There has been a great deal of excitement surrounding the AI/ML field over the past decade, with rapid advancements and significant commercial integration of applications. Deep learning techniques are behind nearly all the current frontier research and successful applications of AI/ML (e.g. image recognition, search engines, drug discovery, deep reinforcement learning). This progress was made possible by the new research approaches to ML problems, combined with huge advances in computing power and the rampant increase in data digitization and availability. PitchBook's latest analyst note provides a snapshot of key trends in artificial intelligence and machine learning, as well as a review of venture financing trends of startups within the field.
• 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 two 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.