Aware Group: Artificial Intelligence consulting

Over breakfast recently I had the opportunity to talk to Brandon Hutcheson,  CEO of the Aware Group. Brandon told me their 17 person company is the fastest growing artificial intelligence consulting group in New Zealand. He shared a little of their history and plans.

Brandon is a serial entrepreneur, having being involved in two successful exits with Cheaphost and Dvelop IT.  The Aware Group got their start in 2016 after successfully winning the Microsoft Excellence Award in Technology Delivery for a tertiary business intelligence implementation. This includes predicting things like student drop out rates (a task Jade had told me they have been working on too) , student prediction,  and  measuring tutor performance. This platform is now being used by various New Zealand tertiary institutions.

They’ve developed their machine vision capability across various projects, including counting and classifying traffic (cars, trucks, vans, bikes, etc) for Local and National Government.  They’ve also put this technology to use to count students going into tertiary classes. The next stage for their product release is the use of facial recognition to identify who is in the class but they don’t feel that we are socially ready for this level of implementation.

Aware group people counting

Their growing data science team is not reinventing the wheel. Where possible they’re using existing models and tweaking them to meet the needs of their project. They’re using Microsoft technologies and are a strong Microsoft partner.

The newest AI capability they’ve developed has been around natural language processing. This has been supported by their recent entry into the Vodafone Xone incubator. They’re applying this technology to understand customer support calls and attempting to surface relevant articles from an internal knowledge base. These articles could help to answer the support call more efficiently, but also to up-sell the customer on relevant services more effectively. This product is currently under development and is being tested in a call centre.

Probably some of their highest profile work has been in demonstrating AI concepts, often in partnership with Microsoft. Some of these are quite quirky, including:

  • demonstrating how patrons with empty glasses can be identified so bar staff can come and give them a top up
  • allowing conference attendees to have a coffee of their choice ordered as soon as they are identified on a camera
  • Using a recurrent neural network to generate Simpsons lines. This won the Just for fun category at the Microsoft Data Insights Summit

aware group simpsons

On the business side, the Aware group are getting most of their revenue from service contracts with an increasing growth in product-based revenue. They have bootstrapped to date, but are preparing themselves for a capital raise.

One of the challenges Brandon is facing is finding good quality AI practitioners.  As well as their team of 12 in Hamilton, they have 5 people based in Seattle and are planning to expand in the US market. They are also eyeing opportunities in Australia, Japan, Israel, and Korea.. With customers in sectors as varied as education, government, large corporates and agriculture, my main advice to Brandon was to focus their small company geographically and within a single sector so they can benefit from getting known within a niche.

The Aware Group was recently acknowledged as a rising star by Deloitte as part of their Fast 50 awards. I’ll be following to see how far and fast their star does rise and wish them all the best.

 

Neuromorphic computing

At a recent AI forum event in Christchurch, one of the presenters was Simon Brown, a physics professor from the University of Canterbury. Simon specialises in nanotechnology and has created a chip that may be capable of low power, fast AI computations. I caught up with Simon after the event to find out more.

The chip is created using a machine consisting of a number of vacuum chambers. It starts with a metal (in this case tin) in vapour form in one vacuum chamber. As it moves through the various chambers the vapour particles are filtered, mechanically and electrically until they are just the right size (averaging 8.5 nanometers diameter) and they are sprayed onto a blank chip. This is done until about 65% of surface of the chip is covered with these tiny droplets.

This is just enough coverage to be almost conductive. The metal droplets on the chips are close enough to each other that an electrical charge in one will induce charges in nearby droplets. Simon describes these as being analogous to synapses in the brain which connect neurons. The strength of the connection between the two droplets is a function of the distance between them. The first chips that were created had two external connections into this nano scale circuit. Interestingly when a voltage was applied to one of the connections the resulting waveform on the other connection had properties similar to those seen in biological neurons.

An important piece of research was showing that this chip was stable, i.e. the performance didn’t change over time. That was proven and so what Simon has been able to create is effectively a tiny neural network with many connections on a chip that has a random configuration. One feature that is unlike artificial neural networks that are used for deep learning, is that the strength of the connections between the neurons (the weights) cannot be changed using external controls. Instead the weights are updated through the atomic scale physical processes that take place on the chip. So while the chips will never be as flexible as artificial neural networks implemented in software, it turns out that these “unsupervised learning” processes have been studied by computer scientists for a long time and have been shown to be very efficient at some kinds of patter recognition. The question is whether there are applications that could leverage the “unsupervised” processing that this chip does very quickly and at low power.

A specific main candidate application is called reservoir computing. Reservoir computing uses a fixed, random network of neurons, just like the one created by Professor Brown, to transform a signal. A single, trainable layer of neurons (implemented in software) on top of this is then used to classify the signal. A Chicago based team  has achieved this using a chip made of memristors.

A standard implementation of reservoir computing would have access to each of the neurons in the random network. With just two connections into the network this chip does not have that access.  When we met, the team had just created a chip with 10 connections into the network.

Their focus now is trying to prove that they can implement reservoir computing or some variant on this chip. If they can do this then there is real potential to commercialise this technology. The larger opportunity is if they could find a way to use this technology to implement deep learning.

NZ Merino using artificial intelligence to monitor sheep well being.

At the last Christchurch AI meetup, I met up with Ian Harris who told me about the work he had done with neXtgen Agri for The New Zealand Merino Company (NZM)  as part of Sensing Wellbeing, a collaborative Sustainable Farming Fund Project. This work involved analysing data collected from accelerometers attached to jaws of sheep to try to identify their behaviour.

SheepWithActivityMonitor
A sheep with an activity monitor. NZM were very particular about ensuring the sheep were treated ethically during this data collection.

The data

Like any machine learning project, the critical part is the data. In this case the raw data came from the tri-axial accelerometers, sampled at 30 Hz. This meant that for each of the three channels there were 300 samples over a 10 second period. This data was collected from 15 sheep over a period of 6 days in conjunction with Massey University.

AccelGrazingObsId29684Sheep1
A example of the data from 3 channels over one 10 second period

6 of the sheep were filmed during that time and their behaviour was categorised into 12 different activities. An initial review of the data showed that there was only good data for 5 of the 12 activities and so the focus was on those activities:

  1. sitting
  2. standing
  3. ruminating while sitting
  4. ruminating while standing
  5. grazing

For those of you (like me) who are not intricately familiar with the lives of sheep, ruminating is the process or regurgitating, re-chewing and re-swallowing food.

Random forest approach

31 different metrics were calculated from the raw data, metrics like energy, maximum, etc. The initial approach Ian took was to use a random forest algorithm with these metrics or features as an input. With this approach the model correctly classifed 81% of the activities. This was a replication of an approach taken by a South African team who got similar results and helped validate the overall set up.

Stratified Sheep RandomForestClassifier Confusion Matrix
This confusion matrix shows that it was difficult to separate sitting and standing with a high degree of accuracy.

Deep learning approach

Ian is an experienced Java developer and had taught himself python and Deep Learning. For this problem he was using Tensorflow and set up a relatively simple 3-layer network that used the raw data for input rather than the calculated features used with the random forest approach. His most successful model had a binary output to detect whether (or should that be wether?) the sheep was grazing or not. This model had 93% accuracy.

Clustering

Only six of the fifteen sheep had their behaviour recorded, so Ian had a lot of unlabelled data. In an effort to leverage this data he used an unsupervised clustering algorithm to try to generate more data from which to learn. This did improve the detection rates, but only for models for which there was relatively low accuracy.

Sheep wellbeing

You could imagine a connected device that could accurately predict whether a sheep is grazing or not may be useful if it could alert a farmer when a sheep has stopped grazing for a period of time so he or she could check on the sheep. It’s been quite a few decades since I lived on a sheep farm – so I’m not able to make any sort of informed commentary on that. Even if this was useful, there are some engineering challenges in designing a device that is cheap enough to make this economic.

That said, I would like to congratulate NZM, and neXtgen Agri for undertaking this project. Whether or not it leads to a commercial product, they will have learned a lot about the capabilities of the AI technologies used along with the data requirements and the time and investment needed. And in Ian, New Zealand has another engineer with AI experience which I’m sure will be put to good use.

Ohmio automation: self driving buses

Last month I had lunch with Yaniv Gal, the artificial intelligence manager at Ohmio. Yaniv is an interesting character who grew up in Israel and has focused his career on computer vision and machine learning, both in academia and industry. Although a lot of his experience was in medical imaging, in New Zealand he had been working in the fresh produce industry as research program manager at Compac Sorting Equipment which uses machine vision to automatically sort fruit. At Ohmio he’s built one of the largest AI teams in New Zealand.

Yaniv explained that Ohmio emerged from electronic sign provider HMI technologies. HMI has been around since 2002 and has thousands of signs operating throughout NZ. To me it seemed unusual that an electronic sign company would spawn a self-driving vehicle company. There were a couple of core reasons:

  1. They had some experience using machine vision with traffic: cameras attached to their signs could be used to count traffic in a much more cost effective and reliable way than digging up the road to install induction powered sensors.
  2. They had experience at installing infrastructure alongside roads. This type of infrastructure could be used to aid a self-driving vehicle along a fixed path

This is a crucial differentiator for Ohmio. They are not trying to compete with the myriad of large companies that are trying to develop level 5 autonomous vehicles: those that can drive without human input in all conditions. This is a difficult problem. Sacha Arnold, the director of engineering at Waymo (owned by Alphabet, Google’s parent) recently said they are about 90% of the way there, but they still have 90% to go. Instead Ohmio are going for the more tractable problem of building a vehicle platform that can navigate along a fixed path. They call this level 4+ autonomy. While this doesn’t have the same broad opportunity as level 5, they believe it is something they can build and that there is still a large market opportunity.

Ohmio LIFT

Their first customer is Christchurch airport. This will allow them to prove the concept and refine the technologies. The economics are obvious, with no driver this just ends up cheaper. It’s not just about the money though, Yaniv is confident it will be safer and with electric vehicles, greener. Since our meeting Ohmio have announced a sale of 150 shuttles to  Korean Company Southwest Coast Enterprise City Development Co Ltd.

For fixed path navigation the path can be learnt and if necessary additional infrastructure can be added along the path to aid the vehicle in localising and navigating. Most of this is done on the vehicle using a variety of sensors. To establish exactly where it is odometery, GPSs and LIDAR are combined to get a more accurate location, with more redundancy than possible with a single sensor. Combining data from multiple sensors like this is called sensor fusion. Company R&D coordinator Mahmood Hikmet described this in his recent AI day presentation.

Machine vision is used primarily for vehicle navigation and collision avoidance. Here collision avoidance is detecting whether there is an object in the vehicle’s path, or if an object may come into its path. Ohmio use a variety of machine vision techniques, including deep learning. Yaniv’s experience predates the recent rise in popularity of neural networks. He is aware of the few disadvantages deep learning suffers compared to more “traditional” machine learning techniques and so he doesn’t feel like every machine vision problem needs the hammer that is a deep neural network.

Yaniv confessed that it will be a nervous moment the first time the system is driving passengers with no safety driver. However, he is confident it is going to be safer than a vehicle with a driver. We talked about how both of us would be even more nervous using a self driving, flying taxi that Kitty Hawk is testing in Christchurch, even though it should be safer than a ground based vehicle because there are less objects to crash into.  We compared this to the fear people felt when elevators were first self-operating, without an elevator operator. It seems like a silly fear now. Maybe the next generation will laugh at our anxiety about self-driving vehicles.

After lunch I told my Uber driver about our conversation. He expressed concern about whether the tech should be developed and the loss of jobs that will come with it. This is an understandable concern given his career (although his masters in aeronautical engineering should see him right). There are too many people working on this type of technology now. If the genie is not out of the bottle, he has his head and shoulders out. The economics are too strong and the world should be a better place with this technology. It’s nice to see a NZ company contributing.

Lincoln Agritech: using machine vision for estimating crop yield

I recently had the opportunity to visit Lincoln Agritech where I met with CEO, Peter Barrowclough, Chief Scientist Ian Woodhead and their machine vision team of Jaco Fourie, Chris Bateman and Jeffrey Hsiao. Lincoln Agritech is an independent R&D provider to the private sector and government employing 50 scientists, engineers and software developers. It is 100% owned by Lincoln University, but distinct from their research and commercialisation office.

Lincoln Agritech have taken a different approach to developing AI capability. Rather than hiring deep learning experts, they have invested in upskilling existing staff by allowing Jaco, Chris and Jeffrey to take a Udacity course in machine vision using deep learning. The investment is in the time of their staff. Having three of them take the course together means that they can learn off each other.

The core projects they are working on involve estimating the yield of grape and apple crops based on photos and microwave images. The business proposition for this is to provide information for better planning for the owners of the crops, both in-field and in-market. Operators of these vineyards and orchards can get a pretty good overall crop estimate based on historical performance and weather information. However, they can’t easily get plant by by plant estimates. To do this they need an experienced person to walk the fields and make a judgement call. A machine vision based approach can be more efficient with better accuracy.

The team elected to tackle the problem initially using photos. They had to take the images carefully at a prescribed distance from the crop, using HDR (this is where you combine light, medium and dark images to bring out the detail in the shadowy canopy). Like most machine learning tasks the biggest problem was getting a tagged data set. The tagging involved drawing polygons around the fruit in the images, including fruit partially occluded by leaves. There was a lot of work trying to train people to do this properly. Inevitably at some stage there were guys with PhDs drawing shapes, such is the glamour of data science. This problem is similar to that faced by Orbica who built a model to draw polygons around buildings and rivers from aerial photography.

In this image labels of a fixed size are added to an image to tell the model where the grape bunches are.
This image shows the result of a trained network automatically finding the areas in the image where the grape bunches are.

They used a convolution neural network to tackle this problem. Rather than train a network from scratch and come up with their own architecture, they used the image net winning inception architecture and adapted that. This network was already trained to extract the features from an image that are required to classify 1000 different classes of images. This technique is called transfer learning. This model works well, with 90% accuracy (on their validation data set).

However, part of the challenge here is that the images do not show all of the fruit that is on the plant. The only way to get the “ground truth” is to have someone go under the canopy and count all the fruit by hand. This is where the microwave technology comes into play.

The company is recognised internationally for their microwave technology in other projects. The way it works is a microwave transmitter emits microwaves and then detects the reflections. The microwaves will travel through leaves, but will be reflected by the water content in reasonably mature fruit.

The machine vision team is working to create a model that can use the microwave image and photo to get superior performance. This is a harder problem because this type of sensor fusing is less common than regular image processing.

The team is using the Tensorflow and Keras platforms on high end machines with Nvidia Titan GPUs. There were a few raised eyebrows from the company when the team were asking for what essentially looked like high end gaming machines.

I applaud Lincoln Agritech for investing in their deep learning capability. The experience they will have gained from their first projects will make each subsequent project easier to apply the technology. The fact they have three people working on this, provides redundancy and the ability to learn off each other. This is a model that other New Zealand organisations should consider, particularly if they’ve having problems finding data scientists. Applying the latest AI technologies to agriculture seems like a real opportunity for New Zealand.

AI day 2018: My take

I noticed the AI day videos were released a few days ago and I’d like to share my thoughts on the day. First I”d like to congratulate the organisers Ben Reid and Justin Flitter for putting this event together. Michelle Dickinson did a great job making the day flow as master of ceremonies. This type of event is just what NZ needs to help people understand how different organisations are using AI so they can make more informed decisions on how they could use the ever evolving set of technologies.

AI day 2018 videos
The AI day 2018 videos

I’d characterise the event as having presentations from small and large organisations, a couple of panels, a politician and a good dose of networking. The highlight for me was from the small companies because they were the ones who had taken various AI technologies and applied them in a way to give them an advantage. In my mind these are the stories that are most likely to inspire other NZ companies. This included:

  • R& D coordinator for Ohmio, Mahmood Hikmet describing the self-driving shuttle that they are building and how their AI team is building a sensor fusion model. This combines data from GPS, lidar and odometry sensors to estimate the position of the shuttle, that is then used for navigating.
  • Kurt Janssen, the founder of Orbica described how they’re using machine vision with aerial and drone footage to automate various GIS tasks.
  • Grant Ryan (or Bro as I call him) describing how Cacophony are using machine vision with thermal cameras to automatically identify pests, and how they might then kill them.
  • Sean Lyons had the most entertaining presentation where he described how Netsafe are using bots to waste scammers time in a project they call Rescam. They’re using IBM Watson for sentiment analysis. It’s been hugely successful, wasting over 5 years of scammers time with 1 million emails.
    netsafe bot
  • Mark Sagar and team are doing some of the most interesting AI work globally at Soul Machines. Unfortunately, his presentation had a few technical glitches, but it was nice to see the latest version of BabyX, complete with arms. Mark talked a little bit about how they are using neural networks for perception and control. I’d love to find out more details.
    Babyx

The other small company that presented was Centrality.ai. Founder Aaron McDonald spent most of the presentation explaining blockchain and how it can be used for contracts. I didn’t come away with any understanding that the company is using AI, or with any comprehension of what the company actually does.

The panels had a selection of interesting entrepreneurs and academics. However, I personally find the panel format a little too unstructured to get much useful information from. I may be an outlier here, Justin told me they got very good feedback about the panels from their post conference surveys.

The other highlight of the conference for me was the networking during the breaks. Everyone you spoke to had some involvement in AI: Entrepreneurs, practitioners, academics and investors. This was an added benefit to an already very stimulating day. I wasn’t able to attend the 2nd day of workshops.

To Justin and Ben: Well done! I look forward to attending next year and hearing how a host of other NZ companies are using AI in interesting ways. For those that didn’t make it, check out the videos.

 

Cognitive modelling, self aware robots Tensorflow, & adversarial attacks

 

This week I’ve been learning about cognitive modelling, self aware robots,  adversarial attacks in reinforcement learning and starting to play with Tensorflow.

Cognitive Modelling

The latest MIT AGI video was released a few days ago. In this Nate Derbinsky gives an overview of different types of cognitive architectures, including SPAUN, ACT-R, Sigma and Soar (his baby). This reminds me of old school AI: symbolic processing. My supervisor’s PURR-PUSS would belong in this category. These are a lot less sexy than deep learning, but in many way’s they are complementary with applications in robotics, game playing & natural language processing.

 

TWiML podcasts

SUM cognitive architectureThis week I listened to an interesting podcast with Raja Chatila on robot perception and discovery. In this Raja talked about the necessity of robot self awareness for true intelligence and the ethics of intelligent autonomous systems. It’s interesting to see that the sort of architectures used for exploring artificial consciousness in robotics have a lot of overlap with the cognitive models described by Nate Derbinsky in the MIT AGI series.

I also had the chance to listen to Google Brainers Ian Goodfellow & Sandy Huang discussing adversarial attacks used against reinforcement learning  Adversarial attacks highlight some of the weakness of deep learning. When used for image classification the image is just a series of numbers that has a set of mathematical operations performed on it to produce a classification. By subtly changing some of the numbers you can fool the classifier, even though to a human the image looks exactly the same. The example of a Panda below was taken from a 2015 Google paper.

panda

In the podcast Ian and Sandy discuss how this can be used against a reinforcement learning agent that has been trained to play computer games. Even changing one pixel can significantly degrade the performance.

Tensorflow

I’m up to the part in my CS231n course where you start to train CNNs using Tensorflow or Pytorch. Despite reading a compelling argument for using Pytorch over Tensorflow on Quora, the people I’ve spoken to locally are using Tensorflow – so I’m going to go with that. I found this introduction useful.

I managed to get the software installed and run Hello World. Apparently there is more you can do with it…

Tensorflow hello world

 

Orbica: Using machine vision in GIS

Last week I had the opportunity to sit down with Orbica CEO’s Kurt Janssen and data scientist  Sagar Soni.

Kurt has worked in the Geographic Information Systems (GIS) industry for more than 14-years. Last year he started his own company, Orbica, which does GIS consulting for organisations in the public and private sector. Orbica invests some of its consulting revenue into developing its own product. A major – and rewarding – investment has been hiring data scientist Sagar.

Sagar was taught machine learning during his master’s degree and had the opportunity to put it into practice developing an earth rock image classification system at Dharmsinh Desai University and using deep learning algorithms like Recurrent Neural Networks to solve medical entity detection problems at US health care solutions provider ezDI. Last year he immigrated to NZ and had just the skills and experience Orbica was looking for.

Orbica’s first product automatically identifyies buildings and waterways from aerial photos. This manually intensive job is traditionally done by geographers and cartographers who draw polygons on maps identifying these features using digitising techniques. The first product identifies buildings in urban areas. The 15 million-pixel (4800×3200 ) photos have each pixel covering a 7.5×7.5cm square . Sagar has built a convolution neural network that takes these photos and outputs the vectors representing the polygons where it believes the buildings are.

They have a good amount of training, test and validation data from Land Information New Zealand that consists of the images and polygons that have been hand drawn. Because of the size of the image, Sagar has tiled them into 512×512 images . He built the model over a couple of months with a little trial and error testing the various hyper parameters. The existing model has nine layers, with the standard 3×3 convolutions. He’s currently getting 90 per cent accuracy on the validation set.

Building outlines

RiverDetection_AIThe water classification is very similar, working with 96 million pixel(12000×8000)  images, but with smaller resolution 30x30cm  pixels. The output is the set of polygons representing the water in the aerial images, but the model also classifies the type of water body, e.g. a lake, lagoon, river, canal, etc.

The commercial benefits of these models are self-evident: Orbica can significantly improve the efficiency of producing this data, whether it does this for a client, or it is sold as a service to city and regional councils. These are done regularly – to identify buildings that have been added or removed, or to track how waterways have changed.

WaterBodiesClassification'

Another opportunity has come from the Beyond Conventions pitch competition in Essen, Germany, where Orbica won the Thyssenkrupp Drone Analytics Challenge and the People’s Choice Award. Orbica’s pitch was to use machine vision to analyse drone footage of construction sites to automatically generate a progress update on the construction project. This is a more complex problem given its 3-dimensional nature. Thyssenkrupp has now resourced Orbica to put together a proof of concept, which Sagar is busy working on. Should this go well, Orbica will probably hire at least one other data scientist. DroneImage_Output

Because the technology is developing quickly, Sagar keeps up to date with the latest developments in deep learning through Coursera and Udacity courses. He’s a fan of anything Andrew Ng produces.

To me, Orbica’s use of machine vision technology is an excellent case study for how New Zealand companies can use the latest advances in artificial intelligence. They have a deep knowledge in their own vertical; in this case GIS. They develop an awareness of what AI technologies are capable of in general and have a vision for how those technologies could be used in their own industry.  Finally, they make an investment to develop that vision. In Orbica’s case, the investment was reasonably modest: hiring Sagar. A recurring theme I’m seeing here is hiring skilled immigrants. New Zealand’s image as a desirable place to live – coupled with interesting work – will hopefully make this a win-win for all involved.

For those that would like to hear more. Kurt is speaking at AI Day in Auckland next week.

 

 

 

 

Imagr: NZ’s version of Amazon Go

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

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.