Is Machine Learning Really AI? (Pt. II)
Continuing a previous article, Ronald Schmelzer explores in a Forbes article whether or not many machine learning projects are truly AI:
If AI is to be a useful term to help delineate different technologies and approaches from each other, then it has to be meaningful. A term that means everything to everyone means nothing to anyone. The previous article on this series “Is Machine Learning Really AI?” went over the various perspectives on what AI has to mean to be useful. The general view is that systems are intelligent when they can 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. You can further define intelligence as being able to increase your future freedom of action, and determine on an individual basis what future outcomes you want based on actions.
Machine learning is the set of technologies and approaches that provide a means by which computer systems can encode learning from experience and data and then apply future information to that learning to come to conclusions. This machine learning is in contrast to explicit programming, where the human uses its own intelligence to accomplish all the goals of cognition. Clearly, machine learning is a prerequisite for AI. However, 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.
