Okun's law shows a strong correlation between GDP and employment. Which runs counter to your previous claim – as I understand it (please forgive me if I'm not understanding it correctly.)

Did you make a comment somewhere else that I missed?

My friends who produced excuses became really good at that. My friends who produced real works and continued to improve became good at that instead.

A year later I was one of the top ten best-selling indie writers on Amazon, I had my first movie deal and representation from an amazing agent who stuck by me through the rough parts.

Luck and timing were big factors. I also locked myself in my house for 12 months straight and did nothing but focus on being a better writer and create as much output as I could to get traction. I spent a tremendous amount of psychotic energy at building my writing business. Most people give up. I was 37 and desperate to make it work.

When I went with a traditional publisher it was frustrating at first. I was getting critical nods (I was a finalist for the Edgar and the Thriller Award) but the sales numbers weren't the same as when I was independent.

Eventually I found a great publisher (an imprint with Amazon Publishing) and it was a perfect match. My first novel with them (The Naturalist) spent six weeks in the number one spot (even outselling Harry Potter.) I now do two books a year with them.

I don't have the quite the sales or name recognition of a James Patterson or Stephen King, but I became a millionaire from publishing and investing on those earnings before I started working in AI.

I can't speak to the part of the article about advances. They're based very heavily on your sales history. I had to work my way up to six-figure advances. If you have John Scalzi sales numbers you can pick your terms. David Baldacci doesn't ask for an advance. He just splits the profits with the publisher. People like him are in a whole different league.

tldr: Luck and timing matter, but so does being an absolutely focused psychopath who gives up a social life, television, games and everything else to get what you want.

The future isn’t a fixed point. But before then H&R Block (or a company like them) will probably shift to AI. Then eventually your AI will mean you don’t even need them.

The disruptors will get disrupted.

Yes. Rich people tend to re-invest their money and not throw it into a Scrooge McDuck vault to swim in. The re-investing is what helps create economic growth.

A. Some parts of the economy will grow. Some will shrink. My claim is that if you believe that AI will create new efficiencies and continue to do so, then overall economic growth will increase at a faster rate than ever.

B. Predicting what architecture could lead to this growth is beyond the scope of this article. The point was to say that if we keep getting increased efficiencies we'll get increased growth.

C. Job creation accelerates. Before I was hired at OpenAI in 2020, nobody on the planet had ever been hired as a prompt engineer. Now it's a career (at least for the time being.)

D. I'm assuming maximizing profit in the most near-sighted way possible. Same as it has ever been.

Is there a specific claim or argument that you disagree with?

My claim is that H&R Block would replace all their workers for more efficient AI if it was more profitable. Then they would re-invest that money in the most productive way they can – other companies, etc. This is the same strategy as Apple, Microsoft and other companies do with large amounts of cash.

I don't claim H&R Block would be directly creating new jobs. They would try to maximize profit by investing their profits and diversifying.

https://andrewmayne.com/2023/03/29/how-to-play-wordle-with-g...

https://www.youtube.com/@johnmpowell

https://m.youtube.com/watch?v=hU83PE68oNY

GPT-3.5 learns how to generalize better when it’s just in the completion.

This is the same problem that vexed the researchers who did the paper on the alleged reversal curse.

(https://andrewmayne.com/2023/11/14/is-the-reversal-curse-rea...)

Segment this text into proper sentences:

Input:

UNDERNEATHTHEGAZEOFORIONSBELTWHERETHESEAOFTRANQUILITYMEETSTHEEDGEOFTWILIGHTLIESAHIDDENTROVEOFWISDOMFORGOTTENBYMANYCOVETEDBYTHOSEINTHEKNOWITHOLDSTHEKEYSTOUNTOLDPOWER

Output:

1. Underneath the gaze of Orion's belt, where the sea of tranquility meets the edge of twilight, lies a hidden trove of wisdom forgotten by many, coveted by those in the know. 2. It holds the keys to untold power.

2. That depends on what you mean by logic. What would be an example of logical reasoning that would settle this?

3. Alan Turing created the Imitation Game thought experiment to show the futility of this question. If intelligence is something that can be observed and tested, then when we should be able to describe what to test for.

4. I don't make any specific claims about LLMs logic or intelligence. I just wanted to put their claim that LLMs can't generalize from B to A to the test.

Spoiler: It doesn't say "Tom Cruise."

I’m the “shark-diving science journalist” in question.

First of all, you can run the experiments like I did and test this yourself. I’m not asking anyone to take my word. Just do what I did: Read the original paper. Test the claims for yourself.

And to clarify a couple things:

1. The shark-diving part is true.

2. I’ve never been a journalist of any kind that I’m aware of unless you count writing for Skeptic Magazine. I’ve had many, many jobs though.

3. I started at OpenAI as a software engineer and member of technical staff. When I started there was just over a hundred people there. The lines between engineering were and are blurry.

4. I was the original prompt engineer at OpenAI and discovered many of the examples for using GPT-3 and wrote a lot of the original documentation. Internally my title was “prompt whisperer.”

5. I’m in the GPT-4 research paper for my contributions to model capability. I helped find abilities for long-text, vision, etc.

6. I was given the title Science Communicator when I started doing background briefings for media, etc., but still worked on model capability and other things.

7. I left OpenAI two months ago to work on a startup.

Best,

Andrew Mayne

The post says that 1000 neurons will give you 130 LLM responses - but of what length?

(LLMs are generally priced by input and output tokens. The longer the tokens the longer the compute time. Without an idea of what you mean by a response it's hard to understand.)

Likewise: 1,250 embeddings – How big is the text size in the example?

I'm VERY excited to see you doing this and understand it's early stages, but I wan't wrap my head around the pricing without context.