When people nowadays talk about A.I., they usually refer to the deep learning methodology and other ANN frameworks. This is great, considering that ANNs were almost considered a dead-end once, due to the inability of technology to help them exhibit their potential. Yet, now computers are more powerful than ever and GPUs are commonplace as add-ons, enabling deep learning and other ANN-based system to function at greater scales. However, there are some other A.I. methodologies that are equally valid and actually predate ANNs. These I refer to as the “hipsters of A.I.” since they were part of the A.I. field before A.I. was cool.
The A.I. hipster methodologies are A.I. frameworks that are not ANN-related. These are systems like Fuzzy Logic (FL), which came about years before ANNs reached a level of development that made them worth using in machine learning. FL systems were used heavily in data analytics, while they were even implemented in hardware. At one point, researchers even experimented with a hybrid system that is part FL and part ANN (this was called ANFIS and was in essence an Artificial Neural network that optimized the membership functions of a Fuzzy Inference System).
Another hipster methodology is the family of optimization methods. These are systems like Genetic Algorithms, Simulated Annealing, and Particle Swarm Optimization (as well as its many variants). Although the scope of these A.I. fields is limited to finding optima of particular functions (aka fitness functions), their usefulness covers a variety of fields. Even dimensionality reduction processes sometimes make use of GAs or some other optimization tool. Note that these system are not the same as the analytical optimization methods known from Calculus, since they tackle very complex search spaces, with oftentimes dozens of variables, and use a stochastic process in the back-end.
If there is one take-away from these hipster A.I. systems it is that there is more than meets the eye when it comes to artificial intelligence. That’s not to say that deep learning systems are not worth your while, but it’s good to keep an open mind about other A.I. systems that may not be as popular today, but may have played (and still play) an important role in the evolution of the field.
Also, having a solid understanding of A.I. through its various methodologies, allows us to be able to think forward in a creative way. Instead of merely trying to extend the methodologies we know, we may come up with new ones, enriching A.I. in ways that we wouldn't be able to fathom if our understanding were limited to a single A.I. framework. Isn't that what A.I. is about, finding novel ways to solve problems, leveraging clever heuristics and imaginative architectures?
Zacharias Voulgaris, PhD
Passionate data scientist with a foxy approach to technology, particularly related to A.I.