2018 TECH TRENDS REPORT — from the Future Today Institute
Emerging technology trends that will influence business, government, education, media and society in the coming year.


The Future Today Institute’s 11th annual Tech Trends Report identifies 235 tantalizing advancements in emerging technologies—artificial intelligence, biotech, autonomous robots, green energy and space travel—that will begin to enter the mainstream and fundamentally disrupt business, geopolitics and everyday life around the world. Our annual report has garnered more than six million cumulative views, and this edition is our largest to date.

Helping organizations see change early and calculate the impact of new trends is why we publish our annual Emerging Tech Trends Report, which focuses on mid- to late-stage emerging technologies that are on a growth trajectory.

In this edition of the FTI Tech Trends Report, we’ve included several new features and sections:

  • a list and map of the world’s smartest cities
  • a calendar of events that will shape technology this year
  • detailed near-future scenarios for several of the technologies
  • a new framework to help organizations decide when to take action on trends
  • an interactive table of contents, which will allow you to more easily navigate the report from the bookmarks bar in your PDF reader



01 How does this trend impact our industry and all of its parts?
02 How might global events — politics, climate change, economic shifts – impact this trend, and as a result, our organization?
03 What are the second, third, fourth, and fifth-order implications of this trend as it evolves, both in our organization and our industry?
04 What are the consequences if our organization fails to take action on this trend?
05 Does this trend signal emerging disruption to our traditional business practices and cherished beliefs?
06 Does this trend indicate a future disruption to the established roles and responsibilities within our organization? If so, how do we reverse-engineer that disruption and deal with it in the present day?
07 How are the organizations in adjacent spaces addressing this trend? What can we learn from their failures and best practices?
08 How will the wants, needs and expectations of our consumers/ constituents change as a result of this trend?
09 Where does this trend create potential new partners or collaborators for us?
10 How does this trend inspire us to think about the future of our organization?




From DSC:
After seeing the article entitled, “Scientists Are Turning Alexa into an Automated Lab Helper,” I began to wonder…might Alexa be a tool to periodically schedule & provide practice tests & distributed practice on content? In the future, will there be “learning bots” that a learner can employ to do such self-testing and/or distributed practice?



From page 45 of the PDF available here:


Might Alexa be a tool to periodically schedule/provide practice tests & distributed practice on content?




Scientists Are Turning Alexa into an Automated Lab Helper — from technologyreview.com by Jamie Condliffe
Amazon’s voice-activated assistant follows a rich tradition of researchers using consumer tech in unintended ways to further their work.


Alexa, what’s the next step in my titration?

Probably not the first question you ask your smart assistant in the morning, but potentially the kind of query that scientists may soon be leveling at Amazon’s AI helper. Chemical & Engineering News reports that software developer James Rhodes—whose wife, DeLacy Rhodes, is a microbiologist—has created a skill for Alexa called Helix that lends a helping hand around the laboratory.

It makes sense. While most people might ask Alexa to check the news headlines, play music, or set a timer because our hands are a mess from cooking, scientists could look up melting points, pose simple calculations, or ask for an experimental procedure to be read aloud while their hands are gloved and in use.

For now, Helix is still a proof-of-concept. But you can sign up to try an early working version, and Rhodes has plans to extend its abilities…


Also see:




Personalized Learning Meets AI With Watson Classroom

Personalized Learning Meets AI With Watson Classroom — from gettingsmart.com by Erin Gohl

Excerpt (emphasis DSC):

Teaching is truly a Herculean challenge. Even the very best teachers can keep only so many of these insights in their heads and make only so many connections between expectations and circumstances. They can be aware of only a fraction of the research on best practices. They have only so much time to collaborate and communicate with the other adults in a particular student’s life to share information and insights. To be the best of themselves, teachers need to have access to a warehouse of information, a research assistant to mine best practices, note takers to gather and record information on each student, a statistician to gauge effective practices, and someone to collaborate with to distill the next best step with each student. In recent years, a plethora of vendors have developed software solutions that promise to simplify this process and give schools and teachers the answers to understand and address the individual needs of each student. One of the most promising, which I recently had a chance to learn about, is IBM’s Watson Classroom.

IBM is clear about what makes Watson different than existing solutions. First of all, it is a cognitive partner; not a solution. Secondly, it does not require proprietary or additional assessments, curriculum, or content. It uses whatever a district has in place. But it goes beyond the performance of tiering difficulty, pace, and reading level that is now standard fare for the solutions promising individualized, adaptive and personalized learning. Watson takes the stew of data from existing systems (including assessments, attendance records, available accommodations), adds the ability to infer meaning from written reports, and is able to connect the quality of the result to the approach that was taken. And then adjust the next recommendation based on what was learned. It is artificial intelligence (AI) brought to education that goes far beyond the adaptive learning technologies of today.

Watson Classroom is currently being piloted in 12 school districts across the country. In those classrooms, Watson Classroom is utilizing cutting-edge computing power to give teachers a full range of support to be the best versions of themselves. Watson is facilitating the kind of education the great teachers strive for every day–one where learning is truly personalized for each and every student. Bringing the power of big data to the interactions between students and teachers can help assure that every student reaches beyond our expectations to achieve their full potential.




Learn with Google AI: Making ML education available to everyone — from blog.google


To help everyone understand how AI can solve challenging problems, we’ve created a resource called Learn with Google AI. This site provides ways to learn about core ML concepts, develop and hone your ML skills, and apply ML to real-world problems. From deep learning experts looking for advanced tutorials and materials on TensorFlow, to “curious cats” who want to take their first steps with AI, anyone looking for educational content from ML experts at Google can find it here.

Learn with Google AI also features a new, free course called Machine Learning Crash Course (MLCC). The course provides exercises, interactive visualizations, and instructional videos that anyone can use to learn and practice ML concepts.



7 Ways Chatbots and AI are Disrupting HR — from chatbotsmagazine.com
Enterprises are embracing AI for automating human resources


Chatbots and AI have become household names and enterprises are taking notice. According to a recent Forrester survey, roughly “85% of customer interactions within an enterprise will be with software robots in five years’ time” and “87% of CEOs are looking to expand their AI workforce” using AI bots.

In an effort to drive increased labor efficiencies, reduce costs, and deliver better customer/employee experiences enterprises are quickly introducing AI, machine learning, and natural language understanding as core elements of their digital transformation strategy in 2018.

Human resources (HR) is one area ripe for intelligent automation within an enterprise. AI-powered bots for HR are able to streamline and personalize the HR process across seasonal, temporary, part-time, and full-time employees.

There are 7 ways in which enterprises can use HR bots to drive increased labors efficiencies, reduced costs, and better employee experiences:

  1. Recruitment
  2. Onboarding
  3. Company Policy FAQs
  4. Employee Training
  5. Common Questions
  6. Benefits Enrollment
  7. Annual Self-Assessment/Reviews


From DSC:
Again, this article paint a bit too rosy of a picture for me re: the use of AI and HR, especially in regards to recruiting employees.




Implementation of AI into eLearning. Interview with Christopher Pappas — from joomlalms.com by Darya Tarliuk


Every day we hear more and more about the impact that Artificial Intelligence gains in every sphere of our life. In order to discover how AI implementation is going to change the eLearning we decided to ask Christopher Pappas to share his views and find out what he thinks about it. Christopher is an experienced eLearning specialist and the Founder of the eLearning Industry’s Network.

How to get ready preparing course materials now, while considering the future impact of AI?
Christopher: Regardless of whether you plan to adopt an AI system as soon as they’re available to the mass market or you opt to hold off (and let others work out the glitches), infrastructure is key. You can prepare your course materials now by developing course catalogs, microlearning online training repositories, and personalized online training paths that fall into the AI framework. For example, the AI system can easily recommend existing resources based on a learners’ assessment scores or job duties. All of the building blocks are in place, allowing the system to focus on content delivery and data analysis.




Can You Trust Intelligent Virtual Assistants? — from nojitter.com by Gary Audin
From malicious hackers to accidental voice recordings, data processed through virtual assistants may open you to security and privacy risks.


Did you know that with such digital assistants your voice data is sent to the cloud or another remote location for processing? Is it safe to talk in front of your TV remote? Are you putting your business data at risk of being compromised by asking Alexa to start your meeting?





Thanks, Robots! Now These Four Non-Tech Job Skills Are In Demand — from fastcompany.com by Christian Madsbjerg
The more we rely on AI and machine learning, the more work we need social scientists and humanities experts to do.


Automation isn’t a simple struggle between people and technology, with the two sides competing for jobs. The more we rely on robots, artificial intelligence (AI), and machine learning, the clearer it’s become just how much we need social scientists and humanities experts–not the reverse.

These four skills in particular are all unique to us humans, and will arguably rise in value in the coming years, as more and more companies realize they need the best of both worlds to unleash the potential from both humans and machines.






Tech companies should stop pretending AI won’t destroy jobs — from technologyreview.com / MIT Technology Review by Kai-Fu Lee
No matter what anyone tells you, we’re not ready for the massive societal upheavals on the way.

Excerpt (emphasis DSC):

The rise of China as an AI superpower isn’t a big deal just for China. The competition between the US and China has sparked intense advances in AI that will be impossible to stop anywhere. The change will be massive, and not all of it good. Inequality will widen. As my Uber driver in Cambridge has already intuited, AI will displace a large number of jobs, which will cause social discontent. Consider the progress of Google DeepMind’s AlphaGo software, which beat the best human players of the board game Go in early 2016. It was subsequently bested by AlphaGo Zero, introduced in 2017, which learned by playing games against itself and within 40 days was superior to all the earlier versions. Now imagine those improvements transferring to areas like customer service, telemarketing, assembly lines, reception desks, truck driving, and other routine blue-collar and white-­collar work. It will soon be obvious that half of our job tasks can be done better at almost no cost by AI and robots. This will be the fastest transition humankind has experienced, and we’re not ready for it.

And finally, there are those who deny that AI has any downside at all—which is the position taken by many of the largest AI companies. It’s unfortunate that AI experts aren’t trying to solve the problem. What’s worse, and unbelievably selfish, is that they actually refuse to acknowledge the problem exists in the first place.

These changes are coming, and we need to tell the truth and the whole truth. We need to find the jobs that AI can’t do and train people to do them. We need to reinvent education. These will be the best of times and the worst of times. If we act rationally and quickly, we can bask in what’s best rather than wallow in what’s worst.


From DSC:
If a business has a choice between hiring a human being or having the job done by a piece of software and/or by a robot, which do you think they’ll go with? My guess? It’s all about the money — whichever/whomever will be less expensive will get the job.

However, that way of thinking may cause enormous social unrest if the software and robots leave human beings in the (job search) dust. Do we, as a society, win with this way of thinking? To me, it’s capitalism gone astray. We aren’t caring enough for our fellow members of the human race, people who have to put bread and butter on their tables. People who have to support their families. People who want to make solid contributions to society and/or to pursue their vocation/callings — to have/find purpose in their lives.


Others think we’ll be saved by a universal basic income. “Take the extra money made by AI and distribute it to the people who lost their jobs,” they say. “This additional income will help people find their new path, and replace other types of social welfare.” But UBI doesn’t address people’s loss of dignity or meet their need to feel useful. It’s just a convenient way for a beneficiary of the AI revolution to sit back and do nothing.



To Fight Fatal Infections, Hospitals May Turn to Algorithms — from scientificamerican.com by John McQuaid
Machine learning could speed up diagnoses and improve accuracy


The CDI algorithm—based on a form of artificial intelligence called machine learning—is at the leading edge of a technological wave starting to hit the U.S. health care industry. After years of experimentation, machine learning’s predictive powers are well-established, and it is poised to move from labs to broad real-world applications, said Zeeshan Syed, who directs Stanford University’s Clinical Inference and Algorithms Program.

“The implications of machine learning are profound,” Syed said. “Yet it also promises to be an unpredictable, disruptive force—likely to alter the way medical decisions are made and put some people out of work.



Lawyer-Bots Are Shaking Up Jobs — from technologyreview.com by Erin Winick


Meticulous research, deep study of case law, and intricate argument-building—lawyers have used similar methods to ply their trade for hundreds of years. But they’d better watch out, because artificial intelligence is moving in on the field.

As of 2016, there were over 1,300,000 licensed lawyers and 200,000 paralegals in the U.S. Consultancy group McKinsey estimates that 22 percent of a lawyer’s job and 35 percent of a law clerk’s job can be automated, which means that while humanity won’t be completely overtaken, major businesses and career adjustments aren’t far off (see “Is Technology About to Decimate White-Collar Work?”). In some cases, they’re already here.


“If I was the parent of a law student, I would be concerned a bit,” says Todd Solomon, a partner at the law firm McDermott Will & Emery, based in Chicago. “There are fewer opportunities for young lawyers to get trained, and that’s the case outside of AI already. But if you add AI onto that, there are ways that is advancement, and there are ways it is hurting us as well.”


So far, AI-powered document discovery tools have had the biggest impact on the field. By training on millions of existing documents, case files, and legal briefs, a machine-learning algorithm can learn to flag the appropriate sources a lawyer needs to craft a case, often more successfully than humans. For example, JPMorgan announced earlier this year that it is using software called Contract Intelligence, or COIN, which can in seconds perform document review tasks that took legal aides 360,000 hours.

People fresh out of law school won’t be spared the impact of automation either. Document-based grunt work is typically a key training ground for first-year associate lawyers, and AI-based products are already stepping in. CaseMine, a legal technology company based in India, builds on document discovery software with what it calls its “virtual associate,” CaseIQ. The system takes an uploaded brief and suggests changes to make it more authoritative, while providing additional documents that can strengthen a lawyer’s arguments.



Lessons From Artificial Intelligence Pioneers — from gartner.com by Christy Pettey

CIOs are struggling to accelerate deployment of artificial intelligence (AI). A recent Gartner survey of global CIOs found that only 4% of respondents had deployed AI. However, the survey also found that one-fifth of the CIOs are already piloting or planning to pilot AI in the short term.

Such ambition puts these leaders in a challenging position. AI efforts are already stressing staff, skills, and the readiness of in-house and third-party AI products and services. Without effective strategic plans for AI, organizations risk wasting money, falling short in performance and falling behind their business rivals.

Pursue small-scale plans likely to deliver small-scale payoffs that will offer lessons for larger implementations

“AI is just starting to become useful to organizations but many will find that AI faces the usual obstacles to progress of any unproven and unfamiliar technology,” says Whit Andrews, vice president and distinguished analyst at Gartner. “However, early AI projects offer valuable lessons and perspectives for enterprise architecture and technology innovation leaders embarking on pilots and more formal AI efforts.”

So what lessons can we learn from these early AI pioneers?



Why Artificial Intelligence Researchers Should Be More Paranoid — from wired.com by Tom Simonite


What to do about that? The report’s main recommendation is that people and companies developing AI technology discuss safety and security more actively and openly—including with policymakers. It also asks AI researchers to adopt a more paranoid mindset and consider how enemies or attackers might repurpose their technologies before releasing them.



How to Prepare College Graduates for an AI World — from wsj.com by
Northeastern University President Joseph Aoun says schools need to change their focus, quickly


WSJ: What about adults who are already in the workforce?

DR. AOUN: Society has to provide ways, and higher education has to provide ways, for people to re-educate themselves, reskill themselves or upskill themselves.

That is the part that I see that higher education has not embraced. That’s where there is an enormous opportunity. We look at lifelong learning in higher education as an ancillary operation, as a second-class operation in many cases. We dabble with it, we try to make money out of it, but we don’t embrace it as part of our core mission.



Inside Amazon’s Artificial Intelligence Flywheel — from wired.com by Steven Levy
How deep learning came to power Alexa, Amazon Web Services, and nearly every other division of the company.


Amazon loves to use the word flywheel to describe how various parts of its massive business work as a single perpetual motion machine. It now has a powerful AI flywheel, where machine-learning innovations in one part of the company fuel the efforts of other teams, who in turn can build products or offer services to affect other groups, or even the company at large. Offering its machine-learning platforms to outsiders as a paid service makes the effort itself profitable—and in certain cases scoops up yet more data to level up the technology even more.





10 Breakthrough Technologies 2018 -- from MIT Technology Review


10 Breakthrough Technologies 2018 — from MIT Technology Review


Dueling neural networks. Artificial embryos. AI in the cloud. Welcome to our annual list of the 10 technology advances we think will shape the way we work and live now and for years to come.

Every year since 2001 we’ve picked what we call the 10 Breakthrough Technologies. People often ask, what exactly do you mean by “breakthrough”? It’s a reasonable question—some of our picks haven’t yet reached widespread use, while others may be on the cusp of becoming commercially available. What we’re really looking for is a technology, or perhaps even a collection of technologies, that will have a profound effect on our lives.

  1. 3-D Metal Printing
  2. Artificial Embryos
  3. Sensing City
  4. AI for Everybody
  5. Dueling Neural Networks
  6. Babel-Fish Earbuds
    In the cult sci-fi classic The Hitchhiker’s Guide to the Galaxy, you slide a yellow Babel fish into your ear to get translations in an instant. In the real world, Google has come up with an interim solution: a $159 pair of earbuds, called Pixel Buds. These work with its Pixel smartphones and Google Translate app to produce practically real-time translation. One person wears the earbuds, while the other holds a phone. The earbud wearer speaks in his or her language—English is the default—and the app translates the talking and plays it aloud on the phone. The person holding the phone responds; this response is translated and played through the earbuds.
  7. Zero-Carbon Natural Gas
  8. Perfect Online Privacy
  9. Genetic Fortune-Telling
  10. Materials’ Quantum Leap




Fake videos are on the rise. As they become more realistic, seeing shouldn’t always be believing — from latimes.com by David Pierson Fe


It’s not hard to imagine a world in which social media is awash with doctored videos targeting ordinary people to exact revenge, extort or to simply troll.

In that scenario, where Twitter and Facebook are algorithmically flooded with hoaxes, no one could fully believe what they see. Truth, already diminished by Russia’s misinformation campaign and President Trump’s proclivity to label uncomplimentary journalism “fake news,” would be more subjective than ever.

The danger there is not just believing hoaxes, but also dismissing what’s real.

The consequences could be devastating for the notion of evidentiary video, long considered the paradigm of proof given the sophistication required to manipulate it.

“This goes far beyond ‘fake news’ because you are dealing with a medium, video, that we traditionally put a tremendous amount of weight on and trust in,” said David Ryan Polgar, a writer and self-described tech ethicist.





From DSC:
Though I’m typically pro-technology, this is truly disturbing. There are certainly downsides to technology as well as upsides — but it’s how we use a technology that can make the real difference. Again, this is truly disturbing.




Mapping the Trends on Our Doorstep: The Pace of Change Has Changed — from an article that I did out at — and with — evoLLLution.com [where LLL stands for lifelong learning]; my thanks to Mr. Amrit Ahluwalia, Managing Editor out at evolllution.com and to his staff as well!
The higher education industry has changed significantly over the past decade, and given the pace and significance of change hitting other industries as a result of technological advances, it’s fair to say the postsecondary space is ripe for further transformation.


From DSC:
From the perspective of those of us working within higher education, we see massive changes occurring in the corporate world, and we see innovations and changes also occurring in the world of K-12. Higher education should also be adapting, changing, questioning, and reflecting upon how we can best prepare our students for a rapidly changing workplace.

Below is another interesting item that I believe gives credence to the idea that we are now on an exponential pace of change. Companies are coming and going on the S&P Index…at an ever faster pace.

The 33-year average tenure of companies on the S&P 500 in 1964 narrowed to 24 years by 2016 and is forecast to shrink to just 12 years by 2027 (Chart 1).


Here is the video:

This is the transcript with the original graphs in it.

This is a nice PDF file from evoLLLution.com with the transcript, with some different graphics and some other





What is Artificial Intelligence, Machine Learning and Deep Learning — from geospatialworld.net by Meenal Dhande







What is the difference between AI, machine learning and deep learning? — from geospatialworld.net by Meenal Dhande


In the first part of this blog series, we gave you simple and elaborative definitions of what is artificial intelligence (AI), machine learning and deep learning. This is the second part of the series; here we are elucidating our readers with – What is the difference between AI, machine learning, and deep learning.

You can think of artificial intelligence (AI), machine learning and deep learning as a set of a matryoshka doll, also known as a Russian nesting doll. Deep learning is a subset of machine learning, which is a subset of AI.






Chatbot for College Students: 4 Chatbots Tips Perfect for College Students — from chatbotsmagazine.com by Zevik Farkash


1. Feed your chatbot with information your students don’t have.
Your institute’s website can be as elaborate as it gets, but if your students can’t find a piece of information on it, it’s as good as incomplete. Say, for example, you offer certain scholarships that students can voluntarily apply for. But the information on these scholarships are tucked away on a remote page that your students don’t access in their day-to-day usage of your site.

So Amy, a new student, has no idea that there’s a scholarship that can potentially make her course 50% cheaper. She can scour your website for details when she finds the time. Or she can ask your university’s chatbot, “Where can I find information on your scholarships?”

And the chatbot can tell her, “Here’s a link to all our current scholarships.”

The best chatbots for colleges and universities tend to be programmed with even more detail, and can actually strike up a conversation by saying things like:

“Please give me the following details so I can pull out all the scholarships that apply to you.
“Which department are you in? (Please select one.)
“Which course are you enrolled in? (Please select one.)
“Which year of study are you in? (Please select one.)
“Thank you for the details! Here’s a list of all applicable scholarships. Please visit the links for detailed information and let me know if I can be of further assistance.”

2. Let it answer all the “What do I do now?” questions.

3. Turn it into a campus guide.

4. Let it take care of paperwork.


From DSC:
This is the sort of thing that I was trying to get at last year at the NGLS 2017 Conference:





18 Disruptive Technology Trends For 2018 — from disruptionhub.com by Rob Prevett


1. Mobile-first to AI-first
A major shift in business thinking has placed Artificial Intelligence at the very heart of business strategy. 2017 saw tech giants including Google and Microsoft focus on an“AI first” strategy, leading the way for other major corporates to follow suit. Companies are demonstrating a willingness to use AI and related tools like machine learning to automate processes, reduce administrative tasks, and collect and organise data. Understanding vast amounts of information is vital in the age of mass data, and AI is proving to be a highly effective solution. Whilst AI has been vilified in the media as the enemy of jobs, many businesses have undergone a transformation in mentalities, viewing AI as enhancing rather than threatening the human workforce.

7. Voice based virtual assistants become ubiquitous
Google HomeThe wide uptake of home based and virtual assistants like Alexa and Google Home have built confidence in conversational interfaces, familiarising consumers with a seamless way of interacting with tech. Amazon and Google have taken prime position between brand and customer, capitalising on conversational convenience. The further adoption of this technology will enhance personalised advertising and sales, creating a direct link between company and consumer.



5 Innovative Uses for Machine Learning — from entrepreneur.com
They’ll be coming into your life — at least your business life — sooner than you think.



Philosophers are building ethical algorithms to help control self-driving cars – from qz.com by Olivia Goldhill



Tech’s Ethical ‘Dark Side’: Harvard, Stanford and Others Want to Address It — from nytimes.com by Natasha Singerfeb


PALO ALTO, Calif. — The medical profession has an ethic: First, do no harm.

Silicon Valley has an ethos: Build it first and ask for forgiveness later.

Now, in the wake of fake news and other troubles at tech companies, universities that helped produce some of Silicon Valley’s top technologists are hustling to bring a more medicine-like morality to computer science.

This semester, Harvard University and the Massachusetts Institute of Technology are jointly offering a new course on the ethics and regulation of artificial intelligence. The University of Texas at Austin just introduced a course titled “Ethical Foundations of Computer Science” — with the idea of eventually requiring it for all computer science majors.

And at Stanford University, the academic heart of the industry, three professors and a research fellow are developing a computer science ethics course for next year. They hope several hundred students will enroll.

The idea is to train the next generation of technologists and policymakers to consider the ramifications of innovations — like autonomous weapons or self-driving cars — before those products go on sale.





From DSC:
Here’s a quote that has been excerpted from the announcement below…and it’s the type of service that will be offered in our future learning ecosystems — our next generation learning platforms:


Career Insight™ enables prospective students to identify programs of study which can help them land the careers they want: Career Insight™ describes labor market opportunities associated with programs of study to prospective students. The recommendation engine also matches prospective students to programs based on specific career interests.


But in addition to our future learning platforms pointing new/prospective students to physical campuses, the recommendation engines will also provide immediate access to digital playlists for the prospective students/learners to pursue from their living rooms (or as they are out and about…i.e., mobile access).


The Living [Class] Room -- by Daniel Christian -- July 2012 -- a second device used in conjunction with a Smart/Connected TV



Artificial intelligence working with enormous databases to build/update recommendation engines…yup, I could see that. Lifelong learning. Helping people know what to reinvent themselves to.




Career Insight™ Lets Prospective Students Connect Academic Program Choices to Career Goals — from burning-glass.com; also from Hadley Dreibelbis from Finn Partners
New Burning Glass Technologies Product Brings Job Data into Enrollment Decisions

BOSTON—Burning Glass Technologies announces the launch of Career Insight™, the first tool to show prospective students exactly how course enrollment will advance their careers.

Embedded in institutional sites and powered by Burning Glass’ unparalleled job market data, Career Insight’s personalized recommendation engine matches prospective students with programs based on their interests and goals. Career Insight will enable students to make smarter decisions, as well as improve conversion and retention rates for postsecondary institutions.

“A recent Gallup survey found that 58% of students say career outcomes are the most important reason to continue their education,” Burning Glass CEO Matthew Sigelman said. “That’s particularly true for the working learners who are now the norm on college campuses. Career Insight™ is a major step in making sure that colleges and universities can speak their language from the very first.”

Beginning an educational program with a firm, realistic career goal can help students persist in their studies. Currently only 29% of students in two-year colleges and 59% of those in four-year institutions complete their degrees within six years.

Career Insight™ enables prospective students to identify programs of study which can help them land the careers they want:

  • Career Insight™ describes labor market opportunities associated with programs of study to prospective students. The recommendation engine also matches prospective students to programs based on specific career interests.
  • The application provides insights to enrollment, advising, and marketing teams into what motivates prospective students, analysis that will guide the institution in improving program offerings and boosting conversion.
  • Enrollment advisors can also walk students through different career and program scenarios in real time.

Career Insight™ is driven by the Burning Glass database of a billion job postings and career histories, collected from more than 40,000 online sources daily. The database, powered by a proprietary analytic taxonomy, provides insight into what employers need much faster and in more detail than any other sources.

Career Insight™ is powered by the same rich dataset Burning Glass delivers to hundreds of leading corporate and education customers – from Microsoft and Accenture to Harvard University and Coursera.

More information is available at http://burning-glass.com/career-insight.




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