The central challenge for companies dealing with large amounts of data is a digitisation strategy. Here it comes to artificial intelligence and semantic technologies. The integration of these megatrends into enterprise processes and workflows will be the focus one of the subjects of the SEMANTiCS 2019, which takes place from 9 to 12 September in Karlsruhe, where top speakers from industry and academia will explore how AI systems can help businesses succeed in the digital age.

The central challenge for companies dealing with large amounts of data is having an effective digitisation strategy
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“Explainable AI is the is the key to progress and acceptance of AI,” says SEMANTICS 2019 General Chair Harald Sack. Explainability can be important in some applications, in particular, when AI is applied to humans. In other applications, it does not play any significant role. Some argue that our brain first acts and then tries to explain why it acted in a particular way, maybe mostly for communication purposes, to justify action and to convince others.

In lectures, panels and workshops numerous enterprise implementation projects and use cases will be presented which will help to bridge that gap between the complex concepts that underpin AI and the practical applications that concretely benefit businesses.

Topics include process optimization and automation, information logistics and data management as well as business development and customer relationship management. In community events, international high-tech experts will also discuss the future and significance of artificial intelligence in a broader context.

Explainable AI is the is the key to progress and acceptance of AI
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One of the core technologies for reducing complexity in business processes are Knowledge Graphs
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The keynotes will be held by prominent experts from the US and Europe, among them Willem Manders, Head of Knowledge Management at Shell, Michel Dumontier, Professor of Data Science at Maastricht University, Valentina Presutti, researcher at the Semantic Technology Laboratory of the National Research Council (CNR) in Rome and Michael J. Sullivan, Principal Cloud Solutions Architect at Oracle.

One of the core technologies for reducing complexity in business processes are Knowledge Graphs.

“Those who stay on their islands will fall back. This is currently the case with a large part of the data which is kept in silos,” says Andreas Blumauer, managing director and founder of the Semantic Web Company, which organizes the conference alongside FIZ Karlsruhe – Leibniz Institute for Information Infrastructure, Fachhochschule St. Pölten, and the Universities of Leipzig and Amsterdam.

“For every knowledge worker, it is a highly time-consuming task to identify, connect, understand, and interpret the right dots and information pieces. SEMANTiCS 2019 wants to show IT decision makers how companies can develop their own Knowledge Graphs along the whole Linked Data Life Cycle, to support decision and process augmentation based on that linked data,” adds Blumauer.

For Volker Tresp, Distinguished Research Scientist at Siemens, the

biggest challenges ahead when it comes to enterprises employing Machine Learning, Autonomous Systems and AI, is coming up with an effective business model that works for that individual company. In other words, there is no one-size-fits-all approach where it comes to leveraging Artificial Intelligence in your own business.

Those who stay on their islands will fall back. This is currently the case with many companies where a large part of their data is kept in silos
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In the coming years we need to build a strong academic basis for deep learning and machine learning in Europe
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“Machine Learning is a very strong and very robust technology. But this does not mean that each company knows how to transfer this success into a business. Currently, no company, whose business is on the internet, can do without AI. If it turns out that your company has an AI business, then you might have to drastically change the culture in your company. You can only be successful if you manage to hire the best talent and if you change the culture in your company so that it values, appreciates and listens to that talent.”

One example of an area where correct AI implementation can have a huge efficiency impact is Healthcare. Hospitals generate huge amounts of data each day, and patients can benefit from the structuring of that data into the Knowledge Graphs mentioned above.

“AI applications in image analysis have already been used for more than 5 years. Siemens Healthineers have been doing pioneering work and are working on many future use cases,” explains Tresp.

Volker Tresp, Distinguished Research Scientist at Siemens

“I am completely convinced that knowledge graphs will become more important. As in the medical area, most domains consist of unstructured data (sensory data, images, …) and structured data (diagnosis, procedures, lab results, blood counts). Roughly speaking structured data is the domain of the knowledge graph and unstructured data is the domain of deep learning. A challenge is to have both talk to one another. That is what we are working on!”

According to Tresp, Deep Learning (DL) was the reason for the recent breakthroughs in AI and it will continue to be the basis for the progress for the next years to come.

“DL builds on available labeled data, new algorithms, computational power, and an incredible community. Deep Learning will interact more strongly with other approaches and we will see more hybrid approaches. For example, we and other research groups combine knowledge graphs with machine learning and deep learning. I am also convinced that the link to cognition will become stronger: one can learn a lot from the organizational structure of the brain. We as humans can manipulate symbols and this will also become more relevant for AI. But we have to go new ways and not warm up old ones. As I like to say: AI is not only Machine Learning, but without Machine Learning there is no AI. In the coming years we need to build a strong academic basis for deep learning and machine learning in Europe and be open to new ideas that can contribute,” he concludes.

No company, whose business is on the internet, can do without AI
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Alice Bonasio is a VR and Digital Transformation Consultant and Tech Trends’ Editor in Chief. She regularly writes for Fast Company, Ars Technica, Quartz, Wired and others. You can follow @alicebonasio on Twitter, connect with her on LinkedIn or subscribe to her Inside VR/AR Newletter for the latest curated immersive news.  

The post What is Your AI Digitisation Strategy? 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.

Care for an AI-le? A British Company is betting that machine learning technology can help brewers turn out a tastier tipple.

One thing that any self-respecting connoisseur knows is that beer is actually alive. Their flavour is constantly evolving, even after you bottle it, and changes again after it’s poured. This is one of the reasons why the micro-brewery movement has taken off in such a big way, as it gives fans of the hoppy stuff (and I count myself among those) a lot more choice.

Beer is actually alive. Their flavour is constantly evolving
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But is there a thing as too much choice? A small-scale brewer faced with limited resources and endless choices of what to brew, what ingredients to use, and what methods to employ might well think so. With so many variables, how do you negotiate that uncertainty and figure out what it is your customers want?

With so many variables, how do you negotiate uncertainty and figure out what your customers want?
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That’s a problem that has long  interested Dr. Rob McInerney, who wrote his Doctoral thesis at Oxford on making decisions under uncertainty. After finishing his PhD he then went on to start Intelligent Layer, a Machine Learning company. And it was in the East London WeWork where they were based that he first met 10x founder Hew Leith, a former M&C Saatchi director. In classic start-up style, they eventually realized – perhaps after a few beers – that they could combine their strengths to fill a gap in the market.

IntelligentX created the world’s first “self improving” range of beers
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Thus a mash-up of the two companies – IntelligentX – was created to make the world’s first “self improving” range of beers that constantly evolve in response to customer feedback data. The system uses machine learning (specifically reinforcement learning and bayesian non-parametrics) to improve itself.

Customers who try the beer can actually “talk” to ABI (short for Automated Brewing Intelligence) directly by using a Facebook Messenger bot, giving feedback on the flavours. The AI then crunches all that data and helps fine-tune the next recipe of the brew to best suit their tastes.

Customers who try the beer can actually “talk” to ABI - or Automated Brewing Intelligence
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If that all sounds a bit too much like having a terminator making your drinks (hey, I think that would be cool, but it’s not for everybody) never fear. ABI also has a bank of wildcard ingredients, like adding fruit to a recipe, which brings in an element of unpredictability and serendipity, all carefully factored and calculated in, of course.

Leith stresses that this isn’t about machines taking over jobs, but about working closely with brewers to supercharge their intuitive, artistic skills using AI.

“We believe the future is a place where A.I. augments humans’ skills. In this case we’re using A.I. to give our brewer superhuman skills, enabling them to test and receive feedback on our beer more quickly than ever before. This means we can respond to consumers’ changing tastes faster than traditional brewers.”

We’re using A.I. to give our brewer superhuman skills
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McIrnewey adds that in a situation where you don’t have millions of datapoints to train a deep learning algorithm (like Google’s Deepmind did with AlphaGo) it is better to employ a Bayesian non-parametric approach to use the information you have much more efficiently. This means effectively rewarding the system every time it makes a correct decision. “We also work very hard to learn as much as possible from the brewer directly, so this really becomes a joint effort between man and machine,” he concludes.

You also know you’ve got your hands on a seriously techie beer when they announce plans to open source all their recipes (on GitHub perhaps?) so that anybody can then recreate their favourite beers at home. “As each batch will be unique, there’s no need to keep the recipe under lock and key,” they explain.

You know you’ve got a techie beer when they open source their recipes
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The beers cost £4.50 a bottle from Ubrew and come in four main styles:

1. Golden AI – From a classic British golden ale recipe featuring Styrian Golding hops
2. Amber AI – derived from a British bitter, with darker appearance and stronger flavour, with a hint of grapefruit
3. Pale AI – Hoppy beer derived from an American pale ale
4. Black AI – From a classic porter recipe, it has a strong smokey flavour

IntelligentX has quite ambitious plans for the future. Not only are they in talks with several Michelin starred restaurants about stocking the AI Beers, but they also have their sights on eventually winning a major brewing award such as CAMRA’s Champion Beer of Britain. So far the beers have already evolved eleven times, so it’s probably only a matter of how many generations it will take to hit that sweet spot of maximum optimisation.

AI will eventually be used to improve all sorts of physical products
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I can certainly see this being a big hit with the Tech City “Beer O’clock” crowd, but there is also a much broader consumer trend emerging here. McInerney and Leith believe this approach of using AI to improve physical products will eventually catch on with all sorts of things, from chocolate and coffee to perfume. Why be stuck with eating the chocolates you don’t like? In future we will all be able to get products fine-tuned to our personal tastes, and it won’t even cost the earth, as personalisation can be quite efficient, with the right tech behind it.

We’ll Drink to that, Cheers!

IntelligentX: The World’s First Beer Brewed by AI from IntelligentX on Vimeo.

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 Brewing Beer with Artificial Intelligence 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.