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Take a look at the device you’re using—it could be argued that there is not one person on earth that could make it from scratch. The process necessitates cooperation between many different people wielding knowledge that has been accumulated over many generations. 

A lot of what we see around us these days is in the same boat. How many people can build a car, house, or even a pen for that matter, from scratch? By putting our heads together, we’ve been able to go so far as building cities, rockets, and supercomputers. 

The business world is heavily reliant upon collaboration. Staying ahead of the curb requires the sharing of ideas and information among potentially hundreds of individuals, each with their own knowledge and experience. 

Adding more people to the mix should, then, logically lead to more creativity and better decisions. While that’s sometimes correct, as the group grows larger the overall composition becomes more complex, making cohesion between all the members more difficult to achieve. 

Collaboration is, at times, a game of chance, dependent on having the right people together at the right time and under the right circumstances. If we want to get the most from working with others, we need to take care in selecting when, where, and with whom this work takes place. If not, a number of things can go wrong. 

Collaboration overload

Understanding how a network of people functions can be a demanding task. Psychologist and anthropologist Robin Dunbar suggested that there is a limit to how many people we can know while also understanding how they relate to the others in the group. As a group increases in size, the number of different relationships between individuals increases by orders of magnitude, eventually becoming too complex for one person to handle. Dunbar’s number, as it’s now known, is 150.  

As organizations expand and take on new projects, there becomes a greater need for effective communication between people and departments. Everyone needs to be kept in the loop regarding new ideas, setbacks, or developments. Information needs to travel through the organization fluidly, to the right people and with minimal disruption. 

When a company outgrows the number of people their structure can handle, we often find ourselves spending an inordinate amount of time dealing with others. Collaboration isn’t effective when people spend all their time in meetings or chatting through text, email, or message boards. At some point, we have to put our heads down and do something constructive. 

Collaboration overload is where we see the usual benefits of a “group effort” decay into a state of stagnation—where we spend more time spreading information than we do creating something. Research into the “collaboration curse” found that knowledge workers spend around 70-85% of their time in meetings, tending to emails, or talking on the phone. Research into the cost of email found that a mid-sized firm could be spending more than $1 million a year processing emails, as each email averaged out to about 95 cents in labor costs. 

This culture of collaboration is found not only in the way we communicate but also in how offices are designed. The rise in open office plans has certainly made it easier for you to share ideas and ask questions of your fellow coworkers, but it’s also just as easy for them to come to you—even when you’re busy trying to focus on something else. Couple this with the general noise of other groups that form around you, and you have a recipe for a great deal of distraction. 

While some of those distractions will be important, when they come often and unexpectedly they can take us out of our frame of mind. Working on complex tasks requires remembering and manipulating information in your mind, every time you are distracted this information is lost, and must be regathered before you can pick up where you left off—research into the cost of interrupted work suggests that it can take up to 25 minutes for people to re-engage themselves in a task after being distracted. 

How groups think

Part of the allure of collaboration is the idea that groups will arrive at better conclusions and make better decisions. The combination of many experts should help capitalize on their strengths while minimizing weaknesses. When one person makes a mistake, others are likely to pick it up; when others show a bias, the others should help to offset it.

But this is not how things always work out. Under certain conditions groups can compound issues and biases. People within groups can behave differently than they would alone or one-on-one—take social loafing, the phenomena in which people tend to exert less effort when they’re part of a group. 

Anchoring is one effect that can undermine creativity. It occurs when a meeting or brainstorming session begins with one person presenting an idea or piece of information. The rest of the group can then too easily fixate on that initial information, expanding and iterating on it as opposed to coming up with unique and varied ideas of their own. 

It is also possible for people to bite their tongue when they believe that their opinion or idea goes against what the others think. When several members of the group do this—effectively conforming to one opinion—it can cause the group to go in a direction counter to the preferences of most of the individuals. This is referred to as the Abilene Paradox.

Adding more people does not necessarily make for more creative ideas or well-rounded decisions. While collaboration can clearly fall victim to the type of errors that plague individuals, there are other situations in which collaboration can make matters worse. One study found that groups are more optimistic than individuals when estimating the time and resources needed to accomplish a task. Another study found that groups are more likely to overcommit to a failing course of action. 

There is also evidence suggesting groups tend to amplify the initial predisposition of their members. That is, if the average member leans in favor of a risky option, after deliberation the group is likely to be even more in favor of the risk. Seeing others agree with your assessment compounds your certainty in it, creating somewhat of a snowball effect within the group.

Preventing collaboration overload

Effective collaboration relies on the coming together of different skill sets and experiences. Of course, people are unique and complex beings, simply sticking a group into a room won’t ensure they work well together. Like a good band, the best ideas emerge from groups whose members understand and can feed off each other. 

When people feel like essential components of some big and important goal, they are likely to be more motivated and inspired—and when this defines every member of the group, overall productivity and results are likely to be enhanced. Just remember not to over-collaborate and let people do their own thing too.

A report from McKinsey estimated that as much as $12 billion was invested globally in artificial intelligence technologies during 2016, including projects focused on machine learning, natural language processing, computer vision, and autonomous vehicles. That figure cuts across industries, and in real-world terms represents thousands of individual R&D projects. 

Unless you’re closely monitoring developments in artificial intelligence, you probably learn about new AI technologies one headline at a time. “New AI system beats champion Go player” or “Advanced AI improves doctors’ diagnoses.” That sort of thing. 

But you can get a much better sense of the scope and diversity of newly emerging AI technologies by learning about a lot of them at once. In this case, 27 different projects transforming 27 different industries. Which is what our research has produced, as you’ll see below.

News of a single AI technology can be pretty exciting, even inspiring. Understanding the diversity and vision of dozens of them underscores just how powerfully transformative this current generation of AI technologies already is, or will soon be.

Here’s our roundup of disruptive AI technologies being designed, built, or deployed in 27 different industries:

1. Aerospace. AI algorithms enable an entirely new type of self-healing aircraft that makes use of new sensors and other on-board recording devices. Smart systems could use data from these tools to spot problems long before failure occurs, making planes safer, lowering maintenance costs, and reducing the number of delays caused by mechanical issues. 

2. Defense. The military and intelligence worlds are changing in a big way, with AI increasingly important to national defense strategies, on the ground and online. New AI cybersecurity platforms are being fed massive amounts of user-generated data so they can learn to spot anomalies associated with cyberattacks. In addition to using AI for cyber defense, the U.S. military wants to build autonomous weapons, such as drones that can conduct real-time surveillance of enemy territory, jam communications, and fire against enemy combatants. 

3. Automotive. Mass adoption of self-driving cars is still some ways off. But AI is already impacting the auto industry in ways that weren’t possible just a few years ago. A new generation of smart features such as accident avoidance, alerts, and automated braking systems contribute to safer driving conditions for everyone. AI systems are being used to predict mechanical failures as well, employing sensors, apps, and Internet of Things technologies to track performance in individual cars. The data collected triggers alerts to drivers to warn them of potential problems, while manufacturers use the same data to improve production and identify faulty or unreliable parts. 

4. Consumer Goods & Services. Clothing and cosmetics companies—just to name a few—are using smart algorithms to bring shopping experiences out of department stores and into consumers’ homes. A consumer who wants to purchase new make-up need only open an app on her smartphone, and she can “test” a range of product shades to see whether they suit her skin tone. Meanwhile, a person in search of new jeans can pop his measurements and style preferences into a simple interface powered by an online shopping algorithm based on a selected brand’s sizing trends. These experiences are powered by AI technologies such as facial recognition and machine learning systems that use partial or uncertain data to make predictions.  

5. Energy & Utilities. It’s difficult to find a segment of the energy industry not being impacted by the opportunities and challenges of AI, including renewable energy producers using big data to better predict supply and demand as well as traditional utilities rolling out smart electrical grid improvements. AI is helping paint a truer picture of the oil industry as well. Not all oil-producing nations are fully transparent about their supply picture, which creates uncertainties in the commodities markets and volatile oil prices. But a combination of convolutional neural networks (CNN), shadow detection, and satellite imaging is shining an AI-powered spotlight on the situation. By analyzing the shadows on oil storage tanks, AI systems can assess how full the containers are and approximate oil supplies in any part of the world, forcing greater transparency on oil-producing nations.

6. Manufacturing. Factory productivity doesn’t have to come at the cost of workers’ safety. Japanese researchers are studying how AI can not only detect defective products and increase quality output, but how it can monitor workers’ physical conditions as well. Fatigue can cause serious mistakes, particularly when a worker is operating heavy machinery. AI systems can determine when an employee becomes drowsy and needs to be reassigned to a safer task. Greater productivity and better working conditions are a win for everyone. 

7. Transportation. Self-driving cars get a lot of media attention, but the entire transportation industry is evolving rapidly with AI technologies. One interesting development is in environmental improvements. The transportation industry accounts for 27% of greenhouse gas emissions in the U.S. When you add up the impact of a cluster of AI-powered autonomous transportation technologies being rolled out for airplanes, cars, trucks, trains, and ships, the potential environmental benefits are significant.

8. Logistics. Ever wonder how goods get from their point of manufacture to your local store? That’s the business of the logistics industry, and it too is undergoing major changes via AI technologies, allowing more goods to get to more places more quickly. The recipe for the supply chain of the future mixes autonomous delivery vehicles—cars and trucks, but also container ships and drones—with predicative analysis to reduce transport times and fuel costs. Taken together, moving goods from point A to B can happen autonomously with greater speed and efficiency.

9. Agriculture. Automation systems are already relieving humans of dangerous farming jobs like picking lettuce, which can expose workers to potentially toxic chemicals. AI may also hold the key to the use of automated farming to solve the global food crises by ensuring better crop yields through targeted farming strategies. Drones can already collect data from vast swaths of farmland to identify which areas are thriving and which are at risk of failing. Some researchers are even attempting to teach drones to cooperate with one another, converging on areas with significant weed problems so they can unleash pesticides on the afflicted sections. 

10. Banking. Advances in natural language processing (NLP) have made financial industry self-service systems capable of increasingly complex functions, such as onboarding new customers and assisting them with major loan decisions. Also, machine learning and optical character recognition are further simplifying banking by allowing people to submit financial documents through their smartphones. A customer can snap photos of the documents and the system will automatically upload the images, extracting the relevant information. 

11. Education. Blended learning, in which teachers use technology to enhance traditional classroom environments, is gaining prominence in American schools. Technologies such as machine learning and NLP create the potential for AI-based lifelong learning companions. These programs would tailor their content to individual students based on the subject areas a child struggles with and which lessons are most effective. AI already serves as a kind of digital teaching assistant, taking over tasks such as grading homework and papers so that teachers can focus more deeply on lesson planning and student engagement. 

12. Food. A powerhouse combination of machine learning and DNA sequencing could lead to food products that help people manage chronic disease. We’re not talking kale and blueberries here, either – these superfoods would be developed around specific peptides and how they impact diseases such as high blood pressure and Type-2 diabetes. The speed of AI-powered analysis could advance a field of study that has long grappled with slow results and extremely high costs, and could lead to breakthroughs in nutrition. 

13. Government. AI is augmenting government work across the spectrum, from data entry to disease outbreak responses. Cognitive applications based on neural networks now analyze data anomalies that impact terrorist threat levels or signal shifts in the markets, events that require urgent government attention. Real-time tracking is also helping the government improve medical outcomes by identifying clusters of serious disease outbreaks. The military is developing technologies that can assess soldiers’ wounds based on data collected through wearable technology, enabling medics to prioritize treatments and treat urgent cases more swiftly. In more ordinary cases, sensors on street lights collect real-time data about traffic and maintenance needs and give citizens a heads up when their parking meters are about to expire. 

14. Healthcare. Machine learning is helping doctors make faster, more precise diagnoses by studying medical records and contrasting images of healthy versus diseased organs. This technology could be used to solve the global caregiver shortfalls with better medical diagnosis and healthcare. In 2015 alone, China’s 80,000 radiologists saw 700,000 new cases of lung cancer. Fortunately, AI programs that can identify lesions and other disease markers are helping radiologists and doctors make earlier diagnoses and therefore prescribe treatment sooner. 

15. Law. The use of AI in the legal discovery process is becoming more mainstream. Technology is expanding into other areas as well, including predictive analysis and contract reviews. The former could prove especially valuable to companies as they determine whether to go to trial and assess their risks. Knowing the likely outcome of a case could save significant resources and shape better policies down the road. 

Although lawyers must be involved in contract reviews, legal industry machine learning platforms can decrease the time lawyers spend on those tasks by 20% to 60%, allowing them to focus on high-level tasks only humans can perform. Litigation strategist James Yoon said clients are still willing to pay a premium for complex, high-stakes legal services. “For the time being, experience like mine is something people are willing to pay for. What clients don’t want to pay for is any routine work.” 

16. Nonprofit. AI is literally saving lives in the nonprofit world. One suicide prevention hotline uses machine learning for the greater good to identify the phrases most often associated with emergency cases so it can prioritize those messages and respond faster to people in need. Another nonprofit, this one aimed at improving students’ writing, uses natural language processing to address users’ problems with sentence fragmentation. The organization had its system analyze 100,000 grammatically correct sentences, then used an NLP platform to break those down. Once the program learned to distinguish sentence fragments from complete thoughts, it showed an 84% accuracy rate on picking out fragments in students’ writing.  

17. Insurance. How much privacy would you trade for cheaper insurance? Artificial intelligence is powered by data. And when it comes to data, often more is better. One distinctive aspect of the insurance industry’s adoption of AI is how these companies intend to collect their data. Insurers are turning to sensors that collect data directly from individuals, including technologies like in-home monitors, automobile transponders, and wearables. These new data sources open the doors to new products and pricing models, but whenever data collection intersects with a real person’s life, privacy questions emerge.

18. Mobile telecommunications. Wireless telecommunication companies have access to volumes of data from their millions of customers. One telecom implemented a real-time customer analytics system that enabled it to track and respond to consumers immediately. The data gathered by the new program facilitated better customer service communication driven by the insights from the custom AI system.

19. Investments. People are not, generally speaking, purely rational investors and their irrationality is what makes markets unpredictable. An artificial intelligence algorithm that can anticipate human behavior while also monitoring economic signals in real-time could be highly disruptive to today’s markets (though some insiders have their doubts). Whether or not an AI “super investor” appears on the scene, the investments industry will require ever-smarter safeguards against exploitation and risk.

20. Travel. Online travel booking is nothing new, but AI-assisted vacation planning? That’s more of a novelty. Beyond aggregating flight times and hotel prices, computer programs now pull data about customers’ online behaviors and use learning systems powered by past preferences to personalize recommendations. When a human agent isn’t in the picture, chatbots can now answer questions and book reservations as well. ‘Nothing will ever replace the expertise and intuitive nature of travel agents,’ said one travel industry veteran. ‘Artificial intelligence brings just another component to their tool kit.’

21. Information Technology. IT professionals in particular find themselves at an exciting turning point in their careers. As more companies integrate AI into their processes, to one extent or another, IT teams are learning how to engage with these new technologies. A 2016 report from Narrative Science and the National Business Research Institute predicted that 62% of enterprises will adopt and use AI by 2018. Given that, IT could soon encompass competencies in machine learning platforms, natural language processing, decision management software, and AI-optimized hardware.

22. News media. The media has been under siege by critics and fake news purveyors during the past several years, but it may find an ally in AI. The Associated Press uses AI software to crank out earnings reports, and data companies are increasingly generating information useful to reporters. The lightning speed at which AI algorithms can gather and process multiple types of data could be a boon to journalists, enabling them to report breaking news as it happens. The Los Angeles Times encountered this firsthand in 2013, when it used a bot to report on an earthquake almost as it was happening.

23. Pharmaceuticals. Pharmaceutical researchers are using machine learning to transform drug creation. These platforms analyze medical histories, chemical databases, and past scientific findings to identify correlations between genetic markers and patient outcomes. This method of drug testing costs 50% less than traditional approaches and provides insight into how a treatment might impact certain types of patients. Pattern-recognition technology can provide a view into how different diseases work as well, allowing researchers to develop drugs that will target them more effectively. Most important, AI deep learning enables doctors to provide more targeted treatment plans based on an individual’s genetics and history.  

24. Online dating. Can autonomous systems make better matches than people? After all, people have been matchmaking practically since there were people to match. Dating itself is ripe for disruption: it is time- and labor-intensive and carries a high failure rate. There’s plenty of room for improvement. So it’s not surprising that the online dating industry is exploring adding AI to the game of love, addressing common online dating complaints like dishonesty in profiles and increasing the relevance of the data underpinning matchmaking algorithms. 

25. Motion pictures. Hollywood loves making movies about AI. Now it’s using AI to make and sell movies. There are AI systems that have been used to create movie preview trailers and even write screenplays. But the movie business might see an even bigger impact from AI systems that predict the likelihood that a given script will be a blockbuster. The system was trained using scripts and box office revenue data going back to the 1980’s. Given that just 20% of movies break even, there is a lot of room to improve the greenlighting process.

26. Publishing. With more than 1 million books published each year—a figure up 400% from just 10 years ago—competition for readers’ attention is fierce. Data can help publishers make decisions about which books to publish, but the best-in-class reader analytics solutions can take up to 4 weeks to process data before providing actionable insights. A new generation of AI publishing systems is rewriting the rules, analyzing the text of books to predict reader engagement and sales performance.

27. Semiconductors. You don’t have to do much more than read business headlines to grasp the impact AI is having on the semiconductor industry. Nvidia, until recently known for its graphics processors used in video games, is emerging as a leader in processors for AI number crunching. Google has launched its own AI-focused chip. The CPU king Intel is making acquisitions to catch up. Winners and losers are to be determined, but clearly the chip industry is being shaken up by the demand for AI processing power.

The advanced artificial intelligence projects we’ve talked about here were first covered in the following Entefy articles:

• AI disruption is already underway in these 8 industries
• New traditions in traditional industries: 9 disruptive artificial intelligence technologies happening now
• Making smart use of smart systems: AI’s disruptive impact in 10 industries

Focus and productivity are closely linked. The more time you spend focused on something, the less time you’ll lose to distraction. But intending to keep focused and actually doing it are two very different things. Because in today’s world of information overload and constant distraction, your brain sometimes just needs a rest. 

Luckily, there are quite a few simple techniques for helping your mind stay sharp and focused in even the most distracting environments. This video summarizes 23 of those techniques. Start with one and add more over time!

You can check out slides about this topic here.

Remember when Kodak dominated the consumer film industry? When digital came along, Kodak’s leadership made the fateful decision to double down on celluloid film instead of going all-in on digital. With the benefits of hindsight, we know now that that decision was the beginning of the end for the company’s dominance. Think of all the one-off decisions that led up to that grand strategic misfire. 

When you run a business or manage a team, decisionmaking comes with the territory – who to hire, which ideas to pursue, how much money to allocate to each department, which products to greenlight. Difficult choices are par for the course for many professionals. Unfortunately, the right calls are rarely obvious. Even after months of planning and analysis, it’s entirely possible to go wrong when making challenging choices.  

The challenges of decisionmaking today

In one study, 78% of professionals responsible for making decisions said they struggle to find the right answers in the constantly-changing business landscape. Fear of making the wrong decisions can prevent action, which in some cases is just as bad as choosing incorrectly. 

Fortunately, business leaders today have access to unprecedented amounts of data as well as artificial intelligence platforms that enable them to make better decisions about their companies’ futures. If Kodak executives had had a better grasp of changing market trends, they might have chosen a different strategic focus when shaping their business. 

One promise of AI automation technologies is that these systems allow humans to reach their potential more fully. Managers and other decisionmakers in particular will be relieved of administrative duties, empowering them to spend more time on high-level tasks like strategy and talent development. AI platforms will also generate data and provide analyses that help leaders make smarter, more informed decisions. Clearly the better we understand our own industry and market, the better we can serve them. 

But becoming a better decisionmaker takes more than simply hooking up new AI tools and relying on their outputs. As one business expert said, “If one of the potential promises of machine learning is the ability to help make decisions, then we should think of technology as being intended to support [managers].” Many leaders are already coming around to this redefined management style. In fact, nearly 80% of managers surveyed by Accenture “believe that they will trust the advice of intelligent systems in making business decisions in the future,” according to the researchers. 

To truly leverage the potential of these new tools, professionals must hone their judgment skills and view AI as a powerful new complement to existing business processes. 

Big data, big decisions 

Managing the personnel and processes around smart decisions is a complex challenge. In part because our understanding of how people make decisions evolves with new discoveries in neuroscience and psychology. 

In 2007, cognitive and business researchers put forth a business-focused decisionmaking framework based on the concept of contextual responsiveness. They wrote that there are five contexts in which professionals make decisions: simple, complicated, complex, chaotic, and disorder. To effectively steer their companies, leaders need to understand when to apply wisdom from past experiences, when to enlist expert counsel, and when to let patterns reveal themselves before deciding how to address a crisis. 

Such a framework provides a powerful guide to thinking about business challenges and making decisions in a data-rich world. Data is everything, and there’s more of it than we humans can handle on our own. By 2020, 1.7 megabytes of data per person will be generated each second. Even if data analysts worked 24/7, there’s no way they could keep up without the aid of tools like artificial intelligence.  

The massive quantity of data is a good thing for decisionmakers. More data means the potential for more accurate predictions about consumer preferences and market trends. Instead of relying on past market events or customer behaviors, data allows businesses to use real-time information and increasingly accurate forecasts to shape their decisions. 

Machine learning improves business processes

Machine learning will play a major role in business decisionmaking because it can be used to adjust resource allocation based on real-time measurements and demands, and can personalize customer experiences. The more data a system gathers about different users’ preferences, the more accurately it can predict what types of products and services they’ll gravitate toward in the future. 

What better way to keep people engaged on a company’s website than to offer constant promotions on exactly the types of goods and services that interest each individual user? The consumer data analyzed by AI algorithms can help create more effective product strategies and marketing campaigns, not to mention mitigate the fallout from supply chain disruptions and other business crises.  

The implications for improved business processes through machine learning and other forms of AI are far-reaching. With AI algorithms becoming smarter all the time, managers will be able to automate customer personalization, resource allocation, and even fraud detection in cybersecurity. They can then focus on bigger picture questions and complex problems that machines simply cannot solve. 

Making decisions is one of the most challenging aspects of business leadership. AI is making that process both easier and more flexible. By providing deep insights into industries, customers, and markets, AI is giving leaders more options and capabilities than ever before. Now it’s up to us to make smarter decisions. 

When you’re in need of motivation, relaxation, or inspiration, do you turn to music? Music can energize us, calm us, make us happy or sad, it can cause us to jump about or to tense up in nervous expectation. Yet when it comes to music at work, all of that stimulation doesn’t always translate into better performance. It turns out that there’s quite a bit of nuance in selecting the right music for the work you’re doing.

One report on the value of music found that 59% of respondents felt that music added to their experience at work, while other surveys have found that music improves people’s job satisfaction and/or performance. But while music may indeed make us feel better and more productive, the research examining actual performance while listening to music isn’t so clear. 

Individual differences in music preferences and personality

The sheer variety of music available today means we are each capable of selecting songs that more precisely match our goals or emotions. This has allowed researchers to begin teasing apart what characteristics of music correlate to what aspects of personality.  

It turns out there is a lot you can tell about someone by simply looking through their music collection. Take sensation seeking, a personality trait where people overlook risk in the pursuit of new and intense experiences. Turns out this trait has close ties to rock music. Then there’s the evidence that extroverts prefer music with exaggerated bass, such as dance and rap. 

Research into how personality relates to music preferences split music along four dimensions: reflective and complex (jazz, blues, classical), intense and rebellious (rock, heavy metal), upbeat and conventional (country, pop), and energetic and rhythmic (rap, electronic). The researchers then correlated these with personality dimensions, and found that openness to experience—a personality construct defined by curiosity, a rich imagination, and aesthetic sensitivity—correlated most strongly with the reflective and intense musical dimensions. Meanwhile, extraversion and agreeableness were correlated to the upbeat and energetic dimensions. 

A study of how musical preferences are linked to cognitive styles went in another direction by dividing people into two categories—empathizers are those who focus on emotions while systemizers are those who focus more on patterns and rules. The empathizers were found to prefer music with low arousal (gentle, warm, and sensual attributes), negative valence (depressing and sad), and emotional depth; the systemizers, on the other hand, preferred music with high arousal (strong, tense, and thrilling), and aspects of positive valence (animated) and cerebral depth (complexity). 

Considering the variability in personality and musical preferences, the first point that should be made is that for us to get a benefit from the music we listen to, we should listen to something we enjoy. A study of music’s link to spatial reasoning found that listening to Mozart increased participants’ scores on a spatial task, giving rise to something called the “Mozart effect,” in which listening to just 10 minutes of a Mozart sonata boosted performance on reasoning tasks. However, it was later found that the Mozart effect is not actually linked to Mozart—the same mental boost could be created simply by playing music people liked listening to. 

All of which tells us that selecting music that you enjoy is the first step in using music to boost productivity. Now let’s look at how the actual selection of your music works.  

Music and cognitive performance

When performing more cognitively-demanding tasks, research suggests that less is more. That’s because as tasks grow in complexity, more of the brain’s mental capacity is needed to meet those demands. Which raises an important point: as good as music feels when you’re listening to it, it is nonetheless a distraction, diverting a portion of your cognitive powers whether you’re actively listening to it or not. 

This was demonstrated in a study into how background music impacts work. The researchers looked at people doing five different tasks with varying types of noise, and found that “Performance was lessened across all cognitive tasks in the presence of background sound (music or noise) compared to silence.” In a related study, people performed worse on a memory task when they had music going in the background—regardless of whether they liked or disliked the music. 

It’s also important to remember that this balance between effort and music exists only when both are taking place simultaneously. Given music’s ability to raise our mood and to motivate us, improvements in performance can be found when music is used before a task. Canadian undergraduates performed better on an IQ test after listening to an up-tempo piece of music composed by Mozart in comparison to a slow piece by Albinoni. 

If your job requires you to think carefully, to focus on something complex, then your music should be turned off or virtually unnoticeable. If music is a must, make it instrumental. People are highly attuned to language, which can make music with lyrics a big distraction. Speech is one of the most distracting aspects of open offices: it is more distracting when it can be understood compared to unintelligible speech; and hearing half of a conversation (like one side of a cell phone call) is more distracting than hearing the whole thing.  

The impact of music on creativity

Where does creativity come into the picture? Creativity is often more fleeting and less focused, involving less rigid thinking and more open-ended contemplation. In fact, creativity can often come as a consequence of being distracted rather than being focused. 

Creativity, unlike focused thinking, can often be improved by dividing or distracting our attention. In 2001 a neuroscientist, Marcus Raichle, identified the brain’s default mode network (DMN), a group of brain regions that become active when we turn our attention away from the world around us, and instead tune into our imagination. This network is responsible for mind wandering—the moments in which our mind is filled with random thoughts as we sit back for the ride. Certain aspects of music have been shown to promote activity in the DMN.  

The role of music largely depends on which type of creativity you’re aiming for. If you’re in a hurry and need to force your inspiration, your music should be sparse, even white or ambient noise. If you’re in need of a flash of insight, the type of a-ha moment that comes when you’re distracted—in the shower, walking through the park—then pump up the volume and lose yourself for a while.  

Learn to pair the right music to the task

The key to selecting music while you’re working depends heavily on the amount of mental effort required by the work you’re doing. The more mentally demanding the work, the less you want complex, active music demanding cognitive resources. As uplifting and inspiring as music can be, it requires mental resources, making it a distraction—although in the case of creative inspiration, a helpful distraction. 

Yet when it comes time to buckle down and get challenging work done, less noise works best. And as your task grows in complexity, your music should sink further into simplicity. By following this simple principle, we might be able to achieve a little more in our intellectual and creative pursuits. 

These days, email is a fact of life. But at the rate it’s growing, it’s practically becoming a fact of nature. In this video enFact, we look at just how big email has become around the world by comparing emails to miles, then calculating just how many trips from Pluto to the Sun and back all of our email messages represent.

No wonder we’re spending hours and hours a day in our inboxes!

Read the original version of this enFact here.

Entefy’s enFacts are illuminating nuggets of information about the intersection of communications, artificial intelligence, security and cyber privacy, and the Internet of Things. Have an idea for an enFact? We would love to hear from you. 

It’s been a banner year for cyber criminals. International cybersecurity disasters such as the WannaCry and Goldeneye ransomware attacks impacted thousands of people around the globe and illustrated just how tenuous a grasp most organizations hold on their security. Then there’s the Equifax debacle, which impacted about 1 in 3 Americans. And if the CIA can’t protect its own data from ending up on Wikileaks, what chance do the rest of us stand against ever-more-sophisticated hackers?   

In theory, artificial intelligence can provide new forms of protection against nefarious actors. But the challenge is that those same nefarious actors will also have access to AI technologies. As every day passes, AI becomes more powerful in the hands of both white hat and black hat hackers. And with more and more data being collected and stored globally, the stakes become much higher with every passing moment. 

Security is becoming an increasingly urgent concern, particularly as experts warn against the gaps in the “wildly insecure” Internet of Things. Smart home features, assisted driving systems, and convenient wearables are all perks of living in the 21st century. Yet these same advantages expose us to digital security violations and cybercrime. Our best hope of defending against AI-powered cyberattacks is to leverage the power of AI in cybersecurity. Fighting fire with fire.

The dangers of AI-powered cyberattacks

Before we dive into how AI helps combat cyberattacks, we first need to understand the challenges. Hackers have been spreading viruses and breaking into databases since networked computing emerged decades ago. Security analysts are always working hard to keep up with new threats. But the sheer amount of data that’s now generated makes it impossible for them to track every anomaly and red flag on their own. After all, humans create 2.5 exabytes of data each day – the equivalent of 250,000 Libraries of Congress. 

“IT security teams are struggling to see what is happening in and around their IT infrastructures,” wrote one business expert. “They struggle to understand where all corporate data lives and who has access to it, not to mention what users are doing with that access.” With hundreds of millions of new security logs created each week, security teams cannot possibly process that data without technological assistance, suggesting new applications for AI. 

As AI has become a growing presence in people’s daily lives – think voice-activated assistants and car autopilots, for instance – our understanding of the opportunities and dangers has matured. 

“The rise of brain-computer interfaces, in particular, will create a dream target for human and AI-enabled hackers. And brain-computer interfaces are not so futuristic — they’re already being used in medical devices and gaming, for example,” one computer science professor wrote about near-future cybersecurity threats. “If successful, attacks on brain-computer interfaces would compromise not only critical information such as social security numbers or bank account numbers but also our deepest dreams, preferences, and secrets.”

Rather than worrying about a malevolent AI rising from the pages of a sci-fi novel to conquer us all, our fears are coming back down to earth. But that doesn’t mean they’re any less scary. In fact, we should be more concerned about malicious or incompetent humans misusing AI and causing unprecedented security challenges. 

At least, that was President Barack Obama’s view of AI. He worried more about hostile actors using AI to commit cyberattacks that harm millions of people than about an autonomous system taking it upon itself to wipe out humanity. 

“There could be an algorithm that said, ‘Go penetrate the nuclear codes and figure out how to launch some missiles,’” Obama told Wired. “If that’s its only job, if it’s self-teaching and it’s just a really effective algorithm, then you’ve got problems.” 

With that in mind, it’s no wonder that the Defense Advanced Research Projects Agency (DARPA), an arm of the Department of Defense, invited high-level hackers to develop proactive AI hacking systems that could detect and repair system vulnerabilities before the bad guys could find them. 

The cybersecurity risks associated with AI are real, and they’re less sci-fi than early hysteria made them out to be. Companies are struggling to recruit qualified cybersecurity professionals due to both a lack of supply and a lack of knowledge, according to an ISACA cybersecurity survey. Even if they can get candidates in the door, hiring managers may not know exactly which skills are needed to defend against attacks. Not only do cybersecurity workers need to understand existing threats, they must also be able to adapt to the ever-shifting, complicated nature of the field. 

Given the growing amount of data, rising number of cyber threats, and the rapid pace of technological change, it’s clear that humans can’t battle cyberattacks alone. We need AI to defend our data against AI – or more specifically, against the humans who would wield AI for less-than-virtuous purposes. 

Artificial intelligence (and humans) to the rescue  

Now that we’ve covered the worst-case scenario side of things, let’s look on the bright side. AI is one of the most powerful tools humans have ever invented, and researchers are already leveraging AI-powered programs to combat cyber threats. In addition to DARPA incentivizing white hat hackers to build autonomous threat-detection systems, experts are using machine learning to beat criminals at their own game. 

Right now, one of the major threats to our virtual and digital well-being is hackers manipulating computer systems through misleading patterns. As a senior editor at MIT Technology Review wrote recently, computer programs “are vulnerable, in part, because they lack actual intelligence.” Without the instinct to reject suspicious-sounding commands, programs may download malware or make incorrect route calculations for self-driving cars. Security experts are experimenting with using machine learning to spot fake patterns and signals so programs will learn to ignore those and avoid cyberattacks. 

Others are taking a hybrid human-AI approach to cyberattacks, developing programs that analyze millions of security logs, flag any suspicious activity, and refer the potential hacks to human analysts. The experts then conduct their own analyses to determine if there’s been a breach. They document the outcomes of these investigations, and the AI system integrates this data into its understanding of what constitutes a threat. In this way, it learns from its mistakes and successes, which leads to a higher accuracy rate and reduced vulnerability.  

AI can dramatically decrease the amount of time it takes to discover security breaches, with some organizations striving to lower alert times from months to minutes. As cyberattacks increase in number and sophistication, time will be increasingly important to mitigating the data and financial fallouts from these attacks. Although many data breaches are caused by system breakdowns or human errors, research indicates that criminal cybersecurity attacks are on the rise. Detecting a hack within the hour after it happens is vastly preferable to finding out months later, when millions of pieces of data have been exposed. 

Becoming our own cybersecurity heroes 

Unfortunately, the cybersecurity war has no end in sight. As new technologies emerge, black hat hackers will find clever new ways to steal high-value data, and white hat security experts will leverage those same technologies to combat the assaults. Companies will likely need to create cybersecurity ecosystems that combine multiple forms of AI to protect their data, and their customers’ data, through fast detection and crisis mitigation protocols. 

There are ways for you and I to protect ourselves as well. Researchers are using AI to create password-generation tools that make it more difficult for attackers to crack, and it’s important that we all monitor such trends and take responsibility for our personal password maintenance. But keep in mind, too, that the more personal information you share online, the more AI-savvy hackers have to work with. 

Some experts predict that hackers will be able to use phishing and machine learning to generate scam emails that sound eerily similar to your own communication style, accurately guess your responses to security questions, or send fraudulent text messages to get you to reveal private information. To stay protected, always check that emails are coming from secured, trusted senders and avoid providing information over unsecured connections. Monitor your accounts for suspicious activity, and trust your instincts when a message or transaction feels off. Hackers are highly motivated and often quite skilled at what they do. But that doesn’t mean victimization is a foregone conclusion.  

As is the case with so many facets of AI, the future of cybersecurity requires both artificial intelligence capabilities and proper human judgment. Humans will need to provide context to the output of AI systems, train both machines and themselves to better determine when there’s a true threat, and find new ways to beat criminals to system vulnerabilities. The battle goes on, but we can use AI to ensure that the bad guys don’t win. 

Personal productivity is a key aspect to professional success. When it comes to ideas for increasing your productivity, there’s no lack of advice. “7 Life Hacks to Accomplish Anything!” “154 Tips for Busy People.” “223 Best Hacks for Work.” The tips and hacks are seemingly endless. But notice how these lists seem to say that there are ever-changing best practices when it comes to professional productivity. As if tomorrow, someone might invent a new and better way to achieve success. 

The problem with improving your productivity using mere hacks isn’t the suggestions themselves. It’s that the suggestions tend to be only narrowly useful. “Do your hardest task first” sounds reasonable, but what if your personal biorhythms make you more productive and creative later in the day? The better habit is to simply know your body clock.

Then there’s the implication that a person can selectively cobble together a set of hacks that will somehow instantly transform them into a super-productive, hyper-successful professional. Success isn’t that simple. 

But it doesn’t have to be all that complicated, either. The true secrets to success aren’t secrets at all. In fact, you probably hear them all the time. A growing body of research shows that the most effective productivity “hacks” are actually the accumulated wisdom of humankind over the past 7,000 years or so. The same habits that have elevated individuals and societies since humans moved from small tribal hunter-gatherer groupings to the much more complex challenges of agricultural and urban living.

The future of productivity is the past

They’re often called the “seven virtues,” a set of habits that show up in virtually every complex civilization, sometimes under different terms, and sometimes in slightly different groupings. Aldous Huxley drew attention to these cross-cultural overlaps in The Perennial Philosophy. What are the seven virtues and what do they represent?

• Prudence, encompassing common sense, reason, wisdom, good judgment, foresight, diligence, and discretion.
• Justice, representing due process, honesty, integrity, law, right, and truth.
• Temperance, or restraint, self-control, frugality, moderation, and humility.
• Courage, comprising bravery, firmness, fortitude, grit, and determination.
• Trust, or broadly, acceptance, belief, confidence, loyalty, and conviction.
• Hope, which is aspiration, confidence, optimism, and a forward-looking perspective. 
• Love, and the habits of commitment, affection, appreciation, friendship, and respect.

Those are some seriously old-fashioned-sounding words. We can perhaps bring them up-to-date by expressing them in the language of productivity advice: 

• Work hard
• Use common sense
• Be fair
• Demonstrate grit and determination
• Trust and be trusted
• Be hopeful
• Maintain self-control
• Invest in relationships

That is a reasonably brief list of life and career habits that is pertinent to a broad range of circumstances. But is there evidence that these productivity habits actually work? Put simply: yes.

Over the centuries and across cultures, a lot has been written about these behaviors, though often limited to philosophical, religious, cultural, or anthropological perspectives. Empirical research into their practical implications is relatively recent. 

One such research project is the World Values Survey, which allows us to measure the differences between national cultures and link those differences to national outcomes. There are many other sources of data on behaviors and virtues and their linkage to life outcomes such as the U.S. Census Bureau’s American Community Survey, longitudinal studies such as The Lewis Terman Study, and national databanks from countries like Sweden and Iceland that maintain integrated databases on issues like health, income, sociology, and crime. 

The big picture is that these and other research projects support the idea that some of the best productivity advice is actually rather timeless.

What the data says about productivity

There is some real insight into productivity to be found by looking at…poverty. One finding that has been independently documented multiple times is a triple-package of personal behaviors that shield a person from poverty. Fewer than 2-4% of people who meet the following criteria end up in poverty:

• Graduate high school, related directly to the habits of Courage, Prudence, Temperance, and Hope.
• Get married, stay married, and don’t have children before marriage, related directly to Courage, Prudence, Temperance, Hope, Love, and Trust.
• Stay employed, regardless of job quality, directly related to Courage, Prudence, Justice, and Temperance.

Looking at each of the “seven virtues” individually, here’s how the research plays out. Let’s start with Trust. Multiple studies have found a strong association between countries with high levels of trust and high levels of prosperity. The causal mechanisms are still being researched and debated, but it appears that cultures that foster trust reduce social and economic friction, thereby improving efficiency and increasing rates of innovation. 

Similarly, countries with strong Justice institutions, reflecting rule-of-law and due process, also have a strong correlation with higher levels of national and individual prosperity. The degree of self-control (Temperance) a person exhibits in childhood is a predictor of beneficial future life outcomes. Grit and time-discounting (Courage) are well-documented precursors of achievement. Hope is also strongly associated with positive life outcomes and is, in fact, an integral part of the placebo effect. Finally, Love (defined broadly as personal and social relationships) has also been demonstrated to be closely affiliated with positive life outcomes.  

In an era of rapid technological change, it is refreshing to see that wisdom dating back thousands of years retains such relevance in the equally-ages-old pursuit of productivity and prosperity. 

Feel like you have a limited attention span these days? You’re not alone. Throughout the day we’re bombarded by new information in one form or another. And it’s difficult to moderate how much information we consume when there’s always something new and intriguing calling out for a click. 

When your attention is a limited resource, the challenge is knowing how to spend it wisely. In this video, we’ll take a look at the many forms of information overload at work in the world, then present ideas for how to tame all that complexity.

You can check out slides about this topic here. 

Feel like you’re being watched? It’s probably because you are. 

Entefy is developing cyber security technologies that protect users’ private data, so we’re naturally very interested in what we call “data trackers,” digital surveillance systems designed to go unnoticed as they capture your sensitive personal data. 

Not all data trackers are secret or even nefarious. Some surveillance is disclosed through Terms of Service or other user agreements. After all, it’s become a rule that if you didn’t pay for the app, you’re the product—your usage will be collected and monetized, usually through targeted advertising. 

Data trackers come in all shapes and sizes. They can take the form of security vulnerabilities—like the example below of hackers stealing smartwatch data to deduce ATM PIN codes from the motion of the victim’s hands. Others we simply accept as facts of 21st century life or fair payment for free services we can’t live without. Still others are just plain disturbing, like toys designed to record children while they play.

Below we’ve assembled a list of 39 different data trackers that Entefy has researched over the past year. The point of this article is education: to provide you with information about data collection activities that can easily go unnoticed. Informed digital consumers can become secure digital consumers. But at the end of the day, it’s difficult to read about all of these data trackers and not end up feeling just a little bit icky about the sheer number of companies profiting from your personal data.

The items below were first covered in the following Entefy articles:

• Hiding in plain sight: 8 digital security threats in everyday life
• 10 signs data privacy is the new Wild West
• Seven new assaults on data privacy 
• Collected, bundled, and sold: your sensitive private data
• Data trackers are watching your every (digital) move

Secret and not-so-secret data trackers and cyber security threats

1. Think you’re safe from privacy violations at work? You’ll probably want to know that one report estimates 15% of the Fortune 500 make use of secret tracking devices hidden in lights and ID badges. One surveillance vendor reports that 350 different companies are using its products to monitor “conference room usage, employee whereabouts, and ‘latency’—how long someone goes without speaking to another co-worker.”
2. The CEO of iRobot, the maker of the popular Roomba automated vacuum cleaner, caused a stir after apparently suggesting the company was seeking deals to sell data about the layout of users’ homes to third parties. The company later clarified that it didn’t have any plans to sell the data without users’ consent. The situation shines a spotlight on the ongoing tension between personal privacy and the monetary value of certain types of consumer data. 
3. Achieving the elite heights of pro sports apparently doesn’t make you immune to privacy threats. The NBA and its players’ union are in conflict over how much data can be collected and shared using wearables like fitness trackers. The player’s union is seeking control over what data is collected and how it gets used. Exactly the same legal issues and ethical considerations that are being raised as more and more employers deploy wearables to their employees. 
4. Your car is watching. Computer systems in many newer cars create records of pretty much everything you do on the road, from logging telephone calls to recording how fast you drive. The challenge for consumers is figuring out what’s being collected, and where it goes afterward. The legal situation in the U.S. is murky, with no one law covering data collection by automobiles.
5. Be careful what you say in front of Barbie. A study from University of Washington researchers demonstrates how the Internet of Toys is raising new privacy questions. In interviews with parents and children about the use of Internet-connected toys, the researchers found that children were unaware that their toys were recording their voices, and that parents worried about privacy pretty much any time the toys were out of the toy boxes. 
6. A lighthearted Facebook meme may unintentionally telegraph answers to your banking security questions. The post, called “10 Concerts I’ve Been To, One is a Lie,” asks users to share information about concerts they’ve attended. The problem is that “Name the first concert you attended” is a common security question used by banks and other financial institutions for online authentication. Phishing aside, the meme can also “telegraph information about a user’s age, musical tastes and even religious affiliation — all of which would be desirable to marketers hoping to target ads.” 
7. Usage-based insurance (UBI) is the term for insurance products that are priced according to specific usage factors. UBI auto insurance, for example, is priced on factors like how often a driver uses their car, how fast they take corners, and their average speed. University researchers were able to demonstrate that it’s possible to reveal personal data by pointing an AI algorithm at usage-based insurance data stored in the cloud. One researcher commented, ‘An attacker only needs one part of the information provided to a UBI company to discover a driver’s whereabouts, home, work, or who they met with.’
8. An audit by the Internet security nonprofit Online Trust Alliance found that 6 of the 13 “Free File Alliance” tax websites approved by the IRS provide inadequate security and privacy protection. The report states, “Criminals are increasingly penetrating IRS systems, targeting e-file service providers and harming consumers through bank account take-overs, identity theft, ransomware and compromising completed returns to redirect tax refunds.” As if April 15 wasn’t stressful enough.
9. The Lumen Privacy Monitor will tell you which apps are collecting your data. 7 in 10 smartphone apps share your data with third-party services. To help users become aware of which apps are collecting data from them, researchers developed an app that lets users “see their information collected in real time and the identity of the entities receiving the information.”
10. Get your Facebook data back. Do you ever wonder how Facebook gains so much insight about its users? The free browser extension Data Selfie sheds light on Facebook’s machine learning algorithms and “tracks all the digital breadcrumbs you would leave behind when using Facebook (hint: it’s a lot of breadcrumbs) and creates your personality profile.”
11. It’s possible to hack a phone through sound waves. Accelerometers measure rest and acceleration in smart devices and are commonly found in smartphones, fitness trackers, and automobiles. Although helpful for navigation and orientation, there’s been a recent discovery that accelerometers are susceptible to vulnerabilities. “Researchers describe how they added fake steps to a Fitbit fitness monitor and played a ‘malicious’ music file from the speaker of a smartphone to control the phone’s accelerometer. That allowed them to interfere with software that relies on the smartphone, like an app used to pilot a radio-controlled toy car.”
12. ESPN collected first-party data on about 106 million of its users. ESPN collects information such as a person’s favorite teams, leagues, and players, as well as displays strategic advertisements based on these preferences. If a Warriors fan visits the website after a win, advertisements for special merchandise will appear when that person checks the website. For the ESPN visitors that do not volunteer their preferences, the network can figure out sport preferences by tracking their behavior online.
13. Smart TVs are known to track personal data, and Vizio got caught. Earlier this year, Vizio paid $2.2 million to settle charges for monitoring viewing habits of more than 11 million TVs without consent. “The main problem was that Vizio TVs had tracking features turned on by default, instead of an opt-in setting like many other manufacturers use…but the situation is now a relatively good one for Vizio TV owners: the company is specifically prohibited from tracking your viewing habits without explicit permission.”
14. Google can track when someone clicks an ad and buys something from a physical store. If you see an online advertisement for a product, then go to a store and buy it with a credit card, Google can track your behavior and report the data to marketers so that they can see how effective their advertisements are. “How does Google know if you bought something at Subway or Aldo? It works with the credit and debit card companies to match up in-store purchases with your online identity. The company has partnerships with companies that account for 70% of credit and debit card purchases in the U.S.”
15. There are “microdots” on printed documents that encode the serial number for the original printer. Research that printers might be spying on us has been around for a while. A recently leaked document has brought it to the forefront of the news again. After a quick analysis of documents related to a National Security Agency leak case, experts “seemed to reveal the exact date and time that the pages in question were printed: 06:20 on 9 May, 2017 – at least, this is likely to be the time on the printer’s internal clock at that moment. The dots also encode a serial number for the printer.”
16. Not all virtual private networks (VPNs) are created equal. VPNs create an encrypted connection between your browser and another private server, and protect users from things like malware. But it can be hard to tell how secure every VPN is and what it’s doing with your data. To optimize security on your VPN, “avoid free services, and…look into setting up your own. Otherwise, make sure a paid VPN has a privacy policy you’re okay with…And on a larger scale, remember that the best solution is still policies that would tackle the problem at the source: ISPs’ ability to sell your data.”
17. Twitter’s new privacy policy with invasive defaults doesn’t “sound good.” Twitter has updated its privacy policy in order to provide users with a more personalized experience, which includes very specific tailored ads. Twitter “will now record and store non-EU users’ off-Twitter web browsing history for up to 30 days, up from 10 days in the previous policy.” This policy is on an opt-out basis. You can “click ‘Review settings’ to opt out of Twitter’s new mechanisms for user tracking.” Wondering why EU users are exempt from this privacy policy? Read on.
18. Amazon Echo Look is collecting a full picture of you and your home. The cloud-connected camera wants to give consumers feedback on their outfits by using advice from fashion experts and machine learning algorithms. “The lookbook is a digital collection of ‘what you wore and when.’” But what’s important here is that “you’re potentially giving the tech giant a lot more data than just the type of chinos you sport. The pictures can reveal socioeconomic status, whether you’re married, religious affiliation (hello cross above your bed), and potentially a lot more.”
19. Evernote attempted to update its privacy policies to make it clear that its employees could read your notes, without the option to opt out. But users protested and the company reversed the changes: ‘We announced a change to our privacy policy that made it seem like we didn’t care about the privacy of our customers or their notes. This was not our intent, and our customers let us know that we messed up, in no uncertain terms. We heard them, and we’re taking immediate action to fix it.’ 
20. A Canadian consumer data privacy advocacy group found that many popular fitness tracking devices transmit your data in ways that make the devices vulnerable to interception or tampering. And the devices can potentially be used to track your movements and profile you: “We discovered severe security vulnerabilities, incredibly sensitive geolocation transmissions that serve no apparent benefit to the end user, and that were not available to users for access and correction, and unclear policies leaving the door open for the sale of users’ fitness data to third parties without express consent of the users.”
21. A study published in the Journal of American Medicine looked at a large collection of diabetes apps on Android and concluded: “Most of the 211 apps (81%) did not have privacy policies. Of the 41 apps (19%) with privacy policies, not all of the provisions actually protected privacy (e.g., 80.5% collected user data and 48.8% shared data). Only 4 policies said they would ask users for permission to share data… Patients might mistakenly believe that health information entered into an app is private (particularly if the app has a privacy policy), but that generally is not the case.”
22. If you’re worried about protecting your activity on Facebook, it’s worth recalling that the social network makes it easy for its advertisers and partners to track you freely: “Most people forget that when they download an app or sign into a website with Facebook, they are giving those companies a look into their Facebook profile. Your profile can often include your email address and phone number as well as your work history and current location.”
23. Meitu, a popular photo-editing app that requires a long list of permissions, has other potential security vulnerabilities: “[Security experts] found numerous serious privacy flaws and avenues for potential leaks of personal data. One eagle-eyed researcher found the Android version of the app asked users for dozens of intrusive permissions, and sends the data to multiple servers in China—including a user’s calendar, contacts, SMS messages, external storage, and IMEI number.”
24. WhatsApp was in the news after a disputed report about a security vulnerability; what emerged from the discussion was awareness that the app’s privacy policies are not clearly defined: “One of the biggest concerns around WhatsApp from a privacy perspective is its opacity, as frequently noted in the Electronic Frontier Foundation’s assessments of which tech providers ‘have your back.’ Whilst [WhatsApp] owner Facebook does have a transparency report, released twice a year, it doesn’t drill down into how many data requests relate to WhatsApp, let alone what kinds of information it can hand over.”
25. Your ambient conversations aren’t believed to be recorded, but Alexa and Google Home listen to everything you say in order to activate each system. You can restore privacy by using the physical mute button on each device. “If you only use Chrome in ‘Incognito Mode,’ put tape over your laptop camera, and worry about snoops sniffing your packets, a web-connected microphone in your home seems risky.”
26. Uber is collecting location information for up to 5 minutes after rides end. Unless you opt out, Uber collects a rider’s location even after closing the app. The company stated that its intention is to improve pickups and drop-offs and measure safety issues like how often riders cross the street after their ride. The company recently announced that it intends to quit user tracking.
27. Some types of wearable devices record the movements of users’ hands. These devices can be hacked in real time to reveal ATM PIN numbers and other key-based security codes. Researchers stated that “Adversaries can obtain sensor readings of wearables via sniffing Bluetooth communications or installing malwares on the devices, and further infer the user’s PIN sequence.”
28. The conversations kids have with these cute toys through an app are being sent to a third-party server in the U.S. without asking for permission first. The app uses a popular voice recognition technology; the problem is that parents aren’t clearly notified that kids’ voices are recorded and sent to a third-party that states in its Terms of Service the data can be used for advertising or further shared with other third-parties.
29. Android apps that are downloaded outside of Google Play are not always secure. Hackers create lookalike apps that, when downloaded, can take over a device, spread ransomware, and steal data. One malware campaign known as “Gooligan” infected more than 1.3 million Google accounts globally, primarily in Asia. You can check to see whether your account was compromised by visiting this site. 
30. Headphones can be hacked into and used as listening devices. Researchers in Israel have “created a piece of code designed to prove it’s possible to hijack a user’s headphones and turn them into a covert listening device…[The malicious code] captures vibrations in the air and converts them to electromagnetic signals able to capture audio.”
31. Even when Shazam is turned off on a Mac, the microphone remains active. The stated purpose was to create a better user experience, but it leaves the app vulnerable to hacking. After users complained, the company stated that it intended to reverse the decision and issue a patch for Mac users.
32. Ultrasonic cross-device tracking uses high frequency audio signals—that you can’t hear—to track your online and offline behavior and assemble a profile of what ads you’ve encountered, what websites you’ve visited, and where you’ve been. Most users are unaware that when they grant an apps permission to access their smartphone’s mics, “apps that use ultrasonic tracking could access their microphone…all the time, even while they’re running in the background.”
33. Individuals can install software that informs them when someone opens their emails without you being notified. The software has legitimate uses, like ensuring important emails reach their intended recipients. But by operating in secret, the technology is ripe for abuse. Like one case of a fan stalking the rapper Jay-Z. 
34. Phone metadata created by calls and texts can reveal private information about you, like the status of your health. Stanford researchers built a smartphone app that collected phone call and text message metadata like the frequency, time of day, and duration of communications. They were then able to determine very specific information about individuals from that metadata, like that one study participant had a heart condition and another owned a particular model of assault rifle.
35. If you download popular free apps on your Android or iPhone, it’s respectively 73% and 47% likely that your personal information has been shared with third parties. “The average Android app sent sensitive data to 3.1 third-party domains, and the average iOS app connected to 2.6 third-party domains.” Many of these connections are disclosed to the user, while many were not. 
36. Your browser settings and battery levels are “fingerprinting” that is personally identifiable and trackable across devices. User tracking has evolved to be far more sophisticated than cookies, those small files that contain information about you. Advanced tracking systems can infer from usage patterns that, for example, a smartphone and a laptop are used by the same individual.
37. Frequent Locations on your iPhone records your every move unless you turn it off. “Apple says that the data is stored only on your device and nowhere else unless you opt into to share it with the company to improve the Maps feature. In that case, the company says it stores user private data anonymously.”
38. Hackers can follow you in real-time while you’re using a traffic app. In a published paper, researchers at UC Santa Barbara studied how Sybil attacks—a type of security threat when a node in a network claims multiple identities—could cause mayhem. They concluded, “Our experiments show that a single Sybil device with limited resources can cause havoc on Waze, reporting false congestion and accidents and automatically rerouting user traffic.” 
39. In another case of spying toys, major toy companies breached the U.S. Children’s Online Privacy Protection Act and collected data about children for third parties. “The companies used technology that allowed third-party vendors to collect and use personal information from children under the age of 13 without parental approval.”

Security guides for protecting your digital identity

In our article, “Now is not the time to take a nap in your security blanket,” we shared resources that describe steps you can take to increase your digital security profile—everything from setting up VPNs to one simple action the FBI recommends for preventing unauthorized use of your laptop camera. 

1. This guide to setting up a Virtual Private Network “in 10 minutes for free” describes the steps you can take to install the secure Opera browser, evaluate VPN providers and products, and start using the Electronic Frontier Foundation’s HTTPS Everywhere browser plug-in.
2. The consumer privacy nonprofit Fight for the Future created an interactive guide to protecting smartphones, laptops, and desktops. This resource is designed to be accessible to computer users of any knowledge level.
3. Following the revelations about C.I.A. hacking, the New York Times produced a guide to protecting iPhone and Android smartphones as well as smart TVs, routers, and personal computers. 
4. Consumer Reports magazine produced a guide with 66 actionable tips for protecting your privacy, covering steps to prevent personal data collection, select better passwords, and even protect your data after death. They published a 10-minute digital privacy tune-up as well.
5. Quick tip from the FBI: cover your laptop’s webcam camera.
6. Famed hacker Kevin Mitnick shared his tips on how to secure your smartphone and laptop.
7. Here is a roundup of 10 low-tech ways to guard your online privacy, covering tips like plugging your headphone jack to thwart hackers from hijacking your smartphone’s mic.
8. Understand more about what encrypting your Internet activity from your ISP does and doesn’t do.

The digital world is complex and constantly evolving. Spend some time determining your comfort level with automated data collection, then evaluate the apps and services you use against your personal standards. There are millions of apps available on the major mobile platforms, so be sure to look for high-quality apps that don’t depend on advertising data collection. Stay informed, stay (digitally) safe.