Month: February 2018

Imagr: NZ’s version of Amazon Go

ShaunLeave a comment

Last Friday during a visit to Auckland I took the opportunity to catch up with Will Chomley, CEO of Imagr. Will is an ambitious entrepreneur, with a finance background. He has surrounded himself with a group of engineers skilled in deep learning. This start-up is focused on using machine vision to identify products as they are added to a shopping cart: NZ’s version of Amazon Go. But this is aimed at cart rather than the whole store. Their product is called SMARTCART and integrates with your phone. 

When I met Will they had just announced their first trial with Foodstuffs New Zealand in their Four Square Ellerslie store . They were busy building their training dataset which at the time of writing contains over 2 million product photos for their machine vision models.

The 12-person company has a handful of machine vision engineers. A portion of these are immigrants because the skills are hard to find in New Zealand. Will is very enthusiastic about running the company from New Zealand because it is such a desirable place to live and it’s easy and quick to move here for people with the right qualifications.

The capabilities of their machine vision look impressive. They’re able to identify very similar looking packages with only subtle differences. I saw an example of two ELF mascara products that on first inspection looked identical, but on closer inspection one had a small brush.  They’re able to identify, with high accuracy, occluded products – partially covered by a hand, or high speed objects being thrown into a basket that I couldn’t recognize, but the blurred images are able to be recognised by their 200+ layer convolutional neural network.

They have designed the cart so the inference engine, which is making the decision about what is going into the basket, can either run on a computer on the basket, or the information can be sent to a server. To get the speed from this model they have developed their own software using C, rather than relying on the easier to use but slower  frameworks such as TensorFlow. This gives the capability to identify and engineer around bottlenecks.

In parallel they’re working on the hardware, having recently decided to use small, cheaper, lower resolution cameras with some added lighting. These can provide the same high accuracy rate as higher resolution, expensive cameras. They have their challenges. Designing and building hardware that can be manufactured at scale and operate reliably and easily is no mean feat. However, they have some engineering chops: their CTO Jimmy Young was Director of Engineering for PowerByProxi, the wireless charging company bought by Apple last year.

They have some backers with deep pockets to help resource these challenges.  Last year they received an undisclosed investment from Sage Technologies Ltd, the technology venture of private investment company QuantRes founder, billionaire Harald McPike.

There’s a large opportunity in front of them and they’re moving quickly. Adding smart carts has to be a lot cheaper than fitting out a store Amazon Go style. They may be able to get a piece of the global grocery market, grabbing a cut of the value of the cart, saving costs for the grocer, improving the experience for the shopper and opening up a world of possibility for the company once they are collecting the shopping data.

One of their challenges is to stay focused. There are so many applications of machine vision technology, even if they stick to retail. They’ve experimented with smart fridges that can identify the gender and age of people taking products, as well as knowing which products they take. They’re talking to others in the industry about applications for their technology.

If they can keep their focus and execute they have a bright future ahead of them.  Their product has global appeal. Retailers can see the threat of technology giants such as Amazon and Alibaba who are moving quickly into bricks and mortar retail. This accelerated last year with Amazon’s purchase of Whole Foods. The convenience of online shopping is coming to a store near you and Imagr may be the one’s powering it.

The limits of deep learning

Shaun1 Comment

There are a couple of articles I’ve read recently that have gelled with my own about the limits of deep learning. Deep learning simply refers to multi layered neural networks, that typically learn using back-propagation to train. These networks are very good at pattern recognition, and are behind most recent advances in artificial intelligence.  However, despite the amazing things they are capable of, I think it’s important to realize that these networks don’t have any understanding of what they’re looking at or listening to.

Gödel, Escher, BachThe first article was by Douglas Hofstadter, a professor of cognitive science and author of Gödel, Escher, Bach. I read that book many years ago and remember getting a little lost. However, his recent article titled The Shallowness of Google Translate clearly demonstrates how  the deep learning powered Google Translate successfully translates the words, but often fails to translate the meaning. Douglas believes that one day machines may be able to do this but they’ll need to be filled with ideas, images, memories and experiences, rather than sophisticated word clustering algorithms.

The second article by Jason Pontin at Wired discusses the Downsides of Deep Learning:

  • They require lots of data to learn
  • They work poorly when confronted with examples outside of their training set
  • It’s difficult to explain why they do what they do
  • They don’t gain any innate knowledge or common sense.

Jason argues that for artificial intelligence to progress we need something beyond deep learning. There are many others saying the same types of things. I’ve recommend watching MIT’s recent lectures on Artificial General Intelligence that covers this as well.