Machine Learning - More than a fad?

Dr Juergen Ude - author of machine-learning article Dr Juergen Ude

Machine learning has recently become popular to the extent that it has received a somewhat mythical status, as is the norm with the latest ‘fads’. Historically there have been many ‘fads’ of which, TQM, Kaizen, JIT, 6 Sigma, Analytics, AI are some examples. All have had a positive influence but at the same time, all have not attained the hyped-up benefits. Many applications have failed, and many have succeeded.

So, what is Machine Learning?

Machine Learning is not really a ‘fad’, it is a natural evolutionary progression of the use of computer power. Unfortunately, it is treated as a fad, and as with previous ‘fads’ its ‘power’ has been hyped out of proportion.

There are many definitions on the internet. The essence is that machine learning is about the use of algorithms to learn from the data to then make a decision, or a future prediction. Some say that it makes use of computers without the need of being specifically programmed or without the need to use specific formulae and functions. This is wrong.

Awareness Limitations

The use of ‘Learning’ in the concept has hyped up the application and its power to the layman. There is an implication that learning is of the same and even higher caliber as human learning, especially when Machine-Learning is associated with Neural Networks.

The reality is that no computer, or machine which will ever be able to truly learn like a human being. Of course, a computer can sometimes learn faster than a human being through processing speed, but there are limitations. Other times, such as with patter recognitions, humans tend to outperform computers at this point in time. A computer is not self-aware, nor is it aware of its changing environment. Self-awareness affects what we are willing to retain for the learning process. Our ‘gut-feel’, is used to decide what we wish to incorporate. If something does not feel right, we are more likely to discard information to learn from. We were given instincts to cope in an uncertain fuzzy environment. Although in an academic environment this could be frowned upon, successful business men and military commanders tend to unanimously believe in the instinctual learning and decision-making process.

A human being though awareness will consider the everchanging environment and modify the learning experience accordingly. A computer is not alive and can only ‘learn’ according to the programmed instruction set and feedback.

All a computer can do is mimic intelligent human learning through code. Artificial intelligence is not real intelligence. It is mimicking intelligence as programmed by programmers guided by a team of experts. It mimics the intelligence, and decision making of the experts. Mimicking intelligence (needed for learning) is becoming more and more sophisticated, but missing is awareness and considering all the other learning experiences. If anything differs from the assumptions used to develop code that mimics learning, the machine learning process fails. A computer cannot adapt outside the preprogrammed instructions no matter how real the mimicking appears. Learning is only as good as the programmer and scientists who use their knowledge to mimic the learning process.

In summary human learning is through awareness of experiences and feelings. Machine learning is through instructions programmed by the programmer mimicking decision processes provided by the programmer.

Technology limitations

Representation algorithms include regression analysis, logistic regression, decision theory, neural networks. Evaluation includes technology such as least squares, likelihood and maximum likelihood. Optimization includes search algorithms, linear programming, dynamic programming and quadratic programming, amongst many others.

Most of these technologies have been around for over one hundred years. Even Neural Networks, which mimic animal nervous systems, was first proposed in the 1940’s. The logistics function was invented in the 19th century, i.e. over one hundred years ago.

There are many issues with most of the technologies and countless of papers have been written about these issues. It is therefore foolish to read more into the power of machine learning then there is. Machine learning is as good as the underlying algorithms used, many of which have flaws. Furthermore, the technologies/algorithms available are not all applicable to every problem. Machine learning depends on matching the best technologies to the application. This depends on human judgement which is programmed into the system.

Machine Learning Applications

Some machine learning applications are in Pattern recognition, Image Recognition, Speech Recognition, Statistical Arbitrage, Prediction, Medical Diagnosis. All these work on the basis that there is some predictability and historical stability. Recognizing that an image is a house only works because there is similarity in all houses. Prediction only works if there is stability I patterns over time. It is not possible to predict into the future if past patterns do not persist. Sometimes instability is not intuitively evident. An example is household water consumption which intuitively may seem highly pattern repetitive, but in practice is not. Seasonal effects vary, occupancy can vary for the same household, illness, stress effecting toilet use can change consumption, changes in garden layout can change consumption.


Machine Learning is a powerful concept but is sometimes misunderstood. The computer does not learn consciously and never will. Programmers have simply written a series of steps in the form of algorithms that the processor must follow to mimic human recognition processes. The effectiveness is dependent on the skills of the programmers and experts. The algorithms cannot cope with situations other than those that were considered by the programmers and experts.

PART 2 - MACHINE POWER can be read here

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