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Element AI: The market is still figuring out how to share data with enterprise AI startups

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VentureBeat | | Jun 7, 2018

element AI - Element AI: The market is still figuring out how to share data with enterprise AI startups

It’s no exaggeration to call Element AI one of the top startups in the world right now. With the help of deep learning pioneer Yoshua Bengio, the company is making AI-powered products for the enterprise. And from its beginning in October 2016, Element AI has broken the rules of what to expect from a startup.

In December 2016, Element AI was the very first company to receive funding from Microsoft Ventures. Six months later, the company raised a $102 million series A round.

Element AI has yet to release a single publicly available product, but the company is already working with customers, has opened offices in Singapore, South Korea, Toronto, and London, and already plays an advisory role to startups that receive funding from the Global AI Fund in South Korea.

At the creative tech conference C2 in Montreal last month, VentureBeat sat down with CEO Jean-François Gagné to talk about challenges enterprise customers face in implementing AI, his company’s first publicly available products, and why he believes AI is allowing startups to challenge incumbent businesses in tech and finance.

This interview has been edited for brevity and clarity.

VentureBeat: Are there any specific kinds of challenges companies encounter in terms of implementation of AI? When you come in the door of a business, what’s stopping them from implementing AI?

Gagné: We’re still all trying to figure out how the IP, data access, and learning component of the technology is going to play out. The market is currently trying to figure that out. The big dynamic that we see is all the conversations about “What piece of IP will your AI keep?”

See:  The forces of change are trumping banks and regulators

Because we totally understand that large chunks of the IP we build on top of the data of our customers is their own, but there is stuff that needs to flow back to our platform so we make the product better. And defining that has been something where we need to spend a lot of time every time educating the customers, making sure they see it’s transparent and understand what are they going to own, what are we going to own, and this is one thing that is right now a big puzzle for the industry to solve as a whole. And because we’re one of the first to really do that at scale, I think we’re opening the way there, and that’s one thing that comes to my mind.

VentureBeat: Lately I’ve heard a fair deal of companies talk about how they want to build common sense and perception into AI; it just keeps coming up as part of some evolution to go beyond narrow applications of artificial intelligence. Where do you think we’re going on that?

Gagné: So here’s the thing: Google doesn’t want to interact with the customer. They want their system to run by their own, so the way they’re approaching the problem is by wanting the assistant to have some sort of common sense and figure things out by themselves, and we have a very different opinion there. We believe in human-in-the-loop systems that are highly efficient, highly flexible, highly agile, but where people are still driving and are in control of the governance, still involved in the decision process.

You look at the work they’re doing with DeepMind and everything, where things are just going to take off by themselves eventually — and first, I don’t believe that this is going to happen anytime soon. The black box effect this creates, going down that road and all the potential downside of having people think that they don’t need to look at what’s going on, is extremely bad given the state of the industry, so I don’t think it’s the right way to go at it.

VentureBeat: They say they want to be more transparent.

Gagné: They don’t want to be evil, they want to be transparent … what matters are the actions. What are people really doing? We have to actually look at that concretely, so the way we’re going at this is really to make sure that we maximize explainability [and] transparency as we’re deploying this, to enable our customers and people who are using our tools to have the right governance on top.

So [we] invest tons of money in all the monitoring systems and explainability of the models.

There’s more effort put in this than in the models themselves, generally speaking, in whatever product we do, whatever thing we roll out, and so of course they don’t want that. They’re at the top already, so they want to make sure that they maintain that edge, so that’s why you’re hearing that from them.

More:  The Age of Artificial Intelligence in Fintech

VentureBeat: So then what should people expect from Element AI in the next six months?

Gagné: At this point it’s all about scale, like we’re literally in the bottom of the hockey stick looking to be 500 people before the end of the year, 1,000 next year, so we’re really starting to hit that phase. And then for us it’s all about repeatable deployments: successful, repeatable deployments. It’s just about repeatability, getting our cybersecurity product out there, our product for insurance, banking, and we’ve got stuff coming for logistics companies.

VentureBeat: You guys are in a bit of a different position than most companies, but do you feel like the monopoly that large companies have stifles the innovation that could be happening in the AI startup ecosystem?

Gagné: No longer, actually. I think they’re [banks] getting challenged to the point that if they don’t do something about it, they know their business is going to start to — their margins and everything are going to start to diminish, and in the financial sector it’s definitely there.

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