Many of the terms most are familiar with when researching A.I. are familiar are the following terms: machine learning, deep learning, unsupervised/supervised learning, big data/interpretation, and many more. Normally, these terms can be very useful when describing the critical components of many A.I. developments. But ultimately, many projects will fail to be implemented in any meaningful way if they remain theoretical processes, algorithms, pseudocode, or programs that are unable to execute real-world tasks. In this way, the assets machine learning presents are only useful when they are coupled with capabilities including programmable logic, program .exes, memory, processing, develop recursions, computation abilities, clock cycling, data acquisition, and sensor fusion. While many of the aspects I’ve listed are oriented towards robotics applications, there is a diverse application and A.I. is only one of many uses. It remains a wide bandwidth component of computer science both in software and hardware. But A.I. is distinguished in the concern that building “smart machines”, whereby they are not only capable of enforcing pre-programmed tasks, but typically require capabilities which enable them to act, adapt, function, and in most cases learn. This final aspect is what makes A.I. unique, learning capabilities. Once an algorithm is made, using some type of feedback, an A.I. may be able to self-regulate or update its content and respond to a problem given new data, actively responding based on new it receives information through each iteration. For most A.I. applications, I find one of the best ways is to learn from how people have used A.I. in the past. Of course, it may not be the only way to learn.
To this day, having written over +200 program kernals myself, I am still learning new methods and trying to understand new libraries that are made and seeing how they may be incorporated to develop my next projects. I never believe that it is simply enough to “know how to program/code”, because any true engineer or data scientist would agree that data structures are naturally inclined to evolve, expressions may require broadened sets of data to interpret in any meaningful ways and in many cases may be re-defined, as is the reasons we constantly have “updates” to most softwares. Much like machine learning, we ourselves are learning and improving based on past data. This is stochastic process in the nature of education. (TBC…)
Here is a link to anyone who might be interested in learning or starting to learn about machine learning from M.I.T’s Open-Course Ware. Please note that this may be only the beginning to a potential lifelong journey with A.I.: