Uwe Weinreich, the author of this blog, usually coaches teams and managers on topics related to strategy, innovation and digital transfor­mation. Now he is seeking a direct confron­tation with Artificial Intelligence.
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Behind AI lies the ability of machines (computers) to learn independently, by building models and thereafter generating and sorting their own solutions.
Algo-rithms Relatively quick to program, stable problem-solving over time. Long and complex development process, prone to mistakes, every dynamic change creates problems. Possibility of not addressing some aspects of the problem.
AI Enormous outlay (acquisition, processing power, energy, training) to solve a straightforward problem. Possibility of differing results over time despite same input data. Reasonable outlay in comparison to the complexity of the problem. Possibility of discovering solutions (models) that wouldn't have been found by humans. Independently adapts to change.
Therefore one needs to be alert when defining the problem. AI is good for complex, dynamic problems, while algorithms are better for simple ones.
Of course one can also use AI for simple problems. The problem then is that it kind of feels as if one is sitting in a seminar led by an average tutor, who's trying to encourage the participants to find the solutions on their own: "Let's say 1 times 1 is 1, 1 times 2 is 2, and 1 times 3 is 3. So, can you figure out how to continue? What would 1 times 4 be? Who can tell me? Mr Smith, you maybe?" In such situations it's embarrassing to answer, and just as embarrassing not to: a lecture hall full of vicarious embarrassment. We should thus avoid this type of situation with AI as well, and use it only where it belongs, right?
A Microsoft video uses exactly this off-putting example to show how AI learns. OK, but only for this fleeting moment as a demonstration!
But thereafter it gets more challenging. The process will include computation of regression, data classification, and clustering. Yes, these processes are used in Machine Learning, but at the moment this doesn't have much to do with AI. All of these processes are also known from the world of classical analysis.
Installing Python and an IDE is not really necessary. Everything works also fine by just using the online editing options.