The basic concept behind machine learning is to get the machine to classify data in a multitude of different ways so that it can easily interpret new data.
All machine learning programs go through extensive training in which they see huge amounts of preliminary data, with the goal being that once the program is used in the real world it will be able to accurately classify and interpret data sets that may not be as clean as what it sees in the controlled training setting. This training must include not just feeding the program training data and then calibrating based on those results, but multiple layers of training and re-testing with new data to ensure that the machine is ready to encounter any kind of complex or unorthodox data sets.
All machine learning algorithms contain three things. The first is a language or set of identities that a computer can learn, which basically defines what can be filtered through the program. If data goes to the machine, it must both be properly labeled and have a pre-existing space within the machine to evaluate it. If either of those requirements are not fulfilled, the computer won’t know what to do with the data that it sees, and it will go to waste. Secondly, it must be able to evaluate which sets of data are performing best, so a scoring function is used to determine the most impactful identifiers and kinds of data. There should also be an external evaluation of the machine’s work as well. Lastly, the machine must be capable of searching among its classifications for that which scored the highest in its evaluations. This helps the machine develop better techniques and determine if there are multiple optimal functions.
Artificial intelligence spans much more broadly than machine learning, which has been much more recently developed. As AI refers to any machine programmed to do a job, machine learning only includes those programs which can use their past experiences to improve their future performance. A constant influx of data will constantly make a machine learning model more productive, whereas any typical AI program sees no internal benefit from more or less data, it processes what it’s given as it’s been programmed. In essence, all machine learning falls under the umbrella of “artificial intelligence,” but not all AI can be called machine learning.
Because the impact of AI is so broad, it really is nearly impossible to predict exactly how far this technology can climb. Artificial intelligence can and has been applied in nearly every major business industry and will most likely lead to the rise of new industries in the coming decades as its advancements will create new possibilities. Improved data collection and computer processing will make AI better than ever before.
But how will corporations and workers adjust to the growth of such a powerful technology, perhaps more impactful than any we’ve ever seen? What role will this play on the micro stage of the individual who is told AI is being implemented and that his role will adjust accordingly, or on the macro stage of the businesses who has to stay at the front lines of AI and ML developments just to survive against their competitors? We will investigate these questions more deeply later, but changes will be felt widely and deeply. Job displacement could be very widespread, especially among the bottom half of the population in education, as easily repeatable or objective jobs will be done by machines.
But simultaneously, as always happens with improvements in technology, new fields will open up and present new opportunities. Those who lose jobs will have the chance to find new ones that have been created by the existence of AI. Amazon offers to pay its employees to train for jobs elsewhere. Other companies must begin to encourage the retraining of their workforce to adjust to a world where simple tasks may all be automated but computer science and programming are vital. Kai-Fu Lee, a groundbreaking computer scientist, states that AI is a tool used to “amplify human creativity” instead of replacing it. Those who can best take advantage of AI are those who are able to take advantage of the insights it offers that are impossible for human brains to decipher while still being able to give the human touch that machines still aren’t capable of.
There are still large breakthroughs yet to be made in the world of artificial intelligence. One large one will be when machines can understand, translate, and express different languages fluently on their own, both computer language and human language. Computers currently can read in data for example and store it, but they are not generally good at expressing the true content and meaning of that information and language. Once they are able to do so, the world available to them will explode, every record in human history will become artificial knowledge, making computers capable of answering more questions than ever before.
A lofty goal, certainly, but one that programmers are working on. Another goal is to give machines the level of memory that humans have, which circles around several different types of memory, such as the knowledge of how to repeat an action upon discovering it without programming help or the ability to recall from previous experiences or remember personal facts.