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.
I grew up reading and watching Sci-Fi. As a child with an Acorn Electron, the idea of smart interactable devices seemed far future rather than near future. I loved the voice interactivity and things ‘just working’ without needing to be controlled. When I got my Echo dot last year, I knew this would be the start of a journey to upgrade my house to a SmartHome and truly be part of the Internet of Things. It’s been four months now and I’ve got a setup with which I’m pretty happy. Here’s what I chose and why… Continue reading Internet of Things: Making a Smart Home
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.
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
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
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?