Machine learning, artificial intelligence, and deep learning are different things. Sometimes people naively use machine learning and artificial intelligence interchangeably. Here, you can learn more about these things.
The differences are very powerful here. These three things give computers different capabilities with different applications. Let’s start with artificial intelligence.
Artificial intelligence is something that it seems everyone is talking about in tech. They imagine what our world would be like if we could outsource certain thinking to machines.
Artificial intelligence can be very exciting. Yet, scientists are still in the early stages of work in this field. They are currently still working with narrow artificial intelligence. This is the type of AI that gives a computer the power to do one specific thing well.
More general artificial intelligence is where a machine can perform complex tasks. To achieve this level, a machine must be about as good as a human at complex tasks. This is not a level that science has reached yet. Maybe someday it will.
For now, we are working at the narrow intelligence level. It’s still very exciting. In some cases, artificial intelligence can save people a lot of time. It can be very good for people who use automation in their businesses.
Machine learning is different from artificial intelligence. Where artificial intelligence as we know it now focuses on a machine performing a narrow task, machine learning is about the computer learning from its environment.
A computer can learn if you give it the right amount of the right algorithms, theoretically. It’s still up to humans to create useful algorithms. But the machine can take a lot of work off of people’s plates if it has good numbers.
There are three basic models for machine learning. They are supervised, unsupervised, and reinforcement learning.
Supervised learning means to tell the machine what certain labels and values are. The machine will be able to finish organizing a set of data based on your labels if it can learn.
Unsupervised learning will allow the machine to make its own labels for each data set.
Reinforcement learning is where the machine interacts with its environment. It learns from penalties and rewards. A chess game with a machine is a good example of machine learning put to work. It “learns” from winning and losing pieces in the game.
Machine learning relies on the computer checking the values of its algorithms. The machine continually tries to get better at solving problems.
But deep learning is about one computer taking the outcomes of another computer’s output. In short, one computer’s output is another computer’s input.
Deep learning relies on layered (although simple) concepts to create meaningful output from simple input. It’s all about layers. Many, many layers make for complex output when it comes to deep learning.
These three things are all similar if you look at the math involved. They are learning and creating data sets and using algorithms. It’s what the machines are doing with the algorithms that is different.
In all three instances, machines are working to create less work for people. In theory, an advanced computer would be able to analyze things faster than a person. It would be a lot less work for some people to have computers do data analysis, for example.
Now, theory and practicality are different. Computers have to be programmed well to do this. It takes a lot of internet capability and storage space to achieve these things in the real world. But it’s something that scientists work on every day.
Data science is evolving at a rapid rate. To answer the question, think about how much data you really need to run a call center.
How much work can you get done relying on humans to analyze and create all the data? Managers and executives know that making decisions based on data is essential. But where can they get, process, and act on all the data?
They will have to rely on AI, machine learning, and deep learning to get the job done.
The world is approaching a future in which machines work on data together with humans. To do this, machines must be able to learn, function, and create at least a little bit like people.
Call centers are probably one of the most data-centric businesses there are. People are constantly on the phone with customer service. The sheer volume of calls and quality control requires technological intervention. People simply cannot deal with all the data efficiently alone.
The future is exciting! It holds a world full of intelligent machines and humans who have more capacity for higher-level thinking.