The State of AI 2019: Divergence


Chapter 1: What is AI?

  • ‘AI’ is a general term that refers to hardware or software that exhibit behaviour which appears intelligent.
  • Basic AI has existed since the 1950s, via rules-based programs that display rudimentary intelligence in limited contexts. Early forms of AI included ‘expert systems’ designed to mimic human specialists.
  • Rules-based systems are limited. Many real-world challenges, from making medical diagnoses to recognising objects in images, are too complex or subtle to be solved by programs that follow sets of rules written by people.
  • Excitement regarding modern AI relates to a set of techniques called machine learning, where advances have been rapid and significant. Machine learning is a sub-set of AI. All machine learning is AI, but not all AI is machine learning.
  • Machine learning enables programs to learn through training, instead of being programmed with rules. By processing training data, machine learning systems provide results that improve with experience.
  • Machine learning can be applied to a wide variety of prediction and optimisation challenges, from determining the probability of a credit card transaction being fraudulent to predicting when an industrial asset is likely to fail.
  • There are more than 15 approaches to machine learning. Popular methodologies include random forests, Bayesian networks and support vector machines.
  • Deep learning is a subset of machine learning that is delivering breakthrough results in fields including computer vision and language. All deep learning is machine learning, but not all machine learning is deep learning.
  • Deep learning emulates the way animals’ brains learn subtle tasks – it models the brain, not the world. Networks of artificial neurons process input data to extract features and optimise variables relevant to a problem, with results improving through training.

“Excitement regarding modern AI relates to a set of techniques called machine learning, where advances have been rapid and significant.”

The Evolution of AI: Deep learning

Source: MMC Ventures

Chapter 2: Why is AI important?

  • AI technology is important because it enables human capabilities – understanding, reasoning, planning, communication and perception – to be undertaken by software increasingly effectively, efficiently and at low cost.
  • General analytical tasks, including finding patterns in data, that have been performed by software for many years can also be performed more effectively using AI.
  • The automation of these abilities creates new opportunities in most business sectors and consumer applications.
  • Significant new products, services and capabilities enabled by AI include autonomous vehicles, automated medical diagnosis, voice input for human-computer interaction, intelligent agents, automated data synthesis and enhanced decision-making.
  • AI has numerous, tangible use cases today that are enabling corporate revenue growth and cost savings in existing sectors.
  • Applications will be most numerous in sectors in which a large proportion of time is spent collecting and synthesising data: financial services, retail and trade, professional services, manufacturing and healthcare. Applications of AI-powered computer vision will be particularly significant in the transport sector.
  • Use cases are proliferating as AI’s potential is understood. We describe 31 core use cases across eight sectors: asset management, healthcare, insurance, law & compliance, manufacturing, retail, transport and utilities.
  • We illustrate how AI can be applied to multiple processes within a business function (human resources).

“AI has numerous, tangible use cases today that are enabling corporate revenue growth and cost savings in existing sectors.”

Sector Core use cases:
Asset Management Investment strategy Portfolio construction Risk management Client service
Healthcare Diagnostics Drug discovery Monitoring
Insurance Risk assessment Claims processing Fraud detection Customer service
Law & compliance Case law Discovery and due diligence Litigation strategy Compliance
Manufacturing Predictive maintenance Asset performance Utility optimisation
Retail Customer segmentation Content personalisation Price optimisation Churn prediction
Transport Autonomous vehicles Infrastructure optimisation Fleet management Control applications
Utilities Supply management Demand optimisation Security Customer experience

Source: MMC Ventures

Chapter 3: Why has AI come of age?

  • After seven false dawns since its inception in 1956, AI technology has come of age.
  • The capabilities of AI systems have reached a tipping point due to the confluence of seven factors: new algorithms; the availability of training data; specialised hardware; cloud AI services; open source software resources; greater investment; and increased interest.
  • Together, these developments have transformed results while slashing the difficulty, time and cost of developing and deploying AI.
  • A virtuous cycle has developed. Progress in AI is attracting investment, entrepreneurship and interest. These, in turn, are accelerating progress.
Convolutional neural networks are delivering human-level image recognition


Chapter 4: The race for adoption

  • AI adoption has tripled in 12 months. One in seven large companies has adopted AI; in 24 months, two thirds of large companies will have live AI initiatives. In 2019, AI ‘crosses the chasm’ from early adopters to the early majority.
  • AI may be the fastest paradigm shift in technology history. In the course of three years, the proportion of enterprises with AI initiatives will have grown from one in 25 to one in three. Adoption has been enabled by the prior paradigm shift to cloud computing, the availability of plug-and-play AI services from global technology vendors and a thriving ecosystem of AI-led software suppliers.
  • Great expectations are fuelling adoption. Executives expect AI to have a greater impact than any other emerging technology, including Blockchain and IoT.
  • Increasing overall adoption masks a growing divergence between leaders and laggards. Leaders are extending their advantage by learning faster and increasing investment in AI at a greater pace than laggards.
  • Globally, China leads the race for AI adoption. Twice as many enterprises in Asia have adopted AI, compared with companies in North America, due to government engagement, a data advantage and fewer legacy assets.
  • Sector adoption is uneven and in a state of flux. ‘Early adopters’ (financial service and high-tech companies)maintain a lead while ‘movers’ (retail, healthcare and media)are rapidly catching up. Government agencies, education companies and charities are laggards in AI adoption. Vulnerable members of society may be among the last to benefit from AI.
  • AI is advancing across a broad front. Enterprises are using multiple types of AI application, with one in ten enterprises using ten or more. The most popular use cases are chatbots, process automation solutions and fraud analytics. Natural language and computer vision AI underpin many prevalent applications as companies embrace the ability to replicate traditionally human activities in software for the first time.
  • Leaders and laggards face different adoption challenges. Laggards are struggling to gain leadership support for AI and to define use cases. Leaders’ difficulties, in contrast, have shifted from ‘if’ to ‘how’. Leaders are seeking to overcome the difficulty of hiring talent and address cultural resistance to AI.
  • AI initiation has shifted from the C-suite to the IT department. Two years ago, CXOs initiated two thirds of AI initiatives. In 2019, as corporate engagement with AI shifts from ‘if’ to ‘how’, the IT department is the primary driver of projects.
  • Companies prefer to buy, not build, AI. Nearly half of companies favour buying AI solutions from third parties, while a third intend to build custom solutions. Just one in ten companies are prepared to wait for AI to be incorporated into their favourite software products.
  • Workers expect AI to increase the safety, quality and speed of their work. As companies’ AI agendas shift from revenue growth to cost reduction initiatives, however, workers are concerned about job security.
One in seven large companies have deployed AI

Source: Gartner, 2019 CIO Survey: CIOs Have Awoken to the Importance of AI, figure 1, 3 January 2019

Chapter 5: The advance of technology

  • While graphical processing units (GPUs) catalysed AI development in the past, and will continue to evolve, hardware innovations are expanding AI’s potential. Hardware is being optimised, customised or re-imagined to deliver a new generation of AI accelerators.
  • Hardware with ‘tensor architectures’ is accelerating deep learning AI. Vendors, including NVIDIA and Google are optimising or customising hardware to support the use of popular deep learning frameworks.
  • We are entering the post-GPU era. Leading hardware manufacturers are creating new classes of computer processor designed, from inception, for AI. Custom silicon offers transformational performance and greater versatility.
  • Custom silicon is also taking AI to the ‘edge’ of the internet – to IoT devices, sensors and vehicles. New processors engineered for edge computing combine high performance with low power consumption and small size.
  • As quantum computing matures, it will create profound opportunities for progress in AI and enable humanity to address previously intractable problems, from personalised medicine to climate change. While nascent, quantum computing is advancing rapidly. Researchers have developed functioning neural networks on quantum computers.
  • Reinforcement learning (RL) is an alternative approach to developing AI that enables a problem to be solved without knowledge of the domain. Instead of learning from training data, RL systems reward and reinforce progress towards a specified goal. AlphaGo Zero, an RL system developed by DeepMind to play the board game Go, developed unrivalled ability after just 40 days of operation. In 2019, developments in RL will enable groups of agents to interact and collaborate effectively.
  • Progress in RL is significant because it decouples system improvement from the constraints of human knowledge. RL is well suited to creating agents that perform autonomously in environments for which we lack training data.
  • Transfer learning (TL) enables programmers to apply elements learned from previous challenges to related problems. TL can deliver stronger initial performance, more rapid improvement and better long-term results. Interest in TL has grown seven-fold in 24 months and is enabling a new generation of systems with greater adaptability.
  • By learning fundamental properties of language, TLpowered models are improving the state of the art in language processing – in areas of universal utility. 2018 was a breakthrough year for the application of TL to language processing.
  • TL is also: enabling the development of complex systems that can interact with the real world; delivering systems with greater adaptability; and supporting progress towards artificial general intelligence, which remains far from possible with current AI technology.
  • Generative Adversarial Networks (GANs) will reshape content creation, media and society. An emerging AI software technique, GANs enable the creation of artificial media, including pictures and video, with exceptional fidelity. GANs will deliver transformational benefits in sectors including media and entertainment, while presenting profound challenges to societies – beware ‘fake news 2.0’.
Reinforcement learning enabled AlphaGo Zero, a system developed by DeepMind to play Go, to achieve unrivalled capability after 40 days of play

Source: Google DeepMind

Chapter 6: The war for talent

  • Demand for AI talent has doubled in 24 months. There is a gulf between demand and supply, with two roles available for every AI professional.
  • The pool of AI talent remains small. AI demands advanced competencies in mathematics, statistics and programming; AI developers are seven times more likely to have a Doctoral degree than other developers.
  • Supply is increasing – machine learning has become the top emerging field of employment in the United States. Greater supply is being driven by: high pay; the inclusion of AI modules in university computer science courses; companies’ investment in staff training; and AI technology companies ‘pump priming’ the market with free educational resources.
  • Over time, AI tools offering greater abstraction will make AI accessible to less specialised developers.
  • Talent shortages are sustaining high salaries. AI professionals are among the best paid developers and their salaries continue to increase; half enjoyed salary growth of 20% or more in the last three years.
  • Winners and losers are emerging in the war for talent. The technology and financial services sectors are absorbing 60% of AI talent.
  • The ‘brain drain’ from academia to industry is real and will have mixed implications, catalysing AI’s immediate impact while inhibiting teaching and moving value from the public domain to private companies.
  • High job satisfaction is intensifying the war for talent. Three quarters of AI professionals are satisfied in their current role.
  • To optimise hiring and retention, companies should align roles to AI professionals’ primary motivators – learning opportunities, office environment and access to preferred technologies.
  • New practitioners in the field are following sub-optimal paths to employment. Company websites and technology job boards are less effective than engaging with recruiters, friends, family and colleagues, according to those already employed in the field.
AI-related job postings as a proportion of the total have doubled in 18 months

Source: Indeed

Chapter 7: Europe’s AI startups

  • Europe is home to 1,600 early stage AI software companies. AI entrepreneurship is becoming mainstream. In 2013, one in 50 new startups embraced AI. Today, one in 12 put AI at the heart of their value proposition.
  • The European start-up ecosystem is maturing. One in six European AI companies is a ‘growth’-stage company with over $8m of funding. Expect: acquisitions to recycle capital and talent; startups competing with ‘scale-ups’ as well as incumbents; and increasing competition for talent.
  • The UK is the powerhouse of European AI with nearly 500 AI startups – a third of Europe’s total and twice as many as any other country. We provide a map of the UK’s AI startups and feature 14 leading companies.
  • Germany and France are thriving European AI hubs. High quality talent, increasing investment and a growing roster of breakout AI companies are creating feedback loops of growth and investment.
With twice as many AI startups as any other country, the UK is the powerhouse of European AI entrepreneurship

Source: MMC Ventures, Beauhurst, Crunchbase, Tracxn

“Availability of talent and access to training data are AI entrepreneurs’ key challenges.”

  • Spain’s contribution to European AI exceeds its size. Immigration, which correlates with entrepreneurship, has deepened the Country’s talent pool.
  • The European AI landscape is in flux. While the UK remains the powerhouse of European AI, its share of European AI startups, by volume, has slightly reduced. Brexit could accelerate this. France, Germany and other countries may extend their influence in the decade ahead, spreading the benefits of entrepreneurship more evenly across Europe.
  • Italy, Sweden and Germany ‘punch above their weight’ in core AI technology, while there is support for Nordic countries’ reputation for deep tech expertise.
  • Nine in ten AI startups address a business function or sector (‘vertical’). Just one in ten provides a ‘horizontal’ AI technology.
  • A quarter of new AI startups are consumer companies, as entrepreneurs address or circumvent the ‘cold start’ data challenge. Many focus on finance or health & wellbeing.
  • Healthcare, financial services, retail and media & entertainment are well served by AI startups. In sectors including manufacturing and agriculture, entrepreneurial activity is modest relative to market opportunities.
  • Health & wellbeing is a focal point for AI entrepreneurship; more startups focus on the sector than any other. In the coming decade, developers will have a greater impact on the future of healthcare than doctors. Activity is thriving given profound new opportunities for process automation and a tipping point in stakeholders’ openness to innovation.
  • The UK is the heartland of European healthcare AI, with a third of the Continent’s startups. UK entrepreneurs benefit from healthcare scale-ups stimulating talent and increasing openness to innovation within the NHS.
  • Marketing and customer service departments enjoy a rich ecosystem of suppliers. A quarter of AI startups serving a business function focus on marketing teams.
  • An influx of AI startups supporting operations teams is driving increasing process automation.
  • AI companies raise larger amounts of capital, due to technology fundamentals and extensive capital supply.
  • Core technology providers attract a disproportionate share of funding. While comprising a tenth of AI startups, they attract a fifth of venture capital.
  • AI entrepreneurs’ key challenges are the availability of talent, access to training data and the difficulty of creating production-ready technology.

The Audio Analytic team (source: Audio Analytic)

AI offers innovation, efficacy, velocity and scalability

Source: MMC Ventures

Chapter 8 : The implications of AI

  • AI’s benefits can be abstracted to: innovation (new products and services); efficacy (perform tasks more effectively); velocity (complete tasks more quickly); and scalability (free activity from the constraints of human capacity). These benefits will have profound implications for consumers, companies and societies.
  • By automating capabilities previously delivered by human professionals, AI will reduce the cost and increase the scalability of services, broadening global participation in markets including healthcare and transport.
  • In multiple sectors including insurance, legal services and transport, AI will change where, and the extent to which, profits are available within a value chain.
  • New commercial success factors – including ownership of large, private data-sets and the ability to attract data scientists – will determine a company’s success in the age of AI.
  • New platforms, leaders, laggards and disruptors will emerge as the paradigm shift to AI causes shifts in companies’ competitive positioning.
  • AI, ‘x-as-a-service’ consumption, and subscription payment models will obviate select business models and offer new possibilities in sectors including transport and insurance.
  • As AI gains adoption, the skills that companies seek, and companies’ organisational structures, will change.
  • By reducing the time required for process-driven work, AI will accelerate innovation. This will compress cycles of creative destruction, reducing the period of time for which all but select super-competitors maintain value.
  • AI will provide profound benefits to societies, including: improved health; greater manufacturing and agricultural capability; broader access to professional services; more satisfying retail experiences; and greater convenience. AI also presents significant challenges and risks.
  • AI-powered automation may displace jobs. AI will enable the automation of certain occupations that involve routine. In other occupations, AI will augment workers’ activities. The short period of time in which select workers may be displaced could prevent those who lose their jobs from being rapidly reabsorbed into the workforce. Social dislocation, with political consequences, may result.
  • Biased systems could increase inequality. Data used to train AI systems reflects historic biases, including those of gender and race. Biased AI systems could cause individuals economic loss, loss of opportunity and social stigmatisation.
  • Artificial media may undermine trust. New AI techniques enable the creation of lifelike artificial media. While offering benefits, they enable convincing counterfeit videos. Artificial media will make it easy to harass and mislead individuals, and weaken societies by undermining trust.
  • AI offers trade-offs between privacy and security. As AI powered facial recognition advances, to what extent will citizens be willing to sacrifice privacy to detect crime?
  • AI enables the high-tech surveillance state, with greater powers for control. China is combining real-time recognition with social scoring to disincentivise undesirable activity.
  • Autonomous weapons may increase conflict. The risk of ‘killer robots’ turning against their masters may be overstated. Less considered is the risk that conflict between nations may increase if the human costs of war are lower.

“If we fail to make ethical and inclusive AI, we risk losing gains made in civil rights and gender equity under the guise of machine neutrality.”

Joy Buolamwini

There are potential harms from algorithmic decision-making

Source: Megan Smith via