HCC 2016: Human Centred Computing Summer School

September 11, 2016 - Geekiness / research / vision

Last week I was invited to present at the first Human Centred Cognition summer school, near Bremen in Germany. Summer schools are a key part of the postgraduate training experience, and involve gathering together experts to deliver graduate level training (lectures, tutorials and workshops) on a particular theme. I’ve been to summer schools before as a participant, but never as faculty. We’re at a crossroads in AI at the moment: there’s been a conflict between “good old fashioned AI” (based upon logic and the symbol manipulation paradigm) and non-symbolic or sub-symbolic AI (neural networks, probabilistic models, emergent systems) for as long as I have known, but right now with deep machine learning in the ascendant it’s come to a head again. The summer school provided us participants with a great opportunity to think about this and to talk about it with real experts.

Typically, a summer school is held somewhere out of the way, to encourage participants to talk to each other and to network. This was no different, so we gathered in a small hotel in a small village about 20 miles south of Bremen. The theme of this one was human centred computing – cognitive computing, AI, vision, and machine learning. The students were properly international (the furthest travelled students were from Australia, followed by Brazil) and were mostly following PhD or Masters programs; more than half were computing students of one kind or another, but a few psychologists, design students and architects came along too.

The view from my room

The bit I did was a workshop on OpenCV, and I decided to make it a hands-on workshop where students would get to code their own simple vision system. With the benefit of hindsight this was a bit ambitious, particularly as a BYOD (Bring Your Own Device) workshop. OpenCV is available for all main platforms, but it’s a pain to install, particularly on macs. I spent a lot of time on the content (you can find that here: http://github.com/handee/opencv-gettingstarted) and not so much time on thinking about infrastructure or thinking about installation. I think about half of the group got to the end of the tutorial, and another 5 or 10 managed to get someway along, but we lost a few to installation problems, and I suspect these were some of the less technical students. I used jupyter notebooks to create the course, which allow you to intersperse code with text about the code, and I think this may have created an extra layer of difficulty in installation, rather than a simplification of practicalities. If I were to do it again I’d either try and have a virtual machine that students could use or I’d run it in a room where I knew the software. I certainly won’t be running it again in a situation where we’re reliant on hotel wifi…

The class trying to download and install OpenCV

The school timetable involved a set of long talks – all an hour or more – mixed between keynote and lecture. The speakers were all experts in their field, and taken together the talks truly provided a masterclass in artificial intelligence, machine learning, the philosophical underpinnings of logic and robotics, intelligent robotics, vision, perception and attention. I really enjoyed sitting in the talks – some of the profs just came for their own sessions, but I chose to attend pretty much the whole school (I skipped the session immediately before mine, for a read through of my notes, and I missed a tutorial as I’d been sat down for too long and needed a little exercise).

It’s hard to pick a highlight as there were so many good talks. Daniel Levin from Vanderbilt (in Tennessee) gave an overview of research in attention and change blindness, showing how psychologists are homing in on the nature of selective attention and attentional blindness. I’ve shown Levin’s videos in my lectures before so it was a real treat to get to see him in person and have a chat. Stella Yu from Berkeley gave the mainstream computer vision perspective, from spectral clustering to deep machine learning. Ulrich Furbach from Koblenz presented a more traditional logical approach to AI, touching on computability and key topics like commonsense reasoning, and how to automate background knowledge.

One of my favourite presenters was David Vernon from Skövde. He provided a philosophical overview of cognitive systems and cognitive architectures: if we’re going to build AI we have a whole bunch of bits which have to fit together; we can either take our inspiration from human intelligence or we can make it up completely, but either way we need to think at the systems level. His talk gave us a clear overview of the space of architectures, and how they relate to each other. He was also very funny, and not afraid to make bold statements about where we are with respect to solving the problem of AI: “We’re not at relativistic physics. We’re not even Newtonian. We’re in the dark ages here“.

Prof Vernon giving us an overview of the conceptual space of cognitive architectures

When he stood up I thought to myself “he looks familiar” and it turns out I actually bought a copy of his book a couple of weeks ago. Guess I’d better read it now.

Kristian Kersting of TU Dortumund, and Michael Beetz of Bremen both presented work that bridges the gap between symbolic and subsymbolic reasoning; Kristian talked about logics and learning, through Markov Logic Networks and the like. Michael described a project in which they’d worked on getting a robot to understand recipes from wikihow, which involved learning concepts like “next to” and “behind“. Both these talks gave us examples of real-world AI that can solve the kinds of problems that traditional AI has had difficulty with; systems that bridge the gap between the symbolic and the subsymbolic. I particularly liked Prof. Beetz’s idea of bootstrapping robot learning using VR: by coding up a model of the workspace that the robot is going to be in, it’s possible to get people to act out the robot’s motions in a virtual world enabling the robot to learn from examples without real-world training.

Prof Kersting reminding us how we solve problems in AI

Each night the students gave a poster display showing their own work, apart from the one night we went into the big city for a conference dinner. Unfortunately the VR demo had real issues with the hotel wifi.

The venue for our conference dinner, a converted windmill in the centre of old Bremen

In all a very good week, which has really got me thinking. Big thanks to Mehul Bhatt of Bremen for inviting me out there. I’d certainly like to contribute to more events like this, if it’s possible; it was a real luxury to spend a full week with such bright students and such inspiring faculty. I alternated between going “Wow, this is so interesting, Isn’t it great that we’re making so much progress!” and “Oh no there is so much left to do!“.

Hotel Bootshaus from the river

› tags: bremen / research / summer school / vision /

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