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Every day, trillions of words are transmitted online in one digital form or another. Emails, texts, social shares, blogs, comments, and so forth. On the one hand, it’s great that so many people are finding their voices and sharing their thoughts. On the other, it’s getting harder and harder to find what you’re looking for among all those words.

In this video enFact, we examine just how chatty everyone has become by comparing our digital words to the contents of the world’s largest library, the Library of Congress. 
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. 

Welcome to the very near future. You’ve just arrived at the offices of AcmeFutureCo where you’re confidently awaiting your first on-site interview for an exciting new job. As artificial intelligence and automation have reshaped the American economy over the past 10 years, you’ve immersed yourself in developing new skills and honing your creative and critical thinking to stay competitive in the modern automated workforce. 

Since you’ve worked as an independent solopreneur for years, you notice immediately that you’re not visiting anything like the corporate workspaces of old. You’re visiting the office of the future. 

Goodbye routine work, hello extra time

As you step into the airy and well-lit office, you’re greeted by a virtual receptionist, a perfectly poised, natural-sounding holographic assistant: “Welcome to AcmeFutureCo. How may I assist you?” These days automated systems like this, using natural language processing (NLP), optical character recognition (OCR), and conversational interfaces, handle repetitive tasks large and small. The company’s former receptionist was promoted just a year earlier to manage logistics and procurement.

The first wave of AI’s takeover of administrative tasks was cheered by employees at all levels of the organization. Freed from mundane repetitive work, they were able to focus on high-value projects that were in many cases also personally relevant and fulfilling. The company’s front-line managers saw their jobs redefined. Pre-AI, they would spend more than 50% of their time on administrative coordination tasks and only 10% of their time on strategy and innovation. Artificial intelligence reversed the equation. With AI systems handling everything from scheduling to report generation, employees have more time to invest into high-level strategy sessions and employee development, initiatives that have boosted morale and retention rates within the company. 

That’s true across the AcmeFutureCo organization where team members are using their working hours far more productively. Back in 2014, it was estimated that people wasted more than half of their workday on email and other distractions. At the company, every one of those lost hours has been reclaimed by offloading low-level correspondence and necessary but monotonous tasks to AI systems.  

New capabilities in Human Resources

The hiring manager leads you into the sleek conference room where you meet the lead managers of your department. After a few minutes of cheerful small talk—some things never change no matter how advanced workplaces become—the department head launches into a presentation about the position you’re applying for. 

If her slides seem especially polished, you can thank AI for that. It used to be that everyone would groan when the boss launched into a series of poorly designed presentation slides littered with hard-to-read text. Artificial intelligence now spares everyone those cringe-worthy presentations by automatically generating context-aware presentations with visually pleasing, easy-to-read slides. For this alone, you are grateful. 

After the presentation, the interview panel engages you in a deep discussion about your work philosophy, professional expertise, and fit for the company. They’re already well-versed in your background, experience, and temperament, thanks to their AI-powered candidate vetting system. AcmeFutureCo uses an artificially intelligent voice analysis algorithm to evaluate candidates during their preliminary interview calls. The system gauges vocal tone and inflection to predict the types of roles for which an applicant might be suited. The company’s recruiters use those results to supplement their other evaluation tools, with the voice analysis providing new insight even as it reduces the overall time it takes to manage the process. 

You suspect that artificial intelligence played a role during each recruitment step leading up to today. 

In the past, recruiters and hiring managers spent hour upon hour reviewing professional profiles and résumés by hand. Without the assistance of AI, a typical recruiter would need to scour 2,000 applications to find qualified candidates to fill 8 to 12 positions each month. To keep up, the average recruiter spent just six seconds reviewing each job application. And that was before time spent scheduling phone screening calls and coordinating interviews with candidates and the hiring team. Very little time was left over to do the deeper vetting necessary to identify superstar talent. 

The company uses machine learning to conduct initial application screenings, helping its talent team identify promising candidates sooner and letting them focus more on assessing the candidates’ skillsets and whether there is a cultural fit. Recruiters end up spending far more time working with potential hires and on initiatives like retention strategy and new employee onboarding improvements. 

Marketing teams with artificial intelligence

Back to your interview. The members of the hiring panel clearly like what they hear from you because you’re invited to meet more of the team. As you’re led around the office, you see a group of creatives from the Marketing team working from their laptops at a shared table, a set-up that allows them to collaborate as they review that week’s predictive analytics report. 

Artificial intelligence helps Marketing anticipate shifts in customer behaviors, providing the data that fuels the creation of high-value marketing content. Armed with up-to-the-second data on the types of messaging that are most likely to resonate with the company’s target audience, the creative team crafts marketing copy, visuals, and other assets that perfectly reflect their audience’s preferences and values.

The team doesn’t use AI to make marketing decisions, but rather to provide them with data-driven insights. The heart of their work lies in how effectively they interpret and implement those insights to shape campaign strategies and tell stories in ways their audience finds interesting and relevant. This sort of complex creative work is a hallmark of creatives in the AI age, and it’s only possible because of automated data collection. Back when marketers would spend 3.5 hours each week gathering data plus another 3.5 hours on email, marketing campaigns were extremely expensive and time-intensive. With AI backing them up, AcmeFutureCo marketers channel their creative resources into crafting better content and serving their customers. Machine learning algorithms revolutionize customers’ online experiences through delivering real-time, context-aware content and recommendations that correspond to the customer or prospect’s stage of the sales funnel.  

The new face of Sales and Service

Your next stop is AcmeFutureCo’s Sales team. Sales representatives use AI to connect with prospects and customers more meaningfully, since they enjoy constant access to predictive analytics and rich customer profiles that are continually updated through deep learning and NLP. With a nuanced picture of each customer’s buying history and pain points, sales teams can humanize the sales process—bye bye “cold” calls. 

The Customer Service team was among the first departments to enjoy the benefits of AI-powered customer service systems. The company started with basic chatbots that used NLP to interpret and respond to common customer questions, shifting the responsibilities of service agents to more complex queries. With more time to spend with customers, agents were better able to develop a rapport with callers, learning more about their needs and upselling when appropriate. 

But AI didn’t just relieve the Customer Service team from answering the same questions over and over. AI platforms also supply agents with dynamic real-time data as they work with customers. This “human in the loop” model is a great example of how AI and humans can work together so effectively. 

Data wizardry

Off to one side of the office you notice small pods where one or two people work. These folks are the company’s data scientists who spend much of their days pouring over the insights being generated from artificial intelligence programs. Once upon a time, data scientists used to spend almost 80% of their time preparing and managing data for analysis. Largely because 90% of the world’s data used to be unstructured. The team built an AI system using technologies like optical character recognition and voice analysis to turn their data stockpiles into something valuable. Today, that data powers AI systems throughout the company, like a custom system that lets senior management glean insight from customer service call transcripts, helping them better understand the company’s performance and make data-driven strategic decisions.  

As your conversation with the data science team draws to a close, you’re led back to the front for farewells and handshakes. You reflect on what you saw: A highly productive workplace built on a foundation of human-AI collaboration. Contrary to fears about machines replacing flesh-and-blood employees, AcmeFutureCo uses artificial intelligence to unleash enormous potential within the organization by removing the constraints of time-wasting and tedious processes. 

Your visit gave you a firsthand look into the symbiotic AI-human relationship that is reshaping business. And as your phone buzzes with a new email, you realize you’ll get the chance to learn a lot more about it: Congratulations, you got the job! 

The reason first impressions are so powerfully important starts with bias. Bias is prejudice for or against a person, an idea, the model of a car—practically anything. Positive bias underpins an optimistic personality, viewing events in their most favorable light. Negative bias is at play in many of the world’s –isms: sexism, racism, elitism, ageism, and so forth.

Confirmation bias is a special type of mental favoritism, defined as the tendency to interpret new information in a way that confirms an existing belief. From an evolutionary standpoint, confirmation bias helps the brain resolve complexity quickly. This cognitive sleight-of-hand is also what allows two people with opposite opinions on, say, climate change to view the same facts as supporting their own polar opposite viewpoints. 

But that’s not quite enough to explain first impressions. You have to consider timing. The order in which we learn information works its own quiet influence. We give more importance to information learned earlier than to information learned later. That early information forms our baseline opinion, then we evaluate later information against it. You can see this tendency in how you ignore a bad habit in an old friend but in anyone else it drives you crazy.

Taken together, these two mental quirks—confirmation bias and the tendency to prefer what we learn first—explain why first impressions matter so much. The first things you learn about a person anchor your opinion of them, and then you’ll tend to interpret everything you learn later as supporting your original opinion. 

You can dive deeper into the science behind confirmation bias in Entefy’s article “The hazards of confirmation bias in life and work.”

Dr. Rudd Canaday is Entefy’s Software Architecture Fellow. He is a co-inventor of the UNIX operating system and was a graduate student at MIT’s pioneering Computer Science & Artificial Intelligence Lab. Rudd shares some of his early AI experiences below.

After graduating cum laude in Physics from Harvard University, I went on to MIT for my graduate work. I started there in 1959, spending the first year and a half completing coursework for my Ph.D. qualifying exams. With that milestone behind me it was time to choose my Master’s thesis topic. It was then that I decided to focus my thesis on the area of artificial intelligence. I didn’t know then that the decision would place me right in the thick of historic advancements in computer science.

In 1961, artificial intelligence was just coming into its own, and MIT was the leader. Two men, Marvin Minsky and John McCarthy, both born in 1927, founded the MIT Computer Science and Artificial Intelligence Laboratory in the same year I entered MIT. These men were pioneers in the field and today are acknowledged as two of the founding fathers of artificial intelligence. 

Minksy was a scientist, inventor, and author who co-wrote the book Perceptrons, a foundational work in the analysis of artificial neural networks. He won the Turning award in 1969 and was inducted into the IEEE Intelligent Systems’ AI Hall of Fame in 2011. Minsky remained at MIT until his death in 2016.

McCarthy coined the term “artificial intelligence” in 1955 and organized the groundbreaking Dartmouth Conference in 1956 that launched AI as a field. In 1958 McCarthy invented LISP, the programming language that soon became the go-to language for AI applications. McCarthy left MIT for Stanford, where he founded Stanford’s Artificial Intelligence Laboratory in 1963. He remained at Stanford until his death in 2011.

Back in the day, MIT’s CS & AI Lab was a wild place. Many of the AI graduate students had their desks in the same room, and it seemed to me noisy and chaotic. Darts were always being thrown (at a dartboard) as were wadded up pieces of paper (at each other). All of this amidst lively discussions and arguments about machine intelligence. I don’t know how anyone got anything done in that atmosphere, but many of the earliest groundbreaking advances in AI happened there.

In 1950, the British mathematician and computer scientist Alan Turing had proposed a test, now called the “Turing test,” to determine whether a machine was intelligent. In the Turing test, you sit at a teletypewriter and converse with a person—or a machine—out of sight at another teletypewriter. If it is a machine that can fool you into thinking it is a person, then the machine is intelligent. Unfortunately, Turing introduced the idea by describing a party game in which you are trying to determine if the unseen person is a man or a woman, thus complicating his explanation by introducing the notion of gender, which for a while obscured the simplicity of his test.

The common belief in the CS & AI Lab at that time was that we would achieve true machine intelligence, a machine that could pass the Turing test, probably within five years, certainly within ten years. Many others believed it also. 

There were early signs that we were on the right path. At MIT during 1964 to 1966, Joseph Weizenbaum wrote a program called ELIZA to analyze natural English sentences. One of the scripts Weizenbaum wrote for ELIZA, named DOCTOR, simulated a Rogerian psychotherapist, who typically work with patients using a series of questions. ELIZA was not at all intelligent, as Weizenbaum was focusing only on analyzing English sentences. However, many people, including many psychotherapists, focused more on ELIZA’s potential than its very limited capabilities. They thought that machines could one day revolutionize the field of psychotherapy.

For AI history enthusiasts, ELIZA can still be found on the Internet. If it does not understand your input it typically replies with something like “Tell me more about your father.” Given advances to cognitive AI since then, it’s hard to believe that anyone could have considered it intelligent back then.

I’ve often thought about why machine intelligence, which we have yet to achieve, is so much harder than we thought 50 years ago. I think that a central issue is worldview, which seems to drive so much of human communication and understanding. Since we share a vast amount of common information with other people, we communicate in shorthand, taking for granted all of that commonality. That can explain why communicating across cultures is sometimes difficult. 

Today, developments in artificial intelligence are happening very fast. The AI system that won the game of Jeopardy against two humans in 2011 was interesting in large part because of its understanding of colloquial English. Jeopardy clues are rich in puns, red herrings, and wordplay. 

With advanced resources (from accelerated computation to efficient architectures to big datasets) now available to many AI systems in development, quality machine intelligence—that once upon a time at MIT we were sure was just 5 or 10 years away—may finally be on the horizon.

“So, um, let’s get started.” 

We all do it. You’re talking away and for a brief moment, a fact eludes you. Or you lose track of exactly what to say next. Or you’re nervous and just plain blank out for a moment. To buy yourself a little time, you throw out an “uh” or “um” or “like” or “sooooo.” There is a long list of verbal fillers like this. In fact, linguists study so-called “disfluency” and have found that as many as 6% of the words we use may be fillers that serve more as punctuation than to express meaning.

In fact, the study of these fillers is quite advanced. Wikipedia maintains a list of fillers in different languages that includes hundreds of examples. One academic study of 14,000 conversations in English found that men are more likely to use “uh” while women use “um.” That usage changes over time, so regardless of your sex you can look forward to saying more “uh” and less “um” as you get older. 

There are some basic tips for stopping the overuse of verbal fillers like “like”: relax, take a deep breath, don’t let your thoughts race too far ahead of your brain, and substitute silence for, uh, verbal fillers.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.

We’ve all experienced the itch that strikes during those in-between moments when you have spare time on your hands. Without thinking, you’re reaching for your mobile device to flick through social media feeds in search of something new. Turns out, what’s happening in your brain in these moments is complex—and fascinating.
In this video, we look at the brain’s love/hate relationship with social media, covering everything from memory formation to dopamine cycles to tips on sustaining deep focus.
You can read more about the neuroscience behind social media here.

A friend of Entefy went off the grid for a “Think Week,” a distraction-free week of personal and professional goal setting. She shared her insights into how to pull off a successful and productive Think Week. 

Earlier this year, Entefy examined the myth of busyness that consumes many people’s lives. Our research revealed that most of us have more time in our days than we realize, but we’re often too “busy” to use those hours effectively. I’m guilty of exactly that. Between professional responsibilities, social plans, and life’s logistics, I often felt that my whole day was spoken for before I got out of bed in the morning. 

As I reflected on my constant busyness, however, I noticed patterns that contributed to the persistent feelings of exhaustion and frustration that came from my seemingly over-packed schedule. My days were peppered with interruptions – text conversations, social media checks, phone calls. Most of these were non-urgent yet I’d respond instantly, which meant it took me at least twice as long as necessary to complete any task. One distraction led to another and then another. I realized I could reclaim a good hour or two of my day, at minimum, if I learned to better manage my habits. 

But my brain craved the dopamine hits that come from checking social media or email every five minutes. Even when I swore I was going to focus, I couldn’t go more than 30 minutes without being seized by the impulse to do something more immediately gratifying. My life stayed busy – too busy. I needed to hit the reset button on how I approached my days and my priorities. I decided to take a Think Week.  

A week for thinking deeply 

In his book Deep Work, Cal Newport describes the famous example of J.K. Rowling checking herself into The Balmoral Hotel to escape the distractions of her daily life as she finished writing Harry Potter and the Deathly Hallows. But you don’t have to be an iconic author to benefit from time and space to reflect—as I proved when I took a Think Week this summer. 

My goal was to reflect productively on my life and career. I hoped that taking time out to consider where I’m at, where I’ve been, and where I’m headed would inspire insights and motivations that had gone dormant in the crush of everyday responsibilities. 

Think Week was an opportunity to disengage from my daily routine, and I found the experience rejuvenating. While it didn’t cure me of all my bad habits, it did help me identify and curb them. Remembering what it was like to not always be on my phone or consuming digital content gave me perspective on the appropriate place of email, social media, and the Internet in my life. 

If you’re seeking a way out of the busyness trap, a Think Week can get you started down that path. Here are five steps you can follow to make the most of the experience:  

1. Schedule your Think Week in advance

Marking your Think Week dates in your calendar will solidify its importance in your mind. It will also give you an opportunity to prepare your family, friends, and colleagues for your week of disconnection. A month out from your start date, create a list of everything you need to get done beforehand. This includes things like work projects, social plans you’ve yet to make, and visits to your parents. 

Find out what your boss or colleagues need from you before you go out of the office and be mindful of how many commitments you take on during these four weeks. Think Week will approach faster than you realize and you don’t want to be pulling all-nighters just to hit your deadlines. The transition will go more smoothly if you’re transparent with everyone about what they can expect during this time and honor your existing commitments before you go off the grid. 

2. Set boundaries 

It’s not enough to say you’re disengaging from your typical patterns for the week. Habits are tough to break, and unless you set boundaries, you’ll find yourself fielding calls and texts while you’re supposed to be in contemplation.  

Depending on your circumstances, you may want to allow contact from a limited number of people. But you should be clear with them and with yourself about what those allowed circumstances are. I told a few family members and close friends that while I wouldn’t be checking texts, email, or social media, I would leave my phone on so they could call me in an emergency. Then I blocked all social media, email, and text apps on my smartphone so I wouldn’t be tempted to check them throughout the week. 

Decide how much connectivity you’ll allow yourself on other devices as well. I opted not to watch TV during my Think Week because I feared that I would fall down the slippery slope into a Netflix binge rather than reading or brainstorming. However, I allowed myself Internet access and watched YouTube videos that I deemed educational or relevant to the topics I was pursuing during the week. Laying out the guidelines in advance gave the week structure, and it helped me overcome my impulses to indulge shallow pursuits while I was supposed to be contemplating the big questions in my life.  

3. Establish clear goals 

Know why you’re doing a Think Week and what you hope to gain from it. You don’t need to set grueling or unrealistic expectations for yourself, such as writing the first draft of a novel or inventing a life-changing product. In fact, it’s probably better if you avoid pursuing such specific outcomes. Paint your goals with broad strokes so that your Think Week has a guiding purpose, but leave room for meandering trains of thought and brainstorming sessions that might lead you to new goals entirely. 

I identified a few key areas in which I was seeking clarity, including long-term career goals and lifestyle changes. Keeping these targets in mind brought focus to my days, so I didn’t wake up each morning feeling aimless. I didn’t follow a set routine during the week, but I knew my purpose each day. By the time Think Week ended, I felt confident that I could put my newly clarified goals into action. 

4. Be spontaneous 

The beauty of Think Week is that you’re out of your daily routine, which can give rise to impulses and interests you’d usually ignore. During my Think Week, I spent large portions of my days reading on the couch. On writing intensive days, I liked to spend a few hours at a local coffee shop because the change of scenery helped me think. Indulging whatever setting felt right in the moment helped me gain momentum in my thinking and planning.  

Make a list of places that you enjoy visiting, and keep it on hand during Think Week. If you’re feeling stuck or restless in your home, go for a walk or wander around a bookstore. Perhaps you do your best thinking in the woods. Allow yourself the luxury of visiting places that inspire you, because spending time in these environments could spark connections that accelerate your thinking. 

5. Plan how to build Think Week findings into your life 

A day or two before Think Week ends, block off a few hours to plan your transition back to your normal schedule. By this point, you’ll have some idea of changes you want to make or goals you want to achieve. Without a plan for how you’ll work these into your routine, however, your Think Week energy will quickly dissipate and little will change. 

Perhaps you can scale back on some social commitments to make more time for the extracurricular work project you want to tackle. Maybe you need to delete certain apps from your smartphone until you’re in the habit of only checking them at certain times, allowing you to cultivate better discipline and greater productivity. Whatever your strategy, write it down and create reminders you can post around your home or workspace to keep your priorities top of mind. 

By the end of my Think Week retreat, I had reconnected with my big picture goals, what I want to achieve in my career, and what matters to me on a personal level. In the noise of constant connectedness, I had let passion projects fall by the wayside and become distant from people I value. After seven days of disconnection, I felt renewed in my priorities and had a clear path toward honoring them. I also had a game plan for having more productive, fulfilling days, which began with strict limitations on how and when I use social media and email. If you never break free of your daily routine, you might be aware that you need a change, but that change will never happen. You need to step outside the stream of input, responsibilities, and stimulation to learn how to flow more effectively within it. 

If the idea of a Think Week appeals to you but you can’t swing a full seven days, try a Think Weekend. Spend Saturday and Sunday offline, do some reading, and give yourself a chance to explore the thoughts and questions that have been lingering at the back of your mind. The act of disconnecting and devoting your attention to deep topics will feel revolutionary, and it could well change the way you structure your life. 

Entefy recently covered AI disruptions already underway in several industries, like utilities and manufacturing. These are industries where you would normally not expect to find advanced AI systems in place and having a disruptive impact. But as the capabilities of algorithmic systems develop rapidly, the surprise these days is finding an industry not experiencing the impact of artificial intelligence.

The story is no different in traditional industries like agriculture and banking. AI is hard at work untangling difficult, longstanding challenges; automating labor- and time-intensive work; and creating entirely new products and markets.

Here are some of the most impactful uses of artificial intelligence technologies in traditional industries:

1. 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. 

2. 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 and extract the relevant information. 

3. Education 

Blended learning, in which teachers use technology to enhance traditional classroom environments, is gaining prominence in American schools, thanks in large part to AI. 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. 

4. 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. 

5. 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. 

6. 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. 

7. 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.” 

8. 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.  

9. 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.

As you’ve seen from the variety and scope of this list, AI is being used to tackle challenges large and small—creating new opportunities for innovation at companies around the world.

You’ve probably seen or heard a public service announcement about how dangerous it is to text while driving. Looking at your smartphone to tap out a message is a sustained distraction from what’s happening on the road. We’ve all been guilty once or twice of almost walking into someone while walking and texting; traveling in a car at highway speeds makes that behavior outright dangerous.

Data points to another dangerous smartphone-related behavior to watch out for while driving. According to a study of 3.1 million drivers by the U.S. National Safety Council, 88% of drivers use their smartphones for 3.5 minutes per hour of driving. The data doesn’t show that talking on the phone is itself dangerous—but making or answering a call is, because doing so requires the driver to look away from the road. And just 2 seconds of distraction increases the risk of an accident by up to 24x.

This is a strong argument in favor of next-generation conversational interfaces to operate devices. But until those technologies mature, be careful starting and ending calls while driving. Those seconds matter.

Data centers are those giant facilities that house the servers that store the world’s digital bits. As more and more people come online globally, these facilities are expanding in number and size. This is causing all sorts of consequences, everything from insatiable energy consumption to a surprising link to how airliners are built. 

In this video enFact, we take a look at how the world’s thirst for data is shaping data centers. 
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.