Tag: artificial intelligence

AI algorithms fall into many classes. Wikipedia reports these:

1. Search and optimization
2. Logic
3. Probabilistic methods for uncertain reasoning
4. Classifiers and statistical learning methods
5. Neural networks
6. Deep feed-forward neural networks
7. Deep recurrent neural networks
8. Control theory
9. Languages
10. Evaluating progress

Most of the above techniques, not to say all of them, are pretty old, and anyone who has some grey hair has seen this stuff 20-30 years ago. We all agree that these techniques are quite successful and that AI is still in its infancy. But that is not the point of this blog.

The question we wish to ask is this: what, at this point, can we do to make AI less artificial and more intelligent, almost indistinguishable from humans? What can we do to take AI to another level, to provoke a quantum leap?

Let us first examine what the word “intelligence” means, let us look at its etymology. From Latin “intelligentia”: understanding, knowledge, power of discerning, from assimilated form of inter “between” ( inter) + legere “choose, pick out. Let us focus on the “choose”, “pick out” part in conjunction with the “inter”, i.e. between. Humans make choices, select options, weigh scenarios and discriminate between different solutions, hundreds if not thousands of times a day. Sometimes this is done using tools, in other cases based on gut, on intuition or experience. It is this capacity to select an option or a strategy that makes humans unique. Life, with all its complexities and nuances, offers almost infinite ways and means of setting goals and then reaching them. No two people will do things in exactly the same manner.

Now we don’t want to get too philosophical here. The idea is to simply state that a generic problem solving process goes more or less like this:

1. Problem statement (definition)
2. Verification if the problem actually has a solution
3. Selection of solution method (there may be many)
4. Solution
5. Verification of result

This is of course a very gross description. A generic example is illustrated below. Suppose that one has to cross the network (or some domain) from left to right.

Suppose that we identify three possibilities represented by the three paths shown in the figure. Suppose, just for the sake of discussion, that each of these paths entails very similar energy expenditure, time, cost, risks, etc. Which path would you choose? All things being equal (or not necessarily all) an experienced and wise individual (or a good engineer!) would probably select the least complex alternative. Humans instinctively imagine multiple scenarios and assess their complexity trying to stay away from the ones that will potentially make life complex in the next few minutes, days, or years. This logic applies to running a family, a corporation or a battle scenario. High complexity leads to fragility.

The key, therefore, is to be able to measure complexity. Since 2005, thanks to Ontonix this is possible. We have now an Italian Standard, the UNI 11613, which shows corporations how to do a “Business Complexity Assessment” and work is in progress to deliver a similar ISO standard, the ISO 22375 (“Guidelines for Business Complexity Analysis”).

The bottom line is that today we have a consolidated technology known as the QCM, or Quantitative Complexity Assessment. QCM provides measures of complexity, not sensations. The way QCM works is simple. Suppose you need to design a turbine and you come up with two candidate designs, which may be represented by the two Complexity maps shown below. The maps illustrate which parameters of our turbine are correlated with which other parameters. More correlations – interdependencies – means the system is intricate, difficult to understand and to fix. The first solution has a complexity of 3.03 cbits. It has 12 correlations between the 10 parameters.

The more complex solution, shown below, has a complexity of 5.2 cbits and has 19 correlations between the said ten parameters.