New generation of investment opportunities
With machine learning and artificial intelligence set to potentially affect almost every industry, a few sectors in particular could see significant disruption, creating new investment opportunities.
With machine learning and artificial intelligence set to potentially affect almost every industry, a few sectors in particular could see significant disruption, creating new investment opportunities.
Artificial intelligence (AI) was the focus of the 1Q2018 outlook for the venture capital environment, from Morningstar-owned private markets company PitchBook.
"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," says PitchBook analyst Cameron Stanfill.
More than US$6 billion was invested across 643 venture capital deals in AI and ML during 2017--12-times higher than the rate of investment in 2008.
" Similarly, after years of negligible exit activity, the last two years represented a substantial uptick in liquidity and a shift to a new stage of the AI/ML exit environment," Stanfill says.
While he acknowledges startups will face tough competition from low-cost, cloud-based options offered by the likes of Google owner Alphabet, Apple, Microsoft, Facebook and others, "they can excel by focusing on more niche areas or datasets".
"Investment in the vertical is on an extended growth trend to levels 12-times 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 two years represented a substantial uptick in liquidity and a shift to a new stage of the AI/ML exit environment.
What is AI?
According to Stanfill, "AI is the area of computer science that focuses on the creation of an intelligent machine that can perceive its environment and make decisions to maximise the chances of reaching its goal."
"Machine learning is a subfield of AI and data analysis, that is working to give computers the ability to learn iteratively, improve predictive models and find insights from data.
"As individuals, we already interact with AI/ML applications every day when we talk to a voice assistant, use facial recognition technology, receive movie or restaurant recommendations, and numerous other instances," Stanfill says.
Within healthcare alone, potential applications of this technology include the automation of menial tasks for doctors and other workers, "especially the tedium of manually inputting patient data into electronic health records".
"New drug discovery is also an exciting use of AI/ML technology--from treating previously incurable diseases, to testing the potential outcomes of gene manipulation.
"Finally, the ability to provide personalised healthcare for every patient will improve patient experience as well as solve a myriad of problems including drastically decreasing the thousands of deaths every year caused by adverse reactions to medication," Stanfill says.
He believes there will be a proliferation of supervised learning applications in AI and ML, "refined from a consumer-facing approach to focus on the automation of tasks, almost to the point of ubiquity".
Self-driving cars
Within the unsupervised and reinforcement learning space--the next step beyond supervised learning--Stanfill says the possibility could offer solutions to an increasing large array of problems. "While there are hurdles to bringing these techniques into commercial products, proof of concept at the bleeding edge can attract significant investment. This has been demonstrated by the amount of capital flowing into the autonomous vehicle space."
He highlights the uptick in mainstream media exposure of AI and ML in recent months, though concedes "it’s clear that the vertical is still in the early innings.
"Even still, there is a segmentation in the market. Some larger companies are already executing on commercial products," Stanfill says. He refers to US-based online lender Avant and used goods marketplace letgo as examples.
Most active acquirers of us VC-backed AI/ML companies
Source: PitchBook. *Data as of 2006 to 31/12/2017
US venture-backed exit activity in AI/ML
Source: PitchBook
A new phase
Stanfill suggests there is increasing maturity within the AI and ML sector, with a number of companies now being acquired and exiting the venture capital environment.
"We expect this trend to continue as current tech giants work to bolster their in-house AI offerings. Additional acquisitions will be fuelled as corporations operating outside the software/internet sphere realise the potential of integrating AI into their business."
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Glenn Freeman is a senior editor at Morningstar.
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