I wonder how they measured this. Could it just be that they get more utilisation? Even per capita is probably not adequate either.
You would need a measure that’s an analogue of per capita. Maybe per result? For instance I could spend half an hour attempting to get just the right set of keywords to bring up the right result, or I could spend 5 minutes in a chat session with an AI honing the correct response.
The wording of the article implies an apples to apples comparison. So 1 Google search == 1 question successfully answered by an LLM. Remember a Google Search in layspeak is not the act of clicking on the search button, rather it’s the act of going to Google to find a website that has information you want. The equivalent with ChatGPT would be to start a “conversation” and getting information you want on a particular topic.
How many search engine queries, or LLM prompts that involves, or how broad the topic, is a level of technical detail that one assumes the source for the number x25 has already controlled for (Feel free to ask the author for the source and share with us though!)
Anyone who’s remotely used any kind of deep learning will know right away that deep learning uses an order of magnitude or two more power (and an order of magnitude or two more performance!) compared to algorithmic and rules based software, and a number like x25 for a similar effective outcome would not at all be surprising, if the approach used is unnecessarily complex.
For example, I could write a neural network to compute 2+2, or I could use an arithmetic calculator. One requires a 500$ GPU consuming 300 watts, the other a 2$ pocket calculator running on 5 watts, returning the answer before the neural network is even done booting.
However many years it takes for these LLM fools to wake up, hopefully they can find a way to laugh at themselves for thinking that it was cutting-edge to jam the internet into a fake jellyfish brain and calling it GPT. I haven’t looked recently, but I still haven’t seen anyone talking about neuroglial networks and how they will revolutionize the applications for AI.
There’s a big*** book, but apparently no public takers in the deep neural network space?
I wonder how they measured this. Could it just be that they get more utilisation? Even per capita is probably not adequate either. You would need a measure that’s an analogue of per capita. Maybe per result? For instance I could spend half an hour attempting to get just the right set of keywords to bring up the right result, or I could spend 5 minutes in a chat session with an AI honing the correct response.
The wording of the article implies an apples to apples comparison. So 1 Google search == 1 question successfully answered by an LLM. Remember a Google Search in layspeak is not the act of clicking on the search button, rather it’s the act of going to Google to find a website that has information you want. The equivalent with ChatGPT would be to start a “conversation” and getting information you want on a particular topic.
How many search engine queries, or LLM prompts that involves, or how broad the topic, is a level of technical detail that one assumes the source for the number x25 has already controlled for (Feel free to ask the author for the source and share with us though!)
Anyone who’s remotely used any kind of deep learning will know right away that deep learning uses an order of magnitude or two more power (and an order of magnitude or two more performance!) compared to algorithmic and rules based software, and a number like x25 for a similar effective outcome would not at all be surprising, if the approach used is unnecessarily complex.
For example, I could write a neural network to compute 2+2, or I could use an arithmetic calculator. One requires a 500$ GPU consuming 300 watts, the other a 2$ pocket calculator running on 5 watts, returning the answer before the neural network is even done booting.
However many years it takes for these LLM fools to wake up, hopefully they can find a way to laugh at themselves for thinking that it was cutting-edge to jam the internet into a fake jellyfish brain and calling it GPT. I haven’t looked recently, but I still haven’t seen anyone talking about neuroglial networks and how they will revolutionize the applications for AI.
There’s a big*** book, but apparently no public takers in the deep neural network space?