If you’ve been following this blog you’ll know that there have been great advances in the past few years with artificial image generation, to the stage where having a picture of something does not necessarily mean that it is real. Image advances are easy to talk about, as there’s something tangible to show, but there have been similar large leaps forward in other areas, particularly in voice synthesis and handwriting.
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
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?
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
“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
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
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.
Continue reading ReWork Deep Learning London 2016 Day 1 Morning
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
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
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