“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
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.
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
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.
So yesterday there was the news that over 1000 people had signed an open letter requesting a ban on autonomous weapons. I signed it too. While AI is advancing rapidly and the very existence of the letter indicates that research is almost certainly already progressing in this area, as a species we need to think about where to draw the line.
Completely autonomous offensive AI would make its own decisions about who to kill and where to go. Battlefields are no longer two armies facing up on some open fields. War is far more complex, quite often with civilians mixed in. Trusting an AI to make those kill decisions in complex scenarios is not something that would sit easily with most. Collateral damage reduced to an “acceptable” probability? Continue reading Military AI arms race
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?
There’s a lot of money, time and brain power going in to various machine learning techniques to take the aggravation out of manually tagging images so that they appear in searches and can be categorised effectively. However, we are strangely fault-intolerant of machines when they get it wrong – too many “unknowns” and we’re less likely to use the services but a couple of bad predictions and we’re aghast about how bad the solution is.
With a lot of the big players coming out with image categorisers, there is the question as whether it’s really worth anyone building their own when you can pay a nominal fee to use the API of an existing system. The only way to really know is to see how well these systems do “in the wild” – sure they have high precision and recall on the test sets, but when an actual user uploads and image and frowns at the result, something isn’t right. Continue reading AI for image recognition – still a way to go