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This is The Age of AI Series, where we talk to the foremost entrepreneurs and innovators around the planet using ML to transform industries. (Join our special mailing list!)
Today we’re talking about machine learning on the “edge” (i.e where devices collect their own data and learn from it themselves, without having to send data to the cloud or back-end for processing).
In recent years, there’s been a lot of buzzword-bingo about the “Internet of Things” (IoT), with people making outlandish projections that everything from your toaster to your teacup will be connected to the internet by 2025.
One reason why I was very skeptical about these predictions and why most of them have been wrong, is that actually building IoT systems is not very simple. The infrastructure and suite of tools available to developers for building connected devices at scale is still maturing, and the landscape is entirely driven by the earliest adopters.
This brings us to my new guest, Jon Lindén, CEO and Co-Founder of Ekkono AI, a software company at the very forefront of this space. They provide the software and tools needed to implement machine learning at the edge for connected devices, and they’re already serving customers in a variety of industries.
Jon and I cut the buzzwords and get down to the actual facts — explaining the actual IoT and edge-learning use cases we’re seeing in the market, what it really takes to build good applications, and how quickly or slowly the landscape is changing:
- 01:13 — Concrete examples of Ekkono’s customers and what they’re building
- 03:23 — “Predictive maintenance”: quick case study on heat exchangers and fluid handling
- 09:41 — Where is the value in edge ML for industrial applications
- 12:53 — Using edge ML for the consumer market: example of intelligent lawn mower
- 16:28 — Purchasing and investment decisions around IoT: how the perception of “value” is changing
- 18:02 — The technology of edge ML explained in simple words
- 20:51 — What Ekkono provides to their customers and what they use it for
- 25:22 — The building blocks of a modern IoT application, and how it works
- 31:40 — Why the IoT space hasn’t matured quickly enough: an unbiased perspective
- 38:00 — “Every billable hour is a failure”: helping buyers get started on their IoT journey, without having to become a services company, and dealing with competition
- 43:16 — Investor sentiment towards IoT companies
- 47:26 — Aman’s summary of the episode
- 49:48 — Jon’s comments on Aman’s summary
As always on the Age of AI Podcast, this is a rare type of conversation you won’t find elsewhere.
Aman’s 2-minute Summary and Key Takeaways:
The fundamental basis for ML on the edge is that the same device or equipment, when used in 2 different places by different people, will have an entirely different life story. The same air conditioner in Costa Rica vs Frankfurt will need completely different maintenance schedules and usage patterns.
Once a product is out of its manufacturer’s hands and into the customer’s, edge ML allows the product to adapt to the operating environment it finds itself in, effectively personalizing itself for its user. That’s the core value proposition.
Applications currently fall under 2 large overlapping umbrellas:
- “Predictive maintenance” for industrial equipment: instead of getting routine check-in visits, the machine can learn to sense the need for maintenance on its own, and caution about impending damage.
- “Personalized products”: if devices can learn about users’ preferences over time, such as your robotic lawn mower learning about YOUR lawn and its eccentricities, therefore giving you a better customer experience. This could also tempt you to buy other lawn care devices from the same company, so that the learnings could be transferred!
The feasibility of such products heavily depends on the hardware sensors available on the device, which I see as the core constraint for this tech. Everything else — the software tools for ML, connectivity tools, semiconductor chips for the embedded computers, etc are getting better.
Ekkono provides a software toolkit for doing edge ML. To buy Ekkono, a customer already needs to have invested in an in-house team working on connected devices, so it’s still a market of early adopters. Some customers ask Ekkono to do everything for them, but being a product-focused company that doesn’t want to carry a services business model, they partner with “solution providers” or consultants to provide that instead.
Jon agrees that while offering end-to-end services would have helped sign more deals, it would have thrown off the company’s focus. (We’ve seen this theme before in Episode 2, where Rolf’s company CollectiveCrunch, also a pioneering company in an early-adopter market, made a similar strategic call not to be a services company.)
As an industry observer, Jon’s take on IoT is that while the landscape is as “hot” as it’s ever been, it hasn’t matured as much as people expected, and he also admitted that he’s surprised their space isn’t more crowded.
That being said, the space is growing enough that they have plenty of paying customers, and being the first mover in a market like this is THE big advantage for a company like Ekkono. After all, if you want to be the go-to toolkit for building edge ML applications, you don’t wait for the market to “mature”; you enable it to happen.
(Ethics Policy: These opinions are 100% my own as an independent observer and educator. I don’t own stock in guests’ companies or their competitors, nor do I get paid by them in any form for any reason at the time of publishing, unless specifically stated. Episodes are also not intended to be an automatic endorsement of any company or its products and services.)
