Understanding Concepts in machine learning

Understanding Concepts in machine learning 

The first one, "The Dark secret at the heart of AI", performs a very good job in explaining the "black box theory", that we have commented on several occasions here: as machine learning's algorithms become more and more sophisticated, the possibility that a human mind understands the procedures it uses to reach a given result are made minor , which leads us, ultimately, to have a black box that generates results that we can test only according to its quality, without understanding how it is reached. 

If we train an algorithm with all the credit history granted and denied by an entity, for example, in order to make decisions in the future that would take a risk committee today, we will end up with a black box from which decisions can be contrasted depending on the quality of the results (if it reduces the number of credits granted and not returned , we'll give it a valid one), but we'll probably be unable to understand how the machine comes to each decision: its operation will be a black box in whose results we rely on its results.

This brings us another question: because when we feed an algorithm, we do it with all the data that we have available and that, according to our human reasoning, contribute to the result, what we find is that the progress of the machine learning redefine the concept that we have of human intelligence, and alter our perceptions of what we can or cannot do.

The starting point for the machine is what we as humans are able to understand, from there, everything is unexplored terrain and methods that require a power of calculation from which our brain simply lacks. So, things that today seems normal for us to make a human, we will see them as absurd as it is a machine that makes them, and things that a machine is capable of making us seem less and more surprising and more normal.

 Very soon, the chatbot will have become the standard for after-sales service and for many more things like "explain" the news, and the first time of disenchantment and disappointment will give way to a moment in which really, as happens to younger generations, we prefer to talk with robots to talk to people, because not only give us a better service , but also eliminate the feeling of being "bothering someone" on the other side (just as a link does not "complain" or "answer us wrong" if we click on it ten times).

 Simply, it is a conversational algorithm that serves you when you have issues related to the product or service of a company will become "normal", and we will see as "of the last century" when there were people dedicated to do that service.

In the same way, there will be activities that will soon be something of the past, be programmed traffic lights to avoid jams, make investment decisions or diagnose a disease, and it will seem "strange" or "primitive" to think that these activities were previously carried out by a person. 

The replacement of taxi or truck drivers will be seen as something so obvious, that we will find it incredible – and dangerous – that this activity was developed in the past manually. Will that mean that many people who did that job go straight to unemployment? Possibly, but the solution will not be to tax those robots who have gone on to perform these activities, but to train and train people to be able to carry out other related activities. And in that sense, cutting social benefits, as the trend in countries like the United States seems, can only lead to a worse problem.

This does not mean logically that we do not have to look for methodologies that somehow allow to increase the traceability of the decisions made by the machines. The article describing the nightmare scenario imagined by Tim Berners-Lee in which decisions in the financial world are taken by machines that create and manage companies, completely apart from typically human notions (and difficult to explain for a machine), as the social impact or the common good is certainly worth reading, and quotes the phrase of that recent Vanity Fair interview with Elon Musk in which he spoke of the same type of dangers, of automatic optimization and of what could happen with an algorithm that optimizes the production of strawberries:

Cartas al CEO: Machine Learning

Cartas al CEO: Machine Learning

My letter has been one of the chosen to be published in open as "aperitif" of the book, and yesterday was published on the page of Kromann Reumert, a Danish legal and business consultancy company that collaborates with THINKERS50, under the title "Changing the Way we understand technology" so I thought it might be interesting to share a Spanish version here. 

My letter is an attempt to explain to a hypothetical CEO the importance of understanding the change in the role of technology, how it has been going from being a simple automation tool, to gradually take a completely different role, that of a tool with a ability to analyze brutal data and is able to learn from these data through various methods to reach processes of deep learning or reinforcement learning that will change the World as we know it.

That a machine is able to win the best human chess players, of jeopardy, of go or of poker is not as such important in itself by the fact, but by what it demonstrates: respectively, that a machine possesses a computational brute force capable of surpassing any human brain, which is also able to understand and process the natural language better than many humans , which can even devise and invent new original strategies competing against itself in a way that no human had ever been able to do, and finally, who is able to study environments with uncertain or unknown information and make better decisions than any human trained for it. The letter, in fact, is an attempt to put into perspective something that many still do not understand and are not able to understand in its true dimension, but which is going to represent the most important change we have experienced in the history of humankind.

I greatly appreciate my friends of BigML, company of which I am strategic advisor, the opportunity that for some time now provide me to learn and understand the technologies involved in the machine learning. Without that fundamental obligation to keep me up-to-date to be able to live up to it, it would have been infinitely more difficult to understand its true scope and dimension.

Then the Spanish text of my letter (original version in English here):

In a few years, the idea we have of computers and computing in general has changed dramatically, although the perception of a generation of executives has not. Changing that perception and being able to reinterpret what technology can do for our business is a more pressing need every day.

For many years, we understood computers as a form of automation. Any task that had repetitive, intensive or tedious components could be able to be automated using a computer with the appropriate program. The arrival of computers in corporate environments was thus, choosing those areas characterized by tedious routines, such as calculating and paying payroll, accounting, etc., or those in which there was a legal requirement for preservation of information.

The idea that we had of a computer was that of a machine that could do the same thing as a person, but faster, cheaper and with fewer errors. This idea of computing as automation has been a constant in the approach of the investments in technology since the beginning of the history of the Corporative informatics.

For some time, this approach has changed dramatically. When we see in the news that a computer has been able to beat chess no less than the world champion Garry Kasparov, who has defeated by wide margin the jeopardy the best players in the history of the program, or has pulverized in the game of go to the most recognized players in the world, and we do not talk about the same computation that we knew so far : To carry out these feats is not enough to have more power of calculation or to do the same operations as a man but at more speed. It is not just a matter of brute force: it is another way of raising things.

The new frontier is called machine learning, and it will provoke such a brutal change in the environment that it will reach the point of minimizing what was at the time the impact of the Internet. A real dimensional change that will define which companies prevail and which simply disappear. It will change what we understand for work, and alter society as a whole. And all of this, within about five years. At the moment, the essence of the true competitive advantage is to be able to provide sufficient data to feed machine learning algorithms that are better, more efficient and more competitive than those of our competitors. There's no more.