Did you know, that last week, during the audio reactive work, you utilized basic machine learning techniques?
Artificial Intelligence, or AI refers to the idea, and field of study concerned with, creating “computer-based systems that can learn new concepts and tasks” and/or “can reason and draw useful conclusions about the world.” (Shukla and Vijay, 2013)
There are popular, Hollywood driven ideas about what AI is. Typically, these include computer systems that have a sense of “consciousness”, or an ability to act and react like a human. However, AI, as with most things in life, is on a spectrum, with varying levels of autonomy, decisions-making capabilities, and domains.
In the current world we live within, we are surrounded by AI systems. These systems have been programmed to understand something about their world and “make decisions when faced with new situations”.
Please read the following brief exploration of AI from MIT Review:
As the above web article hopefully makes clear, AI is a large, complex field, that is still very active, and still very much in its infancy.
Machine Learning (ML) is a subfield of AI. Machine Learning aims to create automatic methods for machines/computers to analyze and understand electronic data. The more specific goal is for machines to “automatically detect patterns in data, and then use the uncovered patterns to predict future data, or to perform other kinds of decisions making under uncertainty.” (Murphy, 2012) The most important distinction between general AI and ML, is that the latter is looking for problems and solutions within very specific topics or related to very specific problems (i.e. what does a human face look like, or what type of purchase is this consumer likely to make next).
To achieve these aims, computer scientists and engineers employee statistical modeling and probability theory to assist machines create models to understand something about specific data. Once a machine can compare new data against these models, the machine is able to make predictions about what something is, or about what may be likely to occur in the future. This data can be literally anything; images, audio, financial data, GPS data, etc.
As with AI, you are obviously surrounded by and interact constantly with systems that employee machine learning techniques to know something about you, or to try and predict your future behavior. Netflix, Spotify, Amazon, Google, etc. all try and predict what you may want to listen to, watch, or search, or buy next. Facebook sells your data marketing companies, so that they can understand purchasing and browsing patterns about you, which then allows their machines to influence you towards your next purchase or content consumption.
Machine learning is also used to solve some of the most important and pressing problems of our time. The COVID-19 rMNA vaccines that were able to come to market so quickly were the result of scientists partially utilizing AI and ML techniques. AI and Machine Learning are used in countless domains, from hard science and engineering, to social science, and important to us, in art and music.
Please read the following to go a bit further:
Please also read the following to get a better sense of where AI comes from:
Please also watch the following videos, which help further solidly the ideas about, and distinctions between AI and ML.