Difference Between AI and Machine Learning.

What’s the Difference Between AI and Machine Learning?

As computer systems become smarter than humans, we should learn the difference between our new overlords.

What is AI?

AI: Artificial Intelligence (AI) is usually defined as the science of making computers do things that require intelligence when done by humans. AI has had some success in limited, or simplified, domains.

First, there are different types of artificial intelligence (AI): weak and strong.  Weak AI might behave as though a robot or manufacturing line is thinking on its own. However, it’s supervised programming, which means there is a programmed output, or action for given inputs.

For example, when an AI program was instructed to obtain the highest score it could in the video game Breakout, it was able to learn how to perform better and was able to outperform humans in just 2.5 hours. Researchers let the program run. To their surprise, the program developed a strategy that was not in the software. It would focus on one spot of bricks to poke a hole so the ball would get behind the wall. This minimizes the work, as the computer no longer has to move the bat while the score would increase. This also minimizes the chances of missing the ball and ending the game.

Keep in mind that the computer isn’t seeing the bat, ball, or rainbow stripped bricks. It “sees” a bunch of numbers. It knows what variables it controls, and how it is able to increase points based on how it controls the variables in relation to the other numbers.

Machine Learning

Machine Learning: A type of AI that can include but isn’t limited to neural networks and deep learning. Generally, it is the ability for a computer to output or do something that it wasn’t programmed to do.

“Deep learning is a special type of machine-learning algorithm—it is multiple layers of neural networks that mimic the connectivity of the brain, and these types of connectivity seem to work much better than pre-existing systems,” said Samarjit Das, a senior research scientist at Bosch. “We currently have to define parameters for machine learning based on our human experience. When we look at images of apples and oranges, we need to define features manually, so that machine-learning systems can identify the difference. Deep learning is the next level because it can create those distinctions on its own. By just showing sample images of apples and oranges to a deep-learning system, it will create its own rules realizing that color and geometry are the key features that distinguish which are which, and not have to teach it based off human knowledge.”