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.

Teradata’s Art of Analytics project is helping people make an emotional connection with the datasets that rule their world.

In her day-to-day work, Yasmeen Ahmad tackles immensely complex datasets, deploying an arsenal of approaches and methodologies that would sound intimidating to most lay people.

Not many people are able to make sense from complex datasets
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From predictive modelling to text analytics, time series analysis to development of attribution strategies, few people can easily wrap their heads around what such terms mean, and even fewer are capable of drawing actionable insights from them – which is why data scientists such as herself are always in such high demand.

Ahmad worked in the life sciences industry before pivoting to commercial work, where she is now Director of Think Big Analytics, the consulting branch of IT service management company Teradata.

Visualisation is a core component of any data science and analytical project
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The best way help people see the meaning of datasets is to paint them a picture
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Over many years helping clients across a variety of industries to make sense of their data, however, she realised that the best way help them see meaning in those datasets was to literally paint them a picture.

“Visualisation is a core component of any data science and analytical project,” she explains. “It is almost always used at the beginning to understand the datasets you are working with, and can help to quickly identify anomalies, outliers and strong correlations in the data.”

As far as data is concerned, she says, a picture really is worth a thousand words, as visualisation helps to add meaning on top of data that is much easier to assimilate for humans than descriptive words or single numbers and values alone.

Visualisation helps to add meaning on top of data that is much easier to assimilate
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The visualisations supported storytelling around a project
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Her team would therefore routinely include such visuals when they presented their key results to clients, and found that even people who might not be well versed in data science or technology could still connect with them. The visualisations supported storytelling around a project, engaging business stakeholders to understand connections, relationships and associations in the data.

“As more investment goes into data platforms and analytical technologies, the artwork helps to provide a face to this investment. We had business leaders commenting on how beautiful the visualisations were. Colour, shape and layout were all dimensions that were used to convey meaning. The choice of how a visualisation was formed is actually a creative process – like creating a piece of art.”

From there, she explains, it was a short leap to the idea behind the Art of Analytics project, which brings together a range of those visualisation pieces from their previous projects.

Art of Analytics brings together a range of visualisation from previous projects.
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Ahmad believes one of the main strengths of the project is its ability to bring data to life for lay audiences, creating a connection between data insights and observers and bridging the technical gap.

“The visualisations push the human to look beyond individual numbers and values, to thinking about data as a series of connections to be explored. They make it particularly easy to see associations, connections, pathways etc. The art is providing form to the complex fields of big data and data science – making them accessible to a wider audience.

Visualisations push the human to think about data as a series of connections to be explored
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Yet the usefulness of data visualisation is not limited to non-technical people. According to Ahmad, it is also a key component of a data scientist’s toolkit:

“During my life sciences research – I was working in a field where everything was abstracted. The data I analysed often came from human cell samples that could only be seen under microscopes. Hence, collecting data about these samples, analysing it to create insight and then relating the insight back to reality was somewhat difficult. Visualisation was key to help portray not only the insights, but also how they linked to human cells and biology in general.”

Art of Analytics was also an opportunity for Ahmad to bring together those creative and scientific sides. Data science, she says, is actually a highly creative discipline that combines technical know-how with lateral thinking and the ability to tease out stories from complex datasets.

Art and science need to come together to solve the world’s most complex problems
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Data science is actually a highly creative discipline
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“I believe that art and science need to come together to help to solve the world’s most complex problems, and the best data scientists are not only great at statistics, maths and analytical subjects, but are also creative problem solvers who can translate their work into meaningful messaging that connects with their business and commercial audiences.”

This is an on-going project, and there are plans to create new data representations as they work with new datasets they haven’t encountered before.

The emotional connection that people establish with the work highlights the power of data
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Ahmad is also keen to explore how other media such as animation, video and perhaps even VR could help add other dimensions to that work. By creating a video, she says, it would be possible to create another level of emotional connection with audiences, by representing how those relationships have evolved over time.

“We have had senior executives request copies of the Art of Analytics to hang in their office. The emotional connection that people can establish with the work highlights the power of data.”

This article was originally published on The Next Web

Art exhibit shows off the beauty of data https://t.co/Mq8uOvFCGH

— TNW Contributors (@TNWcontributors) November 20, 2017

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 Big Data is Beautiful appeared first on Tech Trends.

To leverage Big Data and build an effective Artificial Intelligence infrastructure, enterprises must embrace collaboration.  

Using data smartly can give your business a crucial competitive advantage
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In a digital economy, the rule of thumb tends to be that the smarter your use of data and technology, the more of a competitive edge your business has. A recent report by Teradata, based on over 260 interviews conducted by research firm Vanson Bourne with senior IT and business decision makers, found that there is widespread enthusiasm for adoption of AI, with 80 percent of enterprises reporting that they were already investing in the technology in some capacity, and over 30% planning to expand their investment in the are over the next 36 months.

“C-level executives – namely CIOs and CTOs, maintain they are committed to AI in their enterprise, because of the expected ROI over the next 10 years.” The report therefore concludes that executives will accept those challenges as the long-term benefits clearly outweigh near-term pain. In fact their analysis showed that over a five-year forecast, organisations effectively expected to double their money when investing in AI: For every $1 spent on AI technologies, organisations predicted a return on investment of $1.23 over three years, $1.99 in five years, and $2.87 over a ten-year period.

The Economist – in a phrase that has since become rather cliché – put forward the idea that “data is the new oil,” and World Economic Forum has now designated Big Data as a new kind of economic asset, just like currency or gold.

A study by the MIT Center for Digital Business confirms that data-driven businesses do indeed have the edge. It surveyed 330 leading U.S. Businesses and found that companies that focused strongly on data-driven decision-making had an average of four percentage points higher productivity and six percentage points higher profits.

C-level executives are committed to implementing AI in their enterprise
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Yet as these results indicate, it would perhaps be more accurate to say that data is in fact the new oxygen. Whereas it is still true that businesses that best leverage data and AI will gain a significant competitive advantage, it is probably fair to say that those that fail to make their organisations data-centric will eventually not be able to survive at all in the digital age.

Data is in fact the new oxygen
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While most people agree on the essential role that Artificial Intelligence and Data play in their organisation’s success, there are significant challenges. The overwhelming majority of business leaders surveyed in the Teradata report anticipated major barriers for adoption within their organisation, with roadblocks ranging from an inadequate IT infrastructure to shortage of in-house talent.

The majority of business leaders surveyed predicted significant roadblocks to AI adoption
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In their book The Sentient Enterprise, The evolution of Business Decision Making – launched at the Teradata Partners’s Conference last month – Oliver Ratzesberger (Teradata’s Chief Product Officer) and Mohanbir Sawhney from the McCormick foundation talk about how agility is key to getting the enterprise to the sentient point where it can analyse data and make decisions in real time.

The crux of the problem enterprises face lies not in the difficulty of gathering data, but in extracting insights and then turning these into actionable processes. The reality is that we live in a time of data overload, and companies can easily find themselves trapped in reactive mode, spending most of their time sifting through mountains of data and making decisions only when problems emerge, rather than anticipating them.

The problem enterprises face is in extracting actionable insights from data
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To tackle these problems, enterprises need access to key talent and infrastructure so as to enable the leveraging of Artificial Intelligence and Big Data. Increasingly, this is not being done “in house” but rather in partnerships with dedicated providers. These go beyond the traditional SaaS and becomes much more of a “Platform as a Service” model that incorporates complex customization and consultancy services.

All banks need a scalable, advanced analytics platform
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Teradata’s Think Big Analytics team, for example, worked with Danske Bank to create a fraud-detection platform that uses machine leaning to analyse tens of thousands of latent features, scoring millions of online banking transactions in real-time to provide actionable insight regarding true, and false, fraudulent activity. Together, they built a framework within the bank’s existing infrastructure, crating advanced machine learning models to detect fraud within millions of transactions per year, and in peak times, many hundreds of thousands per minute:

“Using AI, we’ve already reduced false positives by 50% and as such have been able to reallocate half the fraud detection unit to higher value responsibilities,” explains Nadeem Gulzar, Head of Advanced Analytics, Danske Bank. “There is evidence that criminals are becoming savvier by the day; employing sophisticated machine learning techniques to attack, so it’s critical to use advanced techniques, such as machine learning to catch them.”

Using AI, Teradata and Danske Bank reduced false positives by 50%
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“All banks need a scalable, advanced analytics platform, as well as a roadmap and strategy for digitalization to bring data science into the organization.” says Mads Ingwar, Client Services Director at Think Big Analytics. “For online transactions, credit cards and mobile payments, banks need a real-time solution – the platform we developed with Danske Bank scores transactions in less than 300 milliseconds. It means that when customers are standing in the supermarket buying groceries, the system can provide immediately actionable insight. This type of solution is one we’ll begin to see throughout organizations in the financial services industry,” he concludes.

Fintech company Verifi, also collaborates with banks and merchants to connect the multi-layered data-streams and combat fraud. The work the company does with is based on optimising data transmissions using APIs (Application Programming Interfaces). This is much more efficient because whereas legacy systems rely on processing large numbers of files sent in bulk, APIs can process data in real-time.

Verifi collaborates with banks and merchants to connect multi-layered data-streams
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The Verifi system collates APIs from the merchant shopping cart, customer relationship management (CRM) system, shipping data system, and others, and provides the merchant with better information to handle charges disputed by consumers, allowing them to often resolve the issue directly rather than have the bank issue a chargeback. Merchants benefit as they’re able to control the message to the consumer, and banks are happy because the sale remains and they reduce their operating costs. It is the sort of technology enabled, data-driven system that is a true win-win.

Friendly fraud and chargebacks cost $130 million in Q1 2017 in the U.S.
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One of the problems that such systems help tackle is so-called “friendly fraud,” a term used to describe a situation when a customer experiences “buyer’s remorse” and “tries it on” by putting in a claim directly with their bank or card issuer for a refund, when the sale did in fact legitimately occur. Julie Conroy, Research Director of the Aite Group says that in the U.S. friendly fraud and chargebacks would likely near $130 million, in Q1 2017 alone.

“Since many such claims are relatively low-value, banks will often issue the refund rather than dedicating the resources needed to investigate and dispute each transaction,” explains Matthew Katz, Verifi’s CEO Their research in fact showed that up to 86% of cardholders bypass the merchant and contact their issuing bank directly to dispute or question a charge on their bill. “When you add all of these claims up, it amounts to a huge problem for the industry, with billions of dollars in costs being passed onto merchants, and – ultimately – to the consumer,” he says.

Friendly fraud amounts to a serious problem, confirms a report from Javelin Research estimates that the increase in card-not-present (CNP) fraud and chargeback management now consume between 13% and 20% of operational budget for such retailers.

Even with such resources dedicated to fighting fraud, however, not having the right information on hand in real time can mean that businesses are not only vulnerable to fraud, but also risk losing customers and reputational goodwill by challenging legitimate customer claims.

“The key functionality we enable is to connect all the parties to the transaction with the right information at the right time,” says Katz. “Our Order Insight platform provides robust transaction data on each sale, like the merchant’s name and contact information, date of purchase, device name used in the order process and many more important details, directly to the consumer via their bank’s mobile or online interfaces. In most cases, having access to this data can resolve a dispute and avoid a phone call to the issuer altogether, saving the merchant money and keeping customers happy.”

The report by Javelin Research concludes that partnering with third-party providers like Verifi makes sound business sense, in that it frees up merchants to focus on their core business. “It is imperative that merchants stay customer-friendly in the face of growing fraud pressure, yet the amount they have to put into building, managing, and maintaining in-house solutions has significant negative impact on their ability to scale their business over time. The projected growth of CNP fraud will introduce a new level of burden for digital merchants, which they will be unable to combat from a resource and scalability perspective. This will increase the pressure and negative impacts on them because they will be hit by fraud that they can’t handle. Their inability to scale quickly enough may result in measures that will hinder customer experience.”

The key advantage that these technologies – when properly leveraged – bring to enterprise is their ability to streamline and break silos
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The key advantage that these technologies – when properly leveraged – bring to enterprise is their ability to streamline and break silos. Deeper integration and more collaborative approaches to technology can help deliver robust information to the right parties at the right time, ultimately enhancing the experience for customers and users, improving efficiency across the board, and providing higher returns for companies and investors. The pieces of the puzzle are all there, it seems, but stakeholders must all work collaboratively to bring them together.

This article was first published on ERP in News

How Collaboration is key to leveraging AI and Big Data https://t.co/UGfUT6KjYl via @erpinnews@alicebonasio #tw17 #erpinnews #eintechnews

— ERPINNEWS (@erpinnews) November 15, 2017

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 Working Together to Build a Big Data Future appeared first on Tech Trends.

AI companies are getting snapped up in record numbers by tech giants hungry for brains. But are they really worth such big bucks

Magic Pony is hardly a household name. Chances are you never heard of them, or at least not before they were bought for $150 million by Twitter a few weeks ago. They were a company of 14 people, and at that cost this acqui-hiring spree which sees them joining Twitter’s Cortex division priced each of them at over $10 million.

Twitter's latest acqui-hiring spree cost them over $10 million per head. Some argue it's money well spent
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But although that’s certainly at the top end of the scale as far as talent acquisition goes, it’s far from an anomaly in the world of AI these days. A report by Magister Advisors reveals that the average price per high-quality employee in these acquisitions averages $2.4m. The technology advisory firm, which specializes in Mergers and Acquisitions (M&A) tracked 26 AI-driven deals since 2014 in the US, Europe and Israel, 11 of which involved companies with less than 50 employees which were acquired largely, or entirely, for the team and capability.

A good AI engineer is worth more than many company CEOs right now, says Victor Basta, managing director at Magister Advisors.

Technology journalist Luke Dormehl agrees that the valuation of tech companies can sometimes seem baffling, particularly if you compare them with their pre-digital equivalents.

A lot of “traditional” companies producing physical products, rather than ethereal software, employed tens or even hundreds of thousands of employees. Today, tech giants can disrupt industries with a fraction of that. Famously, Instagram employed just thirteen people when it was snapped up by Facebook for $1 billion, explains Dormehl.

In this new market, Israel and the UK are emerging as world centres at the top tier for AI innovation, with many firms such as IBM and Intel expanding their AI footprint in Israel, and the UK capitalising on the influence of institutions such as Cambridge, Oxford, Imperial College and a crop of AI-active VCs such as White Star, Playfair and Notion. This has been reflected in the recent UK exits which – apart from Magic Pony – include DeepMind (Google), Swiftkey (Microsoft), Evi (Amazon), VocalIQ (Apple) and PredictionIO (Salesforce).

Israel and the UK are emerging as world centres for top-tier AI Innovation
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Where there is a broader trend in tech towards building sustainable business models as early as possible and proving sustainability through customer numbers and revenues, in the field of Deep Machine Learning that actually seems to be irrelevant. In fact, the report indicates that acquiring companies prefer to target start-ups with no revenue at all.

Dormehl explains that the reason why the way that companies are valued has changed so dramatically has to do with the potential value of data.

As the Internet has continued to become more and more present in our lives, we’ve seen a rise of ostensibly “free” services. Of course, these aren’t really free at all: they’re often raking in money through advertising or, if it’s not been able to monetise in that way yet, the value of user data.

That makes sense when you think about it: Platforms such as Google, Facebook and Twitter already have all the users they need. They are incredibly pervasive. What they need – very badly indeed – are ways to leverage their user data and keep those people engaging with their content. Companies such as Magic Pony don’t offer a separate product as such. They bring a layer of complexity that can be overlaid on top of existing content such as videos, posts, news, and all manner of experiences. Their engines and algorithms can be embedded directly into the larger company’s offering and match content automatically and intuitively with what users want, need, or might like to discover. This, in nutshell, is the arms race we’re experiencing with AI, yet nobody has their hands on that Holy Grail just yet.

There is an arms race to connect users with relevant content, and AI is the key to achieving that
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The applications of AI technology – machine learning to robotics, virtual assistants, speech and image recognition, predictive analytics and many, many more – are extremely broad. These include major sectors such as advertising, security and healthcare, where, for example, doctors will be able to use image recognition to automate medical diagnosis in future. Since this demand is not segmented or restricted to any one particular sector, this contributes to the chronic shortage of high-quality AI engineers and the resulting price inflation for these companies. Magister’s report points to the fact that ordinarily buyers such as EBay, Twitter, Amazon and Microsoft would not compete for the same M&As. But in the area of AI their interests overlap (in visual search, for example)

Although these sky-high valuations might sometimes be the result of over-hype, Dormehl concludes that there is a solid business case behind the trend:

The concept of a “unicorn” economy, in which venture capitalist are always on the lookout for the next Facebook or Google, means that the potential of a company becomes wrapped up in its valuation. Is that always right? Probably not. Are these companies worth the money when they suddenly create their own version of AdWords and begin raking in billions of dollars? It’s hard to argue that they’re not.

Luke Dormehl’s latest book Thinking Machines: The Inside Story of Artificial Intelligence and Our Race To Build The Future is out August 11 and available on Amazon

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 Rise of the Smart-Up 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.

Data Scientists are the rock stars of the tech world, and London start-up Pivigo helps them get their mojo 

If you’re in the tech business, pray you’re never tasked with recruiting a Data Scientist. Good ones are rarer than hen’s teeth, and the people lucky enough to have them will fight to the death to keep them. One  foolish company – which shall remain unnamed – that recently made its Data Scientist feel rather unloved. That person casually started exploring options on a Monday, and had his pick of six very attractive job offers by Friday that same week.

Data Scientists are people who not only understand data, but can spot amazing things you can DO with it
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But why, you might ask, are they in such high demand? To put it simply, these are people who not only understand data, but can spot the amazing things you can DO with data, then work with others to turn these insights into products.

Economies are now data-driven, and as we go about our ‘always on,’ constantly connected lives, we’re constantly feeding new data streams into a myriad of different systems. There’s more data out there than ever before, and the sheer volume of it continues to grow exponentially, hence Big Data.

Data Scientists spot trends and stories in these complex goldmines of information, helping companies extract actionable insights from them. They communicate well and work with teams across the company, using their analytical super-powers to help optimize processes and come up with new product and services.

A blend of analyst and artist, a good Data Scientist is what you might call a Renaissance person
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Working in the tech industry, I’ve been lucky to work with many gifted individuals over the years. Some were technical geniuses; others had a knack for creating and marketing successful products. Yet it’s very rare to get someone combining both. Those people are Data Scientists. And we need more of them.

And that’s where Kim Nilsson comes in. She co-founded Pivigo in 2013 with Jason Muller to help tackle this chronic industry shortage, and help gifted individuals acquire the right skills to become Data Scientists.

“The problem is that university degrees are great at teaching you theoretical knowledge such as statistical concepts and logic or programming methods, but horribly bad at teaching you the practical skills that the industry needs. What we offer is a sort of finishing school to those with an academic degree, equipping you with the most up-to-date, commercial skills for instant employability.”

Pivigo works with companies on a sponsorship model that allows them to offer heavily discounted rates for academics looking to acquire those industry skills.

“Academia will always struggle with giving students real experience of working with messy, confusing, and uncoordinated data,” says Nilsson. “We prepare them for working in a commercial environment with deadlines, business priorities, communication problems and the pressure that comes with that.”

“We’re now building a platform – a trusted data science marketplace – where we can connect data scientists with organisations continuously. We already have a lot of experience working with companies, and delivered over 45 data science projects to clients like KPMG, Royal Mail, British Gas and Marks & Spencer.”

So far, over 200 students from 40 nationalities have graduated from the programme, with over 35% of these being female (a very healthy representation rate in STEM). Nilsson wants to democratise data science even further, however. She’s hoping that Pivigo’s newly launched online course will reach a much bigger audience in a market worth around £4bn globally, and growing 25% per annum.

Pivigo’s newly launched online course will reach a much bigger audience in a market worth around £4bn
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In this fast-evolving landscape, private initiatives have the advantage of remaining nimble, unlike universities that have to set their curricula years in advance. Nilsson believes it’s crucial for universities to catch up with those standards and speed up learning cycles by incorporating technology tools such as MOOCs into their structure. “Learning should be continuous, adaptive and flexible, with Nano-degrees that can be updated from one month to the next to cover the most recent developments in a field” She says .

Learning should be adaptive and flexible, with Nano-degrees that can be continuously updated
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“There’s also a need to strengthen the prestige and reputation of vocational training and apprenticeships. We need practical skills in society. These may still be of very high complexity, but they must be directly relevant to what industry wants and needs. And this learning is best done on the job.”

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 Changing the World One Byte at the Time appeared first on Tech Trends.