<p>The availability of large data sets.</p>
<p>In symbolic/rule-based AI, the 'knowledge' has to be hand-coded, usually by experts. This is expensive and limited to small-scale problems only.</p>
<p>In statistical AI/Machine Learning approaches, this is replaced by training a system on data. With very large amounts of data it is possible to achieve results that are not possible with rule-based approaches. In the past, such data sets were usually not available.</p>
<p>However, this also means that the AI system has become somewhat of a black box, as it is not possible (at least not easily) <em>why</em> the system has behaved in a particular way.</p>
<p>So in areas where this is important (eg banking and finance), rule-based approaches are still valuable.</p>

CC BY-SA 4.0
2022-09-13T15:30:06.233Z
What about Fuzzy logic?
user366312
CC BY-SA 4.0
2022-09-13T16:23:03.127Z
Fuzzy logic is basically a different way to express rule-based information.
Oliver Mason
CC BY-SA 4.0
2022-09-13T08:56:56.483Z
2022-09-13T08:56:56.483Z
Oliver Mason
<p>What is the fundamental reason that statistics-based AI (e.g., ML and Neural Net) has become more popular than other forms of AI, e.g., Fuzzy Logic and rules-based AI (e.g., Prolog)?</p>

CC BY-SA 4.0
2022-11-27T04:21:47.137Z
Isn't statistics itself rule based from the point of view of human understanding? For example, does cross-validation count as some kind of rule?
cinch
<fuzzy-logic><statistics>
Why has statistics-based AI become more popular than other forms of AI?
user366312
# Why has statistics-based AI become more popular than other forms of AI?

(Asked by: username_0 on 2022-09-11)

What is the fundamental reason that statistics-based AI (e.g., ML and Neural Net) has become more popular than other forms of AI, e.g., Fuzzy Logic and rules-based AI (e.g., Prolog)?

**Tags:** fuzzy-logic, statistics

> **Comments:**
> **username_1**: Isn't statistics itself rule based from the point of view of human understanding? For example, does cross-validation count as some kind of rule?  

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## 1 answers

### Answer by username_2 on 2022-09-13. Score: 5 | Accepted answer

The availability of large data sets.

In symbolic/rule-based AI, the 'knowledge' has to be hand-coded, usually by experts. This is expensive and limited to small-scale problems only.

In statistical AI/Machine Learning approaches, this is replaced by training a system on data. With very large amounts of data it is possible to achieve results that are not possible with rule-based approaches. In the past, such data sets were usually not available.

However, this also means that the AI system has become somewhat of a black box, as it is not possible (at least not easily) *why* the system has behaved in a particular way.

So in areas where this is important (eg banking and finance), rule-based approaches are still valuable.

> **Comments:**
> **username_0**: What about Fuzzy logic?  
> **username_2**: Fuzzy logic is basically a different way to express rule-based information.  

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