Submitting evidence to parliament committees

(c) Parliament committee on Science and Technology

I love the fact that here in the UK everyone can be involved in shaping the future of our country, even if a large number of individuals choose not to and, in my eyes, if you don’t get involved then you don’t have the right to complain.  While this is most generally applied to the election of our representatives from local parish councils to our regional MPs (or actually standing yourself)1 there are also a lot of other ways to be involved.  In addition to raising issues with your local representative, parliament has cross bench committees that seek input from the public and to help create policy or consider draft legislation.

Our elected parliament is not made up of individuals who are experts in all fields.  Even government departments are not necessarily headed by individuals with large amounts of relevant experience.  It is critical that these individuals are informed by those with the experience and expertise in the issues that  are being considered.  Without this critical input, our democracy is weakened. Continue reading Submitting evidence to parliament committees

Algorithmic transparency – is it even possible?

Could you explain to a lay person how this network makes decisions?

The Science and Technology Select Committee here in the UK have launched an inquiry into the use of algorithms in public and business decision making and are asking for written evidence on a number of topics.  One of these topics is best-practise in algorithmic decision making and one of the specific points they highlight is whether this can be done in a ‘transparent’ or ‘accountable’ way1.  If there was such transparency then the decisions made could be understood and challenged.

It’s an interesting idea.  On the surface, it seems reasonable that we should understand the decisions to verify and trust the algorithms, but the practicality of this is where the problem lies. Continue reading Algorithmic transparency – is it even possible?

Anything you can do AI can do better (?): Playing games at a new level

Robot hands dealing cards
Image from BigThink.com

Learning to play games has been a great test for AI.  Being able to generalise from relatively simple rules to find optimal solutions shows a form of intelligence that we humans always hoped would be impossible.  Back in 1997, when IBMs Deep Blue beat Gary Kasparov in chess1 we saw that machines were capable of more than brute force solutions to problems.  20 years later2 and not only has AI mastered Go with Google’s DeepMind winning 4-1 against the world’s best player and IBM’s Watson has mastered Jeopardy,  there have also been some great examples of game play with many of the games I grew up playing: Tetris,  PacMan3, Space Invaders and other Atari games.  I am yet to see any AI complete Repton 2. Continue reading Anything you can do AI can do better (?): Playing games at a new level

Artificial images: seeing is no longer believing

Loom.ai can generate a 3D avatar from a single image

“Pics or it didn’t happen” – it’s a common request when telling a tale that might be considered exaggerated.  Usually, supplying a picture or video of the event is enough to convince your audience that you’re telling the truth.  However, we’ve been living in an age of Photoshop for a while and it has (or really should!!!) become habit to check Snopes and other sites before believing even simple images1 – they even have a tag for debunked images due to photoshopping. Continue reading Artificial images: seeing is no longer believing

How to build a human – review

Gemma Chan, a real human and also now a real synth
Gemma Chan, a real human and also now a real synth

Ahead of season 2 of Channel 4’s Humans, they screened a special showing how a synthetic human could be produced.  If you missed the show and are in the UK, you can watch again on 4OD.

Presented by Humans actress Gemma Chan, the show combined realistic prosthetic generation with AI to create a synth, but also dug a little deeper into the technology, showing how pervasive AI is in the western world.

There was a great scene with Prof Noel Sharkey and the self driving car where they attempted a bend, but human instinct took over: “It nearly took us off the road!” “Shit, yes!”.  This reinforced the delegation of what could be life or death decisions – how can a car have moralistic decisions, or should they even be allowed to? Continue reading How to build a human – review

ReWork Deep Learning London 2016 Day 1 Morning

Entering the conference (c) ReWork
Entering the conference (c) ReWork

In September 2016, the ReWork team organised another deep learning conference in London.  This is the third of their conferences I have attended and each time they continue to be a fantastic cross section of academia, enterprise research and start-ups.  As usual, I took a large amount of notes on both days and I’ll be putting these up as separate posts, this one covers the morning of day 1.  For reference, the notes from previous events can be found here: Boston 2015, Boston 2016.

Day one began with a brief introduction from Neil Lawrence, who has just moved from the University of Sheffield to Amazon research in Cambridge.  Rather strangely, his introduction finished with him introducing himself, which we all found funny.  His talk was titled the “Data Delusion” and started with a brief history of how digital data has exploded.  By 2002, SVM papers dominated NIPs, but there wasn’t the level of data to make these systems work.  There was a great analogy with the steam engine, originally invented by Thomas Newcomen in 1712 for pumping out tin mines, but it was hugely inefficient due to the amount of coal required.  James Watt took the design and improved on it by adding the condenser, which (in combination with efficient coal distribution) led to the industrial revolution1.   Machine learning now needs its “condenser” moment.

Continue reading ReWork Deep Learning London 2016 Day 1 Morning

Amazon Echo Dot (second generation): Review

Echo Dot (c) Amazon
Echo Dot (c) Amazon

When I attended the ReWork Deep Learning conference in Boston in May 2016, one of the most interesting talks was about the Echo and the Alexa personal assistant from Amazon.  As someone whose day job is AI, it seemed only right that I surround myself by as much as possible from other companies.  This week, after it being on back order for a while, it finally arrived.  At £50, the Echo Dot is a reasonable price, with the only negative I was aware of before ordering being that the sound quality “wasn’t great” from a reviewer. Continue reading Amazon Echo Dot (second generation): Review

Formula AI – driverless racing

The AI racing car (c) Roborace
The AI racing car (c) Roborace

We’re all starting to get a bit blasé about self driving cars now.  They were a novelty when they first came out, but even if the vast majority of us have never seen one, let alone been in one, we know they’re there and they work1 and that they are getting better with each iteration (which is phenomenally fast).  But after watching the formula 1 racing, it’s a big step from a 30mph trundle around a city to over 200mph racing around a track with other cars.  Or is it? Continue reading Formula AI – driverless racing

Evolving Machines

I, for one, welcome our new metal overlords ;)
I, for one, welcome our new metal overlords 😉

Following my post on AI for understanding ambiguity, I got into a discussion with a friend covering the limitations of AI if we only try to emulate ourselves.  His premise was that we know so little about how our brains actually enable us to have our rich independent thoughts that if we constrain AI to what we observe, an ability to converse in our native language and perform tasks that we can do with higher precision, then we will completely limit their potential.  I had a similar conversation in the summer of 2015 while at the start-up company I joined1– we spent a whole day2 discussing whether in 100 years’ time the only jobs that humans would do would be to code the robots.  While the technological revolution is changing how we live and work, and yes it will remove some jobs and create others just like the industrial revolution did and ongoing machine automation has been doing, there will always be a rich variety of new roles that require our unique skills and imagination, our ability to adapt and look beyond what we know. Continue reading Evolving Machines

AI for understanding ambiguity

Please do not park bicycles against these railings as they may be removed - the railings or the bikes? Understanding the meaning is easy for us, harder for machines
Please do not park bicycles against these railings as they may be removed – the railings or the bikes? Understanding the meaning is easy for us, harder for machines

Last year I wrote a post on whether machines could ever think1.  Recently, in addition to all the general chatbot competitions, there has been a new type of test for deeper contextual understanding rather than the dumb and obvious meanings of words.  English2 has a rich variety of meanings of words with the primary as the most common and then secondary and tertiary meanings further down in the dictionary.  It’s probably been a while since you last sat down and read a dictionary, or even used an online one other than to find a synonym, antonym or check your spelling3 but as humans we rely mostly on our vocabulary and context that we’ve picked up from education and experience.

Continue reading AI for understanding ambiguity