Artificial Intelligence in the UK: Landscape and learnings from 226 startups

Figure 1 (above): The landscape of early stage UK AI companies.

With every paradigm shift in technology, waves of innovation follow as companies improve and then reimagine processes. Today we are in the early stages of the global artificial intelligence (AI) revolution. Machine learning algorithms, whose results improve with experience, enable us to find patterns in large data sets and make predictions more effectively — about people, equipment, systems and processes. (For an accessible introduction to AI, read our Primer.) But what are the dynamics of AI entrepreneurship in the UK?

We’ve mapped 226 independent, early stage AI software companies based in the UK and met with over 40 of these companies in recent weeks. Below, we share six powerful dynamics we see that are shaping the UK AI market— from changing activity levels and areas of focus, to trends in monetisation and the size and staging of investment.

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The UK AI landscape: 226 companies and counting

Over time, we expect the distinction between ‘AI’ companies and other software providers to blur and then disappear, as machine learning is employed to tackle a wide variety of business processes and sectors. Today, however, it is possible to point to a sub-set of early stage software companies defined by their focus on AI.

We’ve researched 226 early stage AI companies in the UK and met with 40 of them. We’ve developed a map (Figure 1, above) to place the 226 according to their:

• Purpose: Is the company focused on improving a business function (for example, marketing or human resources) or a sector (healthcare, education, agriculture)? Or does the company develop an AI technology with cross-domain application?
• Customer Type: Does the company predominantly sell to other businesses (‘B2B’) or to consumers (‘B2C’)?
• Funding: How much funding has the company received to date? We bracket this from ‘angel’ investment (under $500,000) through to ‘growth’ capital ($8m to ~$100m).

We’ll update our map regularly. We apologise if we’ve omitted or mis-classified your company; we’re aware that many early stage companies may be using, but not presenting, extensive AI. Please get in touch with additions or corrections.

After analysing the market and meeting with 40 companies in recent weeks, we highlight the following six dynamics in the market:

1. A focus on AI for business functions

Most early stage UK AI companies — five in every six — are applying machine learning to challenges in specific business functions or sectors (Figure 2, below). Reflecting the nascent stage of the field, however, one in six is developing an AI technology — a capability, platform or set of algorithms — applicable across multiple domains. These companies’ activities range from the development of computer vision solutions to the creation of algorithms for autonomous decision-making.

To whom are AI companies selling? Nine out of 10 AI companies are predominantly ‘B2B’, developing and selling solutions to other businesses (Figure 3, below). Just one in 10 sells directly to consumers (‘B2C’).

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A ‘cold start’ challenge around data is inhibiting the number of new B2C AI companies. Training machine learning algorithms usually requires large volumes of data. While B2B companies can analyse the varied and extensive data sets of the businesses they serve, in the absence of public or permissioned (e.g. Facebook profile) data, customer-facing companies usually begin without large volumes of consumer data to analyse. Typically, therefore, they deploy machine learning over time as their user bases and data sets grow. Gousto, for example, is an MMC portfolio company that delivers recipes and associated ingredients to consumers for them to cook at home. Today, Gousto’s team of machine learning PhDs, data analysts and engineers leverage AI for warehouse automation and menu design. Since its inception Gousto has had a vision for the use of AI, but the Company has achieved its vision over time.

Given the ‘cold start’ challenge, the reality is that most consumers will first experience machine learning via the world’s most popular consumer applications — Facebook, Google, Amazon, Netflix, Pinterest and others — that leverage vast data sets and machine learning teams to deliver facial recognition, search and entertainment recommendations, translation capabilities and more.

2. AI entrepreneurship is unevenly spread

A heat map highlights areas of early stage activity, as measured by the number of companies in each segment (Figure 4, below).

Figure 4: A heat map of early stage AI companies in the UK

Activity is greatest within:

• The Marketing & Advertising, 
• Information Technology, and 
• Business Intelligence & Analytics functions; and
• The Finance sector.

Activity is extensive within:

• The Human Resources function; and
• The Infrastructure, 
• Healthcare and 
• Retail sectors.

The sectors above are well suited to the application of AI, explaining the concentration of AI activity within them. The opportunity for value creation within each segment is demonstrable as well as significant. In marketing and in finance, for example, improvements in campaign conversion and financial performance against a benchmark are readily quantifiable. All offer numerous prediction and optimisation challenges well suited to the application of machine learning. All offer large data sets for training and deployment. A path to better-than-human performance is technically achievable. The alternatives are impractical or expensive. And all are specialised verticals, distant from the competitive threat posed by the consumer- and horizontal focus of the AI platform providers Google, Amazon, Microsoft and IBM — with the exception of healthcare where Google and IBM both enable and challenge.

As attractive market fundamentals catalyse activity, the strongest AI companies can develop a competitive moat by:

• bringing deep domain expertise to bear in a complex domain; 
• developing proprietary algorithms; 
• creating a network effect around data by leveraging non-public data sets; and 
• by securing adequate capital to build a high quality machine learning team and go-to-market resources.

Activity in Marketing & Advertising dominates; one in five early stage UK AI companies target this function. The fundamentals of modern marketing and advertising represent a sweet-spot for AI. Consumers have billions of touch points with websites and apps, providing a rich seam of available, but complex, data. Further, almost every stage of the marketing and advertising value chain is ripe for optimisation and automation — including

• content processing, 
• consumer segmentation, 
• consumer targeting, 
• programmatic advertising optimisation, 
• purchase discovery for consumers and 
• analysis of consumer sentiment.

Areas of lower activity

In a number of areas, activity appears modest relative to market opportunities. In the Manufacturing sector, for example, there are few startups to address a substantial need.

• Machine learning has the potential to unlock 20% more production capacity through predictive, optimised maintenance of machines. 
• Raw material costs and re-working can be reduced through improved analysis of product quality data. 
• Further, ‘buffering’ — storing raw materials and part-developed products to compensate for unforeseen inefficiencies during production — can be reduced by up to 30% given more predictable production capacity. 
• The proliferation of sensors in the manufacturing industry, including sensor data from the production line, machine tool parameters and environmental data, has also increased significantly the data available for machine learning.

Within the Compliance & Fraud function, there appear few startups capitalising on banks’ ballooning expenditure on compliance.

• 30,000 people in Citi — 12% of the Bank’s workforce — now work in compliance. In its 1Q15 conference call, Citi highlighted that over 50% of the $3.4B it saved through efficiency initiatives was being consumed by additional investments in regulation and compliance. The dynamics are similar among Citi’s peers. 
• JP Morgan increased compliance spend 50% between 2011 and 2015, to $9B, while 
• Goldman Sachs highlighted that its 11% increase in headcount in the last four years has largely been to meet regulatory compliance needs. 

Our discussions with banks highlight particular focus on ‘Know Your Customer’ (KYC) and Anti- Money Laundering (AML) initiatives. Beyond presenting an extensive need, the sector offers large data sets for training, expensive human alternatives and, in some areas at least, an evident ability for machine learning to deliver better-than-human performance given the pragmatic impossibility of humans monitoring the data deluge. There may be few UK compliance companies given in-house efforts by the banks, concern regarding potential client concentration, or competition from US startups — but opportunities appear considerable.

3. AI entrepreneurship has doubled

The number of AI companies founded annually in the UK (Figure 5, below) has doubled in recent years (2014–2016) compared with the prior period (2011–2013). Over 60% of all UK AI companies were founded in the last 36 months. During this period, a new AI company has been founded in the UK on almost a weekly basis.

Entrepreneurship in AI is being fuelled by the broader coming of age of AI as well as factors specific to early stage entrepreneurship.

Regarding AI activity generally,

• seeds planted during the last 20 years of AI research are bearing fruit today. 
• New algorithms, particularly convolutional and recurrent neural networks, are delivering more effective results. 
• A logarithmic increase in the availability of training data has made it possible to tune machine learning algorithms to deliver accurate predictions. 
• Development of graphical processing units (GPUs) has decreased the time required to train a neural network by 5x-10x. And 
• a six-fold increase in public awareness of AI during the last five years has increased buyers’ interest in the technology.

Additional factors are fuelling an increase in new AI startups.

• Venture capital funding of AI companies has increased seven-fold in five years as investors see promise in the sector. 
• The provision of AI infrastructure and services from industry cloud providers (Google, Amazon, Microsoft and IBM) is reducing the difficulty and cost of deploying machine learning solutions. And 
• the growth of open source AI software — particularly TensorFlow, a library of components for machine learning — has reduced barriers to involvement. 

Subject to continued venture capital funding, we expect high levels of AI entrepreneurship in the UK to continue.

Where are new AI companies focusing? 
The HR business function and Finance sector have the highest proportion of new AI companies (Figure 6, below). Two thirds of AI HR and Finance companies are less than two years old.

Recent activity in HR stems from a paradigm shift taking place within the function. HR is evolving from an administrative system of record to a predictive driver of growth and efficiency. Business owners are seeking to leverage previously under-utilised data sets to drive utility — ranging from competency-based recruitment to predictive modelling of employee churn.

It is unsurprising that in the business intelligence, security and compliance functions, and in the retail and infrastructure sectors, the proportion of new AI companies is lower. With large data sets ripe for machine learning, these sectors were the first to attract AI entrepreneurs.

4. A nascent sector relative to global peers

The UK AI sector is at a nascent stage in its development relative to global peers, presenting both opportunities and challenges.

Today, three quarters of UK AI companies are at the earliest stages of their journey, with ‘seed’ or ‘angel’ funding, compared with half of US peers (Figure 7, below). At the other end of the spectrum, just one in 10 UK AI companies is in the late, ‘growth capital’ stage compared with one in five in the US. In 2015, the last full year for which data are available, almost all capital infusions into UK AI companies were at the angel, seed or Series A stage — while among global AI peers a third received later-stage funding (Figure 8, below).

This dynamic presents both opportunities and risks. A vibrant startup scene presents unrivalled opportunities for entrepreneurs, employees and investors in early stage companies. At the same time, more developed and richly funded overseas competitors may increase competitive pressures on UK companies. This effect may be exacerbated by the high proportion of AI companies that sell to enterprises, many of which source providers globally. The UK maintains valuable assets for AI research, including a quarter of the world’s top 25 universities and a growing ecosystem of AI executives and investors following the acquisitions of

• Deep Mind, 
• SwiftKey, 
• Magic Pony and 
• other UK AI companies.

5. The journey to monetisation can be longer

Over 40% of the AI companies we meet are yet to generate revenue (Figure 9, below). This is not an artefact of us meeting ‘early stage’ companies; the median profile of a company we meet is one 

• founded 2–3 years ago that has 
• raised £1.3m, has 
• a team of nine and is 
• spending £76,000 per month.

The idea that most AI companies — applied AI companies, at least — plan to be acquired pre-revenue instead of selling software and services is a myth. All the companies we met were implementing or developing monetisation plans. Why, then, are some AI companies taking longer to monetise or scale than is usual for early stage companies? We see four reasons:

• The bar to a minimum viable product (MVP) in this technically challenging field can be higher, requiring longer development periods. 90% of AI companies are B2B. The long sales cycles typical in B2B sales are exacerbated by many AI companies’ focus on sectors, such as finance, with sprawling and sensitive data sets.
• Deployment periods can be lengthy given
  * extensive per-client data integration,
  * data cleansing and
  * customisation requirements.
  Half the AI companies we meet have a pure software-as-a-service model; as many monetise significant client integration and customisation work in the form of project revenue (Figure 10, above).
• The limited number of personnel available for implementation in early stage companies is inhibiting many AI companies’ growth. In a sentiment echoed by several companies, one told us “we couldn’t implement more sales even if we had them.” One third of many teams are engaged in deployment support.
• Exacerbated by cash burn rates increased by the high cost of machine learning talent, a longer path to monetisation can pose a challenge to AI companies. 

We recommend that AI companies raise sufficient capital to last them through this period of risk, to go-to-market and beyond.

6. Investments are larger and staging is atypical

Globally at least, investments into AI firms are typically 20% to 60% larger than average capital infusions (Figure 11, below, shows 2015 data). This reflects company fundamentals and dynamics in the supply and demand of capital. AI companies’ capital requirements can be higher given

• longer development periods prior to product viability, 
• the high cost of machine learning talent and 
• the larger teams required for complex deployments. 

Beyond these fundamentals, however, capital infusions are being inflated by

• extensive supply (many venture capitalists seek opportunities to invest in artificial intelligence companies) and 
• limited demand (there are relatively few AI companies in which to invest). 

Venture capital investment in early stage AI companies has increased seven-fold in five years, while the number of investable prospects remains limited.

Further, in the UK a sizeable minority of companies jump from a seed rounds to a much larger raise than is typical for a subsequent round (Figure 12, below). 1 in 3 UK AI companies that raised more than $8m in a funding round raised less than $1m previously. As above, this dynamic is driven in part by AI companies’ capital requirements, but as much by the limited number of attractive investment opportunities in AI. Companies’ valuation expectations, meanwhile, are being supported by ‘acquihire’ offers for nascent teams.

Conclusion: An inflection point in UK AI

The last 36 months have marked an inflection point in early stage UK AI. Entrepreneurship has doubled, as AI technology comes of age and investment has increased. Yet, companies are early in their development relative to global peers, offering entrepreneurs and employees unprecedented opportunity and challenge. Three quarters of UK AI companies are at the earliest stages of their journey and activity remains uneven. Startups have concentrated on readily addressable business functions, where data sets are plentiful and optimisation challenges are pronounced. Today, business processes are being optimised. In the future, they will be re-imagined. Within the last 24 months, additional functions and sectors are starting to be tackled by AI entrepreneurs. The path to monetisation for today’s AI companies can be longer, but effective entrepreneurs are taking advantage of attractive capital dynamics to raise sufficient sums of money earlier in their journey.

As the AI revolution continues, the distinction between ‘AI companies’ and other software providers will further blur. Today, however, we are pleased to highlight the dynamics of a group of companies delivering remarkable benefits. Together, they are shaping the ‘fourth industrial revolution’.

ORIGINAL: Medium

By David Kelnar. Investment Director & Head of Research at MMC Ventures. 2x CEO/CFO. Love tech, venture capital, trends and triathlon. 
Dec 21, 2016

Microsoft Neural Net Shows Deep Learning can get Way Deeper

PAUL TAYLOR/GETTY IMAGES

COMPUTER VISION IS now a part of everyday life. Facebook recognizes faces in the photos you post to the popular social network. The Google Photos app can find images buried in your collection, identifying everything from dogs to birthday parties to gravestones. Twitter can pinpoint pornographic images without help from human curators.

All of this “seeing” stems from a remarkably effective breed of artificial intelligence called deep learning. But as far as this much-hyped technology has come in recent years, a new experiment from Microsoft Research shows it’s only getting started. Deep learning can go so much deeper.

'We're staring at a huge design space, trying to figure out where to go next.' 

PETER LEE, MICROSOFT RESEARCH

This revolution in computer vision was a long time coming. A key turning point came in 2012, when artificial intelligence researchers from the University of Toronto won a competition called ImageNet. ImageNet pits machines against each other in an image recognition contest—which computer can identify cats or cars or clouds more accurately?—and that year, the Toronto team, including researcher Alex Krizhevsky and professor Geoff Hinton, topped the contest using deep neural nets, a technology that learns to identify images by examining enormous numbers of them, rather than identifying images according to rules diligently hand-coded by humans.

Toronto’s win provided a roadmap for the future of deep learning. In the years since, the biggest names on the ‘net—including Facebook, Google, Twitter, and Microsoft—have used similar tech to build computer vision systems that can match and even surpass humans. “We can’t claim that our system ‘sees’ like a person does,” says Peter Lee, the head of research at Microsoft. “But what we can say is that for very specific, narrowly defined tasks, we can learn to be as good as humans.”

Roughly speaking, neural nets use hardware and software to approximate the web of neurons in the human brain. This idea dates to the 1980s, but in 2012, Krizhevsky and Hinton advanced the technology by running their neural nets atop graphics processing units, or GPUs. These specialized chips were originally designed to render images for games and other highly graphical software, but as it turns out, they’re also suited to the kind of math that drives neural nets. Google, Facebook, Twitter, Microsoft, and so many others now use GPU-powered-AI to handle image recognition and so many others tasks, from Internet search to security. Krizhevsky and Hinton joined the staff at Google.

Deep learning can go so much deeper.

Now, the latest ImageNet winner is pointing to what could be another step in the evolution of computer vision—and the wider field of artificial intelligence. Last month, a team of Microsoft researchers took the ImageNet crown using a new approach they call a deep residual network. The name doesn’t quite describe it. They’ve designed a neural net that’s significantly more complex than typical designs—one that spans 152 layers of mathematical operations, compared to the typical six or seven. It shows that, in the years to come, companies like Microsoft will be able to use vast clusters of GPUs and other specialized chips to significantly improve not only image recognition but other AI services, including systems that recognize speech and even understand language as we humans naturally speak it.

In other words, deep learning is nowhere close to reaching its potential. “We’re staring at a huge design space,” Lee says, “trying to figure out where to go next.”

Layers of Neurons

Deep neural networks are arranged in layers. Each layer is a different set of mathematical operations—aka algorithms. The output of one layer becomes the input of the next. Loosely speaking, if a neural network is designed for image recognition, one layer will look for a particular set of features in an image—edges or angles or shapes or textures or the like—and the next will look for another set. These layers are what make these neural networks deep. “Generally speaking, if you make these networks deeper, it becomes easier for them to learn,” says Alex Berg, a researcher at the University of North Carolina who helps oversee the ImageNet competition.

Constructing this kind of mega-neural net is flat-out difficult.

Today, a typical neural network includes six or seven layers. Some might extend to 20 or even 30. But the Microsoft team, led by researcher Jian Sun, just expanded that to 152. In essence, this neural net is better at recognizing images because it can examine more features. “There is a lot more subtlety that can be learned,” Lee says.

In the past, according Lee and researchers outside of Microsoft, this sort of very deep neural net wasn’t feasible. Part of the problem was that as your mathematical signal moved from layer to layer, it became diluted and tended to fade. As Lee explains, Microsoft solved this problem by building a neural net that skips certain layers when it doesn’t need them, but uses them when it does. “When you do this kind of skipping, you’re able to preserve the strength of the signal much further,” Lee says, “and this is turning out to have a tremendous, beneficial impact on accuracy.”

Berg says that this is an notable departure from previous systems, and he believes that others companies and researchers will follow suit.

Deep Difficulty

The other issue is that constructing this kind of mega-neural net is tremendously difficult. Landing on a particular set of algorithms—determining how each layer should operate and how it should talk to the next layer—is an almost epic task. But Microsoft has a trick here, too. It has designed a computing system that can help build these networks.

As Jian Sun explains it, researchers can identify a promising arrangement for massive neural networks, and then the system can cycle through a range of similar possibilities until it settles on this best one. “In most cases, after a number of tries, the researchers learn [something], reflect, and make a new decision on the next try,” he says. “You can view this as ‘human-assisted search.'”

Microsoft has designed a computing system that can help build these networks.

According to Adam Gibson—the chief researcher at deep learning startup Skymind—this kind of thing is getting more common. It’s called “hyper parameter optimization.” “People can just spin up a cluster [of machines], run 10 models at once, find out which one works best and use that,” Gibson says. “They can input some baseline parameter—based on intuition—and the machines kind of homes in on what the best solution is.” As Gibson notes, last year Twitter acquired a company, Whetlab, that offers similar ways of “optimizing” neural networks.

‘A Hardware Problem’

As Peter Lee and Jian Sun describe it, such an approach isn’t exactly “brute forcing” the problem. “With very very large amounts of compute resources, one could fantasize about a gigantic ‘natural selection’ setup where evolutionary forces help direct a brute-force search through a huge space of possibilities,” Lee says. “The world doesn’t have those computing resources available for such a thing…For now, we will still depend on really smart researchers like Jian.”

But Lee does say that, thanks to new techniques and computer data centers filled with GPU machines, the realm of possibilities for deep learning are enormous. A big part of the company’s task is just finding the time and the computing power needed to explore these possibilities. “This work as dramatically exploded the design space. The amount of ground to cover, in terms of scientific investigation, has become exponentially larger,” Lee says. And this extends well beyond image recognition, into speech recognition, natural language understanding, and other tasks.

As Lee explains, that’s one reason Microsoft is not only pushing to improve the power of its GPUs clusters, but exploring the use of other specialized processors, including FPGAs—chips that can programmed for particular tasks, such as deep learning. “There has also been an explosion in demand for much more experimental hardware platforms from our researchers,” he says. And this work is sending ripples across the wider of world of tech and artificial intelligence. This past summer, in its largest ever acquisition deal, Intel agreed to buy Altera, which specializes in FPGAs.

Indeed, Gibson says that deep learning has become more of “a hardware problem.” Yes, we still need top researchers to guide the creation of neural networks, but more and more, finding new paths is a matter of brute-forcing new algorithms across ever more powerful collections of hardware. As Gibson point out, though these deep neural nets work extremely well, we don’t quite know why they work. The trick lies in finding the complex combination of algorithms that work the best. More and better hardware can shorten the path.

The end result is that the companies that can build the most powerful networks of hardware are the companies will come out ahead. That would be Google and Facebook and Microsoft. Those that are good at deep learning today will only get better.

ORIGINAL: Wired

01.14.16

MORE AI

 

Google Just Open Sourced TensorFlow, Its Artificial Intelligence Engine

Facebook Aims Its AI at the Game No Computer Can Crack

Google Made a Chatbot That Debates the Meaning of Life

Facebook Joins Stampede of Tech Giants Giving Away Artificial Intelligence Technology

Leading computing companies are helping both themselves and others by open-sourcing AI tools.

Facebook designed this server to put new power behind the simulated neurons that enable software to do smart things like recognize speech or the content of photos.

Facebook is releasing for free the designs of a powerful new computer server it crafted to put more power behind artificial-intelligence software. Serkan Piantino, an engineering director in Facebook’s AI Research group, says the new servers are twice as fast as those Facebook used before. “We will discover more things in machine learning and AI as a result,” he says.

The social network’s giveaway is the latest in a recent flurry of announcements by tech giants that are open-sourcing artificial-intelligence technology, which is becoming vital to consumer and business-computing services. Opening up the technology is seen as a way to accelerate progress in the broader field, while also helping tech companies to boost their reputations and make key hires.

In November, Google opened up software called .TensorFlow, used to power the company’s speech recognition and image search (see “.Here’s What Developers Are Doing with Google’s AI Brain”). Just three days later Microsoft released software that distributes machine-learning software across multiple machines to make it more powerful. Not long after, IBM announced the fruition of an earlier promise to open-source SystemML, originally developed to use machine learning to find useful patterns in corporate databanks.

Facebook’s new server design, dubbed Big Sur, was created to power deep-learning software, which processes data using roughly simulated neurons (see “.Teaching Computers to Understand Us”). The invention of ways to put more power behind deep learning, using graphics processors, or GPUs, was crucial to recent leaps in the ability of computers to understand speech, images, and language. Facebook worked closely with Nvidia, a leading manufacturer of GPUs, on its new server designs, which have been stripped down to cram in more of the chips. The hardware can be used to run Google’s TensorFlow software.

Yann LeCun, director of Facebook’s AI Research group, says that one reason to open up the Big Sur designs is that the social network is well placed to slurp up any new ideas it can unlock. “Companies like us actually thrive on fast progress; the faster the progress can be made, the better it is for us,” says LeCun. Facebook open-sourced deep-learning software of its own .in February of this year.

LeCun says that opening up Facebook’s technology also helps attract leading talent. A company can benefit by being seen as benevolent, and also by encouraging people to become familiar with a particular way of working and thinking. As Google, Facebook, and other companies have increased their investments in artificial intelligence, competition to hire experts in the technology has intensified (see “.Is Google Cornering the Market in Deep Learning?”).

Derek Schoettle, general manager of IBM Cloud Data Services unit, which offers tools to help companies analyze data, says that machine-learning technology has to be opened up for it to become widespread. Open-source projects have played a major role in establishing large-scale databases and data analysis as the bedrock of modern computing companies large and small, he says. Real value tends to lie in what companies can do with the tools, not the tools themselves.

“What’s going to be interesting and valuable is the data that’s moving in that system and the ways people can find value in that data,” he says. Late last month, IBM transferred its SystemML machine-learning software, designed around techniques other than deep learning, to the Apache Software Foundation, which supports several major open-source projects.

Facebook’s Big Sur server design will be submitted to the Open Compute Project, a group started by the social network through which companies including Apple and Microsoft share designs of computing infrastructure to drive down costs (see “.Inside Facebook’s Not-So-Secret New Data Center”).


ORIGINAL: .Technology Review

By .Tom Simonite 

December 10, 2015