Practical AI and how it is done
Artificial Intelligence in business is practical. When you think about neural networks, don’t think about abstract mathematical structures, but rather computer systems that need data to learn business processes and how to operate within them.
Data Science is not a real science, it’s an experimentation domain, where you need to constantly adjust, test, build prototypes from scratch, and rebuild what you have. It’s a framework for approaching problems rather than a specific set of tools. This paradigm of using neural networks, statistics on steroids, is what makes AI both practically and theoretically complex, with such a broad range of applications, which we’re going to cover in the next chapter.
So how Data Science or Artificial Intelligence is currently done? You could split the actual work into two parts, connected strongly with each other:
- implementation,
- research.
Implementation phase is focused...