It seems like robots have been on everyone’s mind as of late, with various news sources constantly reporting technological advancements that make bots one step closer to what we all envisioned when Terminator was first released.
Although we still have a long time to go until robots become so advanced as to resemble a human, scientists from all over the world have certainly been trying, working hard to develop artificial intelligence that they hope one day will grow to be as complex as the human brain.
Understanding how a robot functions can be very hard, though, especially once we start throwing around terms like “machine learning.” We understand that the curiosity to learn about the latest news in automation development can get a little overwhelming, which is why we’ve decided to help you process the subject.
Let’s break into the topic so you will never again ask yourself, “What is machine learning used for?”
What Is a Bot?
As of right now, the definition of a bot is that of a program or software that has been designed to respond to specific triggers or parameters with a particular set of actions. Imagine that you’ve been invited to a megacorporation, and, as you’re walking through the doors of a particularly impressive lobby, you step by a couple of sensors that detect your movement.
That would be the trigger to a verbal welcome by a bot connected to a series of loudspeakers. “Welcome to our company!” it says, echoing across the lobby and making you desperately look around, trying to figure out if that disembodied voice is addressing you.
Dramatic examples aside, bots are usually seen in the form of chatbots, such as the ones you’re forced to deal with if you call your phone carrier to pose a couple of questions, or, in some circumstances, to order food from more unique, cutting-edge locales.
There are many varieties to a chatbot. Some of the most complex chatbots make use of machine learning, which is a fancy way of describing their ability to gather new information and put it to use – just as a human would.
Machine Learning Explained
A simple way of explaining machine learning would be to say that it is a way for bots to make use of data and algorithms to vaguely imitate the way in which humans learn. We’d like to put emphasis on “vaguely,” since mankind still hasn’t managed to replicate the complexity of the human brain.
Machine learning is thus a way for bots to develop themselves and learn new abilities without being explicitly programmed to do so. The framework for the line of thought that leads to learning new experiences has been set in place, but from that point onwards, it is solely up to the bot to reach the end of the journey.
Not all machine learning methods are the same, though. There are several main algorithms:
- Supervised – The bot makes use of internal labels to categorize new information and predict future events. This requires the application of a training dataset, which is essentially a small collection of commands to help direct the bot to the sort of knowledge it needs to gather.
- Unsupervised – This means learning patterns from data that has not been labeled. The bot, therefore, has to identify the main subject of the topic they are reading about by itself, comparing it to previous sources of information in an attempt to determine whether it is worth learning or not.
- Reinforcement – The bot essentially interacts with its environment through a variety of actions, trying to discover which one is rewarded and which is punished. This is a relatively human way of learning since it requires a whole lot of trial and error.
Top of the Iceberg
There’s a lot more to machine learning than we can cover in this article, so if you’re interested in the subject, then we wholeheartedly encourage you to do your own research. Bots can be outright fascinating to learn about, especially since we know for a fact that they’re going to influence our lives in the not-so-distant future.
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