The impact and opportunity of AI in NZ

I’ve just read the AI forum report analysing the impact and opportunity of artificial intelligence within New Zealand. This was released last week. At 108 pages it’s a substantial read. You can see the full report here.

AI forum report

The timing of this report is very good. There is a lot of news about AI and a growing awareness of it. But at the same time, I believe there is a lack of understanding of what AI is capable of and how organisations can take advantage in the recent advances.

I think the first level of misunderstanding is that people over estimate what the technology can do. This is driven by science fiction, a misinformed media and fuelled by marketers who want their company and products to be seen to be using AI. AI is nowhere near human level intelligence and doesn’t understand concepts like a human (see my post on the limits of deep learning). That may change, but major breakthroughs are needed and it’s not clear when or if those will occur (see predictions from AI pioneer Rodney Brooks for more on this).

Although AI does not have human level intelligence, there are a host of applications for the technology. I think the second level of misunderstanding is around how difficult and expensive it is to take advantage of this AI technology. The assumption is that it’s expensive and you need a team of “rocket scientists”. From what I’ve seen studying deep learning and talking to NZ companies that are using AI, the technology is very accessible and the investment required is relatively small.

The report is level-headed: it’s not predicting massive job losses. I’m not going to comment further on the predictions on the economic impact. They’ll be wrong – because, to quote Niels Bohr – predicting is very difficult, especially about the future.

In my opinion the report did not place enough emphasis on the importance of deep learning. The recent rise of this technology has driven the resurgence of AI in recent years. Their history of AI missed the single most important event which was the AlexNet neural network winning the ImageNet competition. This bought deep learning to the attention of the worlds AI researchers and triggered a tsunami of research and development. I would go so far as to suggest that the majority of the focus on AI should be on deep learning.

image_classification_006

The key recommendation of the report is that NZ needs to adopt an AI strategy.  I agree. Of the 6 themes they suggested I think they key ones are:

  1. Increasing the understanding of AI capability. This should involve educating the decision makers at the board and executive level about the opportunities to leverage AI technology and the investment required. The outcome of this should be more organisations deciding to invest in AI.
  2. Growing the capability. NZ needs more AI practitioners. While we can attract immigrants with these skills, we also need to educate more people. I was encouraged to see the report advocating the use of online learning. I agree that NZQA should find a way to recognise these courses but think we should go further. Organisations should be incentivised to train existing staff using these courses (particularly if they have a project identified) and young people should be subsidised to study AI either online or undergrad/postgrad at universities.

I am less worried about the risks. I think it would be good to have AI that was biased, opaque, unethical and breaking copyright law. At least then we would be using the technology and we could address those concerns as they came up. I am also not worried about the existential threat of AI. First, I think human level intelligence may be a long time away. Second, I’m somewhat fatalistic – I can’t see how you could stop those breakthroughs from happening. We need to make sure that humans come along for the ride.

From my perspective the authors have done a very good job with this report. I encourage you to take the time to read it.  I encourage the government to adopt its recommendations.

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

 

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.