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It has become common in the technology world that the presence of ML in a company, in a development process, or in a product is viewed as a certification of technical superiority, something that will outstrip all competition.
Machine Learning is what has enabled the new assistants in our houses such as the Amazon Echo Alexa and Google Home by allowing them to reliably understand as we speak to them. Machine Learning is how Google chooses what advertisements to place, how it saves enormous amounts of electricity at its data centers, and how it labels images so that we can search for them with key words.
Machine learning is how DeepMind a Google company was able to build a program called Alpha Go which beat the world Go champion.
Machine Learning is how Amazon knows what recommendations to make to you whenever you are at its web site.
Machine Learning is how PayPal detects fraudulent transactions. Machine Learning is how Facebook is able to translate between languages.
And the list goes on! While ML has started to have an impact on many aspects of our life, and will more and more so over the coming decades, some sobriety is not out of place.
Neither AI programs, nor robots, wander around in the world ready to learn about whatever there is around them. Every successful application of ML is hard won by researchers or engineers carefully analyzing the problem that is at hand. They select one or many different ML algorithms, and custom design how to connect them together and to the data.
In some cases there is an extensive period of training on very large sets of data before the algorithm can be run on the problem that is being solved.
In that case there may be months of work to do in collecting the right sort of data from which ML will actually learn.
In other cases the learning algorithm will be integrated in to the application and will learn while doing the task that is desired—it might require some training wheels in the early stages, and they too must be designed.
In any case there is always a big design project about how, when the ultimate system is operational, the data that comes in will be organized, processed and mapped before it reaches the ML component of the system.
When we are tending plants we pour water on them and perhaps give them some fertilizer and they grow. I think many people in the press, in management, and in the non-technical world have been dazzled by the success of Machine Learning, and have come to think of it a little like water or fertilizer for hard problems.
But while ML can sometimes have miraculous results it needs to be carefully customized after the DNA of the problem has beed analyzed.
And even then it might not be what is needed—to extend the metaphor, perhaps it is the climate that needs to be adjusted and no amount of fertilizer or ML will do the job. How does Machine Learning work, and is it the same as when a child or adult learns something new?
The examples above certainly seem to cover some of the same sort of territory, learning how to understand a human speaking, learning how to play a game, learning to name objects based on their appearance.
They had been built, using the technology of vacuum tubes, to calculate gunnery tables and to decrypt coded military communications of the enemy.
Even then, however, people were starting to think about how these computers might be used to carry out intelligent activities, fifteen years before the term Artificial Intelligence was first floated by John McCarthy. Alan Turing, who in had written the seminal paper that established the foundations of modern computation, and Donald Michie, a classics student from Oxford later he would earn a doctorate in geneticsworked together at Bletchley Park, the famous UK code breaking establishment that Churchill credited with subtracting years from the war.
Turing contributed to the design of the Colossus computer there, and through a key programming breakthrough that Michie made, the design of the second version of the Colossus was changed to accommodate his ideas even better.
Meanwhile at the local pub the pair had a weekly chess game together and discussed how to program a computer to play chess, but they were only able to get as far as simulations with pen and paper. While the computer was still being built he planned out how to program it to play checkers or draughts in British Englishbut left in to join IBM before the University computer was completed.Expert advice on finding college scholarships, applying for them, writing great essays, and winning a scholarship.
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