The story was interesting but the title is misleading. This wasn't the first hedge fund. Benjamin Graham started his first fund in the 1920s which would be what we call a hedge fund today. Graham's fund might not be the first hedge fund but it came before Jones'.
During the US bull market of the 1920s, there were numerous private investment vehicles available to wealthy investors. Of that period, the best known today is the Graham-Newman Partnership, founded by Benjamin Graham and his long-time business partner Jerry Newman. This was cited by Warren Buffett in a 2006 letter to the Museum of American Finance as an early hedge fund, and based on other comments from Buffett, Janet Tavakoli deems Graham's investment firm the first hedge fund.
The sociologist Alfred W. Jones is credited with coining the phrase "hedged fund" and is credited with creating the first hedge fund structure in 1949. Jones referred to his fund as being "hedged", a term then commonly used on Wall Street to describe the management of investment risk due to changes in the financial markets.
In other words, it depends on who you ask and what your exact understanding is of what defines a hedge(d) fund.
This reminds me of a story (I don’t know the authenticity of it) where someone before Black-Scholes had invented the Black-Scholes model but didn’t publish rather they were making ton of money by putting it to work.
You would make money because the other tools in use at the time were much worse and people were pricing options poorly. So plenty of opportunities. Hence why Ed thorp did well.
It will not work today. For starters, it makes a bunch of simplifying assumptions. And there are better models. It also misses a number of important dynamics.
The answer is that aw jones was the first to use the long short hedging strategy. That is why his fund is called a hedge fund and is considered the first of its kind. Others have used shorting before but he was the first to use the strategy of specifically entering a short position to protect a different long position. Well, he was the first one to officially theorize it and market it, at least. That is why when carol loomis described his fund, she coined the word hedge fund.
> Read the intro in the original paper "Attention is all you need"
I wouldn't call this the original "attention" paper. Definitely not the first paper to use the phrase. If you want clear proof of this, let's read the paper
> Attention mechanisms have become an integral part of compelling sequence modeling and transduction models in various tasks, allowing modeling of dependencies without regard to their distance in the input or output sequences.
I do think a lot of people's lack of understanding of attention is because they are so focused on DP(S)A that they miss a lot of the broader picture. And math. Not enough people dig into the math.
This is an interesting feature. But you don't need to use just OpenAI's embeddings. You can generate your own embeddings with open source SOTA transformer models which would probably work just the same. You could generate a couple hundred thousand embeddings with a rented A100 for less than 2 dollars. And the point of converting text or other objects (like images) into embeddings is to compare a large number of documents to a source document very fast. It's more useful to put the embeddings in something like Redis. This pgvector data type would be good for an offline backup of vectors.
> And the point of converting text or other objects (like images) into embeddings is to compare a large number of documents to a source document very fast.
But a "source document" could also be a natural language query that you need to convert to an embedding - if you want to enable natural language search on your corpus? (maybe along with handling queries in French getting good semantic hits in English etc?)?
Do you have any examples that come to mind for this? I'd love to understand what other models exist and what Redis extensions exist to compare embeddings.
It's not. Managers lie about performance of their employees in order to either save themselves or save their favorites in the group. I'm a current Amazon employee.
