(Artificial) Intelligence, which one?

After we had heard a lot of talking about Cognitive, then Data Science, then Machine Learning, the concept of Artificial Intelligence is back in headlines. Indeed, most of these terms are about the idea of machine understanding problems and taking decisions as intelligent humans would. Intelligence covers a lot of things as the Wikipedia quote suggests:

Intelligence has been defined in many ways to include the capacity for logic, understanding, self-awareness, learning, emotional knowledge, reasoning, planning, creativity, and problem solving.

So it should not be surprising that Artificial Intelligence is as diverse as Human Intelligence, including different approaches to problem solving, different types or levels of intelligence, with different benefits and inconvenients.

Recently, I have been on holidays with my family and we all had to prepare our luggage. This is a simple and common decision making problem. What should I put in my luggage? How many socks? Should I bring one book or too? Should I take shirts or sweaters?

The different approaches used illustrates nicely the different types of Artificial Intelligence.

Me

In my personal case, these are easy decisions. I more or less randomly take always the same stuff and then I manage to adapt on site. I remember the first time I went to Chicago in winter, with -11 degrees Celsius, the adaptation was not so easy...

We might agree that taking decisions randomly cannot really be considered intelligence, even if we could agree that a huge part of human decision making is based on this, even for very important decisions. Human decisions are affected by quite a few other human specific factors.

Limited memory. Humans have limited memory, and sometimes, even faced to the exact same situation that before, they are able to take the exact same bad decision. My great mother most repeated recommendations was: “making a mistake is not important, but doing the same mistake twice is really stupid”. Some commonly heard similar advice is that “either we succeed or we learn”.

Limited calculations. When calculations are required (e.g. total weights of items in the luggage) and when the problem size grows, large calculations are required, and humans are definitely limited.

Emotions. Humans have emotions. Emotions are probably the most impacting factor for human decision making. Studies show the same person does not take the same decision on the same problem depending on their emotional state this day.

Environment. Under pressure, when tired, with severe weather, human decisions will be impacted.

Adrián

In the case of my younger kid, Adrián, he just followed the rules provided by his mother: “Mum told me to put one set of socks, shorts and shirt per day, then to put one book, and then some toys if I can make it fit”

This could be considered as a first level of intelligence.

Most of our education system is dedicated to teach our children this ability to understand instructions in the form of rules and execute them and, by the way, this level is largely enough for a minimal survival in human society. At home, at school, and at work, lots of human don’t need or use any higher level of intelligence.

The quality of the decision is mostly impacted by the quality of the rules and the ability to execute them correctly.

This corresponds to Business Rules Management Systems.

Hugo

My older kid, Hugo, has gone to the next level. He has learnt on his own based on looking at his mum packing luggage several dozens of times.

Indeed, in our complex world, you cannot explicitly provide all the rules on everything to your kids, so you expect them to learn, looking at you and looking after other models you tell them are good. You eat healthy food, do sports, and read books, expecting your kids to look at you and reproduce these good habits.

This is another level of intelligence, when humans can just learn on their own. Some humans (not all)  use such kind of intelligence. A very limited type of learning is at least not to repeat the same mistake  twice (see great mother advice above)

Hugo had to see his mother pack luggage a dozen of times before he could be autonomous. The quality of his packing highly depends on whether he learnt from someone or alone, and who he learnt form. If he would have learnt looking at me, then the outcome could have been quite worse.

Also this is interesting to note that as he has seen his mother pack many times for summer holidays (to the beach), so he would still be in trouble if we would have to pack luggage to go skiing…

This one corresponds to Machine Learning.

Mónica

The third type of intelligence is what my wife, Mónica uses. She knows that the airline luggage weight limit is 23kg, she knows what items are mandatory or not, and take into account destination weather to set preferences between optional items.

Based on these constraints and preferences, she mentally calculate that this item plus this item would not comply with the weight constraints, or that this set of items offers a better global satisfaction than this other set.

There are not so many decisions, constraints and objectives here so that she can optimize the decisions mentally.

Reasons for the packing being wrong could be, for example, a bad prediction of the destination weather, or a misunderstanding on airline regulations.

This is how Decision Optimization works.

So how to decide?

Intelligent humans know when to use each level of intelligence. Sometimes, deciding does not require to develop an optimization model (e.g. decide where to go out for dinner tonight), but sometimes it helps (e.g. to decide how to invest your savings).

With Artificial Intelligence, a similar situations occurs, where different approaches, which still not always easily combine, may have different benefits and inconveniences. This is why this is critical to understand these different techniques and be able to identify when each one would better apply and when they can combine efficiently.