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Wednesday, June 06, 2018

Not all Machine Learning (ML) Is Artificial Intelligence (AI): Part II | CTOvision

"No two companies define Artificial Intelligence (AI) the same way, but they all insist they are doing it. Or at least some version of it" reports Ronald Schmelzer, principal analyst, managing partner, and founder of the Artificial Intelligence-focused analyst and advisory firm Cognilytica



However, if AI is to mean something and be a useful term to help delineate different technologies and approaches from others, then it has to be meaningful. A term that means everything to everyone means nothing to anyone.

In one of our previous articles “Is Machine Learning Really AI?” we go over our opinions on what we believe AI has to mean to be useful. In summary, our view is that AI systems need to be able to sense and understand their environment, learn from past behaviors and apply that learning to future behaviors, and adapt to new circumstances by reasoning from experience and learning and then generating new learning from those new circumstances and experiences. As we have defined in the above article, Machine Learning is the set of technologies and approaches that provide a means by which computer systems can encode learning and then apply future information to that learning to come to conclusions. Clearly, Machine Learning is a prerequisite for AI. But as we’ve said before, ML is necessary, but not sufficient for AI.  Likewise, not all ML systems are operating in the context of what we’re trying to achieve with AI.

So, Which Parts of ML are not AI? 
In the above newsletter article, we talk about what parts of AI are not ML, but we didn’t dive into what parts of ML are not AI. In our conversations with customers we seem to find two divergent perspectives of ML.  Some say that even the narrowest form of AI is still AI.  Since we have not yet achieved Artificial General Intelligence (AGI), despite some attempts to get us close, then all practical implementations of AI in the field are narrow AI of one form or another. We find this reductio ad absurdum unhelpful. It’s not useful to call a data science effort that uses random decision forests (a form of ML) for the specific task of achieving a very specific learning outcome to be at the same level as attempts to build systems that can learn and adapt to new situations.

On the other hand, we’re in the camp with those that say that forms of predictive analytics that use the methods of Machine Learning are indeed ML projects, but they are not AI projects in themselves. In essence, using ML techniques to learn one narrow specific application, and in which that training model cannot be applied to different situations or has any way to evolve or adapt to new situations is not an AI-focused ML project. It’s ML without the AI.  Hopefully this Venn diagram might be helpful as a way of explaining which parts of ML are contributory to AI and which parts are not:

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Source: CTOvision 


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