I think they share a lot (e.g. machine learning is a subset of both, right?), but maybe both have elements the other doesn't have?

Could you name some in that case? Or is one a subset of the other?

What is the relationship between AI and data science?

For example, when it comes to the relationship of AI and ML, I always say AI is a superset of ML. And the distinguishing set is search algorithms, which I would include in AI but not in ML. Would search algorithms be included in data science?

  • $\begingroup$ Actually my personal interpretation ìs that AI is a big field that ML is a branch of that. At least based on what I have learnt these years from professor Russel's book about definitions of AI. $\endgroup$ May 12, 2018 at 9:24
  • $\begingroup$ I think it's a decent question, though several people would consider it too opinion-based. I'd like to leave it as a wiki. $\endgroup$
    – Sean Owen
    Feb 15, 2019 at 14:30
  • $\begingroup$ @SeanOwen I've added the "reference-request" tag. This means answers can be opinion-based, but should be able to support it by somebody who published that view. $\endgroup$ Feb 15, 2019 at 15:14

2 Answers 2


Though neither are well defined, as commonly used they are somewhat orthogonal concepts.

In my opinion, AI has a fairly narrow definition - it is about optimization through actions. AI is about decision making, either in deterministic or probabilistic environments. Typically, this is operationalized as action selection to maximize some reward function, or equivalently to minimize some loss function. Supervised or unsupervised learning (i.e. machine learning) about your environment can be helpful in using your experience to aid in selecting optimal actions.

As it's commonly used, data science has no rigorous definition - businesses use the term to refer to anything from creating charts Excel to deep reinforcement learning models that can win Go. From the point of view of a practitioner, these have absolutely nothing in common. From the point of view of a business owner, the common thread is extracting meaning, and therefore value, from raw data. A data scientist is a 'meaning extraction layer'. How this operation is performed, and the techniques used (again in my experience), make no difference to the employer of a data scientist. The job title may as well be 'data magician'. But the point is that data + data science = business value, whether that comes in the form of insights into customer trends, causal analysis of marketing campaigns, or an AI bot that is 'rewarded' when it recommends you a movie that you like. I suppose that means that AI is a subset of data science - but you could also say the same thing about clear communication, so it's a bit of a non-statement.


Artificial intelligence is just the big set of what can be fully automated. A simple calculator is a form of AI. Everything that makes a human tas automatic is AI.
Different is for machine learning which is a subset of AI and it is always mislead with it. ML focuses on those statistical techniques that allow us to automatically make predictions on something. Data science makes use of the techniques offered by AI to process and extract info from raw data which is meaningless as it is. Hope I've been clear.


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