“Gradient descent” ≈ on a “hilly” (mathematical) surface, try to find the lowest point by finding the lowest point near an initial guess. Hopefully, the lowest point near your initial guess is low enough to pass as a solution to your problem.
“Gradient” is basically the steepness, or rate that the thing you’re trying to optimize changes as you move through “space”. The gradient tells you mathematically which direction you need to go to reach the bottom. “Descent” means “try to find the minimum”.
I’m glossing over a lot of details, particularly what a “surface” actually means in the high dimensional spaces that AI uses, but a lot of problems in mathematical optimization are solved like this. And one of the steps in training an AI agent is to do an optimization, which often does use a gradient descent algorithm. That being said, not every process that uses gradient descent is necessarily AI or even machine learning. I’m actually taking a course this semester where a bunch of my professor’s research is in optimization algorithms that don’t use a gradient descent!
This is a decent explanation of gradient descent but I’m pretty sure the meme is referencing the color gradients often used to highlight when something is AI generated haha
Gradient descent is a common algorithm in machine learning (AI* is a subset of machine learning algorithms). It refers to using math to determine how wrong an answer is in a particular direction and adjusting the algorithm to be less wrong using that information.
The way your phrased that perfectly illustrates the current problem AI has: In a problem space as large as natural language, there are nearly an infinite number of ways it can be wrong. So no matter how much data we feed it, there will always be some “brand new sentence” someone asks that breaks it and causes a wrong answer.
Absolutely. It’s why asking it for facts is inherently bad. It can’t retain information, it is trained to give output shaped like an answer. It’s pretty good at things that don’t have a specific answer (I’ll never write another cover letter thank blob).
Now, if someone were to have the good sense to have some kind of lookup to inject correct information between the prompt and the output, we’d be cooking with gas. But that’s really human labor intensive and all the tech bros are trying to avoid that.
I thought it meant that all the icons/interfaces for AI seem to have a graphical gradient between colors, usually cool colors like blue/purple/pink. (Like the face in the meme)
No. Nobody uses gradient descent anymore, it’s just the technique you learn about in beginner level machine learning courses. It’s about the color gradient in all the AI logos.
Yes this is the correct answer. The words in the meme are written to a hypothetical end user. They would not reference technology like the other person said.
“It has a gradient so you know it’s AI.” <- Uh, what does this mean?
What are you talking about asking questions? It’s AI … it’s all we need to know
Ai logos and buttons tend to be “shiny” with gradient color scheme.
This is the actual answer, the other replies are over thinking it. There’s a gradient on his face ffs
“gradient descent” is a jargon word for one kind of training method.
“Gradient descent” ≈ on a “hilly” (mathematical) surface, try to find the lowest point by finding the lowest point near an initial guess. Hopefully, the lowest point near your initial guess is low enough to pass as a solution to your problem.
“Gradient” is basically the steepness, or rate that the thing you’re trying to optimize changes as you move through “space”. The gradient tells you mathematically which direction you need to go to reach the bottom. “Descent” means “try to find the minimum”.
I’m glossing over a lot of details, particularly what a “surface” actually means in the high dimensional spaces that AI uses, but a lot of problems in mathematical optimization are solved like this. And one of the steps in training an AI agent is to do an optimization, which often does use a gradient descent algorithm. That being said, not every process that uses gradient descent is necessarily AI or even machine learning. I’m actually taking a course this semester where a bunch of my professor’s research is in optimization algorithms that don’t use a gradient descent!
This is a decent explanation of gradient descent but I’m pretty sure the meme is referencing the color gradients often used to highlight when something is AI generated haha
Gradient descent is a common algorithm in machine learning (AI* is a subset of machine learning algorithms). It refers to using math to determine how wrong an answer is in a particular direction and adjusting the algorithm to be less wrong using that information.
The way your phrased that perfectly illustrates the current problem AI has: In a problem space as large as natural language, there are nearly an infinite number of ways it can be wrong. So no matter how much data we feed it, there will always be some “brand new sentence” someone asks that breaks it and causes a wrong answer.
Absolutely. It’s why asking it for facts is inherently bad. It can’t retain information, it is trained to give output shaped like an answer. It’s pretty good at things that don’t have a specific answer (I’ll never write another cover letter thank blob).
Now, if someone were to have the good sense to have some kind of lookup to inject correct information between the prompt and the output, we’d be cooking with gas. But that’s really human labor intensive and all the tech bros are trying to avoid that.
I thought it meant that all the icons/interfaces for AI seem to have a graphical gradient between colors, usually cool colors like blue/purple/pink. (Like the face in the meme)
No. Not at all. It’s about gradient descent, an optimization technique.
No. Nobody uses gradient descent anymore, it’s just the technique you learn about in beginner level machine learning courses. It’s about the color gradient in all the AI logos.
Yes this is the correct answer. The words in the meme are written to a hypothetical end user. They would not reference technology like the other person said.
I thought they meant gradient descent