There are not many though. Rachel permalink August 20, Hi there — thank you for the informative article! I am currently considering making a career change, and would like some input from you if you have the time. I have a background in behavior analysis, with an undergraduate psychology degree from Western Michigan University.
A common thread that I have observed is how people tend to underestimate how long new technologies will take to be adopted after proof of concept demonstrations. This is precisely a realization of the early optimism about how things would be deployed and used did just not turn out to be.
However, I do not think that I am a techno-pessimist.
Rather, I think of myself as a techno-realist. In my view having ideas is easy. Turning them into reality is hard. Turning them into being deployed at scale is even harder.
And in evaluating the likelihood of success at that I think it is possible to sort technology and technology deployment ideas into a spectrum running from relatively easier to very hard.
But simply spouting off master thesis structured products daily this is rather easy to do as there is no responsibility for being right or wrong. That applies not just to me, but to pundits ranging from physicists to entrepreneurs to academics, who are making wild predictions about AI and ML.
I am going to take this opportunity to make predictions myself, not just about the coming year, but rather the next thirty two years. I am going to write them in this blog with explicit dates attached to them. Hence they are my dated predictions. And they will be here on this blog and copies that live on elsewhere in cyberspace for all to see.
I am going to take responsibility for what I say, and make it so that people can hold me to whether I turn out to be right or wrong. If I am unfortunate, some of my predictions will at some point seem rather dated! So the furtherest out date I am going to consider is January 1st, I specify dates in three different ways: Now in reality precision on defining what I am predicting is almost impossible.
Nevertheless I will try. I had an experience very recently that made me realize just how hard people will try, when challenged, to hold their preconceived notions about technologies and the cornucopia they will provide to humanity. I tweeted out the following rodneyabrooks: When humans next land on the Moon it will be with the help of many, many, Artificial Intelligence and Machine Learning systems.
My intent with this tweet was to say that although AI and ML are today very powerful and useful, it does not mean that they are the only way to do things, and it is worth remembering that. One of the responses to this tweet, which itself was retweeted many, many times, was that Kalman filtering was used to track the spacecraft completely truethat Kalman filtering uses Bayesian updating completely trueand that therefore Kalman filtering is an instance of machine learning complete non sequitur and that therefore machine learning was used to get to the Moon a valid inference based on a non-sequitur, and completely wrong.
Kalman filtering uses multiple data points from a particular process to get a good estimate of what the data is really saying. It does not save anything for later to be used for a similar problem at some future time.
So, no, it is not Machine Learning, and no, we did not use Machine Learning to get to the Moon last time, no matter how much you want to believe that Machine Learning is the key to all technological progress.
That is why I am going to try to be very specific about what I mean by my predictions, and why, no doubt, I will need to argue back to many people who will want to claim that the things I predict will not happen before some future time have already happened.
I predict that people will be making such claims! What is Easy and What is Hard? Building electric cars and reusable rockets is easy.
Building flying cars, or a hyperloop system or a palletized underground car transport network underground is hard. What makes the difference? Cars have been around, and mass produced, for well over a century.
If you want to build electric cars rather than gasoline cars, you do not have to invent too much stuff, and figure out how to deploy it at scale. There has been over a hundred years of engineering and production of windscreen wipers, brakes, wheels, tires, steering systems, windows that can go up and down, car seats, a chassis, and much more.
There have even been well over 20 years of large scale production of digitalized drive trains. To build electric cars at scale, and at a competitive price, and with good range, you may have to be very clever, and well capitalized.
But there is an awful lot of the car that you do not need to change.
For the majority of the car there are plenty of people around who have worked on those components for decades, and plenty of manufacturing expertise for building the components and assembly.MOVEMENT PROGRAMMES AS A MEANS TO LEARNING READINESS by DR SOEZIN KROG Submitted in accordance with the requirements for the degree MASTER OF EDUCATION WITH.
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