I’ve just uploaded a new Mandelbrot.py demo for pyCUDA, it adds a new calculation routine that straddles the numpy (C based math) and the pure-CUDA implementations. In total there are 4 variants to choose from. The speed differences are huge!

Update – this Reddit thread has more details including real-world timings for two client problems (showing 10-3,677* speed-ups over a C task).

This post builds upon my earlier pyCUDA on Windows and Mac for super-fast Python math using CUDA.

You’ll need CUDA 3.1 and pyCUDA installed with a compatible NVIDIA graphics card. This version of the Mandelbrot code forces single precision math – this means it’ll work on all CUDA cards (even the older ones – full list). It runs on my MacBook (Leopard) and Windows, the Windows machines use a 9800 GT and GTX 480. Here’s what it generates:

The big-beast graphics card for my physics client is a GTX 480 – this is NVIDIA’s top of the line consumer card (costing £420GBP in the UK a few weeks back). It is huge – it covers two slots, uses one PCIe 2.0×16 slot and has a requirement for 300-400W of power (I’m using a 750W PSU to be safe on a Gigabyte GA H55M S2H motherboard):

The mandelbrot.py demo has four options (e.g. ‘python mandelbrot.py gpu’):

• ‘gpu’ is a pure CUDA solution on the GPU
• ‘gpuarray’ uses a numpy-like CUDA wrapper in Python on the GPU
• ‘numpy’ is a pure Numpy (C-based) solution on the CPU
• ‘python’ is a pure Python solution on the CPU with numpy arrays

The default problem is a 1000*1000 Mandelbrot plot with 1000 max iterations. I’m running this on a 2.9GHz dual core Windows XP SP3 with Python 2.6 (only 1 thread is used for all CPU tests). The timings:

• ‘gpu’ – 0.07 seconds
• ‘gpuarray’ – 3.45 seconds – 49* slower than GPU version
• ‘numpy’ – 43.4 seconds – 620* slower than GPU version
• ‘python’ – 1605.6 seconds – 22,937* slower than GPU version
• ‘python’ with psyco.full() – 1428.3 seconds – 20,404* slower than GPU version

By default mandelbrot.py forces single precision for all the math. Interestingly on my box if I let numpy default to numpy.complex128 (two double precision floating point numbers rather than numpy.complex64 with two single precision floats) then the Python result is faster:

• ‘numpy’ – 34.0 seconds (double precision)
• ‘python’ – 627 seconds (double precision) – 2.5* faster than the single precision version

The ’22,937*’ figure is a little unfair in light of the 627 second result (which is 8,957* slower) but I wanted to use only single precision math for consistency and compatibility across all CUDA cards (the older cards can only do single precision math).

On my older dual core 2.66GHz machine with a 9800 GT I get:

• ‘gpu’ – 1.5 seconds
• ‘gpuarray’ – 7.1 seconds – 4.7* slower than GPU version
• ‘numpy’ – 51 seconds – 34* slower than GPU version
• ‘python’ – 1994.3 seconds – 1,329* slower than GPU version

If we compare the 0.07 seconds for the GTX 480 against the 1.5 seconds for the 9800 GT (albeit on different machines but the runtime is just measuring the GPU work) then the GTX 480 is 21* faster than the 9800 GT. That’s not a bad speed-up for a couple of years difference in architectures.

If you take a look at the source code you’ll see that the ‘gpu’ option uses a lump of C-like CUDA code, behind the scenes all pyCUDA code is converted into this C-like code and then down to PTX via their compiler. This is the way to go if you understand the memory model and you want to write very fast code.

The gpuarray option uses a numpy-like interface to pyCUDA which, behind the scenes, is converted into CUDA code. Because it is compiled from Python code the resulting CUDA code isn’t as efficient – the compiler can’t make the same assumptions about memory usage as I can make when hand-crafting CUDA code (at least – that’s my best understanding at present!).

The numpy version uses C-based math running on the CPU – generally it is regarded as being ‘pretty darned fast’. The python version uses numpy arrays with straight Python arithmetic, this makes it awfully slow. Psyco 2.0.0 makes it a bit faster.

Feedback and extensions are welcomed via the wiki!

If you want to get started then make sure you have a compatible CUDA card, get pyCUDA (installation instructions), compile pyCUDA (takes 30 minutes from scratch if you’re on a well-known system), try the examples and run mandelbrot.py. The mailing list is helpful.

It’d be nice to see some comparisons with PyPy, ShedSkin and other Python implementations. You’ll find links in my older ShedSkin post. It’ll also be interesting to tie this in to some of the A.I. projects in the A.I. Cookbook, I’ll have to ponder some of the problems that might be tackled.

Books:

The following two books will be useful if you’re new to CUDA. The first is very friendly, I’m still finding it very useful.

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Ian applies Artificial Intelligence for companies (Mor Consulting), programs Python, produces professional screencasts (ProCasts), writes The Screencasting Handbook and is also a sea-side dweller and consumer of fine coffees.

A couple of weeks back I presented an Artificial Intelligence evening at FlashBrighton with John Montgomery and Emily Toop. The night covered optical character recognition, face detection, robots and some futurology. A video link should follow.

Optical Character Recognition to Read Plaques

Recently I’ve been playing with OCR to read photos with text, a particular example I care about is extracting the text from English Heritage Plaques for the OpenPlaques project:

I gave an overview of the tesseract open source OCR tool (originally created by HP). Some of the notes I explained came from this tesseract OSCON paper. Some notes:

• tesseract ranked highly in international competitions for scanned-image text extraction
• it works better if you remove non-text regions (e.g. you isolate just the blue plaque in the above image) and threshold the image to a grey scale
• it runs very quickly – it’ll extract text in a fraction of a second so it will run on a mobile phone (iPhone ports exist)

To get people thinking about the task from the computer’s point of view I had everyone read out the text from this blurry photo. Treating the image as a computer would see it shows that you need several passes to learn which country is involved and to guess at some of the terms:

You can guess that the domain is music/theatre (which helps you to specialise the dictionary you’re using), based in the US (so you know that 1.25 is $1.25USD) and even though the time is hard to read it is bound to be 7.30PM (rather than 7.32 or 7.37) because events normally start on the hour or half hour. General knowledge about the domain greatly increases the chance that OCR can extract the correct text.

I talked about the forthcoming competition to write a Plaque-transcriber system, that project is close to starting and you can see demo Python source code in the AI Cookbook.

Optical Character Recognition Web Service and Translator iPhone Demo

To help make OCR a bit easier to use I’ve setup a simple website: http://ocr.aicookbook.com/. You call a URL with an image that’s on the web (I use flickr for my examples) and it returns a JSON string with the translated text. The website is a few lines of Python code created using the fabulous bottle.py.

The JSON also contains a French translation and mp3 links for text to speech, this shows how easy it is to make a visual-assist device for the hard of sight.

Emily built an iPhone demo based on this web service – you can a photograph of some text, it uploads the text to flickr, retrieves the JSON and then plays the mp3s and shows you the translated text.

OCR on videos

The final OCR demo shows a proof of concept that extracts keywords from ShowMeDo‘s screencast videos. The screencasts show programming in action – it is easy to extract frames, perform OCR and build up strong lists of keywords. These keywords can then be added back to the ShowMeDo video page to give Google more indexable content.

There’s a write-up of the early system here.

OCR futurology

Text is all around us and mobile phones are everywhere. It strikes me that sooner or later we’ll be pointing our mobile phone at a poster like this and we’ll get extra information in return:

From the photo we can extract names of places, we also know the phone’s location so a WikiPedia geo-lookup will return relevant pages. Probably we can also extract dates and costs from posters and these can go into our calendar. I used tesseract on this image and extracted enough information to link to several WikiPedia pages with history and a map.

Face Detection for Privacy Invasion

John and I built a system for correlating gowalla check-ins with faces seen in images from the SkiffCam – the webcam that’s hosted in the Skiff co-working space. The goal was to show that we lose quite a lot of privacy without realising it – the SkiffCam has 29,000 images (1Gb of data) dating back over several years.

Using openCV’s face detection system I extracted thousands of faces. John retrieved all the gowalla check-ins based at the Skiff and built a web service that lets us correlate the faces with check-ins. We showed faces for many well-known Brightoners including Seb, Niqui, Paulo, Jon & Anna and Nat.

Given a persons face we could then train a face recogniser to see other occurrences of that person at the Skiff even if they’re not checking in with gowalla. We can also mine their twitter accounts for other identifying data like blogs and build a profile of where they go, who they know and what they talk about. This feels pretty invasive – all with open source tools and public data.

Emotion detection

Building on the face detector I next demonstrated the FaceL face labeling project from Colorado State Uni, built on pyVision. The tool works out of the box on a Mac – it can learn several faces or poses during a live demo. Most face recognisers only label the name of the person – the difference with FaceL is that it can recognise basic emotional states such as ‘happy’, ‘neutral’ and ‘sad’. This makes it really easy to work towards an emotion-detecting user interface.

During my demo I showed FaceL correctly recognising ‘happy’ and ‘sad’ on my face, then ‘left’ and ‘right’ head poses’, then ‘up’ and ‘down’ poses. I suspect with the up/down poses that it is really easy to build a nod-detecting interface!

Headroid2 – a Face Tracking Robot

Finally I demo’d Headroid2 – my face tracking robot (using the same openCV module as above) that uses an Arduino, a servo board, pySerial and a few lines of code to give the robot the ability to track faces, smile and frown:

Here’s a video of the earlier version (without the smiling face feedback):

For full details including build instructions see building a face tracking robot.

EuroPython

I’ll bring Headroid3 (this adds face-seeking behaviour) to EuroPython in a few weeks, hopefully I can find a few other A.I. folk and we can run some demos.

Reading material:

If you’re curious about A.I. then the following books will interest you:

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Ian applies Artificial Intelligence for companies (Mor Consulting), programs Python, produces professional screencasts (ProCasts), writes The Screencasting Handbook and is also a sea-side dweller and consumer of fine coffees.

Here’s a random moment – on Blackman Street just down from Brighton Station is this abandoned petrol pump. I’m curious to know what kind of business it supported – anyone know?

This is the cheapside area of Brighton (meaning ‘market area‘ in olde English) known now as the New England Quarter – a few streets from the new sustainable housing developments, green corridor and New England House.

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Ian applies Artificial Intelligence for companies (Mor Consulting), programs Python, produces professional screencasts (ProCasts), writes The Screencasting Handbook and is also a sea-side dweller and consumer of fine coffees.

Last week I played with the Optical Character Recognition system tesseract applied to video data. The goal – extract keywords from the video frames so Google has useful text to index.

I chose to work with ShowMeDo‘s screencasts as many show programming in action – there’s great keyword information in these videos that can be exposed for Google to crawl. This builds on my recent OCR for plaques project.

I’ll blog in the future about the full system, this is a quick how-to if you want to try the system yourself.

First – get a video. I downloaded video 10370000.flv from Introducing numpy arrays (part 1 of 11).

Next – extract a frame. Using ffmpeg I extracted a frame at 240 seconds as a JPG:

ffmpeg -i 10370000.flv -y -f image2 -ss 240 -sameq -t 0.001  10370000_240.jpg

Tesseract needs TIF input files (not JPGs) so I used GIMP to convert to TIF.

Finally I applied tesseract to extract text:

tesseract 10370000_30.tif 10370000_30 -l eng

This yields:

than rstupr .
See Also
linspate : Evenly spaced numbers with  careful handling of endpoints.
grid: Arrays of evenly spared numbers  in Nrdxmensmns
grid: Grid—shaped arrays of evenly spaced numbers in  Nwiunensxnns
Examples
>>> np.arange(3)
¤rr¤y([¤. 1.  2])
>>> np4arange(3.B)
array([ B., 1., 2.])
>>>  np.arange(3,7)
array([3, A, S, 6])
>>> np.arange(3,7,?)
·=rr··¤y<[3.  5])
III
Ill

Obviously there’s some garbage in the above but there are also a lot of useful keywords!

To clean up the extraction I’ll be experimenting with:

• Using the original AVI video rather than the FLV (which contains compression artefacts which reduce the visual quality), the FLV is also watermarked with ShowMeDo’s logo which hurts some images
• Cleaning the image – perhaps applying some thresholding or highlighting to make the text stand out, possibly the green text is causing a problem in this image
• Training tesseract to read the terminal fonts commonly found in ShowMeDo videos

I tried four images for this test, in all cases useful text was extracted. I suspect that by rejecting short words (less than four characters) and using words that appear at least twice in the video then I’ll have a clean set of useful keywords.

Update – the blog for the A.I. Cookbook is now active, more A.I. and robot updates will occur there.

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Ian applies Artificial Intelligence for companies (Mor Consulting), programs Python, produces professional screencasts (ProCasts), writes The Screencasting Handbook and is also a sea-side dweller and consumer of fine coffees.

Last night we ran our 22nd £5 App event, videos for each speaker are listed below. The lovely Thayer Prime of Data.Gov.Uk provided us with 3 copies of Programming the Semantic Web to give away, this was particularly well timed given the semantic nature of our two main talks. Thanks Thayer!

WildLife Near You by Simon Willison and Natalie Downe (wildlifenearyou.com)

£5 App #22 WildLifeNearYou by Simon Willison and Natalie Downe from IanProCastsCoUk on Vimeo.

OpenPlaques by Simon Harriyott and Jez Nicholson (openplaques.org)

£5 App #22 OpenPlaques.org from IanProCastsCoUk on Vimeo.

BotBuilder’s Robots by Steve Carpenter (botbuilder.co.uk)

£5 App #22 Robots from BotBuilder (Steve Carpenter) from Ian Ozsvald on Vimeo.

Google Go by Jamie Campbell (golang.org)

£5 App #22 Google’s Go by Jamie from Ian Ozsvald on Vimeo.

SureScore Golf Pro by Chris Holden (scoresure.co.uk)

£5 App #22 Chris on ScoreSure Golf Pro from Ian Ozsvald on Vimeo.

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Ian applies Artificial Intelligence for companies (Mor Consulting), programs Python, produces professional screencasts (ProCasts), writes The Screencasting Handbook and is also a sea-side dweller and consumer of fine coffees.

On Tuesday 30th March we’ll have our 22nd £5 App night running from 8pm at The Skiff. We’ll have:

• Wildlife Near You by Simon Willison and Natalie Downe
• Open Plaques by Jez Nicholson and Simon Harriyott
• BotBuilder robots by Steve Carpenter
• ScoreSure Golf Pro by Chris (video) as a Show n’Tell
• Jamie Campbell on Google’s Go

I was hoping to do a short demo of speech and facial recognition technologies I’m working with but due to feeling a bit under the weather I might leave that until a future £5 App.

As usual please sign-up on Upcoming so we know how much beer to buy and cake to bake.

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Ian applies Artificial Intelligence for companies (Mor Consulting), programs Python, produces professional screencasts (ProCasts), writes The Screencasting Handbook and is also a sea-side dweller and consumer of fine coffees.

I’ve just launched my second Twitter bot – @ConceptNetDaily takes a random concept from the A.I. site ConceptNet and posts it to Twitter with a link back to the site. A tweet looks like:

“When humans own horses, humans groom and ride horses.” http://tinyurl.com/ydvf7vg

The TinyURL expands out to an address like: http://openmind.media.mit.edu/en/assertion/143313/

The aim of the site is to build a large repository of common-sense knowledge, exactly the kind of knowledge that humans take for granted and never write down as statements for a computer to understand.  Currently it tracks over 1,026,553 statements.

Using the link you can vote on the concept.  Vote up if the concept is solid (i.e. something a human would say is ‘right’) or down if it is wrong, silly or erroneous.  The site supports OpenID which makes starting a touch easier.

My goal with this bot is to remind people every day to vote on the concepts and to add new knowledge.  If a concept has many votes then we can have faith that it is ‘common-sense knowledge’.  If a concept is voted down enough then we can have faith that it is ‘unhelpful or wrong’.

You’ll find a searchable list of Concepts and some random examples on the English homepage.  For good examples see all the information that ConceptNet knows about humans, chess and girls.

Details:

I’ve written the bot in Python using PyYAML, Python-tinyurl and Python-twitter.  It runs every day via a cron job.  It works by guessing a random id for a raw_assertion and checking to see if a concept lives at the URL.  See this XML example for id 143313, I extract the .yaml version via PyYAML but the .xml version renders nicely in your browser if you want a peek.

ConceptNet’s web API is well documented.  ConceptNet itself is written in Python using Django but I’m not using the downloaded version here, just the web API.

My first Twitter bot – @BrightonJobDoom:

Just in case you live here in Brighton you might want to track @BrightonJobDoom to see how healthy (or…not) the job market is in the UK during this rather wobbly recession   I wrote this bot for our £5 App’s 5k coding competition.

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Ian applies Artificial Intelligence for companies (Mor Consulting), programs Python, produces professional screencasts (ProCasts), writes The Screencasting Handbook and is also a sea-side dweller and consumer of fine coffees.

A month back I tried to build an Ubuntu-based Eucalyptus cloud/cluster environment for a client for a parallel processing research project.  The project was thwarted by an overly aggressive corporate firewall and my lack of understanding of low-level network config-fu.

I’ve revisited the project using the same machines but with an external public internet connection (no firewall – yay!).

Grub2

On the node machine I still needed to dual-boot to Windows.  Unfortunately whilst reboots to Linux are fine, if Windows is booted it ‘does something’ to the MBR and the machine is unbootable.  I delved into the boot-loader and had to learn some Grub2-fu.

Grub2 was introduced in Ubuntu 9.10, it replaces Grub which in turn replaced boot managers like lilo.  The wiki page is pretty good for recovering a boot-loader using an Ubuntu LiveCD but it didn’t work quite to plan.

The step for ‘sudo chroot /mnt’ fails as bash or sh can’t be run from within /mnt (which at this point is looking at the originally installed hd).  There is something odd going on with the LiveCD, much googling didn’t seem to reveal the answer.

To run grub-install on the hd, rather than via the CD (because chroot fails) I used ‘sudo grub-install –root-directory=/mnt /dev/sda’, it reports that ‘(hd0) /dev/sda’ is installed.

Sidenote – on later attempts somehow a reference to (fd0) got involved and this broke the boot process.  I edited /mnt/boot/grub/device.map to remove the fd0 reference, leaving the hd0 reference.  I ran grub-install again and all was fine.  Now the machine can boot again.

Mounting a USB memory stick

Whilst a 8Gb memory stick was recognised, it didn’t get mounted.  I had to edit /etc/fstab and add:

/dev/sdf1 /mnt/stick auto umask=0,user,iocharset=iso8859-1,sync,codepage=850,noauto,exec,users 0 0

After this I used ‘sudo mkdir /mnt/stick’, ‘sudo mount /dev/sdf1′ and it mounted just fine.

Installing Eucalyptus

The install process this time around was much the same as before, except this time without the firewall it all ‘just worked’.  Seeing the fnords part 1 took me through the basic install.

I got the feeling from later steps that the cloud controller needs a static IP so I switched the cluster controller from DHCP to a static IP and rebooted.

The discover nodes process (‘sudo euca_conf –no-rsync –discover-nodes’) for euca_conf also required that I’d setup ssh keys on the Node, step 6 in the NodeInstall doc has the instruction.  Typo note – if you spell ‘eucalyptus’ wrong you’ll go round in circles trying to figure out why the password won’t work!

Sometimes I couldn’t get ‘euca-describe-availability-zones verbose’ to work, it’d just report ‘No route to host’.  It seems that a reboot of the CC and Node are required, plus a minute or so of patience after boot for Apache to sort itself out, before this problems just goes away.

Using the Ubuntu Store

Having installed the CC and registered a Node, next I ran the web interface via ‘https://10.0.0.4:8443′.  Note ‘https’.  If you visit the website too soon after a reboot (i.e. <1 minute) then the webapp won’t respond or maybe it won’t recognise the admin user.  Having logged in, the first login forces a password change.

Next check the ‘Configuration’ tab and verify the IP addresses.  For reasons beyond my understanding our switch rebooted during my first attempt to setup the cluster and it switched from the ’192.168.x.x’ address range to ’10.x.x.x’ – this royally barfed my configuration.  I chose to re-install the CC from scratch (I was plagued by ‘no route to host’ problems no matter how much tweaking I tried).

Next visit the ‘Store’ tab and download an image, I’m using ‘Ubuntu 9.10 Karmic Koala (i386)’.  Today this works – I’ve spent 2.5 days building and re-building the cluster to get it to this point.  Often the Store would download an image and then report ‘no route to host’.  This process is pretty darned frustrating and seems to lack useful error messages.

But ultimately – no cigar

Rather frustratingly I can’t get my Node to run an image.  I can see that the Node exists though ‘euca-describe-availability-zones verbose’ shows that a Node exists but doesn’t list its IP address which is odd, the online docs say it should be shown.

If I run an image then it enters the ‘pending’ state and then the ‘terminating’ state.  Digging around in Google shows that other people currently have the same problem, it might be related to the lack of Hypervisor instructions on my Node machine (though they’re not supposed to be required…).  Possibly also the current build in unstable, there’s a lot of bug-fixing going on.

Debug notes

Eucalyptus has a trouble-shooting guide, this blog series is very useful.

Conclusion

Eucalyptus should give you an EC2-like cloud that runs on your own machines, using an EC2-compatible API so you could move to the cloud when you want to scale up or are less concerned about the privacy of your data.  Currently I can’t get it to work but others do have it working – it seems to depend upon your hardware.  It also lacks clear error messages so debugging is hard – I resorted to clean installs on three occasions.

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Ian applies Artificial Intelligence for companies (Mor Consulting), programs Python, produces professional screencasts (ProCasts), writes The Screencasting Handbook and is also a sea-side dweller and consumer of fine coffees.

I’m using WingIDE on my MacBook and I couldn’t get copy/paste to work between WingIDE (running in X11) and native apps.  This meant copying URLs and code snippets was impossible…hugely frustrating!

There is a simple fix, as outlined here just run the Property List Editor, open the specifed .plist, tick the 5 checkboxes, save, restart all of X11 and then the clipboard is shared between X11 and native apps.  Phew.

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Ian applies Artificial Intelligence for companies (Mor Consulting), programs Python, produces professional screencasts (ProCasts), writes The Screencasting Handbook and is also a sea-side dweller and consumer of fine coffees.

On Wednesday night we ran our music-themed £5 App Xmas Special (fivepoundapp.com).

It was fab!  John and I had a fab time organising things and watching the night run so down-to-earthly – it seems that many others did too.  I particularly like:

“I bloomin’ love £5 app! The event that’s happy to be itself, and is more rewarding for all as a result. Here with @ribot & @lastminute teams” – ribotminimus

“Home from #fivepoundapp, letting the awesomeness sink in.” – j4mie

Get to the end of the very last video and you’ll hear a special £5 App rendition of Jingle Bells.

Note: I want photos!  Email me links to flickr’d images please.  I also want your blog write-ups, mail them to me or comment down below.

Particular thanks for our sponsors Alan Newman (Sensible Development) and Paul Silver (PaulSilver.co.uk) along with John (psychicorigami) and my ProCasts for putting up cash to fund a few hours of free beer.  Also super-huge thanks to the Ribots for supplying piles of mince pies (yummy!) and John for baking a batch of crunchy cookies.

The event was organised through Philip and Declan of PlayGroup, they use Hector’s House for arts and science gigs (thanks BuildBrighton for the connection!).  Cheers chaps, it was exactly the space we needed!

“Seb’s Slightly Failed Music Career”

Seb spoke on the highs and lows of forming a band, showed previously-unseen footage and generally gave the lowdown on how it all works. Rick-rolling was included.  Seb has his own write-up.

Sadly Seb’s hard-drive died after the talk taking all his transcoded footage but on the flip-side Seb inspired Simon to share footage from his old cover band.

Here’s the 60 minute video of Seb’s talk:

£5 App #20 “Seb’s Slightly Failed Music Career” for the 2009 Xmas Special from IanProCastsCoUk on Vimeo.

We were absolutely honoured that Seb and Jenny unveiled their new Xmas song tonight, see it here and share it around:

“Toby Cole – Zero to Theremin in 20 days” (with demo)

Toby Cole shows the ThereThing constructed through BuildBrighton and unveiled at a live gig the previous month.

Paul Silver took a video of the ThereThing in action:

Sadly the ThereThing is slightly out of shot during the video of the talk but you can hear Toby and see the screen just fine (and the ThereThing link shows it in detail).

£5 App #20 “Toby Cole – Zero to Theremin in 20 days” for the 2009 Xmas Special from Ian Ozsvald on Vimeo.

“Jim – Mrmr/LiveAPI guitar-mounted iPhone ableton live interface”

Jim Purbrick showed Mrmr, the LiveAPI guitar mounted iPhone Ableton live interface.  Jim’s also the head of Second Life (UK) and is known for building robots.

£5 App #20 “Jim Purbrick – Mrmr/LiveAPI guitar mounted ableton live interface” for the 2009 Xmas Special from Ian Ozsvald on Vimeo.

“Lastminute.com Lab’s with Bottle-Rock-It” (with an additional proper demo video)

Richard, Sam and Mathias (LastMinute.com Labs) came down from London (thanks guys!) to demo Bottle-Rock-It, a group iPhone musical instrument.

The background talk gives loads of detail, sadly the demo went a bit sideways so we sang Jingle Bells as a loud (and slightly tipsy) group instead.

Check this BBC News story to see Bottle Rock It in action.

£5 App #20 “Lastminute.com’s Bottle-Rock-It” for the 2009 Xmas Special from Ian Ozsvald on Vimeo.

100 Robots (band)

After the talks finished Jim Purbrick and Max went on to play live n’loud as 100 Robots.

2010 and beyond…

If you want to keep in touch with future £5 App events then join the £5 App Google Group – it is very low volume and is mostly there just for the announces.

We’ll probably run some more competitions next year, the 5k competition went very well and John wants to do more around that idea and I want to play with some open-source A.I. kits.  Details to follow.

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Ian applies Artificial Intelligence for companies (Mor Consulting), programs Python, produces professional screencasts (ProCasts), writes The Screencasting Handbook and is also a sea-side dweller and consumer of fine coffees.