I get very tired of the clickbaity journalism hyping up minor advances in AI, making news stories out of nothing or even the ones for those in the industry. You know the type: “Facebook AI had to be shut down”, “Google creates self learning AI”.
Throughout my academic career one thing that was repeatedly enforced was that if you were claiming something to be true in a paper, you needed to show results to prove it or cite a credible source that had those results. It took a lot of effort in those pre-Google Scholar and pre-Arxiv days1. Reading the journals, being aware of retractions and clarifications and building the evidence to support your own work took time2. Writing up my thesis was painful solely because of finding the right references for things that were “known”. I had several excellent reviewers who sent me back copies of my thesis with “citation needed” where I’d stated things as facts without a reference. My tutor at Oxford was very clear on this: without a citation, it’s your opinion not a fact. Continue reading Citation Needed – without it you have opinion not facts
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
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.
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
In my last post I talked a little about logic as it applies to generic statements. Now it’s time to think about more mathematics proofs and different techniques. As part of MS221 there are two proof types that we need to consider: proof by exhaustion and proof by induction. This all lays the foundations for building more and more complex mathematical statements so it’s important to get the basics right.
Firstly, proof by exhaustion. This simply means that we try every possible valid input and check that the result is true. A single false result would disprove our proposition. So let’s consider an example: Continue reading Proof by Induction
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.