About 2,200 people gathered to hear about use cases, technology and business models for AI/machine learning (ML). It was a diverse group at least in terms of levels of understanding of AI and machine learning, if not in terms of gender. I attend a lot of technology conferences and most are majority-male, this one must’ve been 80% male. About 20% of attendees came from outside the US. But those that were there were eager to engage, learn and discuss.
And what’s driving that interest is, of course, the huge potential new revenue and new costs saving there are to be made. Heath Terry, an MD at Goldman Sachs had some startling numbers to hand, such as annual cost savings in healthcare of $54bn by 2025, or the same number in retail coupled with a $41bn increase in revenue by 2025. And the energy market would enjoy cumulative cost savings in the seven years to 2025 of $140bn. Terry also said he’d never seen a market getting this sort of level of investment that has not spawned new, large companies.
The startup lightning round on the evening of day one – judged by an all-female VC panel of three, incidentally – had some interesting pitches from Avata Intelligence, BI Brainz, Clickworker.com, Flamingo.ai, indico.io, Synaptik and ZyloTech with software aimed at solving issues such as customer analytics and assistants, process automation, and something called AI-as-a-service, among other things.
One small criticism would be that panellists need to think about the conference as a whole and the likelihood that the audience has already heard multiple – and sometimes conflicting – definitions of what AI, ML and deep learning (DL) are so we don’t need another discussion about definitions when we could be discussing use cases, for instance.
Veritone CEO Chad Steelberg had an intriguing proposition that we’ll cover soon in our research at 451 – and a right to claim to be the first publicly-traded pure-play machine learning software company in the US markets.
I had the honour of closing out the conference with chair Eliot Weinman and chief scientist of Narrative Science Kris Hammond on the daunting subject of the Future of AI. I had to quickly gather some thoughts together so I chose brief comments focused on:
- More data acquisition & consolidation by large vendors (think how Oracle Data Cloud was formed, or IBM-Weather Company or Microsoft-LinkedIn) as data is the feedstock of machine learning and large application vendors infusing their stacks with machine learning will want more of it
- More focus on how human brains actually work – neuroscience and computer science folks trying to figure out if neural nets do actually work in a similar way to the brain. Nobody seems to know right now
- More specialist AI chips – Apple, Facebook, Google, Graphcore, Intel, Microsoft, Nvidia, Qualcomm…..some are making them, some are merely designing them for others to make, but all are focused on increasing the performance by baking functions into the chips, especially for the increased requirements of deep learning
- Government involvement will increase – both in a regulatory sense and promoting national interests sense
- Unsupervised machine learning – because there are only so many labeled datasets around to train models. I explored this further in a recent 451 report you can read here.
Earlier in the day I had three excellent speakers on my panel about driving innovation in the enterprise:iven we were on day three and a lot of panels about various aspects of AI/ML had preceded us, I wanted to focus as much on innovation as on machine learning. So we were 15 minutes into our allotted 45 before we steered the conversation back to the main topic of the conference but we got good feedback from the audience on what was said about how SAP and Intel foster innovation at large tech vendors and how it’s done at IHG.
AI World next year is expanding to the Boston Seaport Hotel & World Trade Center in Boston December 3-5. Mark your calendar.