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.

Hard-of-hearing and deaf students are, unfortunately, often left at a disadvantage as they have an extra set of challenges to contend with on top of the difficulties that come with adjusting to a classroom environment.

This extra work – keeping up with sign language interpreters while simultaneously reading course material and taking notes – often results in learning challenges and lower grades. However, as technology integration grows in schools, new ways to bridge classroom accessibility gaps emerge.

Prior to working with Verbit, the university was facing major challenges due to a lack of resources, a high demand for services and a large backlog of inaccessible content that required transcription
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And that’s where technology can really make a difference, according to Tom Livne, CEO of Verbit. His company has developed a cutting edge transcription process, powered by machine learning technology, that provide 99% transcription accuracy – which he claims is the fastest turnaround time in the industry at any volume.

“Our hybrid model fuses the latest AI technology as well as seasoned transcribers who edit and review each file to polish them to perfection. Our platform is the only solution on the market that incorporates contextual data, specific acoustic models and current events to provide each customer with a tailored model that keeps improving with time.”

Founded in 2016, Verbit has secured $11M in seed funding backed by several prominent venture capitalists. It is now being used by various institutions including Brigham Young University in Idaho, which has used their technology to provide fast and accurate transcripts and captions for their students, particularly those who are deaf or hard of hearing. Prior to working with Verbit, the university was facing major challenges due to a lack of resources, a high demand for services and a large backlog of inaccessible content that required transcription.

Keeping up with sign language interpreters while simultaneously reading course material and taking notes – often results in learning challenges and lower grades for deaf students
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This is part of a growing trend which we’ve been seeing at events such as this year’s Microsoft Build, where Satya Nadella launched their company’s AI for Accessibility program
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Deploying the technology resulted in a much faster turnaround without compromising on accuracy. Automation also allowed the services to be delivered at a much lower cost, which is of course a crucial issue for many education institutions trying to balance their budgets.

“We chose Verbit because, out of the multitude of companies on the market, they were a perfect fit for us. We stay with Verbit because the people are fantastic. The customer service has been incredible. The turnaround time, the accuracy, the editing time – all of those things are truly best- in-class.” Valerie Sturm, BYU-I Coordinator of Services for the Deaf and Hard of Hearing.

Prior to piloting the automation route, BYU-Idaho employed 11 transcribers on campus, tasked with covering over 500 hours of transcription per week. A high demand for services and too few people to provide them made it impossible to meet students’ needs and recover from a lengthy backlog of requests.

The system relied mostly on student transcribers, who would graduate in due course, meaning a constant need to re-train personnel from scratch, which was highly inefficient. The university found itself faced with a constant backlog of requests for content transcription, which they weren’t able to meet under the old system. At one point the institution had over 1,000 pieces of audio that were never transcribed or captioned. Activities like tutoring sessions or extra content that took place outside of the traditional classroom setting only added to this growing backlog.

Deploying the technology resulted in a much faster turnaround without compromising on accuracy
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This is part of a growing trend which we’ve been seeing at events such as this year’s Microsoft Build, where Satya Nadella launched their company’s AI for Accessibility program. There is little doubt that with the fast pace of innovation we’re seeing in fields such as machine learning, this type of capability has great potential for making society more inclusive for people with disabilities, and that has to be a good thing.

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 Tech Bridging the Accessibility Gap appeared first on Tech Trends.

The Internet as we know it today was not made up of high-speed broadband, smartphones or the Cloud.

In fact, the original network was called ARPANET and was a single homogenous network of packet switches called IMPs (Interface Message Processors) connected by dedicated telephone circuits. The mainframe computers, called hosts, were connected to the packet switches. So the basic network was a set of specialized computers, IMPs, that facilitated the exchange of packets between the host computers.

The original network was called ARPANET and was a single homogenous network of packet switches called IMPs
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The Internet introduced us to the concept of a network of heterogenous packet switched networks interconnected by specialized computers called gateways. The hosts on each network knew they were part of the Internet and knew how to send and receive Internet packets. The gateways knew they were part of the Internet and they routed Internet packets from gateway to gateway across multiple networks until they arrived at the destination network at which point they were relayed to the destination host on the destination network.

Throughout the years, the Internet has undergone an explosive growth
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From connecting minds across continents to introducing eCommerce, the Internet has become something most of us now rely on every day for pretty much everything. Yet despite all of that, few of us know its history. This infographic by SSL2BUY sheds light onto it.

From connecting minds across continents to introducing eCommerce, the Internet has become something most of us now rely on every day
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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 Infographic: A Brief History of the Internet appeared first on Tech Trends.

Can deep learning and blockchain technology help us slow or even reverse the ravages of the aging process?

Longevity research has momentous implications for our lives, because it goes beyond merely living longer. We’re talking about living better, healthier lives, and potentially also shifting the goalposts where it comes to the “ticking biological clock.” Just think for a second about what that could do for gender equality in levelling the career playing field for women considering whether or not they want to – or can afford to – have children. It quite literally changes the rules of the game.

So it’s no wonder there was such buzz around Insilico Medicine – a Baltimore-based AI company specialized in the application of deep learning for drug discovery and aging research – at the Pioneers Conference in Vienna last month. As Dr. Alex Zhavoronkov, Founder and CEO of Insilico says in the video below:

“Modern artificial intelligence now has the ability to capture some of the most important biological features that change during aging and we can now use it to reverse some of those processes.”

Longevity research has momentous implications for our lives, because it goes beyond merely living longer
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“More than 90% of the molecules discovered the traditional techniques and tested in mice fail in human clinical trials. Our goal at Insilico Medicine is to develop advanced end-to-end AI solutions to discover the optimal pre-clinical candidates,” explains Zhavoronkov. “Our collaboration with WuXi AppTec enables us to focus on artificial intelligence without the need to invest in the expensive laboratory infrastructure. It also enables us to collaborate with some of the world’s brightest medicinal chemists and biologists to optimize the drug development process.”

Now Insilico has just announced a new research collaboration with WuXi AppTec, which will enable the company to perform the rapid experimental validation of the molecules discovered using AI and generate the valuable data to advance its AI research.

“We devised series of experiments to test the capabilities of the novel molecular generators and generate valuable data for the AI-powered drug discovery. The results we saw so far look very promising and provide some confidence that artificial intelligence and deep learning can be helpful for drug discovery,” said Dr. Tao Guo, Vice President, Head of International Discovery Service Unit of WuXi AppTec. “The compounds generated by Insilico’s approaches to artificial intelligence have the potential to shorten cycle times and improve the quality of pre-clinical candidates.”

The amount of their latest investment round (which also included Pavilion Capital, BOLD Capital and longevity therapeutics company Juvenescence) was not disclosed, but the Insilico had previously raised over $14 million according to Crunchbase.

Modern artificial intelligence now has the ability to capture some of the most important biological features that change during aging and we can now use it to reverse some of those processes
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Insilico is headquartered at the Emerging Technology Centers at the Johns Hopkins University Eastern campus in Baltimore, with R&D and management resources in Belgium, Russia, UK, Taiwan and Korea sourced through hackathons and competitions. Their work is dedicated to extending human productive longevity and transforming every step of the drug discovery and drug development process through excellence in biomarker discovery, drug development, digital medicine and aging research.

More than 90% of the molecules discovered the traditional techniques and tested in mice fail in human clinical trials
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Insilico devised experiments to test the capabilities of the novel molecular generators and generate valuable data for the AI-powered drug discovery
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It pioneered the applications of the generative adversarial networks (GANs) and reinforcement learning for generation of novel molecular structures for the diseases with a known target and with no known targets. Since 2016, Insilico Medicine published multiple research papers, demonstrating the capabilities of GANs and RL systems for de novo generation of diverse molecules with the desired molecular properties, and preliminary experimental validation uncovered the most promising research directions.

“What impressed us the most about Insilico Medicine was the breadth of its technological capabilities and applications, ranging from target identification and small molecule generation using next-generation artificial intelligence to expertise in blockchain and aging research,” explained Mr. Edward Hu, Chief Financial Officer and Chief Investment Officer of WuXi AppTec.

In addition to working collaborations with the large pharmaceutical companies, the company is pursuing internal drug discovery programs in cancer, dermatological diseases, fibrosis, Parkinson’s Disease, Alzheimer’s Disease, ALS, diabetes and many others, so needless to say Tech Trends will be monitoring their progress with great interest, watch this space.

Insilico Medicine is pursuing internal drug discovery programs in cancer, dermatological diseases, fibrosis, Parkinson's Disease, Alzheimer's Disease, ALS and diabetes
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For companies looking to get into Immersive technologies such as VR/AR/MR/XR our Virtual Reality Consultancy services offer guidance and support on how best to incorporate these into your brand strategy.

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 Fighting Aging with Artificial Intelligence appeared first on Tech Trends.

Innovation is challenging just how far we trust technology – and tech providers – to play an increasingly central role in daily life: can the law keep up?

By Dan Tozer, Partner at Harbottle & Lewis

It is difficult to find surety in our current legal frameworks as we head into the AI age – particularly when it comes to ownership of ideas and actions
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The march of the robots into daily life is now seemingly inevitable.

Almost a third of London jobs are expected to be in the hands of machines within the next twenty years, according to Think Tank the Centre for London, while the Royal Society of Arts predicts 4 million UK private sector jobs could be taken by robots within the decade. From data use (see the Cambridge Analytica scandal) to robot liability, innovations are bringing the issue of trust and responsibility in technology to the fore.

Amazon Alexa placed an order of cat food on its own, after being ‘triggered’ by an advert for a cat food brand on a nearby TV
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The recent and much anticipated House of Lords report on AI highlighted this issue. While rightly underlining the UK’s leading role in AI advancement, peers also noted the urgent need for legal clarity over AI, particularly with regards to liability. Law and regulation, based to such a large extent on precedent, traditionally struggles to keep up with technological advancements at the best of times, and we are now at a significant turning point.

The march of the robots into daily life is now seemingly inevitable
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With machines playing an increasingly central role in daily life, a degree of surety over where responsibility for AI lies, and where public trust resides, is required. As it stands, it is difficult to find this surety in our current legal frameworks as we head into the ‘AI age’ – particularly when it comes to ‘ownership’, in its broadest sense, of ideas and actions.

Law and regulation, based to such a large extent on precedent, traditionally struggles to keep up with technological advancements at the best of times, and we are now at a significant turning point
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Can a robot own its own creations?

With machines now being used to create content largely independently, recent years have seen the emergence of a debate, particularly in the creative and arts industries, over the extent to which robots can own the intellectual property inherent in their creations.

In 2016, Gaetan Hadjeres and Francois Pachet at the Sony Computer Science Laboratories in Paris developed a machine learning neural network that has learned to produce choral cantatas of its own in the style of Bach. More recently, Shun Matsuzaka, creative planner for Japanese ad agency McCann, created the world’s first AI creative director, capable of directing a TV commercial. We are now left wondering how far these artistic creations belong to the machine, or belong to the human behind the machine.

In this context, it is worth considering to what extent a ‘non-human’ can own IP at all…

For example, in 2016, after a protracted legal battle revolving around a “selfie” taken by Naruto, a macaque money from the jungle of Indonesia, using a camera owned by photographer David Slater, a federal judge in San Francisco ruled that a monkey could not be declared a copyright owner. While of course not a robot issue, this highlighted the difficulty of ascribing ownership to a non-human ‘being’.

With machines playing an increasingly central role, a degree of surety over where responsibility for AI lies, and where public trust resides, is required
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Crucially, to what extent and where ownership is attributed is central to the question of who retains responsibility, and where public trust ultimately needs to lie – particularly when machines expand in their ‘intelligence’, ‘learn’ and begin to take independent actions…

With machines now being used to create content largely independently, we have seen the emergence of a debate over the extent to which robots can own the intellectual property in their creations.
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Assigning responsibility

Earlier in 2018, an Amazon Alexa personal assistant placed an order of cat food on its own, after being ‘triggered’ by an advert for a cat food brand on a nearby TV. The case was dealt with by the Advertising Standards Authority, who ruled that the mistake was not the advertiser’s fault. This was an isolated case but, as robot assistants increase in sophistication, questions remain around who takes the blame and who takes the ownership, when a robot’s actions go wrong.

The recent tragedies involving driverless cars, including in Arizona during an Uber autonomous vehicle test, places clear focus on the issue of liability for AI decisions. Many questions remain in the autonomous vehicles space. For instance, if a vehicle’s algorithm is programmed to ‘preserve itself’ – i.e. to limit any physical damage to the vehicle and its inhabitants – are other road-users, or indeed occupants, potentially more at risk? Further, how far liability can be assigned to the manufacturer, the software developer, and the machine itself, will continue to be debated.

If a vehicle’s algorithm is programmed to ‘preserve itself’ – i.e. to limit any physical damage to the vehicle and its inhabitants – are other road-users, or indeed occupants, potentially more at risk?
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How far would you trust a robot?

Of course, there may be situations where we are more likely to be more trusting of machines to perform tasks and services. One of the arguments for driverless vehicles is they are not susceptible to issues such as drink-driving, while opinions are still divided as to how far, for instance, patients would trust a robot surgeon.

The recent tragedies involving driverless cars place clear focus on the issue of liability for AI decisions
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Nils Bohr said, “prediction is very difficult, especially if it’s about the future.” However, we can safely predict that the robots are coming – indeed, to a large extent they are already here, driving many of the systems and processes we often take for granted. As AI becomes ever more embedded within our daily lives, clarity over issues including ownership and responsibility will need to be sought, and solutions developed, so we can venture into the brave new world of AI with trust in the innovation.

The post Expert View: Trusting Technology appeared first on Tech Trends.

The airline industry is investing heavily in pilotless planes, but should it be fixing existing systems first?

The holidays bring that familiar mixture of anticipation and dread that comes with traveling at the busiest time of the year. And this year passengers have even more reason to feel trepidation as they head out to the airport, even if they’re not flying with United.

42% of current US pilots will retire over the next decade
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Hundreds of American Airlines passengers recently discovered that a computer glitch had jeopardized their holiday plans. In September, Ryanair also experienced a similar failure that led to a much larger scheduling meltdown, affecting over 700,000 passengers and causing no fewer than 20,000 flight cancellations.

Ryanair has since gone on to hire more than 200 pilots, capitalizing in part on the collapse of some of its competitors – such as Monarch Airlines – but critics point to the fact that more staff won’t solve the flaws that led to the fiasco in the first place.

The immediate crisis facing the airline industry is keeping up with demand among rising concerns about crewing shortages
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So why don’t the airlines simply implement new solutions to optimize their operations? Tom LaJoie, the president of airline crew and flight schedule management firm eTT Aviation, says that the aviation industry is notoriously conservative and generally loathe to make changes to its mission-critical structures.

“Their operational systems have many tentacles into other systems, and those interfaces must be updated with a new system,” he explains. “Some airlines get caught in a situation of waiting too long to upgrade their systems due to costs and complexities of the implementation phase.”

Watching all those scenes of miserable passengers stranded at airports, and given the industry’s resistance to change, you’d be forgiven for thinking that airlines are in crisis. And yet, profits are actually expected to hit record levels in 2018, and global passenger numbers are up by 6 percent on average.

So the more immediate crisis facing the airline industry is keeping up with demand among rising concerns about crewing shortages. According to a Boeing report, at current rates of growth airlines would need to recruit and train around 600,000 new pilots over the next 20 years – over 253,000 in the Asia Pacific region alone.

Why don’t airlines implement new solutions to optimize their operations?
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Expansion in China and elsewhere is indeed putting pressure on established markets like Europe, and aviation groups broadly agree that recruitment is going to become increasingly challenging as the prospect of pilot shortages looms.

Add to this the fact that 42% of current US pilots will retire over the next decade, and that new regulations brought in by the Federal Aviation Administration (FAA) have limited pilot flight time in an effort to reduce fatigue-induced errors, and you have something of a perfect storm brewing which could be bad news for passengers.

Airlines are spurred by the promise that automation could potentially save the industry an estimated $30 billion over the next two decades
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So it doesn’t come as much of a surprise that research conducted by investment bank UBS found growing momentum to develop technologies which would allow aircraft to be flown remotely. At this rate, the study claims, pilotless planes could be possible by 2025

Autonomous flight is already being tested and conceptualized, in fact. Lilium Aviation recently tested its all-electric, two-seater, vertical take-off and landing (VTOL) prototype, while Airbus recently completed a successful test of Sagitta, an uncrewed aerial vehicle, which flew autonomously for seven minutes on a pre-programmed course in Overberg, South Africa.

“In the not-too-distant future, we would expect to see a situation where flights are pilotless or the number of pilots shrinks to one, with a remote pilot based on the ground and highly secure ground-to-air communications,” the UBS study claimed, saying the changes would eventually make flying safer by minimizing human cockpit errors, as well as bringing peripheral efficiencies in terms of fuel gains through optimized operations, for instance.

Paul Eremenko, Chief Technology Officer at Airbus, recently said in an interview with Bloomberg Television that the company is already pursuing single-pilot operation as a potential option, but doesn’t rule out going even further: “A lot of the technologies needed to make that happen have also put us on the path towards unpiloted operation.”

These companies are spurred by the promise that such technology could potentially save the industry an estimated $30 billion over the next two decades. Yet on the other hand, unmanned cockpits pose obvious security concerns around hacking and glitches, not to mention removing the ability of a human pilot to deal with unexpected emergency situations on board.

The data solution powered by the Sisense business intelligence helps schedulers on the ground to work with crewmembers to solve problems before they cause delays or cancellations
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So even though such a move could save passengers as much as 10 per cent on the cost of their fares, 54 per cent of the 8,000 respondents surveyed by UBS said they would be unlikely to board a pilotless flight.

“While it is true fully automated flight will be possible in commercial passenger operations, the public’s willingness to travel on such aircraft will likely be a bigger obstacle to its full implementation than mastering the technology itself,” notes eTT’s LaJoie.

In his view, the investment in autonomous passenger travel is a “a very long play,” and might even prove something of a distraction for airlines that should be focusing on upgrading their existing systems to make them more intelligent and dynamic.

“Automated tools are particularly important now, not just for regulatory reasons, but because the shortage of pilots – particularly at regional airlines – necessitates scheduling each pilot for maximum utilizations and efficiency within regulatory boundaries,” he says.

LaJoie’s company specializes in flight schedule management, taking into account a complex set of variables including crew readiness and vessel location to maximize pilot availability. Their complex data solution, powered by the Sisense business intelligence platform, helps schedulers on the ground to work with crewmembers to solve problems before they become large enough to cause delays or cancellations.

Much like the broader debate around driverless cars, automation is not a silver bullet that will solve the industry’s problems. And although greater use of big data analytics and AI will inevitably play a large part in building the future mobility mix, it is not necessarily about replacing humans, but about empowering them to be more efficient and productive in their jobs.

Much like the broader debate around driverless cars, automation is not a silver bullet that will solve the industry’s problems
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Even though overhauling logistics and improving business intelligence may not sound as sexy as autonomous flying cars, there is a case for addressing the improvement and integration of existing systems infrastructure, as these can potentially yield the greatest returns for both industry and passengers.

This article was originally published on The Next Web

Is automated flying the future of air travel? https://t.co/s88qy98HJj

— TNW Contributors (@TNWcontributors) December 20, 2017

For companies looking to get into VR/AR/MR our Virtual Reality Consultancy services offer guidance on how these technologies can enhance and support your brand strategy.

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 Is automated flying the future of air travel? 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.

From 3D printing to Artificial Intelligence, Lush shows how even old-school companies can embrace technology.

Over the past twenty years, Lush has sold countless tons their strong-smelling handmade cosmetics ranging from colourful bath bombs to soap which is sold by weight, cut from giant slabs in their shops. For environmental reasons, they are very much against using packaging unless strictly necessary, so prices and product information is usually written by hand in chalkboard style signs, and everything is left “naked” on display (hence the strong smell of the mingling essential oils that you get every time you walk past one of their shops, of which there are about 1000 worldwide).

Which is why it wasn’t really surprising that the most high-tech thing you could spot in a Lush shop for many years was a set of scales much like you’d find in a street market.

Sure, they had a website where you could mail order, but to be honest for many years it was rather a cumbersome affair and I always found it easier (and nicer) to just go to my nearest shop. Which I supposed was the whole idea. Instead, their main vehicle for updating the loyal customer base on product news was an old-fashioned newspaper which was printed and distributed in the shops every month. The Lush Times.

Lush has pivoted to embrace digital technology over the past few years
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But that has all pivoted a few years ago, and Lush’s transformation into modernity has been driven largely by Jack Constantine, the son of Lush Founders Mark and Mo who is currently the company’s Chief Digital Officer – and inventor. The family still owns most of the business and are very much a part of its day-to-day running (his sister Claire and brother Simon also work at Lush).

“Lush Digital is my baby. It’s been something I’ve been working on for the past 5 years and as the digital landscape evolved it became clear we needed to set up a new arm of the business,” Constantine explains.

Jack trained as a graphic designer and then as “online became a thing” moved on to digital marketing, while still getting his hands dirty with inventing new bath bombs in the Lush factory in Dorset, England. He quickly understood that for digital marketing to succeed they needed to overhaul the company’s existing website. But although these days Lush does have a perfectly adequate e-commerce setup, Constantine tells me they will soon be overhauling their online structure in a much more fundamental way. His vision for the new website is rather ambitious:

“It’s not just a website – it’s a cloud based, mobile first global technology infrastructure for the whole Lush business.  All built in-house, using open source software, and aligning to our own ethical policies,” he says.

Even Lush's till system will be entirely run on open source software
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Even the till system that all their shops use will run on open source software, which Lush believes will not only save on running costs, but also make its customer-facing operations much more customizable and efficient. And to do that, they are busy recruiting in-house tech talent for the team.

But although Poole – where Lush’s UK Headquarters and factory are still based – is one of the top 10 digital tech hubs in the country, recruiting in-house tech talent hasn’t always been easy. “It’s been challenging getting the digital community to understand that Lush  – a cosmetics company – is serious about digital and is building a software & media house, and over time our reputation has developed to where it is today.”

And where it is today involves dabbling with a lot of different tech. At the showcase I spotted 3D printers sculpting intricate bath melts, and a machine that laser-etched soap bars, as well as a fully-fledged media studio, and a setup that captured biometric feedback such as brainwaves to turn it into a relaxing “spa experience” installation.

Now that it has decided to embrace technology, the company seems determined to dabble in them all, and Constantine is particularly excited about the possibilities that Machine Learning and Augmented Reality bring. “Lush is such a tactile brand, and our products are so hands on that having tools that bridge the real world and the digital world is going to enhance everything,” he says.

Lush is keen to incorporate its trademark playfulness into their tech too
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But while it is clear that Lush is keen to incorporate some of its trademark playfulness into its technology, some of the applications I demoed had a much more practical bend. The “Lush Lens” app, for example, used image recognition technology to give users real-time information on their products. They are also looking at how best to incorporate Google Machine Learning APIs to build a “Lush Personal Assistant” functionality into that offering. Which is all really useful, considering that most people are rather unfamiliar with how you go about using things like hair oil, massage bars, solid bubble bath, shower jelly, or a “toothie tab”.

What Lush shows is that in the digital age every business is at least in part a technology business. And the more you embrace that and integrate tech into your company’s DNA, the easier that journey of modernization will be, and perhaps you can even manage to have some fun along the way.

This article was originally published on The Next Web

From 3D printing to Artificial Intelligence, old-school companies can go digital too https://t.co/5mxi5ksnp6

— TNW Contributors (@TNWcontributors) September 8, 2017

To find out how to leverage VR/AR/MR in your enterprise, Tech Trends offers bespoke Virtual Reality Consultancy support

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 Hand-making the Digital Revolution appeared first on Tech Trends.

In data – like many things in life – what counts is not what you have, but what you do with it.

Employing a data analytics culture is the first step to creating value from data
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By 2020 the world will generate 50 times the amount of data as it did in 2011. But while all this information provides huge opportunities, it is meaningless unless you can use it to make informed decisions. I caught up with James Eiloart from Tableau Software, which makes software that translates these gigantic datasets into meaningful insights for businesses.

Eiloart told me that companies in the private sector would do well to follow some of the approaches to data adopted by schools and universities.

“Several of today’s leading public institutions are implementing data strategies that can teach even the largest, most commercially-minded organisations a few lessons. For example, using visual analytics platforms to gather important insights into student attrition, growth rates and performance.

Here are three approaches to data analytics that he found are making a difference at schools and universities and could perhaps be adapted to other sectors:

James Eiloart, SVP EMEA, Tableau Software

Predicting student problems before they escalate

League tables are an important metric for assessing school performance. But what they cannot measure are the more intangible elements of the school environment. Specifically, there is an increasing focus on student well-being, particularly as today’s students report high levels of stress and mental health issues. Such factors could be taken into account using data to give parents and local authorities a more holistic view of overall performance and student retention rates.

 

At Spokane School District in Washington State (US), a large school of nearly 30,000 students with a higher level of poverty than the state average, administrators wanted to see if they could explain why some students were dropping out of school early and whether they could do a better job reading the early warning signs.

 

By analysing their data, the school discovered a series of tipping points – changes in the student environment that tended to trigger disengagement. “We learned that transitions are critical for kids,” says Steven Gering, Chief Academic Officer. “For example, when kids transition up to the middle school… about 20 percent of our dropouts start showing warning signs. They start having behavior problems or discipline problems or their test scores start dropping. At the transition to high school, about 40 percent of our kids start exhibiting some warning signs that we’d never seen before. Some of our data visualisations are helping catch kids fast and intervene.”

 

Harnessing the power of data with visual analytics, the Spokane School District is able to develop a better understanding of what is happening in their schools and why, giving administrators the power to provide support and guidance to students when it matters most.

Private sector would do well to follow some data approaches adopted by schools and universities.
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Visualising data to manage growth

In the same way that unpredictable growth can be a challenge for businesses, fast growth rates can put a strain on university infrastructure and budgets. With over 50,000 students and 3,000 faculty members, the University of Texas, Austin is a huge organisation, and demand for places is growing. To manage this growth, the university’s operations team uses self-service analytics to track growth and its impact on areas such as human resources, the physical environment, and the university’s finances.

 

The University found that by visualizing their data, they could identify “high-spend” buildings, and hone in on worrying outliers across the campus. Capturing this data for analysis, the operations team was able to find valuable efficiencies across the organisation and eventually discover a rare trend: steady expansion accompanied by shrinking budgets.

The University of Texas found that by visualizing their data, they could identify high-spend buildings
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The journey to data centricity

Employing a data analytics culture is the first step to creating value from data. It can seem daunting to many. In order to make informed decisions and uncover insights into areas like operations, human resources or stakeholder engagement, building a culture around data – where data is at the center of all conversations and decisions are driven by fact rather than gut-feel – is a first and critical step.

 

While university administrators drive a data-centric culture across the campus, students are discovering the importance of visualizing data to enrich their learning experience. At the University of Washington, data analytics is used in multiple departments across the institution.

 

“Our goal is a cultural change around data, where people are able to access the data quickly, ask advanced questions, explore, tell stories and derive insights,” says Anja Canfield-Budde, Director, Enterprise Data and Analytics, UW Information Technology. Administrators currently assess academic data, including major enrollments, student credit hours, degrees awarded and student progress. The team is looking to add budgets, revenue and expenditures, undergraduate applications and admissions data in future.

 

Just as in the private sector, schools and universities today face increased pressure to find efficiencies, amidst growth and budget constraints. The use of data analytics means that teachers and administrators have a more holistic view of how students and the wider institution are performing.

 

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Data enables teachers and administrators to have a more holistic view of students and the wider institutions
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The post Schools Can Teach Businesses a Lesson in Using Big Data appeared first on Tech Trends.

Artificial Intelligence is literally embedded everywhere these days, and as automation advances at breakneck speed, the goalposts have already shifted further than most of us realise.

This has implications for every industry, because while creative jobs are likely to prove more resilient to the threat of automation than task-based repetitive work, there is plentiful evidence that the rise of the machines will leave no stone undisrupted.

Artificial Intelligence will leave no stone undisrupted
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In this article for the Huffington Post I look at some of the ways in which AI and Machine Learning have already transformed PR, and how creatives can best leverage the power of automation.

In the Age of Post-Truth and Artificial Intelligence, what is the Future of PR? https://t.co/gfIl0NJlH0

— Alice Bonasio (@alicebonasio) January 27, 2017

For companies looking to get into VR/AR/MR our Virtual Reality Consultancy services offer guidance on how these technologies can enhance and support your brand strategy.

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 Is Artificial Intelligence the Future of PR? appeared first on Tech Trends.