests, and ensure the service works well for everyone.

“We have strict technical and operational safeguards, and have a zero tolerance policy for the abuse of our system. Employees do not have direct access to information that can identify the person or account as part of this workflow. All information is treated with high confidentiality and we use multi-factor authentication to restrict access, service encryption and audits of our control environment to protect it.”

Amazon, in its marketing and privacy policy materials, doesn’t explicitly say humans are listening to recordings of some conversations picked up by Alexa. “We use your requests to Alexa to train our speech recognition and natural language understanding systems,” the company says in a list of frequently asked questions.

In Alexa’s privacy settings, Amazon gives users the option of disabling the use of their voice recordings for the development of new features. The company says people who opt out of that program might still have their recordings analyzed by hand over the regular course of the review process. A screenshot reviewed by Bloomberg shows that the recordings sent to the Alexa reviewers don’t provide a user’s full name and address but are associated with an account number, as well as the user’s first name and the device’s serial number.

The Intercept reported earlier this year that employees of Amazon-owned Ring manually identify vehicles and people in videos captured by the company’s doorbell cameras, an effort to better train the software to do that work itself.

“You don’t necessarily think of another human listening to what you’re telling your smart speaker in the intimacy of your home,” said Florian Schaub, a professor at the University of Michigan who has researched privacy issues related to smart speakers. “I think we’ve been conditioned to the [assumption] that these machines are just doing magic machine learning. But the fact is there is still manual processing involved.”

“Whether that’s a privacy concern or not depends on how cautious Amazon and other companies are in what type of information they have manually annotated, and how they present that information to someone,” he added.

When the Echo debuted in 2014, Amazon’s cylindrical smart speaker quickly popularized the use of voice software in the home. Before long, Alphabet Inc. launched its own version, called Google Home, followed by Apple Inc.’s HomePod. Various companies also sell their own devices in China. Globally, consumers bought 78 million smart speakers last year, according to researcher Canalys. Millions more use voice software to interact with digital assistants on their smartphones.

Alexa software is designed to continuously record snatches of audio, listening for a wake word. That’s “Alexa” by default, but people can change it to “Echo” or “computer.” When the wake word is detected, the light ring at the top of the Echo turns blue, indicating the device is recording and beaming a command to Amazon servers.

 Inside An Amazon 4-Star Store
An Echo smart speaker inside an Amazon 4-star store in Berkeley, California.
Photographer: Cayce Clifford/Bloomberg

Most modern speech-recognition systems rely on neural networks patterned on the human brain. The software learns as it goes, by spotting patterns amid vast amounts of data. The algorithms powering the Echo and other smart speakers use models of probability to make educated guesses. If someone asks Alexa if there’s a Greek place nearby, the algorithms know the user is probably looking for a restaurant, not a church or community center.

But sometimes Alexa gets it wrong—especially when grappling with new slang, regional colloquialisms or languages other than English. In French, avec sa, “with his” or “with her,” can confuse the software into thinking someone is using the Alexa wake word. Hecho, Spanish for a fact or deed, is sometimes misinterpreted as Echo. And so on. That’s why Amazon recruited human helpers to fill in the gaps missed by the algorithms.

Apple’s Siri also has human helpers, who work to gauge whether the digital assistant’s interpretation of requests lines up with what the person said. The recordings they review lack personally identifiable information and are stored for six months tied to a random identifier, according to an Apple security white paper. After that, the data is stripped of its random identification information but may be stored for longer periods to improve Siri’s voice recognition.

At Google, some reviewers can access some audio snippets from its Assistant to help train and improve the product, but it’s not associated with any personally identifiable information and the audio is distorted, the company says.

A recent Amazon job posting, seeking a quality assurance manager for Alexa Data Services in Bucharest, describes the role humans play: “Every day she [Alexa] listens to thousands of people talking to her about different topics and different languages, and she needs our help to make sense of it all.” The want ad continues: “This is big data handling like you’ve never seen it. We’re creating, labeling, curating and analyzing vast quantities of speech on a daily basis.”

Amazon’s review process for speech data begins when Alexa pulls a random, small sampling of customer voice recordings and sends the audio files to the far-flung employees and contractors, according to a person familiar with the program’s design.

 Amazon.com Inc. Holds Product Reveal Launch
The Echo Spot
Photographer: Daniel Berman/Bloomberg

Some Alexa reviewers are tasked with transcribing users’ commands, comparing the recordings to Alexa’s automated transcript, say, or annotating the interaction between user and machine. What did the person ask? Did Alexa provide an effective response?

Others note everything the speaker picks up, including background conversations—even when children are speaking. Sometimes listeners hear users discussing private details such as names or bank details; in such cases, they’re supposed to tick a dialog box denoting “critical data.” They then move on to the next audio file.

According to Amazon’s website, no audio is stored unless Echo detects the wake word or is activated by pressing a button. But sometimes Alexa appears to begin recording without any prompt at all, and the audio files start with a blaring television or unintelligible noise. Whether or not the activation is mistaken, the reviewers are required to transcribe it. One of the people said the auditors each transcribe as many as 100 recordings a day when Alexa receives no wake command or is triggered by accident.

In homes around the world, Echo owners frequently speculate about who might be listening, according to two of the reviewers. “Do you work for the NSA?” they ask. “Alexa, is someone else listening to us?”

— With assistance by Gerrit De Vynck, Mark Gurman, and Irina Vilcu

Source: https://www.bloomberg.com/news/articles/2019-04-10/is-anyone-listening-to-you-on-alexa-a-global-team-reviews-audio

How Companies Learn Your Secrets

Andrew Pole had just started working as a statistician for Target in 2002, when two colleagues from the marketing department stopped by his desk to ask an odd question: “If we wanted to figure out if a customer is pregnant, even if she didn’t want us to know, can you do that? ”

Pole has a master’s degree in statistics and another in economics, and has been obsessed with the intersection of data and human behavior most of his life. His parents were teachers in North Dakota, and while other kids were going to 4-H, Pole was doing algebra and writing computer programs. “The stereotype of a math nerd is true,” he told me when I spoke with him last year. “I kind of like going out and evangelizing analytics.”

As the marketers explained to Pole — and as Pole later explained to me, back when we were still speaking and before Target told him to stop — new parents are a retailer’s holy grail. Most shoppers don’t buy everything they need at one store. Instead, they buy groceries at the grocery store and toys at the toy store, and they visit Target only when they need certain items they associate with Target — cleaning supplies, say, or new socks or a six-month supply of toilet paper. But Target sells everything from milk to stuffed animals to lawn furniture to electronics, so one of the company’s primary goals is convincing customers that the only store they need is Target. But it’s a tough message to get across, even with the most ingenious ad campaigns, because once consumers’ shopping habits are ingrained, it’s incredibly difficult to change them.

There are, however, some brief periods in a person’s life when old routines fall apart and buying habits are suddenly in flux. One of those moments — the moment, really — is right around the birth of a child, when parents are exhausted and overwhelmed and their shopping patterns and brand loyalties are up for grabs. But as Target’s marketers explained to Pole, timing is everything. Because birth records are usually public, the moment a couple have a new baby, they are almost instantaneously barraged with offers and incentives and advertisements from all sorts of companies. Which means that the key is to reach them earlier, before any other retailers know a baby is on the way. Specifically, the marketers said they wanted to send specially designed ads to women in their second trimester, which is when most expectant mothers begin buying all sorts of new things, like prenatal vitamins and maternity clothing. “Can you give us a list?” the marketers asked.

“We knew that if we could identify them in their second trimester, there’s a good chance we could capture them for years,” Pole told me. “As soon as we get them buying diapers from us, they’re going to start buying everything else too. If you’re rushing through the store, looking for bottles, and you pass orange juice, you’ll grab a carton. Oh, and there’s that new DVD I want. Soon, you’ll be buying cereal and paper towels from us, and keep coming back.”

Continue reading the main story

The desire to collect information on customers is not new for Target or any other large retailer, of course. For decades, Target has collected vast amounts of data on every person who regularly walks into one of its stores. Whenever possible, Target assigns each shopper a unique code — known internally as the Guest ID number — that keeps tabs on everything they buy. “If you use a credit card or a coupon, or fill out a survey, or mail in a refund, or call the customer help line, or open an e-mail we’ve sent you or visit our Web site, we’ll record it and link it to your Guest ID,” Pole said. “We want to know everything we can.”

Also linked to your Guest ID is demographic information like your age, whether you are married and have kids, which part of town you live in, how long it takes you to drive to the store, your estimated salary, whether you’ve moved recently, what credit cards you carry in your wallet and what Web sites you visit. Target can buy data about your ethnicity, job history, the magazines you read, if you’ve ever declared bankruptcy or got divorced, the year you bought (or lost) your house, where you went to college, what kinds of topics you talk about online, whether you prefer certain brands of coffee, paper towels, cereal or applesauce, your political leanings, reading habits, charitable giving and the number of cars you own. (In a statement, Target declined to identify what demographic information it collects or purchases.) All that information is meaningless, however, without someone to analyze and make sense of it. That’s where Andrew Pole and the dozens of other members of Target’s Guest Marketing Analytics department come in.

Almost every major retailer, from grocery chains to investmentbanks to the U.S. Postal Service, has a “predictive analytics” department devoted to understanding not just consumers’ shopping habits but also their personal habits, so as to more efficiently market to them. “But Target has always been one of the smartest at this,” says Eric Siegel, a consultant and the chairman of a conference called Predictive Analytics World. “We’re living through a golden age of behavioral research. It’s amazing how much we can figure out about how people think now.”

The reason Target can snoop on our shopping habits is that, over the past two decades, the science of habit formation has become a major field of research in neurology and psychology departments at hundreds of major medical centers and universities, as well as inside extremely well financed corporate labs. “It’s like an arms race to hire statisticians nowadays,” said Andreas Weigend, the former chief scientist at Amazon.com. “Mathematicians are suddenly sexy.” As the ability to analyze data has grown more and more fine-grained, the push to understand how daily habits influence our decisions has become one of the most exciting topics in clinical research, even though most of us are hardly aware those patterns exist. One study from Duke University estimated that habits, rather than conscious decision-making, shape 45 percent of the choices we make every day, and recent discoveries have begun to change everything from the way we think about dieting to how doctors conceive treatments for anxiety, depression and addictions.

This research is also transforming our understanding of how habits function across organizations and societies. A football coach named Tony Dungy propelled one of the worst teams in the N.F.L. to the Super Bowl by focusing on how his players habitually reacted to on-field cues. Before he became Treasury secretary, Paul O’Neill overhauled a stumbling conglomerate, Alcoa, and turned it into a top performer in the Dow Jones by relentlessly attacking one habit — a specific approach to worker safety — which in turn caused a companywide transformation. The Obama campaign has hired a habit specialist as its “chief scientist” to figure out how to trigger new voting patterns among different constituencies.

Researchers have figured out how to stop people from habitually overeating and biting their nails. They can explain why some of us automatically go for a jog every morning and are more productive at work, while others oversleep and procrastinate. There is a calculus, it turns out, for mastering our subconscious urges. For companies like Target, the exhaustive rendering of our conscious and unconscious patterns into data sets and algorithms has revolutionized what they know about us and, therefore, how precisely they can sell.

Inside the brain-and-cognitive-sciences department of the Massachusetts Institute of Technology are what, to the casual observer, look like dollhouse versions of surgical theaters. There are rooms with tiny scalpels, small drills and miniature saws. Even the operating tables are petite, as if prepared for 7-year-old surgeons. Inside those shrunken O.R.’s, neurologists cut into the skulls of anesthetized rats, implanting tiny sensors that record the smallest changes in the activity of their brains.

An M.I.T. neuroscientist named Ann Graybiel told me that she and her colleagues began exploring habits more than a decade ago by putting their wired rats into a T-shaped maze with chocolate at one end. The maze was structured so that each animal was positioned behind a barrier that opened after a loud click. The first time a rat was placed in the maze, it would usually wander slowly up and down the center aisle after the barrier slid away, sniffing in corners and scratching at walls. It appeared to smell the chocolate but couldn’t figure out how to find it. There was no discernible pattern in the rat’s meanderings and no indication it was working hard to find the treat.

The probes in the rats’ heads, however, told a different story. While each animal wandered through the maze, its brain was working furiously. Every time a rat sniffed the air or scratched a wall, the neurosensors inside the animal’s head exploded with activity. As the scientists repeated the experiment, again and again, the rats eventually stopped sniffing corners and making wrong turns and began to zip through the maze with more and more speed. And within their brains, something unexpected occurred: as each rat learned how to complete the maze more quickly, its mental activity decreased. As the path became more and more automatic — as it became a habit — the rats started thinking less and less.

This process, in which the brain converts a sequence of actions into an automatic routine, is called “chunking.” There are dozens, if not hundreds, of behavioral chunks we rely on every day. Some are simple: you automatically put toothpaste on your toothbrush before sticking it in your mouth. Some, like making the kids’ lunch, are a little more complex. Still others are so complicated that it’s remarkable to realize that a habit could have emerged at all.

Take backing your car out of the driveway. When you first learned to drive, that act required a major dose of concentration, and for good reason: it involves peering into the rearview and side mirrors and checking for obstacles, putting your foot on the brake, moving the gearshift into reverse, removing your foot from the brake, estimating the distance between the garage and the street while keeping the wheels aligned, calculating how images in the mirrors translate into actual distances, all while applying differing amounts of pressure to the gas pedal and brake.

Now, you perform that series of actions every time you pull into the street without thinking very much. Your brain has chunked large parts of it. Left to its own devices, the brain will try to make almost any repeated behavior into a habit, because habits allow our minds to conserve effort. But conserving mental energy is tricky, because if our brains power down at the wrong moment, we might fail to notice something important, like a child riding her bike down the sidewalk or a speeding car coming down the street. So we’ve devised a clever system to determine when to let a habit take over. It’s something that happens whenever a chunk of behavior starts or ends — and it helps to explain why habits are so difficult to change once they’re formed, despite our best intentions.

To understand this a little more clearly, consider again the chocolate-seeking rats. What Graybiel and her colleagues found was that, as the ability to navigate the maze became habitual, there were two spikes in the rats’ brain activity — once at the beginning of the maze, when the rat heard the click right before the barrier slid away, and once at the end, when the rat found the chocolate. Those spikes show when the rats’ brains were fully engaged, and the dip in neural activity between the spikes showed when the habit took over. From behind the partition, the rat wasn’t sure what waited on the other side, until it heard the click, which it had come to associate with the maze. Once it heard that sound, it knew to use the “maze habit,” and its brain activity decreased. Then at the end of the routine, when the reward appeared, the brain shook itself awake again and the chocolate signaled to the rat that this particular habit was worth remembering, and the neurological pathway was carved that much deeper.

The process within our brains that creates habits is a three-step loop. First, there is a cue, a trigger that tells your brain to go into automatic mode and which habit to use. Then there is the routine, which can be physical or mental or emotional. Finally, there is a reward, which helps your brain figure out if this particular loop is worth remembering for the future. Over time, this loop — cue, routine, reward; cue, routine, reward — becomes more and more automatic. The cue and reward become neurologically intertwined until a sense of craving emerges. What’s unique about cues and rewards, however, is how subtle they can be. Neurological studies like the ones in Graybiel’s lab have revealed that some cues span just milliseconds. And rewards can range from the obvious (like the sugar rush that a morning doughnut habit provides) to the infinitesimal (like the barely noticeable — but measurable — sense of relief the brain experiences after successfully navigating the driveway). Most cues and rewards, in fact, happen so quickly and are so slight that we are hardly aware of them at all. But our neural systems notice and use them to build automatic behaviors.

Habits aren’t destiny — they can be ignored, changed or replaced. But it’s also true that once the loop is established and a habit emerges, your brain stops fully participating in decision-making. So unless you deliberately fight a habit — unless you find new cues and rewards — the old pattern will unfold automatically.

“We’ve done experiments where we trained rats to run down a maze until it was a habit, and then we extinguished the habit by changing the placement of the reward,” Graybiel told me. “Then one day, we’ll put the reward in the old place and put in the rat and, by golly, the old habit will re-emerge right away. Habits never really disappear.”

Luckily, simply understanding how habits work makes them easier to control. Take, for instance, a series of studies conducted a few years ago at Columbia University and the University of Alberta. Researchers wanted to understand how exercise habits emerge. In one project, 256 members of a health-insurance plan were invited to classes stressing the importance of exercise. Half the participants received an extra lesson on the theories of habit formation (the structure of the habit loop) and were asked to identify cues and rewards that might help them develop exercise routines.

The results were dramatic. Over the next four months, those participants who deliberately identified cues and rewards spent twice as much time exercising as their peers. Other studies have yielded similar results. According to another recent paper, if you want to start running in the morning, it’s essential that you choose a simple cue (like always putting on your sneakers before breakfast or leaving your running clothes next to your bed) and a clear reward (like a midday treat or even the sense of accomplishment that comes from ritually recording your miles in a log book). After a while, your brain will start anticipating that reward — craving the treat or the feeling of accomplishment — and there will be a measurable neurological impulse to lace up your jogging shoes each morning.

Our relationship to e-mail operates on the same principle. When a computer chimes or a smartphone vibrates with a new message, the brain starts anticipating the neurological “pleasure” (even if we don’t recognize it as such) that clicking on the e-mail and reading it provides. That expectation, if unsatisfied, can build until you find yourself moved to distraction by the thought of an e-mail sitting there unread — even if you know, rationally, it’s most likely not important. On the other hand, once you remove the cue by disabling the buzzing of your phone or the chiming of your computer, the craving is never triggered, and you’ll find, over time, that you’re able to work productively for long stretches without checking your in-box.

Some of the most ambitious habit experiments have been conducted by corporate America. To understand why executives are so entranced by this science, consider how one of the world’s largest companies, Procter & Gamble, used habit insights to turn a failing product into one of its biggest sellers. P.& G. is the corporate behemoth behind a whole range of products, from Downy fabric softener to Bounty paper towels to Duracell batteries and dozens of other household brands. In the mid-1990s, P.& G.’s executives began a secret project to create a new product that could eradicate bad smells. P.& G. spent millions developing a colorless, cheap-to-manufacture liquid that could be sprayed on a smoky blouse, stinky couch, old jacket or stained car interior and make it odorless. In order to market the product — Febreze — the company formed a team that included a former Wall Street mathematician named Drake Stimson and habit specialists, whose job was to make sure the television commercials, which they tested in Phoenix, Salt Lake City and Boise, Idaho, accentuated the product’s cues and rewards just right.

Video

TimesCast | Retailers‘ Predictions

February 16, 2012 – In a preview of this Sunday’s New York Times Magazine, Charles Duhigg details how some retailers profit by predicting major changes in your life.

By Kassie Bracken on Publish Date February 17, 2012. . Watch in Times Video »

The first ad showed a woman complaining about the smoking section of a restaurant. Whenever she eats there, she says, her jacket smells like smoke. A friend tells her that if she uses Febreze, it will eliminate the odor. The cue in the ad is clear: the harsh smell of cigarette smoke. The reward: odor eliminated from clothes. The second ad featured a woman worrying about her dog, Sophie, who always sits on the couch. “Sophie will always smell like Sophie,” she says, but with Febreze, “now my furniture doesn’t have to.” The ads were put in heavy rotation. Then the marketers sat back, anticipating how they would spend their bonuses. A week passed. Then two. A month. Two months. Sales started small and got smaller. Febreze was a dud.

The panicked marketing team canvassed consumers and conducted in-depth interviews to figure out what was going wrong, Stimson recalled. Their first inkling came when they visited a woman’s home outside Phoenix. The house was clean and organized. She was something of a neat freak, the woman explained. But when P.& G.’s scientists walked into her living room, where her nine cats spent most of their time, the scent was so overpowering that one of them gagged.

According to Stimson, who led the Febreze team, a researcher asked the woman, “What do you do about the cat smell?”

“It’s usually not a problem,” she said.

“Do you smell it now?”

“No,” she said. “Isn’t it wonderful? They hardly smell at all!”

A similar scene played out in dozens of other smelly homes. The reason Febreze wasn’t selling, the marketers realized, was that people couldn’t detect most of the bad smells in their lives. If you live with nine cats, you become desensitized to their scents. If you smoke cigarettes, eventually you don’t smell smoke anymore. Even the strongest odors fade with constant exposure. That’s why Febreze was a failure. The product’s cue — the bad smells that were supposed to trigger daily use — was hidden from the people who needed it the most. And Febreze’s reward (an odorless home) was meaningless to someone who couldn’t smell offensive scents in the first place.

P.& G. employed a Harvard Business School professor to analyze Febreze’s ad campaigns. They collected hours of footage of people cleaning their homes and watched tape after tape, looking for clues that might help them connect Febreze to people’s daily habits. When that didn’t reveal anything, they went into the field and conducted more interviews. A breakthrough came when they visited a woman in a suburb near Scottsdale, Ariz., who was in her 40s with four children. Her house was clean, though not compulsively tidy, and didn’t appear to have any odor problems; there were no pets or smokers. To the surprise of everyone, she loved Febreze.

“I use it every day,” she said.

“What smells are you trying to get rid of?” a researcher asked.

“I don’t really use it for specific smells,” the woman said. “I use it for normal cleaning — a couple of sprays when I’m done in a room.”

The researchers followed her around as she tidied the house. In the bedroom, she made her bed, tightened the sheet’s corners, then sprayed the comforter with Febreze. In the living room, she vacuumed, picked up the children’s shoes, straightened the coffee table, then sprayed Febreze on the freshly cleaned carpet.

“It’s nice, you know?” she said. “Spraying feels like a little minicelebration when I’m done with a room.” At the rate she was going, the team estimated, she would empty a bottle of Febreze every two weeks.

When they got back to P.& G.’s headquarters, the researchers watched their videotapes again. Now they knew what to look for and saw their mistake in scene after scene. Cleaning has its own habit loops that already exist. In one video, when a woman walked into a dirty room (cue), she started sweeping and picking up toys (routine), then she examined the room and smiled when she was done (reward). In another, a woman scowled at her unmade bed (cue), proceeded to straighten the blankets and comforter (routine) and then sighed as she ran her hands over the freshly plumped pillows (reward). P.& G. had been trying to create a whole new habit with Febreze, but what they really needed to do was piggyback on habit loops that were already in place. The marketers needed to position Febreze as something that came at the end of the cleaning ritual, the reward, rather than as a whole new cleaning routine.

The company printed new ads showing open windows and gusts of fresh air. More perfume was added to the Febreze formula, so that instead of merely neutralizing odors, the spray had its own distinct scent. Television commercials were filmed of women, having finished their cleaning routine, using Febreze to spritz freshly made beds and just-laundered clothing. Each ad was designed to appeal to the habit loop: when you see a freshly cleaned room (cue), pull out Febreze (routine) and enjoy a smell that says you’ve done a great job (reward). When you finish making a bed (cue), spritz Febreze (routine) and breathe a sweet, contented sigh (reward). Febreze, the ads implied, was a pleasant treat, not a reminder that your home stinks.

And so Febreze, a product originally conceived as a revolutionary way to destroy odors, became an air freshener used once things are already clean. The Febreze revamp occurred in the summer of 1998. Within two months, sales doubled. A year later, the product brought in $230 million. Since then Febreze has spawned dozens of spinoffs — air fresheners, candles and laundry detergents — that now account for sales of more than $1 billion a year. Eventually, P.& G. began mentioning to customers that, in addition to smelling sweet, Febreze can actually kill bad odors. Today it’s one of the top-selling products in the world.

Andrew Pole was hired by Target to use the same kinds of insights into consumers’ habits to expand Target’s sales. His assignment was to analyze all the cue-routine-reward loops among shoppers and help the company figure out how to exploit them. Much of his department’s work was straightforward: find the customers who have children and send them catalogs that feature toys before Christmas. Look for shoppers who habitually purchase swimsuits in April and send them coupons for sunscreen in July and diet books in December. But Pole’s most important assignment was to identify those unique moments in consumers’ lives when their shopping habits become particularly flexible and the right advertisement or coupon would cause them to begin spending in new ways.

In the 1980s, a team of researchers led by a U.C.L.A. professor named Alan Andreasen undertook a study of peoples’ most mundane purchases, like soap, toothpaste, trash bags and toilet paper. They learned that most shoppers paid almost no attention to how they bought these products, that the purchases occurred habitually, without any complex decision-making. Which meant it was hard for marketers, despite their displays and coupons and product promotions, to persuade shoppers to change.

But when some customers were going through a major life event, like graduating from college or getting a new job or moving to a new town, their shopping habits became flexible in ways that were both predictable and potential gold mines for retailers. The study found that when someone marries, he or she is more likely to start buying a new type of coffee. When a couple move into a new house, they’re more apt to purchase a different kind of cereal. When they divorce, there’s an increased chance they’ll start buying different brands of beer.

Consumers going through major life events often don’t notice, or care, that their shopping habits have shifted, but retailers notice, and they care quite a bit. At those unique moments, Andreasen wrote, customers are “vulnerable to intervention by marketers.” In other words, a precisely timed advertisement, sent to a recent divorcee or new homebuyer, can change someone’s shopping patterns for years.

And among life events, none are more important than the arrival of a baby. At that moment, new parents’ habits are more flexible than at almost any other time in their adult lives. If companies can identify pregnant shoppers, they can earn millions.

The only problem is that identifying pregnant customers is harder than it sounds. Target has a baby-shower registry, and Pole started there, observing how shopping habits changed as a woman approached her due date, which women on the registry had willingly disclosed. He ran test after test, analyzing the data, and before long some useful patterns emerged. Lotions, for example. Lots of people buy lotion, but one of Pole’s colleagues noticed that women on the baby registry were buying larger quantities of unscented lotion around the beginning of their second trimester. Another analyst noted that sometime in the first 20 weeks, pregnant women loaded up on supplements like calcium, magnesium and zinc. Many shoppers purchase soap and cotton balls, but when someone suddenly starts buying lots of scent-free soap and extra-big bags of cotton balls, in addition to hand sanitizers and washcloths, it signals they could be getting close to their delivery date.

As Pole’s computers crawled through the data, he was able to identify about 25 products that, when analyzed together, allowed him to assign each shopper a “pregnancy prediction” score. More important, he could also estimate her due date to within a small window, so Target could send coupons timed to very specific stages of her pregnancy.

One Target employee I spoke to provided a hypothetical example. Take a fictional Target shopper named Jenny Ward, who is 23, lives in Atlanta and in March bought cocoa-butter lotion, a purse large enough to double as a diaper bag, zinc and magnesium supplements and a bright blue rug. There’s, say, an 87 percent chance that she’s pregnant and that her delivery date is sometime in late August. What’s more, because of the data attached to her Guest ID number, Target knows how to trigger Jenny’s habits. They know that if she receives a coupon via e-mail, it will most likely cue her to buy online. They know that if she receives an ad in the mail on Friday, she frequently uses it on a weekend trip to the store. And they know that if they reward her with a printed receipt that entitles her to a free cup of Starbucks coffee, she’ll use it when she comes back again.

In the past, that knowledge had limited value. After all, Jenny purchased only cleaning supplies at Target, and there were only so many psychological buttons the company could push. But now that she is pregnant, everything is up for grabs. In addition to triggering Jenny’s habits to buy more cleaning products, they can also start including offers for an array of products, some more obvious than others, that a woman at her stage of pregnancy might need.

Pole applied his program to every regular female shopper in Target’s national database and soon had a list of tens of thousands of women who were most likely pregnant. If they could entice those women or their husbands to visit Target and buy baby-related products, the company’s cue-routine-reward calculators could kick in and start pushing them to buy groceries, bathing suits, toys and clothing, as well. When Pole shared his list with the marketers, he said, they were ecstatic. Soon, Pole was getting invited to meetings above his paygrade. Eventually his paygrade went up.

At which point someone asked an important question: How are women going to react when they figure out how much Target knows?

“If we send someone a catalog and say, ‘Congratulations on your first child!’ and they’ve never told us they’re pregnant, that’s going to make some people uncomfortable,” Pole told me. “We are very conservative about compliance with all privacy laws. But even if you’re following the law, you can do things where people get queasy.”

About a year after Pole created his pregnancy-prediction model, a man walked into a Target outside Minneapolis and demanded to see the manager. He was clutching coupons that had been sent to his daughter, and he was angry, according to an employee who participated in the conversation.

Video

How to Break the Cookie Habit

Charles Duhigg explains the science of habits.

By Random House on Publish Date February 16, 2012. . Watch in Times Video »

“My daughter got this in the mail!” he said. “She’s still in high school, and you’re sending her coupons for baby clothes and cribs? Are you trying to encourage her to get pregnant?”

The manager didn’t have any idea what the man was talking about. He looked at the mailer. Sure enough, it was addressed to the man’s daughter and contained advertisements for maternity clothing, nursery furniture and pictures of smiling infants. The manager apologized and then called a few days later to apologize again.

On the phone, though, the father was somewhat abashed. “I had a talk with my daughter,” he said. “It turns out there’s been some activities in my house I haven’t been completely aware of. She’s due in August. I owe you an apology.”

When I approached Target to discuss Pole’s work, its representatives declined to speak with me. “Our mission is to make Target the preferred shopping destination for our guests by delivering outstanding value, continuous innovation and exceptional guest experience,” the company wrote in a statement. “We’ve developed a number of research tools that allow us to gain insights into trends and preferences within different demographic segments of our guest population.” When I sent Target a complete summary of my reporting, the reply was more terse: “Almost all of your statements contain inaccurate information and publishing them would be misleading to the public. We do not intend to address each statement point by point.” The company declined to identify what was inaccurate. They did add, however, that Target “is in compliance with all federal and state laws, including those related to protected health information.”

When I offered to fly to Target’s headquarters to discuss its concerns, a spokeswoman e-mailed that no one would meet me. When I flew out anyway, I was told I was on a list of prohibited visitors. “I’ve been instructed not to give you access and to ask you to leave,” said a very nice security guard named Alex.

Using data to predict a woman’s pregnancy, Target realized soon after Pole perfected his model, could be a public-relations disaster. So the question became: how could they get their advertisements into expectant mothers’ hands without making it appear they were spying on them? How do you take advantage of someone’s habits without letting them know you’re studying their lives?

Before I met Andrew Pole, before I even decided to write a book about the science of habit formation, I had another goal: I wanted to lose weight.

I had got into a bad habit of going to the cafeteria every afternoon and eating a chocolate-chip cookie, which contributed to my gaining a few pounds. Eight, to be precise. I put a Post-it note on my computer reading “NO MORE COOKIES.” But every afternoon, I managed to ignore that note, wander to the cafeteria, buy a cookie and eat it while chatting with colleagues. Tomorrow, I always promised myself, I’ll muster the willpower to resist.

Tomorrow, I ate another cookie.

When I started interviewing experts in habit formation, I concluded each interview by asking what I should do. The first step, they said, was to figure out my habit loop. The routine was simple: every afternoon, I walked to the cafeteria, bought a cookie and ate it while chatting with friends.

Next came some less obvious questions: What was the cue? Hunger? Boredom? Low blood sugar? And what was the reward? The taste of the cookie itself? The temporary distraction from my work? The chance to socialize with colleagues?

Rewards are powerful because they satisfy cravings, but we’re often not conscious of the urges driving our habits in the first place. So one day, when I felt a cookie impulse, I went outside and took a walk instead. The next day, I went to the cafeteria and bought a coffee. The next, I bought an apple and ate it while chatting with friends. You get the idea. I wanted to test different theories regarding what reward I was really craving. Was it hunger? (In which case the apple should have worked.) Was it the desire for a quick burst of energy? (If so, the coffee should suffice.) Or, as turned out to be the answer, was it that after several hours spent focused on work, I wanted to socialize, to make sure I was up to speed on office gossip, and the cookie was just a convenient excuse? When I walked to a colleague’s desk and chatted for a few minutes, it turned out, my cookie urge was gone.

All that was left was identifying the cue.

Deciphering cues is hard, however. Our lives often contain too much information to figure out what is triggering a particular behavior. Do you eat breakfast at a certain time because you’re hungry? Or because the morning news is on? Or because your kids have started eating? Experiments have shown that most cues fit into one of five categories: location, time, emotional state, other people or the immediately preceding action. So to figure out the cue for my cookie habit, I wrote down five things the moment the urge hit:

Where are you? (Sitting at my desk.)

What time is it? (3:36 p.m.)

What’s your emotional state? (Bored.)

Who else is around? (No one.)

What action preceded the urge? (Answered an e-mail.)

The next day I did the same thing. And the next. Pretty soon, the cue was clear: I always felt an urge to snack around 3:30.

Once I figured out all the parts of the loop, it seemed fairly easy to change my habit. But the psychologists and neuroscientists warned me that, for my new behavior to stick, I needed to abide by the same principle that guided Procter & Gamble in selling Febreze: To shift the routine — to socialize, rather than eat a cookie — I needed to piggyback on an existing habit. So now, every day around 3:30, I stand up, look around the newsroom for someone to talk to, spend 10 minutes gossiping, then go back to my desk. The cue and reward have stayed the same. Only the routine has shifted. It doesn’t feel like a decision, any more than the M.I.T. rats made a decision to run through the maze. It’s now a habit. I’ve lost 21 pounds since then (12 of them from changing my cookie ritual).

After Andrew Pole built his pregnancy-prediction model, after he identified thousands of female shoppers who were most likely pregnant, after someone pointed out that some of those women might be a little upset if they received an advertisement making it obvious Target was studying their reproductive status, everyone decided to slow things down.

The marketing department conducted a few tests by choosing a small, random sample of women from Pole’s list and mailing them combinations of advertisements to see how they reacted.

“We have the capacity to send every customer an ad booklet, specifically designed for them, that says, ‘Here’s everything you bought last week and a coupon for it,’ ” one Target executive told me. “We do that for grocery products all the time.” But for pregnant women, Target’s goal was selling them baby items they didn’t even know they needed yet.

“With the pregnancy products, though, we learned that some women react badly,” the executive said. “Then we started mixing in all these ads for things we knew pregnant women would never buy, so the baby ads looked random. We’d put an ad for a lawn mower next to diapers. We’d put a coupon for wineglasses next to infant clothes. That way, it looked like all the products were chosen by chance.

“And we found out that as long as a pregnant woman thinks she hasn’t been spied on, she’ll use the coupons. She just assumes that everyone else on her block got the same mailer for diapers and cribs. As long as we don’t spook her, it works.”

In other words, if Target piggybacked on existing habits — the same cues and rewards they already knew got customers to buy cleaning supplies or socks — then they could insert a new routine: buying baby products, as well. There’s a cue (“Oh, a coupon for something I need!”) a routine (“Buy! Buy! Buy!”) and a reward (“I can take that off my list”). And once the shopper is inside the store, Target will hit her with cues and rewards to entice her to purchase everything she normally buys somewhere else. As long as Target camouflaged how much it knew, as long as the habit felt familiar, the new behavior took hold.

Soon after the new ad campaign began, Target’s Mom and Baby sales exploded. The company doesn’t break out figures for specific divisions, but between 2002 — when Pole was hired — and 2010, Target’s revenues grew from $44 billion to $67 billion. In 2005, the company’s president, Gregg Steinhafel, boasted to a room of investors about the company’s “heightened focus on items and categories that appeal to specific guest segments such as mom and baby.”

Pole was promoted. He has been invited to speak at conferences. “I never expected this would become such a big deal,” he told me the last time we spoke.

A few weeks before this article went to press, I flew to Minneapolis to try and speak to Andrew Pole one last time. I hadn’t talked to him in more than a year. Back when we were still friendly, I mentioned that my wife was seven months pregnant. We shop at Target, I told him, and had given the company our address so we could start receiving coupons in the mail. As my wife’s pregnancy progressed, I noticed a subtle upswing in the number of advertisements for diapers and baby clothes arriving at our house.

Pole didn’t answer my e-mails or phone calls when I visited Minneapolis. I drove to his large home in a nice suburb, but no one answered the door. On my way back to the hotel, I stopped at a Target to pick up some deodorant, then also bought some T-shirts and a fancy hair gel. On a whim, I threw in some pacifiers, to see how the computers would react. Besides, our baby is now 9 months old. You can’t have too many pacifiers.

When I paid, I didn’t receive any sudden deals on diapers or formula, to my slight disappointment. It made sense, though: I was shopping in a city I never previously visited, at 9:45 p.m. on a weeknight, buying a random assortment of items. I was using a corporate credit card, and besides the pacifiers, hadn’t purchased any of the things that a parent needs. It was clear to Target’s computers that I was on a business trip. Pole’s prediction calculator took one look at me, ran the numbers and decided to bide its time. Back home, the offers would eventually come. As Pole told me the last time we spoke: “Just wait. We’ll be sending you coupons for things you want before you even know you want them.”

Source: https://www.nytimes.com/2012/02/19/magazine/shopping-habits.html February 2012

Google’s DeepMind AI can accurately detect 50 types of eye disease just by looking at scans

Mustafa Suleyman 1831_preview (1)DeepMind cofounder Mustafa Suleyman.DeepMind
  • Google’s artificial intelligence company DeepMind has published „really significant“ research showing its algorithm can identify around 50 eye diseases by looking at retinal eye scans.
  • DeepMind said its AI was as good as expert clinicians, and that it could help prevent people from losing their sight.
  • DeepMind has been criticised for its practices around medical data, but cofounder Mustafa Suleyman said all the information in this research project was anonymised.
  • The company plans to hand the technology over for free to NHS hospitals for five years, provided it passes the next phase of research.

Google’s artificial intelligence company, DeepMind, has developed an AI which can successfully detect more than 50 types of eye disease just by looking at 3D retinal scans.

DeepMind published on Monday the results of joint research with Moorfields Eye Hospital, a renowned centre for treating eye conditions in London, in Nature Medicine.

The company said its AI was as accurate as expert clinicians when it came to detecting diseases, such as diabetic eye disease and macular degeneration. It could also recommend the best course of action for patients and suggest which needed urgent care.

OCT scanA technician examines an OCT scan.DeepMind

What is especially significant about the research, according to DeepMind cofounder Mustafa Suleyman, is that the AI has a level of „explainability“ that could boost doctors‘ trust in its recommendations.

„It’s possible for the clinician to interpret what the algorithm is thinking,“ he told Business Insider. „[They can] look at the underlying segmentation.“

In other words, the AI looks less like a mysterious black box that’s spitting out results. It labels pixels on the eye scan that corresponds to signs of a particular disease, Suleyman explained, and can calculate its confidence in its own findings with a percentage score. „That’s really significant,“ he said.

DeepMind's algorithm analysing an OCT eye scanDeepMind’s AI analysing an OCT scan.DeepMind

Suleyman described the findings as a „research breakthrough“ and said the next step was to prove the AI works in a clinical setting. That, he said, would take a number of years. Once DeepMind is in a position to deploy its AI across NHS hospitals in the UK, it will provide the service for free for five years.

Patients are at risk of losing their sight because doctors can’t look at their eye scans in time

British eye specialists have been warning for years that patients are at risk of losing their sight because the NHS is overstretched, and because the UK has an ageing population.

Part of the reason DeepMind and Moorfields took up the research project was because clinicians are „overwhelmed“ by the demand for eye scans, Suleyman said.

„If you have a sight-threatening disease, you want treatment as soon as possible,“ he explained. „And unlike in A&E, where a staff nurse will talk to you and make an evaluation of how serious your condition is, then use that evaluation to decide how quickly you are seen. When an [eye] scan is submitted, there isn’t a triage of your scan according to its severity.“

OCT scanA patient having an OCT scan.DeepMind

Putting eye scans through the AI could speed the entire process up.

„In the future, I could envisage a person going into their local high street optician, and have an OCT scan done and this algorithm would identify those patients with sight-threatening disease at the very early stage of the condition,“ said Dr Pearse Keane, consultant ophthalmologist at Moorfields Eye Hospital.

DeepMind’s AI was trained on a database of almost 15,000 eye scans, stripped of any identifying information. DeepMind worked with clinicians to label areas of disease, then ran those labelled images through its system. Suleyman said the two-and-a-half project required „huge investment“ from DeepMind and involved 25 staffers, as well as the researchers from Moorfields.

People are still worried about a Google-linked company having access to medical data

Google acquired DeepMind in 2014 for £400 million ($509 million), and the British AI company is probably most famous for AlphaGo, its algorithm that beat the world champion at the strategy game Go.

While DeepMind has remained UK-based and independent from Google, the relationship has attracted scrutiny. The main question is whether Google, a private US company, should have access to the sensitive medical data required for DeepMind’s health arm.

DeepMind was criticised in 2016 for failing to disclose its access to historical medical data during a project with Royal Free Hospital. Suleyman said the eye scans processed by DeepMind were „completely anonymised.“

„You can’t identify whose scans it was. We’re in quite a different regime, this is very much research, and we’re a number of years from being able to deploy in practice,“ he said.

Suleyman added: „How this has the potential to have transform the NHS is very clear. We’ve been very conscious that this will be a model that’s published, and available to others to implement.

„The labelled dataset is available to other researchers. So this is very much an open and collaborative relationship between equals that we’ve worked hard to foster. I’m proud of that work.“

 

https://www.businessinsider.de/google-deepmind-ai-detects-eye-disease-2018-8?r=US&IR=T