Not to mention they will need to research how to make their models faster and cheaper to run in order to fit some margin within what people are actually willing to pay.
453 karma · joined December 1, 2011
It's never the technology that's the problem, it's the owners and operators who decide how to use it.
CS as the only path to programming was always too narrow, and often people with a broader education are better at creative solutions. With AI-assisted programming I'd argue they have an even clearer advantage now.
Why not give us nice things for integrating with knowledge graphs and rules engines pretty please?
I know the article title says "integration tests" but when a lot of functionality is done inside PostgreSQL then you can cover a lot of the test pyramid with unit tests directly in the DB as well.
The test database orchestration from the article pairs really well with pgTAP for isolation.
> if you have carbon-based life forms, you will have water and CO2.
..can lead to statements like:
> it is just way more likely than any other form
I totally agree on the observation, but what is fascinating to me is why a deductive statement can be considered to indicate likelihood in probability. It seems there is a bit of abductive reasoning going on behind the scenes which neither the deductive logic or inductive probability can really capture on their own.
It's more of an ethics and compliance issue with the cost of BS and plausible deniability going to zero. As usual, it's what humans do with technology that has good or bad consequences. The tech itself is fairly close to neutral as long as training data wasn't chosen specifically to contain illegal substance or by way of copyright infringement (which isn't even the tech, it's the product).
The reverse would be true where poor people could be ruined, unless the value provided is worth significantly more than the debt created, which seems doubtful.
Actually I paid for Blinkist recently and really enjoyed it at first. They have a lot of "blinks" that state at the end that the voice was synthetic and I was legitimately surprised at the quality, having not even noticed until they told me.
This seems like a good move for YT to maintain a basic level of quality (which I'm amazed can actually get worse), but I suspect it's a pretext to avoid paying out to "illegitimate creators" for commercial reasons in a way that makes them look like they care about people.
After using RAG with pgvector for the last few months with temperature 0, it's been pretty great with very little hallucination.
The small context window is the limiting factor.
In principle, I don't see the difference between a bunch of fine-tuned prompts along the lines of "here is another context section: <~4k-n tokens of the corpus>", which is the same as what it looks like in a RAG prompt anyway.
Maybe the distinction of whether it is for "tone" or "context" is based on the role of the given prompts and not restricted by the fine-tuning process itself?
In theory, fine-tuning it on ~100k tokens like that would allow for better inference, even with the RAG prompt that includes a few sections from the same corpus. It would prevent issues where the vector search results are too thin despite their high similarity. E.g. picking out one or two sections of a book which is actually really long.
For example, I've seen some folks use arbitrary chunking of tokens in batches of 1k or so as an easy config for implementation, but that totally breaks the semantic meaning of longer paragraphs, and those paragraphs might not come back grouped together from the vector search. My approach there has been manual curation of sections allowing variations from 50 to 3k tokens to get the chunks to be more natural. It has worked well but I could still see having the whole corpus fine-tuned as extra insurance against losing context.
https://help.openai.com/en/articles/5722486-how-your-data-is...
That said, for enterprises that use the consumer product internally, it would make sense to pay to opt-out from that input being used.
We've done this in NLP and search forever. I guess even SQL query planners and other things that automatically rewrite queries might count.
It's just that now the parameters seem squishier with a prompt interface. It's almost like we need some kind of symbolic structure again.
One of the ways that I've learned to practice respect is actually not really getting to know people personally too much. I've been manipulated before by "leaders" who try to use personal information like family needs or other goals as a fake carrot that is not in their power to exchange. As a result I have a pretty "no questions asked" policy on the privacy rights of my direct reports when it comes to approving any time off or facilitating whatever it is they want to do with their careers, and it doesn't require "getting to know them" beyond their own voluntary sharing in private. Sometimes it will grow into a closer relationship but it totally doesn't have to.
Maybe it's an extreme reaction to the bad taste from interacting with folks who like to pretend that work is a family. I just don't trust workplaces enough to be fully vulnerable anymore, and by extension would never demand that another employee be unconditionally trusting with their full humanity, because it is never a two-way street. When the company wants to mess with livelihoods it does not need to seek permission, so it is unfair to ask for the disclosure of personal information beyond what is necessary to carry out the work.
For just one more recent example, I fell for the advertised inclusive corporate culture by putting my pronouns on my Slack profile (having never been publicly out as a queer person at work before), I experienced weird behaviours from people that made it harder to do my job normally. So I ended up going back into the workplace closet because it's just easier to project what "regular" folks want to see in order to solve problems efficiently.
I'd love to hear your take on healthy boundaries, because sometimes I wish there could be more connection with people in the workplace besides the naturally occurring camaraderie that arises through direct collaboration and the inevitable small handful of friends with similar values. Is it possible to have a deeper connection with everyone as a general approach?
At least unions can still exist inside the standard model of employment which everything else is built around. In a "benevolent" tech company that actually treats its people well, I have trouble seeing unions as the ideal vehicle for balancing fairness with innovation. Of course there is inherent exploitation in any employment relationship within the standard model. I would love to see an example of it working in tech though, because I can imagine a union that is equally as interested in the success of the business and prioritizes sustainable growth on top of enforcing equitable treatment as a backstop.
There has to be some combination of legal structures that can somehow represent all interests fairly without adding friction. That is unions, or unions plus other things.
For example, employee ownership trusts could in theory be very aggressive and help smooth out the continuum between periodic bargaining and striking as the main/only leverage. Similar to a share purchase plan or RSU with a pool that has a majority of voting power, or at least equal to founders and investors with all the usual tie-breakers and dilution protections.
If I remember correctly some countries (Germany?) also have legislation requiring a certain amount of employee representation on corporate boards, but I don't know if that actually makes a big difference.