Over the past few days, my social media timeline has been full of angry people, predominantly in the UK and predominantly attacking people of note within the UK1. This is, sadly, nothing new. However I have noticed a further decline in the quality of debate, perpetuated by the strong emotions of what has happened in the world. People whose opinions I respect and who normally make reasoned arguments have posted some pretty vile language that is literally “[person] is a [expletive]” in regard to Brexit, the NHS, Southern Rail and a whole host of regional issues. If this was directed at someone with whom they agreed, then there would be, legitimately, outrage. Name calling someone is never a valid argument. Didn’t we all grow out of this at school? Apparently not. Continue reading Debate the idea, don’t attack the person
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
As we enter 2017, most people will start to think about resolutions – “new year, new me”. The main resolution seems to be to get healthier: drink less, more exercise, eat better. Year after year the resolutions will mainly peter out, leaving us all the ability to reuse the same ones next year. What I’ve seen over 20161 is a worldwide lack of critical thinking. Social media posts are the main sources of the problem, but recently news outlets have fallen foul of this too. It worries me that all it seems to take is an image and some text and then something becomes “true”. What’s really irritating is this has all been made acceptable by saying we live in a post-truth era, without anyone really trying to do anything about it. Continue reading Have a resolution to think critically
“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
The following tweet appeared on my timeline today:
The Turing test is like saying planes don’t fly unless they can fool birds into thinking they’re birds. (h/t Peter Norvig) #AI
— Pedro Domingos (@pmddomingos) July 19, 2015
Initially I thought “heh, fair point – we are defining that the only true intelligence is described by the properties humans exhibit”, and my in-built twitter filter1 ignored the inaccuracies of the analogy. I clicked on the tweet as I wanted to see what the responses were and whether there was a better metaphor that I could talk about. There wasn’t – the responses were mainly variants on the deficiencies of the analogy and equally problematic in their own right. While this didn’t descend into anything abusive2, I do feel that the essence of what was trying to be conveyed was lost and this will be a continual problem with twitter. One of the better responses3 did point out that cherry-picking a single feature was not the same as the Turing Test. However, this did get me thinking based on my initial interpretation of the tweet.
In order to answer a big question we are simplifying it in one way. Turing simplified “Can machines think?” to “can machines fool humans into thinking they are human?”. Continue reading Can machines think?
I’m four weeks in to my new role and one of the threads of work I have is looking into machine learning and how this has advanced since my own thesis. The current approach to machine intelligence is via learning networks where the data is abstracted: rather than recognising specifics about the problem, the algorithm learns the common elements of the problem and solution to match an input to the expected output, without needing an exact match. Our brains are very good at this: from a very early age we can recognise familiar faces from unfamiliar ones and quickly this progresses to identification in bad light, different angles, when the face is obscured. Getting machines to do the same has been notoriously difficult. Continue reading Machine intelligence – training and plasticity
The final part of block D in MS221 of my OU Maths degree is all about mathematical proofs and deduction, which I find absolutely fascinating. A big part of this block was clarity on some logical fallacies that we encounter all the time and that many people use to trick us into agreeing with their arguments.
With one week to go until the General Election in the UK it seems like a good time to revisit logic and proof from both the political and mathematical sides.
I love science. My parents fostered a great sense of curiosity in me and the need to learn. Part of this was the ability to question what was presented and come to my own conclusions as to whether it was correct or not. It was okay to change my mind as new evidence was presented, included my own experiences and this is how we grow as individuals.
At university we were taught to go to the primary sources for information – not the summaries or reviews but read the original papers and decide whether the research was sound for ourselves. Corrections are regularly published for papers (or retractions made) and these are not always referenced when the original paper is cited, perpetuating the error. (I don’t want to get into a discussion of specific examples as this will detract from the point of this post).