Last week I was on a remote island in the Outer Hebrides, we had WiFi at our cottage but it was slow, we didn’t have any mobile signal. There was a mobile signal in the nearby town, 3 miles away, but again, that was slow and covered a small portion of the island. It was a great reminder of how much we take connectivity for granted and that for much of the world that assumption is invalid.
Whilst I was away though there have been a number of AI, Machine Learning and Robotics related things happening:
As seems to be the case for all technologies a point is reached where people need to talk about the negative aspects including the hype-levels of the current hot tech. That has been the case this last week with MIT and others running stories. The MIT Technology Review one is looking specifically at IBM Watson and the perceived rate of progress it is making in healthcare. Progress in any technology is rarely a smooth ride and some high visibility failures are normal.
The MIT Technology Review has also been looking at the progress GE are making by using AI and Machine Learning. GE is going through a huge transformation that will embed advanced technologies and robotics into many of its products. This transformation is also radically changing the way people work with people taking on what would have previously been two separate roles. The role change is one that is already happening and I’ve got an post brewing on that.
The Guardian is the latest organisation to do a round up of the research and current thinking into the impact of automation on jobs:
“in the last 60 years automation has only eliminated one occupation: elevator operators.”
I’m not sure that’s really true, but I get the point. Aside from the statistics the core question people want to know the answer to is: “what can I do to prepare?” It’s not an easy question to answer, the only sure thing is that change is going to happen and humans have adapted to change for hundreds of thousands of years. That ability to adapt is what’s going to be key in the future, one way of being adaptable is to diversify and be able to take on multiple roles.
If you want to know more about Machine Learning then this nice Visual Introduction from R2D3 will get you started.
Jacques Mattheij had a dilemma, how to sort 2 metric tonnes of Lego which he did with some Lego (what else?), some hardware, python and a neural network. Although there won’t be many people with 2 metric tonnes of Lego I’m sure that many of us would love to be able to sort the boxes that do exist.
A Reality Check for IBM’s AI Ambitions via MIT Technology Review
None of those companies has garnered anywhere near the attention that Watson has, thanks to its victory on the television quiz show Jeopardy! in 2011 and zealous marketing by IBM ever since. But lately, much of the press for Watson has been bad. A heavily promoted collaboration with the M.D. Anderson Cancer Center in Houston fell apart this year. As IBM’s revenue has swooned and its stock price has seesawed, analysts have been questioning when Watson will actually deliver much value. “Watson is a joke,” Chamath Palihapitiya, an influential tech investor who founded the VC firm Social Capital, said on CNBC in May.
General Electric Builds an AI Workforce via MIT Technology Review
When Jason Nichols joined GE Global Research in 2011, soon after completing postdoctoral work in organic chemistry at the University of California, Berkeley, he anticipated a long career in chemical research. But after four years creating materials and systems to treat industrial wastewater, Nichols moved to the company’s machine-learning lab. This year he began working with augmented reality. Part chemist, part data scientist, Nichols is now exactly the type of hybrid employee crucial to the future of a company working to inject artificial intelligence into its machines and industrial processes.
What jobs will still be around in 20 years? Read this to prepare your future by The Guardian.
Today’s technological revolution is an entirely different beast from the industrial revolution. The pace of change is exponentially faster and far wider in scope. As Stanford University academic Jerry Kaplan writes in Humans Need Not Apply: today, automation is “blind to the color of your collar.” It doesn’t matter whether you’re a factory worker, a financial advisor or a professional flute-player: automation is coming for you.
A visual introduction to machine learning by R2D3
In machine learning, computers apply statistical learning techniques to automatically identify patterns in data. These techniques can be used to make highly accurate predictions.
Sorting 2 Metric Tons of Lego by Jacques Mattheij
Computer skills to the rescue! A first proof of concept was built of – what else – lego. This was hacked together with some python code and a bunch of hardware to handle the parts. After playing around with that for a while it appeared there were several basic problems that needed to be solved, some obvious, some not so obvious. A small collection: