Searching for a keyword data science on Google, you might find a repeated pattern of keywords such as “sexiest job of the 20th century.” I use to find it interesting, particularly when a data scientist role is less known than the data engineers. Moreover, most of the experienced ones who have completed a large scale of machine learning aren’t working at a large enterprise. They work in academia such as a college professor, which leaves the market full of asymmetric information; unlike developed markets, information hasn’t flowed. Many people actually learning that every IT vendor can do a chatbot, but which one can get the job done?
“We can do everything from AI to basic statistics, even machine learning, and very good at them!!”
It’s like someone telling you he can be a doctor and a dentist. Don’t you believe that? And when should we start believing that him as a genius man who has two doctors inside him. In practice, you only trust this guy if he can show two medical degrees and two doctor practice licenses. Same with Data science, it is a big word. Think of Google as a prime example of leading machine learning company. Google can’t even make language translation from English to Thai right. Even Google hires over 100 machine learning engineers/data scientist / Ph.D. fellowships- one hundred. The Google product sucks. I don’t mean to say Google free API translation sucks, but scared resources of scientist, time, and finances force them to focus on a few things at a time. Google needs to focus on the core brain of machine learning. Another similar example of Google is OpenAI, privately owned by Elon Musk. OpenAI builds an AI to learn and play DOTA 2 against professional players, and it has defeated humans on many single battles. However, it has not proved that the AI can win as a team play yet. I hope that two examples can give you a bigger picture of AI development in the World today. AI has been developed by humans and increasingly become an important step in the industrial revolution. However, it’s will take a lot of time for us to teach one AI at a time. It won’t be creating ten AIs by hiring two college students.
To be continued…in the next chapter
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