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Practical Projects & AI

On the topic of being afraid to look foolish in the field of artificial intelligence

It can hardly be said that I am adventurous if I have hardly left home, metaphorically speaking. Is it not better to be revealed as lacking knowledge than to claim any kind of certain knowledge? Then, proceed somehow to attempt to gaining knowledge of what you lacked?

Practice can be: the actual application or use of an idea, belief, or method, as opposed to theories relating to it.

I can claim to know a great deal about artificial intelligence (AI) from a theoretical perspective, but I cannot claim a wealth of practical experience.

I have written one new article about AI every day for more than 400 days, yet that does not make me any vivid (or dull) practitioner. It only makes me someone who has written about AI for an extended period of time.

One can wonder whether it is fair to criticise so heavily without moving into the field of AI. The simple answer is ‘yes’, of course one should be allowed to criticise and it is fair to do so!

However, it is more of a question of what role one wants to take or what one want to contribute.

What do I want to contribute?

It is fair to be a food critic, but then you are not expected to be a good chef or cook food. Do I want to be a food critic? Not really.

So, what do I want to be? Do I want to be a questioner, critic, practitioner or a strange mix?

Pratyusha Kalluri on the 7th of July 2020 wrote an article titled:

“Don’t ask if artificial intelligence is good or fair, ask how it shifts power.”

It is hard to disagree.

She also gives a call to action, arguing that those who could be exploited by AI should be shaping its projects.

The field of AI is not simply neutral and beneficial. She continues:

“Many researchers think that AI is neutral and often beneficial, marred only by biased data drawn from an unfair society. In reality, an indifferent field serves the powerful.”

To a certain extent I feel that if I am to become less indifferent I need to get more involved.

Getting more involved means taking a risk and allowing myself to make more mistakes.

I wonder if that is what developers tell themselves? “Move fast and break things,” is a much known saying in the technology industry that decorated the walls of Facebook. Although this has allegedly by Zuckerberg changed into: “Move fast with stable infra[structure].”

That is one hand the conundrum of perceived speed in learning. As opposed to or accompanied by consistent progression.

Failure, however, is to some extent inevitable.

Have you ever seen someone able to ride a bicycle on their first try?

Should one decide never to ride a car simply because one did not pass the first driver’s test or one feels too old to learn how to drive? One could, certainly.

Would you believe: a self-proclaimed AI writer knows little about the life of being a practitioner in the field of AI.

I am ignorant, foolish even for not wanting to go deeper. Shallow learning.

I have begun programming the last year, and it has been interesting alongside subjects in social science, however it is a question as well about how much time I have for aspects of life that I appreciate.

A balancing act. How much social science can I read, how much can I write, and how long can I learn programming?

Programming for 100 days — it is not even an option to some extent if other aspects of life is taken into consideration.

I could do one project for 100 days or 99 days now.

Maybe, we will see tomorrow. That is what I could say to delay this further.

No, it is decided working on a practical project for 100 days to prepare and clean data then undertake machine learning.

For me rudimentary text based machine learning on a set of public documents seem an interesting way to go.

Many would likely think that this is too easy, but that could be said of many things in life and depends on your progression.

If I keep telling myself that I am not ready to ‘ride the bicycle’ I will not likely be able to ever ‘ride’ – learning the more practical aspects of machine learning applied to a certain extent in the field of AI.

99 days left and I will make them count.

This is #500daysofAI and you are reading article 401. I am writing one new article about or related to artificial intelligence every day for 500 days.



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