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

— Tecntrend (@alicebonasio) January 27, 2017

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From the Internet of Things to Augmented Reality, Artificial Intelligence and Machine Learning, the Great Detective gives us some enduring insight into cutting-edge Technology.

In this article published in Quartz I examine how the Victorians who lived in Holmes’ London also lived in a time of huge technological upheaval, and how that revolution informed Arthur Conan Doyle’s stories.

The picture, in case you’re wondering, is of my dog Watson.

The Victorians lived at a time of huge technological disruption
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Sherlock Holmes was the original technology disrupter https://t.co/FRc5iivXv5

— Quartz (@qz) November 30, 2016

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A recent report by Age UK helped to illustrate how urgent the crisis of care is becoming. As the world’s population becomes older and social care budgets are slashed in line with austerity policies, the responsibility for looking after people often falls upon those least able to cope. In the United Kingdom, one in seven people over 80 – an estimated 417,000 – is thought to clock in about 35 hours of unpaid work caring for a family member or friend. The situation is fast becoming unsustainable, and the old systems simply aren’t built to cope with the problem.

Technology has the potential to transform the way society supports vulnerable people
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But all is not lost. This is an area where technology has some amazing potential to make a real difference, transforming the way society supports vulnerable people.

We’ve heard a lot about HealthTech and the trend in personalised healthcare: Millennials track their physical activity with Fitbits and Apple Watches. The Internet of Things is populating homes with all sorts of interconnected gadgets. But these innovations are still restricted to a relatively small group of early adopters, and tend to exclude those who would potentially benefit the most from such advances.

Take, for example, an 85-year old widow living by herself; we’ll call her “Mary” for the purpose of this story. Mary’s only income is her retirement pension and she’s not very mobile, so she has neither the money nor the confidence to use the latest technology. Therefore, the closest thing to a gadget you’re likely to find in her house is a TV or the electric kettle she uses to make herself a cup of tea. Mary represents the generation that tech forgot. But some people are looking to change that.

I recently caught up with Stephen Kong, CEO of Think Eco, a leading IoT company based in British Columbia. He believes that we can use existing technology to help people like Mary without needing her to change their routine or learn any new skills.

Reality Mining collects data from everyday objects and interprets these to trigger reactions
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Their solution involves an approach called reality mining, which collects data from everyday objects and applies advanced pattern-recognition techniques, interpreting these into behaviours and triggering appropriate reactions.

In the case of someone like Mary, this could mean that there is a small box, situated outside her house, which monitors the harmonic frequencies that are emitted whenever one of her appliances is switched on.

“Each appliance emits a different frequency, so we can tell the difference between the TV and the kettle, for example.” Explains Kong.

This technology is the same that Think Eco currently uses to monitor energy consumption levels and patterns and help people cut down on their energy bills and be more environmentally responsible. Since these devices are installed onto the power grid itself, the solution doesn’t require people to put in expensive or intrusive cameras and sensors in the home, thus protecting personal privacy.

Each appliance emits a different frequency, so we can tell the difference between the TV and the kettle
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People who are elderly and living alone often have quite regular habits, so when Mary doesn’t switch on the kettle to make her cup of tea, that could send up a red flag that means hear support worker can give her a quick call to make sure she’s OK.

As well as helping elderly and vulnerable people live more safely and retain their independence for longer, Kong says the potential for governments and healthcare providers to improve efficiency and save money is enormous:

“About one-third of all people over 65 will experience a fall each year. For those over 80, this rises to around 50%. Falls now account for over half of all hospital admissions for accidental injury. In the UK alone, this costs the NHS a staggering £4.6m each day (£1.7bn per year). This figure could be reduced substantially if older people are assisted in managing their health and have injuries quickly addressed, making longer hospital stays less likely,” he says.

The potential for governments and healthcare providers to improve efficiency and save money is enormous
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The same technology can also provide useful information for tackling many additional healthcare problems. For instance, if Mary begins taking a new heart medication with certain side effects, her patterns of appliance usage can be compared against previous patterns (or typical ones) to indicate whether the medication is adversely affecting her. Repeated patterns of usage during the night or a delayed first usage in the morning might indicate that her sleep is disturbed. Conversely, a more regular and typical schedule of appliance usage could suggest that the medication is making a positive difference. Patterns of health can be monitored over a longer time frame, helping people continue living independently

But it’s not just the elderly who could benefit from this less intrusive form of personalised health monitoring: These reality-mining techniques can be used to prevent and detect risk factors for diseases such as Type 2 diabetes in younger people, a rising problem currently affecting over 422 million people. By collecting datasets around people’s habits and aggregating these, it could eventually help empower physicians, parents and policy-makers to address risk factors such as junk food consumption and start reversing those trends.

“In time, everyone’s health could be improved through creative application of these non-invasive techniques, cutting the public health bill and increasing longevity and quality of life.” Kong concludes.

In an increasingly connected world, we’ve only just started scratching the surface of what Big Data can achieve when properly connected and contextualised. Advances in healthcare have so far made amazing strides in extending our life expectancy, but the next wave of smart, data-based Healthtech has the potential to do something even more amazing; to improve the quality of life for everyone, not just techies.

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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 - was created to create 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 also know you’ve got your hands on a seriously techie beer when they open source all 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.

Using AI to improve physical products will eventually catch on with all sorts of things
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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.

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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 Founders Rob Bishop and Zehan Wang

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

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