A.I. is great. There is no doubt about that. It’s been around long enough to be a respectable field of science and survive many years of skepticism, becoming more hands-on in the process. Nowadays, it’s been experiencing a Renaissance as it has become the favorite tool of many data scientists. Some people (not data scientists necessarily) even go so far as to claim that it will replace data science, as it is bound to automate the whole pipeline. Yet, whether it manages to replace the actual people involved in the data science process is still debateable.
Contrary to what the blind advocates of A.I. think, data scientists are not some mindless automatons who apply a formula until the hit an insight. In my experience, even the most mediocre data scientists out there has some intelligence and the know-how to apply it with some effectiveness. The aforementioned A.I. advocates probably never experienced that, as they tend to base their ideas on stuff they have read on some blog or some news article. Still, even though A.I. has displaced some of the traditional models that data scientists employ, there is more to the work a data scientist does than just crunching numbers. This is something that these A.I. fanboys fail to comprehend. This is probably beacuse this part of the data scientist’s work is not that appealing to the masses, so it rarely gets mentioned in those articles the A.I. fans are reading.
A data scientist’s role involves a lot of communication. That’s something that is yet to be accomplished by machines, even those running good A.I. systems on the back-end. Because communication is not just figuring out what the words you hear or read mean, it’s also about understanding intent and those subtle cues that are often in the words that are not there. I’d like to see an A.I. system handle that, especially when the communicator it has to understand is stressed out and fails to articulate properly what he expects, or if he is in the dark about what’s possible with the available data. A.I. is excellentfor NLP, but there is more to communication than this niche aspect of language-related data streams.
Moreover, a data scientist has to communicate the findings she comes up with or the roadblocks she encounters. Sometimes it takes several meetings to accomplish that and she needs to liaise with several other people in the company, many of whom are not data scientists and/or have a very limited view of the data at hand. Also, she needs to do that in a way that is succinct and comprehensible. Will an A.I. system be able to cope with that, within a reasonable timeframe? I doubt it.
So, without neglecting the value that A.I. adds and will continue to add to data science, it is important to manage our expectations of it. A.I. systems like the one in the movie “Her” may never become mainstream in the data science world, even if they do come about eventually. Say that company X invents such a system, do you honestly think that every company out there will be able to afford a license for it? If so in the beginning, for how long do you think it will remain affordable? These business-related aspects of technology may not be as exciting but they are as important as the technical ones. After all, someone has to pay the bills and that someone is not going to spend a lot of money on a system that may or may not be cost-effective.
A more realistic view of how things will be in the A.I.-imbued data science world is as follows. Most likely, A.I. will dominate in the data science pipeline, in those steps that can be automated. This will yield great efficiency, making the data scientist’s job somewhat different. So, instead of her focusing on building the models and fine-tuning them, she will concentrate on the more high-level aspects of the role. The A.I. is not going to replace her, but there is bound to be a synergy between the two players, with the human providing guidance and insight, while the machine takes care of all the low-level work. The future doesn’t have to be bleak like some Hollywood movies like to portray it (since that makes for a more interesting story). It can be something worth looking forward to, especially when it comes to data science.
Zacharias Voulgaris, PhD
Passionate data scientist with a foxy flair when it comes to technology, technique, and tests.