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PitchBook’s 1Q 2018 Analysis of the Investment in Artificial Intelligence (AI) & Machine Learning (ML) market confirms that investment in AI Technology will not restrict its growth but will become more targeted. The report identified the following key issues;
As a general-purpose technology, artificial intelligence (AI) and machine learning (ML) have potential use cases in virtually every industry and the ability to reshape the way people live and do business. Breakthroughs in deep learning in the past decade have engendered a proliferation of artificial intelligence applications into daily life and paved the way for further advancement in the field.
VC investment in the vertical is on an extended growth trend to levels 12x above what we saw in 2008. 2017 recorded $6 billion invested across 643 VC deals in AI / ML. Similarly, after years of negligible exit activity, the last 2 years represented a substantial uptick in liquidity and a shift to a new stage of the AI/ML exit environment.
For now, almost all commercially successful ML applications use supervised learning, which encompasses a vast number of applications but is limited to areas that have clean, labeled data. Startups will face stout competition from low-cost options available via the cloud from technology giants, but they can excel by focusing on more niche areas or datasets.
￼￼￼￼￼￼In the near term, we think there will continue to be a proliferation of supervised learning applications in AI / ML developed and refined from a consumer-facing approach to focus on the automation of tasks, almost to the point of ubiquity.
Over the next year, these companies will produce the most commercially viable AI / ML products and will represent many acquisitions in the vertical. However, the largest AI / ML market prospects will derive from a potential expansion into a host of enterprise applications and new industries.
Opportunities in unsupervised and reinforcement learning present some exciting scenarios at the frontier of the vertical. While there are still a fair number of obstacles to proliferation of these techniques, the possibilities could offer solutions to an increasingly large array of problems. One of the most significant early achievements in reinforcement learning was AlphaZero, originating from Google’s DeepMind. This algorithm mastered the games of chess, Go and Shogi purely via simulated games against itself.
Further potential use cases where reinforcement learning can improve on more traditional solutions include resource allocation problems, a plethora of personalized user interfaces (healthcare, content such as headlines, ads, etc.), and applications in robotics and autonomous vehicles.
While there are hurdles to bringing these techniques into commercial products, proof of concept at the bleeding edge of AI / ML can attract significant investment. This has been demonstrated by the amount of capital flowing into the autonomous vehicle space. In the end, the most adaptable VC-backed companies and investors will succeed in the AI-centric world.
Memoori believes that AI Video Technology has one of the lowest barriers to establish itself in the video surveillance market and accordingly will achieve one of the highest rates of growth over the next five years.
That said, many of these applications will take more time to come to fruition due to the complexity of the problems and the computation power that they require. In response to these issues, advances are being pursued in supporting hardware—quantum / high performance computing and hybrid computing (GPUs/CPUs/FPGAs/TPUs) as well as decomposition techniques to break down complex problems into manageable segments.
The U.S. venture capital (VC) industry finished strong in 2017 with $84 billion invested in 8,035 companies across 8,076 deals, the highest annual amount of capital deployed to the entrepreneurial ecosystem since the early 2000’s, according to the PitchBook NVCA Venture Monitor.
The EU and UK both announced major investments in artificial intelligence research in April this year, with more than 50 tech companies contributing to a £1 billion deal in the UK, and the European Commission announcing it would be allocating €1.5 billion to AI research until 2020.
The UK’s deal, as detailed in a government press release, will include funding for “8,000 specialist computer science teachers, 1,000 government-funded AI PhDs by 2025,” and development for a “prestigious global Turing Fellowship” program to attract top talent. Per the release, the U.K. will also be developing “a world-leading Centre for Data Ethics and Innovation,” to emphasize ethical standards with AI research. The EU’s deal also includes laying out clear ethical guidelines by the end of 2018.
For more information on the impact of Artificial Intelligence on the Video Surveillance Industry, take a look at our report “The Global Market for Intelligent Video Analytics 2018 to 2023”.