The Future of Technology - Where Might It Take Us
Discuss:
What changes have there been in the last 100 years?
What have the results of these changes been? Which have been positive? Which have been negative?
What businesses and careers have been impacted by changing technology?
Consider:
How might technology change the world in your lifetime? Where will technology take us?
The Rise of Artificial Intelligence
How do AIs learn and make decisions?
We used to use an If/Then model of programming.
Would your rules work for these pictures?
Now we're building neural networks. The algorithm is given many examples. It is tested. Changes are made. It is retested. And, the process repeats again and again and again, creating increasingly accurate and powerful neural networks.
In the words of ChatGPT:
AI neural networks are like super-smart computer programs that can learn things by themselves, just like you learn new things at school or by practicing. They are trained using a special technique called machine learning.
Imagine you have a big puzzle to solve, but you don't know how to solve it. You start by trying different pieces in different places until you find the right combination. That's how neural networks work, but instead of a puzzle, they have a special task to learn, like recognizing pictures of animals.
At first, the neural network doesn't know anything, just like you when you start learning something new. It's like a baby that needs to learn everything from scratch. So we give the neural network a lot of examples, like pictures of different animals, and tell it which animals they are.
AI neural networks are like super-smart computer programs that can learn things by themselves, just like you learn new things at school or by practicing. They are trained using a special technique called machine learning.
Imagine you have a big puzzle to solve, but you don't know how to solve it. You start by trying different pieces in different places until you find the right combination. That's how neural networks work, but instead of a puzzle, they have a special task to learn, like recognizing pictures of animals.
At first, the neural network doesn't know anything, just like you when you start learning something new. It's like a baby that needs to learn everything from scratch. So we give the neural network a lot of examples, like pictures of different animals, and tell it which animals they are.
Huge data set.
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The neural network looks at these pictures and tries to find patterns or features that are common to each animal. It starts by making random guesses, and we tell it if it's right or wrong. It keeps adjusting its guesses and learning from its mistakes until it gets better and better at recognizing animals.
Think of it like playing a game of "guess who" with the neural network. You show it a picture of an animal, and it tries to guess which animal it is based on the features it has learned. If it's wrong, we tell it the correct answer and it learns from that. The more examples we give the neural network, the smarter it becomes. It learns to recognize different animals by looking at their shapes, colors, and other features. Eventually, it gets so good that it can even recognize animals it has never seen before! |
Training a neural network takes time and lots of data, just like it takes time for you to become really good at something. But once it's trained, it can do amazing things like recognizing objects, playing games, or even helping doctors diagnose diseases.
So, AI neural networks are like smart learners that start with no knowledge and use examples to figure things out. They learn from their mistakes and get better over time, just like you do when you learn something new!
So, AI neural networks are like smart learners that start with no knowledge and use examples to figure things out. They learn from their mistakes and get better over time, just like you do when you learn something new!
How can an algorithm be biased?
From the UBC site choose the AI Image Filter lesson for instructions.
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