In a first-of-its-kind study, Elsevier has provided comprehensive analysis of the global research landscape and future trends in Artificial Intelligence (AI)
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The report’s data shows how China is set to become the global leader in AI research, having already overtaken the U.S. in 2004 and fast closing in on Europe’s lead in that arena. At this rate, the report predicts that the country will become the biggest source of AI research globally within four years.

The report predicts that China will become the biggest source of AI research globally within the next four years
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Key findings from the report include:

• Europe is suffering from AI Brain Drain: Over the 20 year period, Europe has been losing academic talent in this area, mostly to the corporate sector in the United States.
• Globally, research in AI has grown by 12.9% annually over the last 5 years
• There have been several key milestones over 20 years which have led to spikes and growth in AI research
• The ethics of AI is a research blind spot. Despite the increasing societal relevance of AI and widespread media attention on ethical implications, research remains limited.

The Elsevier analysis finds that industry in the United States attracts the most AI talent from both local and international academia
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The report shows that, globally, AI research has accelerated, growing by more than 12 percent annually in the past five years (2013-2017), compared to less than 5 percent in the previous 5 years (2008-2012). By contrast, research output overall, globally across all subject areas, has grown by 0.8 percent annually over the past five years (2013-2017).

Enrico Motta, Professor of Knowledge Technologies at the Open University in the UK, was one of the many experts who contributed to the report. He believes it is the most comprehensive characterization of AI outputs across different sectors delivered so far: “This report applies extensive text mining and semantic analytics across literature from different sectors to uncover how to more comprehensively define the AI field – essentially using AI to map AI,” he adds.

Reviewing 600 documents and over 700 field-specific key words across four sectors – research, education, technology, and media – the semantic analysis reveals that the field of AI focuses on seven distinct research areas:

• Search and Optimization,
• Fuzzy Systems,
• Natural Language Processing and Knowledge Representation,
• Computer Vision,
• Machine Learning and Probabilistic Reasoning,
• Planning and Decision Making, and
• Neural Networks.

Globally, AI research has accelerated, growing by more than 12 percent annually in the past five years
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Of these areas, research in Machine Learning and Probabilistic Reasoning, Neural Networks, and Computer Vision show the largest volume of research output and growth.

Keyword clusters and co-occurrences in the AI field, 2017; source: Scopus

Machine Learning and Probabilistic Reasoning, Neural Networks, and Computer Vision show the largest volume of research output and growth
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Regional findings highlighted in the report:

• In terms of research areas within the field of AI, Europe is the largest and most diverse region with high levels of international collaboration
• After China and the US, India became the third largest country in terms of AI research output, followed by Germany and Japan.
• Iran is ninth in AI publication output, on par with France and Canada.
• International mobility and collaboration patterns suggest that China operates in relative isolation from the wider research community.

“With this comprehensive study of research performance in AI we aim to provide insights into the field’s dynamics, trends and parameters, says Dan Olley, Chief Technology Officer at Elsevier. “The report is not a conclusion, but the start of a discussion on how we best enter the era of AI and increasingly symbiotic technology,” he concludes.

Data used in the report comes from Elsevier’s Scopus, Fingerprint Engine, PlumX, ScienceDirect, and SciVal, RELX’s TotalPatent, and further draws on public sources, including dblp, arXiv, Stanford AI Index, kamishima.net, and Kaggle, as well as datasets provided by the Institute of Automation, Chinese Academy of Science.

Alice Bonasio is a VR and Digital Transformation Consultant and Tech Trends’ Editor in Chief. She also regularly writes for Fast Company, Ars Technica, Quartz, Wired and others. Connect with her on LinkedIn and follow @alicebonasio on Twitter.

The post Report: Global AI Research appeared first on Tech Trends.

Even in an Antarctic research ship, ninja tech skills can come in handy, as one software engineer found in the trip of a lifetime.

A research trip around the Antarctic gave a Software Engineer Carles Pina i Estany a new perspective on how the very product he helped build was being used by scientists. Aboard a research vessel thousands of miles from shore and the nearest reliable internet connection, these modern explorers routinely relied on Mendeley to do their work, even in subzero temperatures.

A research trip around the Antarctic gave a Software Engineer Carles Pina i Estany a new perspective on how the very product he helped build was being used by scientists
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The opportunity to take part in this unique expedition came when Carles’ partner, Jen Thomas, was invited to become Data Manager on a a research trip led by the newly created Swiss Polar Institute, which has a mission to connect researchers active in polar or extreme environments, promotes public awareness of these environments, and facilitates access to research facilities in those extreme environments. As they were short of an IT person and Carles was already on a sabbatical from his work at Elsevier – during which he had already planned to travel around the world – he seized the opportunity to fulfill a longstanding ambition to take part in a polar expedition.

Aboard a research vessel thousands of miles from shore, modern explorers rely on technology to do their work in subzero temperatures
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Carles joined the Mendeley team as a developer in June 2009, back when the start-up consisted of about a dozen people working in a small office near Farringdon, a historic section of London. Four years later, the company he helped build was acquired by Elsevier. Carles, along with the founders and the now much larger team, stayed on through the transition. Now he’s a Senior Software engineer.

I first met Carles when I was also part of the Mendeley team back in 2013-15 and saw how resourceful and creative he and his colleagues were with technology (specially during the monthly hack days he helped organise). I also knew he had a penchant for adventure, as many a fascinating tale was told by the espresso machine at our old White Bear Yard office when he returned from trips to places like China and Africa.

Carles joined the Mendeley team as a developer in June 2009, back when the start-up consisted of about a dozen people working in a small office near Farringdon
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So I wasn’t exactly surprised when I learned that he had taken this rather adventurous sabbatical as IT Systems Engineer aboard the Akademik Tryoshnikov research vessel on an ambitious Antarctic circumnavigation via Cape Town, South Africa, Hobart, Tasmania and Punta Arenas, Chile.

When he returned to London and his work at Mendeley — now in the Alphabeta building in London’s Tech City — I caught up with him to find out more about the trip. As it turns out, the experience had been an eye-opener about how important the work he had done for all those years has helped to advance science. Seeing the sometimes unexpected ways researchers in this Antarctic expedition used tools like Mendeley left him with a sense of renewed appreciation for how his own work has played a part in enabling scientific research.

The opportunity to take part in this unique expedition came when Carles’ partner, Jen Thomas, was invited to become Data Manager on a a research trip led by the newly created Swiss Polar Institute, which has a mission to connect researchers active in polar or extreme environments, promotes public awareness of these environments, and facilitates access to research facilities in those extreme environments. As they were short of an IT person and Carles was already on a sabbatical from his work at Elsevier – during which he had already planned to travel around the world – he seized the opportunity to fulfill a longstanding ambition to take part in a polar expedition.

Carles joined the Mendeley team as a developer in June 2009, back when the start-up consisted of about a dozen people working in a small office near Farringdon, a historic section of London. Four years later, the company he helped build was acquired by Elsevier. Carles, along with the founders and the now much larger team, stayed on through the transition. Now he’s a Senior Software engineer.

I first met Carles when I was also part of the Mendeley team back in 2013-15 and saw how resourceful and creative he and his colleagues were with technology (specially during the monthly hack days he helped organise). I also knew he had a penchant for adventure, as many a fascinating tale was told by the espresso machine at our old White Bear Yard office when he returned from trips to places like China and Africa.

So I wasn’t exactly surprised when I learned that he had taken this rather adventurous sabbatical as IT Systems Engineer aboard the Akademik Tryoshnikov research vessel on an ambitious Antarctic circumnavigation via Cape Town, South Africa, Hobart, Tasmania and Punta Arenas, Chile.

When he returned to London and his work at Mendeley — now in the Alphabeta building in London’s Tech City — I caught up with him to find out more about the trip. As it turns out, the experience had been an eye-opener about how important the work he had done for all those years has helped to advance science. Seeing the sometimes unexpected ways researchers in this Antarctic expedition used tools like Mendeley left him with a sense of renewed appreciation for how his own work has played a part in enabling scientific research.

Here, Carles talks about those experiences and shares the photos he and Jen took during the voyage. (He explained that they were not allowed to go on deck or outside when the weather was rough, so all of the pictures are in calm weather.)

What was this expedition about, and what role did you actually have in it?

I was the software engineer on board the ACE expedition, which had 22 cross-disciplinary teams of scientists, about 150 researchers in all, who changed around at various stages of the expedition, each of which lasted about a month. Some were PhD students, some veterans of many expeditions with many years of experience, so it was a real mix.

Carles Pina i Estany diagnoses a broken hard disk. (Photo by Jen Thomas)

On the ship, each team set their own equipment up for their labs. Quite often these pieces of equipment are either connected to a computer or have a computer built into them already, to collect data and operate a machine, for example. I’d help them with things such as backing up data and any other issues that came up with those computers. I learnt a lot about oceanography and had to think on my feet because of the limited resources. Think of programming almost off-line for four months! No Google, Stack Overflow or any documentation besides the off-line versions.

Were many of those scientists using Mendeley, and how did you find them?

I knew that many of them would be Mendeley users. I do like talking to users and finding out how they use Mendeley (specially Mendeley Desktop, since that is the part of it I’ve been most involved in for many years). As they explain to me their favourite features and tell me what they would add, I can often point out useful features in the product that they are missing out on. Sometimes the features they ask for are already there, so I love helping them discover these.

 

Personally though, I like meeting users because what I do every day is for them, and it reminds me that there are humans using the software and that they have feelings. So if the software works well they love it, but they can also hate it if it ends up causing them stress, makes them lose work, miss a deadline for writing a paper or a thesis… At the end of the day, it’s very emotional and personal work, and it helps to keep that into perspective, even when you’re deep in the code. I like it when I can put faces to our users – I think in terms of “this user does this” rather than “just 23 percent of our users do this.”

How did the researchers react when they learned about your background?

Sometimes I’d get talking to them, and as I explained that this was my “year off” from my job working on the Mendeley Desktop team, their first reaction was that it was pretty intense for a year off. But the second one was that often they would say, “Oh, Mendeley Desktop – I love it! Ah, OK, OK, do whatever you want with this equipment!” So I ended up getting extra leeway for doing things around the ship because they liked and trusted Mendeley (and therefore me).

If I worked on some unrelated industry that they didn’t know about, they would have been more reluctant to let me change things around, I believe. They were really happy that I came from a place where I actually understood part of their work, what they actually do, etc. But I have to say that after this trip I now know waaaaaaaaaaaaay more than before!

An iceberg near Mount Siple in February. (Photo by Jen Thomas)

Were there any awkward moments?

One user was scared about the fact that Mendeley had been acquired by Elsevier, so we had a long talk about how Mendeley was publisher agnostic, and how that could still work in the Elsevier business model. Many users came up with long lists of ideas. Some of them turned out to be things that we already had tools for – deduplication, for example – but sometimes there were things that we could do, or that we hadn’t considered. I always encouraged them to tell me about these so I could either email them to the Desktop team back in London or consider them once I got back – which I have!

Did you get any surprising product insights during your time on the ship?

We were mostly working from the “expedition office,” where we had two “hot desks,” so lots of different people were coming and going to do their printing, use the intranet, send something over the Internet, etc. One of my favourite things to do was spot whether they were using Mendeley Desktop, and if they were, I’d sometimes ask if they liked it or not before confessing that I’d actually done part of that. I felt a bit like a “mystery shopper” or perhaps an undercover “Mendeley investigator.”

 

One really amazing thing for me was seeing how Mendeley Desktop was being used offline during the expedition because the Internet connection was almost non-existent (we only had a limited satellite link, but this was only for email and data, not normal use). It was good to see that users really appreciated that they could carry on accessing all their notes and information easily even when there was no Internet connection.

One of the researchers said to me (and I think he was only half joking) that usually he did not use Mendeley himself because he was a professor now, and “that’s the reason why I have assistants –they use it for me!” I thought, well, maybe that’s one of the reasons that some of our users stop using Mendeley – they become professors!

 

Some days, when I was feeling like talking to more researchers about Mendeley, I’d just put on my Mendeley T-shirt, and that would soon get some conversations started.

King penguins in South Georgia in March (Photo by Carles Pina i Estany)

My favourite day was when four of us were having dinner round a table – myself, my partner Jen, who was the expedition’s Data Manager, one researcher who already knew I worked at Mendeley, and one who didn’t.

The one that didn’t know where I was from saw the T-shirt and started saying, “OH WOW! MENDELEY! I LOVE MENDELEY! I use it every day! It’s fantastic! I added all my papers, I can find them, cite them, share. I really like how it’s done and what it can do! It’s AWESOME!”

So I was sitting there blushing, until she asked me where I got the t-shirt, and when I told her I worked at Mendeley, she actually jumped up and went “WHAAAAAAAAAAT??!!! YOU WORK AT MENDELEY?!?! It’s best thing ever!!!!”

As I said, it can be an emotional thing for researchers, and it sometimes feels good to be reminded of that.”

This article was originally published on Elsevier Connect

Antarctic exploration in the digital agehttps://t.co/EAkPBDwQya (w/ @mendeley_com) pic.twitter.com/Lmz0MrMvGa

— Elsevier (@ElsevierConnect) January 16, 2018

Alice Bonasio is a VR Consultant and Tech Trends’ Editor in Chief. She also regularly writes for Fast Company, Ars Technica, Quartz, Wired and others. Connect with her on LinkedIn and follow @alicebonasio on Twitter.

The post When Polar Exploration Gets Techy appeared first on Tech Trends.

Making the huge volume of data out there useful to humans is about contextualizing it, but if Facebook has taught us anything is that this must be done responsibly.

Back in 2012 Forbes Magazine declared that data was “the new oil,” and nobody these days disputes the fact that there is a ridiculously large amount of useful data available out there. The use of HTTP as an access method and semantic web languages as interchange formats have turned the Web into the largest decentralised database the world has ever seen. The problem we face, however, is that there are major issues around reliability, accessibility and socialisation of that data that stop it from being as universally useful as it could be.

There are challenges in extracting scientific data from PDFs
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It was Tim Berners-Lee who once said that the next evolution of the World Wide Web – or Web 3.0 if you prefer – would be about the “Giant Global Graph”. What he was talking about was Big Data, but in a social dynamic context which people can easily access and take advantage of. In the words of Gerhard Weikum, Research Director at the Max Planck Institute for Computer Science, nearly all experimental “Big Data” is “utterly boring,” with evaluations ending up in “completely synthetic data with a synthetic workload that has been beaten to death for the last twenty years”. To make this data “interesting”, what he proposes is to bring Big Data and Open Data together, creating Linked Open Data.

Major administrative authorities already publish their statistical data in a Linked Data aware format, but the actual value of these datasets is not unleashed or fully exploited, because data needs context to be of value, and “socialising” is what provides such context. One example of this is the Digital Agenda EU Portal, which has a huge number of datasets on important European indicators, but does not allow people to share their findings or to discuss its interpretations. This means that the context, which gives the data most of its meaning, is simply missing.

As a researcher, you tend to spend most of your time trying to make sense of datasets
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That is the problem that a group of EU-funded researchers are trying to tackle, together with industry partners such as London-based research collaboration platform Mendeley. They launched an open beta version of 42-data, a portal that aims- as the name will suggest to any fans of “Hitchhiker’s Guide to the Galaxy” – to provide answers to the universe and everything by socialising statistical data. This is the main output of theCODE project, which has a remit of facilitating “community-driven insight generation” by lifting non-semantic web data silos in an ecosystem around Linked Open Data, bootstrapped by micropayments and trust mechanisms. Their goal is to essentially create a “flea market for research data” by combining crowd sourced workflows with offline statistical data. This would create a Linked Open Data cloud capable of generating customised datasets to backup and answer all manner of research questions.

Scientific articles are obviously the perfect fodder for this cloud database, but they come with a major problem attached: most papers are in PDF format, which means that it’s difficult (not to say impossible) to extract the primary research data contained in tables and figures. The CODE project, which Mendeley participates in, addresses this by reverse-engineering the paper to extract this information in a format that can then be easily processed and analysed.

“We have a long-standing partnership with Mendeley which started with the TEAM-Beam project, and was then extended to CODE,” says Professor Michael Granitzer, from the University of Passau, Germany, the academic partners responsible for the 42-data portal. “The vision with CODE is basically to make the daily lives of researchers a bit easier, and Mendeley is the perfect partner for that, because it already offers so many tools like the group collaboration and the open API. In the scope of the CODE project, we developed and deployed lots of tools to analyse research publications. Most of that analysis consists of information from inside the paper itself, the primary statistical research data such as tables. We enrich this analysis with linked Open Data to generate meaningful insights and broaden a researcher’s view in a user-friendly way, with sophisticated visualisations that can generate interactive charts and other assets for their research,” he explains.

As a researcher, you tend to spend most of your time actually making (or trying to make) sense out of datasets, and this process means that you have less time to come up with interesting insights and advance research in your field. Take, for example, a researcher preparing to write up their paper: Why is the proposed approach better? The hypothesis they’re putting forward must be backed up by meaningful data, so they are faced with the task of extracting and aggregating statistical primary research data that is stored in tables within various research papers, and then combining, comparing and contrasting this with their own evaluation data. Without integrating these workflows, you’d need a plethora of tools, specially since copying and pasting from PDFs does not work for this type of data. Within 42-data, however, Mendeley hosts and pre-processes those papers, using the Know-Center services to extract the information in a format that is easily processed and manipulable.

The platform itself collects that table-based data and the University of Passau uses Mendeley’s API to merge all those single results accordingly, creating a “data cube” of merged and linked data. This data cube presents the researcher with an integrated view of all those disparate data sources. “But that’s not even the end of it, as a data cube can then be enriched with Open Data to offer up even more insights,” concludes Granitzer. The analysis and discovery is thus not limited to the initial dataset, as the platform offers virtually endless possibilities for customised mixing and matching within what 42-data calls “Data Cubes” to address specific research questions and needs. Individual cubes can be interconnected and aggregated using a graphical interface, which guides the user through and warns of any integrity constraint violations, and how these can be solved, by modifying its structure.

This uncovers some exciting possibilities for accelerating scientific discovery; if some of the sensemaking legwork was automated by such portals, we could see the emergence of a virtual meeting place for people interested in getting insights from such Open Data sets, similarly to how Mendeley users interact in groups based around their research interests. “It is a well-known fact that discoveries in academia come out of intense communication processes, and that is what we’re looking to support,” says Florian Stegmaier, Senior Researcher at the University of Passau. “In addition, the social/crowdsourcing aspect of the platform means that we’re going way beyond the text-based model of asking questions, broadening the scope of discussions to include virtually everything. You could assess the suitability of your research ideas based on existing data, ask for statistics to be included in a paper, or simply discuss a range of published papers to get an in-depth view of the subject,” he enthuses.

But analysing, integrating and sharing data comes with associated costs, as does running such a portal. Beyond the initial EU Seventh Framework Programme grant, how does 42-data actually propose to fund itself? “It’s crucial to establish a value chain for data that creates a positive benefit-to-cost ratio, and we are doing that through two main mechanisms: Reputation and Donations,” Michael Granitzer explains. Reputation is certainly the core motivation driver in the crowdsourcing ecosystem, as we’ve seen withStackExchange and Github, amongst many other high-profile examples. They set out to provide a similar proposition, where users contribute to open-source data projects, analysing data sets and creating interesting insights.

In order for this reputation model to work within the Web of Data you need to establish provenance. This means there is a solid chain of data, which tells you the origin or source of every individual piece of information within that chain. That includes records about which individuals were involved in creating, changing or extracting the data at any given point in time. If a particular person generates a data cube with their query, their ID is stored in that cube to guarantee this reproducible mapping (in the case of data extracted from a paper hosted on Mendeley, the metadata referring to the author name, abstract, publication date, academic status, discipline, research interests, etc. is automatically extracted and linked to the cube). The plan as the platform develops is to triangulate this information with community ratings and recommendation algorithms to produce a “user trust score” that will further feed the reputational ecosystem.

What we see with data today is a similar situation to what we had in the era prior to Web 2.0
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Donations also provide monetary incentives, in the community-driven financing model that Wikipedia pioneered. A “revenue chain” is created by allowing people to donate to users, questions, answers or resources that they find particularly helpful. The idea is to explore the long tail of micro-payments by keeping it flexible. You can target your donation to a specific user, or if it’s a collaborative effort, this can be sent to multiple targets, with user’s trust and reputation scores on the site also influencing how well they do out of those transactions, which is hoped will foster a stronger and more cooperative community. “The complete ecosystem is driven by trust and reputation mechanisms. The higher the trust is, the more likely one will donate for something,” says Granitzer.

What we see with data today is a similar situation to what we had in the era prior to Web 2.0, where there was a lot of content around, but socialisation over that content was not enabled. Just as we’ve seen with the social media boom of recent years, however, there is now an opportunity and appetite for creating communities of interest around the socialisation of data. Through exploring Linked Open Data, users should be empowered to aggregate and integrate interesting data, quickly tailoring it to their specific research needs. That is, however, just the first step, as this increased socialisation could make these datasets accessible to non-scientists as well. The growing momentum of the Citizen Science movement goes to show the enormous potential of opening up science in this way, and the possibilities that this opens up are truly amazing.

Exploring Linked Open Data, users could aggregate interesting data and tailor it to their specific research needs
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Originally published in the Huffington Post

Alice Bonasio is a VR Consultant and Tech Trends’ Editor in Chief. She also regularly writes for Fast Company, Ars Technica, Quartz, Wired and others. Connect with her on LinkedIn and follow @alicebonasio and @techtrends_tech on Twitter.

The post Making Big Data More Social appeared first on Tech Trends.

17% growth expected in the Education Technology sector over the next  4 years according to EdTechX Global and IBIS Capital report.

Mendeley is one example of a start-up that thrived in the London Education Technology ecosystem
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The fastest growth is currently being experienced in Asia, showing that the current dominance of US companies in that space is shifting. The UK looked set to play a large part in that trend, as the country had experienced something of an EdTech boom in recent years. It’s unclear, however, how the decision to leave the European Union will affect the technology industry in general,  the Education sector in particular over the long term.

Mendeley is one example of a start-up that thrived in the London Education Technology ecosystem; it was founded in 2008 by 3 German Entrepreneurs and later acquired by Dutch Publishers Elsevier in 2013 . It is hard to imagine this type of success story happening as easily without the collaboration and free movement of talent that the European Union fostered, and without London being a gateway to the broader European Market and technology ecosystem.

At the EdTechX Europe summit, which took place in June just before the Brexit vote, the mood had been optimistic. The conference attracted record numbers, and many European companies were ranked as global technology champions in the first “Makers and Shakers of Education Technology Index” published prior to the event. The index highlighted the role of innovators such as Cambridge-based Raspberry Pi and Lego Education in advancing Education Technology.

There is an increasingly pressing need for investment in the sector to accelerate the digitisation of education, especially in Europe
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Stavros N Yiannouka, CEO of WISE – the World Innovation Summit for Education which co-curated the rankings said the purpose of the list was to recognise thinkers and doers pioneering innovative approaches to addressing challenges in education. The criteria used in compiling the shortlist included disruptive innovation, individual vision, impact, and contribution to education.

According to Benjamin Vedrenne-Cloquet, Co- Founder of EdTechXGlobal, the index elevates education innovators and thought leaders to the level of “social rock stars”.

Beyond social recognition for the sector, however, there is little doubt that the enormous commercial opportunities around EdTech remain largely underexplored, in spite of the growth we’ve seen so far. The digital market is now worth over $5tn (this is 8 times the size of the software market and 3 times larger than the media and entertainment industry) yet education is only 2% digitised. There is, the report concludes, an increasingly pressing need for investment in the sector to accelerate the digitisation of education, especially in Europe.

There is little doubt that the enormous commercial opportunities around EdTech remain largely underexplored
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Digital models offer new routes for re-skilling as well as addressing widespread labour shortages
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There is also a growing realisation of the impact EdTech will have in the way the 21^st Century workforce acquires and develops skills, and an acknowledgement that this will be driven by mobile. The most substantial growth is expected across emerging markets, with mobile being a catalyst for delivering and supporting learning. 90% of the world’s population under 30 live in those markets, and it follows that education and vocational training in the area will be led by mobile-first strategies. This is a view that strongly chimes with the strategy set out by Aape Pohjavirta, from Finnish Mobile Learning start-up Funzi.

The report also predicts a continued rise of MOOC platforms such as Coursera (which is set to reach yearly revenues of $30m) as the global demand for education rises and traditional provision models will simply not be able to grow in step with rise in global population numbers. At the same time, we can expect a global workforce crisis powered by extreme automation, a so-called ‘Fourth Industrial Revolution’ which will see AI replacing not only highly repetitive low-skill jobs, but routine medium-skill too.

“50% of current jobs won’t exist in 2025,” says Vedrenne-Cloquet, “so there will be a growing need to re-train the workforce in order to address current skill gaps and increase the use of continuous learning.”

Digital models, he concludes, offer a way to capture these changes and offer new routes for re-skilling as well as addressing widespread labour shortages.

There is a growing realisation of the impact EdTech will have in the way the 21st Century workforce acquires and develops skills
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Alice Bonasio is a VR Consultant and Tech Trends’ Editor in Chief. She also regularly writes for Fast Company, Ars Technica, Quartz, Wired and others. Connect with her on LinkedIn and follow @alicebonasio and @techtrends_tech on Twitter.

The post Edtech Market to Reach $252bn by 2020 appeared first on Tech Trends.