By Fernando Monteiro, SVP EMEA
The two most common technology acronyms you may find yourself reading and talking about are Artificial Intelligence (AI) and Machine Learning (ML). However, despite hearing them so often, many are stumped to explain the definition, what they actually do, and how they differentiate from each other.
Throughout this article, discover the basics of AI and ML.
Artificial Intelligence (AI) is not easy to define as it is an ever-evolving field. Some authors define it as the part of computer science concerned with designing systems that exhibit characteristics we associate with intelligence in human behavior: understanding language, learning, reasoning, solving problems, and so on. (Barr & Feigenbaum, 1981).
That is why I find the way Russel & Norvig frame the concept more interesting and comprehensive. As an analogy they say that “the quest for ‘artificial flight’ succeeded when engineers and inventors stopped imitating birds and started using wind tunnels and learning about aerodynamics.”
Russel & Norvig describe two main dimensions to define AI.
One dimension is related to the concept of intelligence, which can 1) either be related to human performance or 2) rationally doing the “right thing”.
The other dimension is related to the subject matter itself: it can be 1) seen as an internal thought process or 2) focused on intelligent behavior that generates external results.
These two dimensions generate four combinations:
These four fields have been researched over the last few decades, proving to be relevant disciplines. However, the rational-agent approach to AI (“acting rationally”) has prevailed over the other dimensions as it is more appropriate for science development.
Therefore, another way to define AI is to say it focuses on creating agents that do the right thing.
Now that we understand a little more about AI, let’s discuss ML. Today, many confuse the two or use them interchangeably when in reality they are not the same thing.
Essentially, ML is a discipline of AI. So, although some AI systems use ML to achieve their results, there are many AI applications that do not use ML.
In ML programming, the pre-written algorithm is designed in such a way that the program learns how to solve the problem over time according to received data. It is a mathematical model that depends on sample data. The ML program adjusts itself to perform better as it is exposed to more data.
I hope this helps to better understand these important concepts. In future posts, I will talk about Data Science, real-life application of AI, and how we apply all of it here at Cognira.
Main source: Russell, Stuart; Norvig, Peter. Artificial Intelligence (Pearson Series in Artificial Intelligence). Pearson Education. Kindle Edition.
Fernando Monteiro, SVP EMEA, is helping the international expansion of Cognira. He brings 15+ of experience from management consulting with a particular focus in consumer and retail sectors. As a management consultant, Fernando realized the potential of AI to create value and started working in partnership with Cognira.
Fernando is Brazilian, with dual citizenship (Portuguese and Brazilian). He lives in Amsterdam, the Netherlands, with his wife, two kids, one cat and many bikes.
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