AI For Data Science

There is no doubt that Artificial Intelligence (commonly abbreviated AI) is making waves these days, perhaps more than the world anticipated as recently as the mid-2010s. Back then, AI was an esoteric topic that was too math-heavy to attract the average computer scientist, but now, it seems to be a household term. While it was once considered sci-fi lingo, it’s now common to see and hear the term “AI” featured in ads about consumer products like smart phones. This is to be expected, though; once an idea or technology reaches critical mass, it naturally becomes more acceptable to a wider audience, even if just on the application level. This level refers to what AI can do for us, by facilitating certain processes, or automating others. However, all this often gives rise to a series of misunderstandings. As AI itself has become more well-known, so have spread various ominous predictions about potential dangers of AI — predictions that are fueled by fear and fantasy, rather than fact. Just like every other new technology, AI demands to be discussed with a sense of responsibility and ethical stature. An AI practitioner, especially one geared more towards the practical aspects of the field, understands the technology and its limitations, as well as the possible issues it has, which is why he talks about it without hyperbole and with projections of measured scope – that is, he projects realistic applications of AI, without talking about scenarios that resemble sci-fi films. After all, the main issues stemming for the misuse of a technology like this have more to do with the people using it, rather than the technology itself. If an AI system is programmed well, its risks are mitigated, and its outcomes are predictably positive.