ImageNet has been a deep learning benchmark data set since it was created. It was the competition that showed that DL networks could outperform non-ML techniques and it’s been used by academics as a standard for testing new image classification systems. A few days ago an exciting paper was published on arxiv for training ImageNet in four minutes. Not weeks, days or hours but minutes. This is on the surface a great leap forward but it’s important to dig beneath the surface. The Register sub headline says all you need to know:
If you’ve read pretty much any other of my artificial intelligence blog posts on here then you’ll know how annoyed I am when the slightest advance in the achievements of AI spurs an onslaught of articles about “thinking machines”, that can reason and opens up the question of robots taking jobs and eventually destroying us all in some not-to-be-mentioned1 film franchise style. Before I get onto discussing if and when we’ll get to a Detroit Become Human scenario, I’d like to cover where we are and the biggest problem in all this. Continue reading Thinking machines – biological and artificial
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
While there may be disagreements on whether AI is something to worry about or not, there is general agreement that it will change the workforce. What is a potential concern is how quickly these changes will appear. Anyone who has been watching Inside the Factory1 can see how few people are needed on production lines that are largely automated: a single person with the title “manager” whose team consists entirely of robots. It wasn’t too long ago that these factories would have been full of manual labour.
The nature of our workforce has changed. It’s been changing constantly – the AI revolution is no different in that respect. We just need to be aware of the speed and scale of potential change and ensure that we are giving everyone the opportunity to be skilled in the roles that will form part of our future. There is an inevitability about this. Just as globalisation made it easy for companies to outsource work to cheaper locations (and even easier with micro contract sites) AI will make it cheaper and easier for companies to do tasks so it will be adopted. Tasks that aren’t interesting enough or wide market enough or even too difficult right now to be automated will still need human workers. Everything else will slowly be lost “to the robots”. Continue reading Is a Robot tax on companies using AI a way of protecting the workforce?
While I like to kid myself that maybe I’m only a quarter or third of the way through my life, statistics suggest that I’m now in the second half and my future holds a gradual decline to the grave. I’m not afraid of my age, it’s just a number1. I certainly don’t feel it. My father recently said that he doesn’t feel his age either and is sometimes surprised to see an old man staring back at him from the mirror.
As an atheist, death terrifies me. My own and that of those I love. I don’t have the easy comfort blanket of an afterlife and mourn the loss of everything an individual was when they cease to be. Continue reading Chatbot immortality
Artificial intelligence has progressed immensely in the past decade with the fantastic open source nature of the community. However there are relatively few people, even in the research areas, that understand the history of the field from both the computational and biological standpoints. Standing on the shoulders of giants is a great way to step forward, but can you truly innovate without understanding the fundamentals?
I go to a lot of conferences and I’ve noticed a subtle change in the past few years. Solutions that are being spoken about now don’t appear to be as far forward as some of those presented a couple of years ago. This may be subjective, but the more I speak to people about my own background in biochemically accurate computational neuron models, the more interest it sparks. Our current deep learning model neurons are barely scratching the surface of what biological neurons can do. Is it any wonder that models need complexity and are limited in their scope? Continue reading Biologically Inspired Artificial Intelligence
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
I love the fact that here in the UK everyone can be involved in shaping the future of our country, even if a large number of individuals choose not to and, in my eyes, if you don’t get involved then you don’t have the right to complain. While this is most generally applied to the election of our representatives from local parish councils to our regional MPs (or actually standing yourself)1 there are also a lot of other ways to be involved. In addition to raising issues with your local representative, parliament has cross bench committees that seek input from the public and to help create policy or consider draft legislation.
Our elected parliament is not made up of individuals who are experts in all fields. Even government departments are not necessarily headed by individuals with large amounts of relevant experience. It is critical that these individuals are informed by those with the experience and expertise in the issues that are being considered. Without this critical input, our democracy is weakened. Continue reading Submitting evidence to parliament committees
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.