How and what we observe?

Hello, readers!

Today I'd like to invite you to make an experiment.

Look at the image below.

Now, say out loud what you've just saw and observed. [1]

Please, don't skip this process and do not start to imagine how this is stupid. I think this exercise is going to be extremely important to have a grasp of the whole point of this post.

What did you observe?

There are many ways you could observe it. To list a few:

1. A table;
2. A photo of a table;
3. A 3d model of a real table;
4. A particular composition of woods that looks like a table. Since a table is just an idea (a concept), I'm not looking at an idea;
5. your answer...

If you are a computer scientist (like me) you could answer something like "I can observe a sequence of pixels, since this is a definition of an image and I'm looking at an image". Evidently, there is no correct answer to the question.

The reason why there is no right answer is because the question was too broad. No scope was defined. No contexts. No directions. No theories. [2]

The importance of theories

Karl R. Popper proved in his book: 'Conjectures and Refutations' that there was no way Isaac Newton could devise his laws from pure observation of the world. This is a strong assertive and I suggest you to read the proof if you are interested in more details.

Popper believed that you cannot successfully reach a general theory exclusively through observations. The point is we have to have a system of references to make an observation. It's like a guide in what perspective to use.

Thus, in order to answer the table-observation-problem, you picked out one theory from your experience, probably unconsciously, and thought something like: "I'm gonna look with the eyes of a Platonist (through Platonic ideas), so I'm gonna answer like 4: "A particular composition of woods...".

And not the other way, from observation to theory. [3]

Few words on big data

Today, the term "big data" is a well known buzzword. But what people mean with "doing" big data?

Doing big data, as far as I have seen and known, is trying to fit the data to one of our human theories. The most used are statistics and machine learning. In this particular case of big data, we clearly make a conscious decision of what theory to use to support our observations.

And maybe we should use other theories to look at it...

Till the next post!
Ronald Kaiser

[1] Feel free to comment in this post your answer, ;)

[2] Nevertheless, someone would frown if you say "I can see my grandma through this table!".

[3] Please, don't mess the words look and observe.

On making mistakes

Hello readers!

Today I'm gonna talk briefly about making mistakes.

We, human beings, usually regret ourselves on making mistakes. Avoiding them, undoubtedly, has been an advantage in an evolutionary point of view. Imagine a prehistoric man hunting in a savana. When he stumbled upon a rock while trying to catch a lion he was punished for his mistake with his life.

The work of Karl R. Popper, which I'm reading a lot nowadays, delineates another standpoint about errors, or perhaps, I should say, a discussion in another level.

Popper believed that the only way to get closer to truth (if it exists...) was to trust in reason and be critical. Thus, according to him, making mistakes is not just inevitable, but very necessary to improve our understanding about the world. In few words this is what his critical rationalism is all about.

Similar ideas are pointed out by other 'successful' people:

Seth Godin:

"You say you want innovation...If you're serious about this, you need to celebrate and promote failures" - starts at 10'47''.

Garry Kasparov:

"Let's forget about avoiding mistakes...We want to limit our ability to make mistakes. Making mistakes is like a crime. No! It's a normal part of the thinking process." - starts at 33'30''.

Sherlock Holmes:

When Irene Adler is in scene you can observe that Sherlock Holmes makes more mistakes. And as far as I know, it is an invariant in all Sherlock Holmes movies/series. Watch this video:

In this particular scene, Holmes is easily poisoned. Immediately after being poisoned, he still strives to understand how he was deceived, mentally reconstructing Adler's poisoning strategy. In a nutshell, learning from his mistake.

So, next time you make a mistake, laugh. It probably means that you are trying something new. And most importantly, learn with it!

Till the next time, readers!

Ronald Kaiser