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.
Earlier this month, Dove launched their new baby range with another of their fantastic adverts challenging stereotypes and questioning is there a “perfect mum”. As a mum myself I can relate to the many hilarious bloggers1 who are refreshingly honest about the unbrushed hair, lack of make-up, generally being covered in whatever substances your new tiny human decides to produce, and all other parenting frustrations. I’m really pleased that there are lots of women2 out there challenging the myths presented in the media – we don’t all have a team to make us beautiful, nor someone photo-shopping the results to perfection, and the pressure can be immense. This is where Dove’s campaign is fantastic. Rather than just creating a photoshoot with a model and doctoring the results, the image is actually completely artificial, having been generated by AI. Continue reading Artifical image creation takes another step forward in advertising
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?
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
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
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
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.