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🔴 036. Tricking AI: Making Mistakes on Purpose
Join Us | Newsletter : https://buymeacoffee.com/marlonbonajos/membership
Try this | 7 Days Challenge : https://tinyurl.com/7-Days-Challenge
Description:
Imagine you're showing a picture of a cat to a computer program that's learned to identify animals.
Usually, it gets it right! But what if someone adds a tiny bit of weird "digital noise" to the picture, so small that you can't even see it? Surprisingly, this tiny change can trick the AI into thinking the cat is a dog! This is called adversarial example generation. It's like creating sneaky inputs that look normal to us but totally confuse AI models.
Why is this important? Well, we use AI for lots of serious things, like self-driving cars that need to see road signs correctly or medical tools that help doctors diagnose illnesses.
If someone could use these tricky inputs to fool the AI in these situations, it could cause big problems. Scientists are studying how to create these tricky inputs to find out the weaknesses in AI systems. Some of the ways they do this are with methods called FGSM (which is like making quick, small changes) and C&W attack (which is more complicated but can be very effective at fooling the AI).
They even try to trick AI systems when they don't know exactly how the AI works, which is like trying to guess someone's password by trying different combinations.
Learning about these "AI attacks" helps us build more secure and reliable AI that can't be easily fooled. It's like testing the locks on a door to make sure they can't be picked!
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By Marlon BonajosJoin Us | Newsletter : https://buymeacoffee.com/marlonbonajos/membership
Try this | 7 Days Challenge : https://tinyurl.com/7-Days-Challenge
Description:
Imagine you're showing a picture of a cat to a computer program that's learned to identify animals.
Usually, it gets it right! But what if someone adds a tiny bit of weird "digital noise" to the picture, so small that you can't even see it? Surprisingly, this tiny change can trick the AI into thinking the cat is a dog! This is called adversarial example generation. It's like creating sneaky inputs that look normal to us but totally confuse AI models.
Why is this important? Well, we use AI for lots of serious things, like self-driving cars that need to see road signs correctly or medical tools that help doctors diagnose illnesses.
If someone could use these tricky inputs to fool the AI in these situations, it could cause big problems. Scientists are studying how to create these tricky inputs to find out the weaknesses in AI systems. Some of the ways they do this are with methods called FGSM (which is like making quick, small changes) and C&W attack (which is more complicated but can be very effective at fooling the AI).
They even try to trick AI systems when they don't know exactly how the AI works, which is like trying to guess someone's password by trying different combinations.
Learning about these "AI attacks" helps us build more secure and reliable AI that can't be easily fooled. It's like testing the locks on a door to make sure they can't be picked!