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
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.
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
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.
Ahead of season 2 of Channel 4’s Humans, they screened a special showing how a synthetic human could be produced. If you missed the show and are in the UK, you can watch again on 4OD.
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.
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.