Token Cost of GPT-4 level models over time
Cost of 1M tokens has dropped from $180 to $0.75 in ~18 months = 240x cheaper.
— FWIW, none of the cheap ones are quite to the quality of real GPT-4 on coding, the only real job where AI matters right now, and who cares if they’re cheaper when they’re not yet quite good enough to really do the jobs they could.
Industry wasted last 2 years making models cheaper rather than pushing forward the state of the art.
Cost of 1M tokens has dropped from $180 to $0.75 in ~18 months = 240x cheaper.
— FWIW, none of the cheap ones are quite to the quality of real GPT-4 on coding, the only real job where AI matters right now, and who cares if they’re cheaper when they’re not yet quite good enough to really do the jobs they could.
Industry wasted last 2 years making models cheaper rather than pushing forward the state of the art.
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OpenAI launches new ChatGPT model, o1, with reasoning capabilities of a PhD student
“Similar to how a human may think for a long time before responding to a difficult question, o1 uses a chain of thought when attempting to solve a problem. Through reinforcement learning, o1 learns to hone its chain of thought and refine the strategies it uses. It learns to recognize and correct its mistakes. It learns to break down tricky steps into simpler ones. It learns to try a different approach when the current one isn’t working. This process dramatically improves the model’s ability to reason.“
OpenAI Announcement
“Similar to how a human may think for a long time before responding to a difficult question, o1 uses a chain of thought when attempting to solve a problem. Through reinforcement learning, o1 learns to hone its chain of thought and refine the strategies it uses. It learns to recognize and correct its mistakes. It learns to break down tricky steps into simpler ones. It learns to try a different approach when the current one isn’t working. This process dramatically improves the model’s ability to reason.“
OpenAI Announcement
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Just 30 messages weekly limit for new o1 model
Unsurprised.
O1 uses an obscene amount of resources, as the announcement confirms.
At the same time, this always was the correct way forward.
There are no rich, energy-poor nations.
Just use vastly more compute.
Bitter lesson
Unsurprised.
O1 uses an obscene amount of resources, as the announcement confirms.
At the same time, this always was the correct way forward.
There are no rich, energy-poor nations.
Just use vastly more compute.
Bitter lesson
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Hiding the Chain-Of-Thought Reasoning from Users Will Enable OpenAI to Better Manipulate the Users
They don’t even hide it, they openly admit it.
OpenAI will now be hiding the AI reasoning in order to better enable manipulating the users.
Purpose of a system is what it does.
Article
They don’t even hide it, they openly admit it.
OpenAI will now be hiding the AI reasoning in order to better enable manipulating the users.
Purpose of a system is what it does.
Article
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Chat GPT
The Bitter Lesson
We could have been talking to our desktop computers in English since the 90s!
"Somebody got one of the small versions of Llama to run on Windows 98…”
“We could've been talking to our computers in English for the last 30 years"
- Marc Andreessen
Correct.
The hardware already existed, for decades.
What stopped us?
Extreme aversion to investing money into training much larger AI models.
No one was willing to invest the many millions needed to train an AI model of this size.
In fact, even a decade later in 2011, people were still hardly willing to spend more than TEN DOLLARS on electricity costs to train a state-of-the-art model, e.g. the AlexNet image model
Many truly under-estimate how unwilling to people have been to spend money on AI training, until very recently
And this wasn’t unrealized, many of us had screamed this for decades.
No one cared.
Incredible testiment to man’s unwillingness to invest in certain critical areas of future tech.
— happens in AI, advanced market mechanisms, proof systems, and a few other similar areas, that are unquestionably the future.
We could have been talking to our desktop computers in English since the 90s
Bitter Lesson
"Somebody got one of the small versions of Llama to run on Windows 98…”
“We could've been talking to our computers in English for the last 30 years"
- Marc Andreessen
Correct.
The hardware already existed, for decades.
What stopped us?
Extreme aversion to investing money into training much larger AI models.
No one was willing to invest the many millions needed to train an AI model of this size.
In fact, even a decade later in 2011, people were still hardly willing to spend more than TEN DOLLARS on electricity costs to train a state-of-the-art model, e.g. the AlexNet image model
Many truly under-estimate how unwilling to people have been to spend money on AI training, until very recently
And this wasn’t unrealized, many of us had screamed this for decades.
No one cared.
Incredible testiment to man’s unwillingness to invest in certain critical areas of future tech.
— happens in AI, advanced market mechanisms, proof systems, and a few other similar areas, that are unquestionably the future.
We could have been talking to our desktop computers in English since the 90s
Bitter Lesson
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We could've been talking to our computers in English for the last 30 years
35.9 tok/sec on a 26 year old Windows 98 Intel Pentium II CPU, with 128MB RAM
Using a 260K LLM with Llama-architecture
35.9 tok/sec on a 26 year old Windows 98 Intel Pentium II CPU, with 128MB RAM
Using a 260K LLM with Llama-architecture
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We could've been talking to our computers in English for the last 30 years
Somebody got one of the small versions of Llama to run on Windows 98…
We could've been talking to our computers in English for the last 30 years
- Marc Andreessen
Somebody got one of the small versions of Llama to run on Windows 98…
We could've been talking to our computers in English for the last 30 years
- Marc Andreessen
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