These are great examples. I have tried similar things by prompting LLMs with random ideas before then asking it to attempt some task (with admittedly mixed success).
Randomness is a recurring pattern as the source of novelty and creativity in many AI algorithms. You can see it pop-up again and again.
This great video on the 3blue1brown channel (actually its from Welch Labs) covers how the diffusion models of image generation work by adding noise to images and learning how to reverse-engineer the image.
When neural networks learn, the weights are randomly initialized – and that random initialization is critical to getting the learning algorithm to work.
Normally, this is a one-time injection, which is one of the reasons these models have trouble continually learning. This paper, from Rich Sutton and others, explains that continuing to inject randomness can overcome some of those barriers and allow these models to continue to learn.
When LLMs choose the next token in their output, they do it based on probability distribution that can be adjusted with a ‘temperature’. The higher the temperature the more random the next token. If the ‘temperature’ is too low the output starts to look like when you write a text on your iphone by clicking the next suggested word over and over again…
In reinforcement learning you build models that are continually balancing between taking an action they understand vs. ‘exploring’ (which often means introducing randomness to encourage them to pick actions whose outcomes they know less about).
Very awesome artwork you have been sharing on your feed by the way. Love it!
Indeed randomness is a key resources in so many places! Clearly essential for creativity in all kinds of forms. Appreciate sharing that paper by Sutton and friends, it looks interesting indeed.
These are great examples. I have tried similar things by prompting LLMs with random ideas before then asking it to attempt some task (with admittedly mixed success).
Randomness is a recurring pattern as the source of novelty and creativity in many AI algorithms. You can see it pop-up again and again.
This great video on the 3blue1brown channel (actually its from Welch Labs) covers how the diffusion models of image generation work by adding noise to images and learning how to reverse-engineer the image.
But How do AI Images Work?: https://www.youtube.com/watch?v=iv-5mZ_9CPY
When neural networks learn, the weights are randomly initialized – and that random initialization is critical to getting the learning algorithm to work.
Normally, this is a one-time injection, which is one of the reasons these models have trouble continually learning. This paper, from Rich Sutton and others, explains that continuing to inject randomness can overcome some of those barriers and allow these models to continue to learn.
Continual Backprop: Stochastic Gradient Descent with Persistent Randomness: https://arxiv.org/abs/2108.06325
When LLMs choose the next token in their output, they do it based on probability distribution that can be adjusted with a ‘temperature’. The higher the temperature the more random the next token. If the ‘temperature’ is too low the output starts to look like when you write a text on your iphone by clicking the next suggested word over and over again…
In reinforcement learning you build models that are continually balancing between taking an action they understand vs. ‘exploring’ (which often means introducing randomness to encourage them to pick actions whose outcomes they know less about).
Very awesome artwork you have been sharing on your feed by the way. Love it!
Indeed randomness is a key resources in so many places! Clearly essential for creativity in all kinds of forms. Appreciate sharing that paper by Sutton and friends, it looks interesting indeed.
Thanks for the kind words!