Hi I study AI by myself with "AI a modern approach" I've just finished the chapters about bayesian network and probabilities, and I found them very interesting. Now I want to implement the differents algorithms and test them on differents cases and environments. But the question is : is it worth it to spend time on these techniques ?
*AI, A Modern Approach," was given that title to break from previously narrow approaches to duplicating desirable qualities of human thinking.
Although Bayesian networks require somewhat resource intensive computational elements, the importance of Bayesian inference and probability are still of paramount importance in that some of the highest scientific thinking require mastery of them. Furthermore, developing in silicon dies (or possibly graphene nanites) the machinery to perform elementary probability computations in massively parallel architectures may arise over the next few decades. The use of video DSP circuits to implement ANNs is a notable segue into this kind of development.
I would not dismiss the techniques you just read about. If your intention is to capitalize on the recent crazes, you may enter the river of wannabees chasing every current trend, implement many systems that other people will rewrite later, and have a meaningless yet profitable career. I would recommend following your inquisitiveness instead.
The chapters for Bayesian Networks are:
- Quantifying Uncertainty
- Probabilistic Reasoning
- Dynamic Bayesian
- Naive Bayes, hidden variables, Markov
. Are We Going in the Right Direction? ... p.1049
If you find them interesting then invest more time to it. You might improve them and break new scientific ground.
Recent trend goes Deep Convolutional Neural Networks (ex: AlphaGo)