> the person who said that obviously never checked on book and magazine and newspaper typesetting
That's one of the "everywheres" the author of the statement was talking about, which is the point - you could see every part of the economy seemingly improved, but it did not show up at the macroeconomic measurement level.
It was until the 2000s that computers caused an uptick in measured productivity at the GDP level, though.
The number of books being published has been steadily increasing for decades since computers became prevalent in typesetting --- is that not measurable?
Admittedly part of that is caused by the change in tax laws making it untenable for publishers to maintain a backlist.
It was not on a level that nudges GDP growth for a long time. The irony is that maybe the productivity didn't increase until book publishing plummeted, since enabling a lack of physical books was a huge productivity win.
This isn't a debate about whether industries improved. When you look at GDP, do you see an uptick in GDP or not?
The original quote was often seen as a paradox. At least one major interpretation of the original quote is that "the computer revolution is so obviously helping the economy, that if it doesn't show up in GDP, is it even a meaningful measurement, and how can we fix that?"
But it is very fitting to discuss that quote - the original article discusses multifactor productivity, the same relevant measurement, and earnings of the S&P 493. There are two ways to interpret it - is AI not increasing productivity, or are we not measuring productivity, and is there "dark matter" somewhere in the economy that is not being properly measured?
If companies can better understand their customers so they can give me products that better fit my needs, or inform me of products I couldn't have known about otherwise, that's a win for everyone.
Scala doesn't have an IO monad by default, but ZIO, CatsEffect, and probably others are fairly popular in at least some parts of the community. IMO it's kind of the worst of both worlds (all the complexity, none of the guarantees), but it's definitely there.
You can get ZDR for OpenAI models through AWS Bedrock or Azure without OAI ever touching your data.
OpenAI never even gets the data, those orgs get to host OAI's model as local within their datacenters, OAI never touches it - you still have to trust Google, MS, Amazon, but I would trust any of them before OAI directly.
Are there keywords to search for to read more? This is the first time I've heard about this! This is super interesting, what are a few well known other stable matches people deeper into this talk about?
The problem is it's a lattice and not fully ordered. There are cases where there are two or more pairings that are worse for women and better for men, but we cannot determine which pairing is superior to the other overall.
Because tesla has always been selling dollars for $0.50. Investing in their product lines doesn't help them, it's always been a grift and a shell game. Some great products have come out of it, but the valuation has never been one where selling more products helps the business, unless it can lead to the hype train.
Sure, but if they're losing money, you go looking for ways to make a bit more money without having to spend too much. My contention is at least some of these things could be done relatively cheaply and would generate additional sales.
Tesla may be getting belted from pillar to post elsewhere in the world by the Chinese but they a) still have a near-monopoly in EVs in the US, and b) have a cost-competitive EV manufacturing operation in China from which they can supply other parts of the world.
That still doesn't help their image problem, which is still very real for many left-leaning affluent consumers around the world, but that's less of a problem for commercial vehicles.
I have not personally reviewed the benchmarks, but recall seeing a post where a Linear SVM with bag-of-words features outperformed Jev on many simple NLP classification tasks, like the ones you cite. You don't even need embeddings!
I've used LLM's to classify things for numerous projects and it's always really hard to beat linear classifier/decision tree over simple embeddings or even the sklearn HashingVectorizer. However, you DO need to trust your validated data, ground truths for this to work - but you should have these anyway to validate a Jev or similar solution.
The biggest disadvantage of Jev is that it's a proprietary product and you need to send them your data. A bespoke solution makes much more sense in many scenarios.
That’s not what I’m asking about - the text prompted for rank and it output “rank” in the response object. How did it do that? Like if I’d asked it to group similar items, how would it know how to structure that output and know that the key should be “groupings”
The output key is determined through code by the harness from the inputs, and the model generates 255 floats in order that follow that schema by reading the expected return type "rank" in the input. The harness then programmatically uses the floats of the 255 floats that are useful. The harness can assume that the correct float will be in the correct position as the model is trained to follow the schemas
That's one of the "everywheres" the author of the statement was talking about, which is the point - you could see every part of the economy seemingly improved, but it did not show up at the macroeconomic measurement level.
It was until the 2000s that computers caused an uptick in measured productivity at the GDP level, though.
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