The difference is that CEOs fired a lot of people, using "AI can replace human coders" as an excuse. Also: there are claims all over the headlines along the lines of "We built something amazing without human coders."
Both claims are loud and are flooding the discussion, but under the hood it's mostly a slop disaster.
So the negative sentiment is a natural response (and a dose of realism).
Bayesian optimization of, say, hyperparameters is the canonical modern usage in my view, and there are other similar optimization problems where it's the preferred approach.
My take is that the Rasmussen book isn't especially approachable, and that this book has actually held back the wider adoption of GPs in the world.
The book has been seen as the authoritative source on the topic, so people were hesitant to write anything else. At the same time, the book borders on impenetrable.
This perspective under-appreciates the role of a leader's charisma when it comes to attracting staff that will actually execute the ideas of that leader.
Anyone who has worked in a presidential administration (or a congressional office) can tell you that a leader is effective if and only if they have staff that believes in their message and agenda, and that is willing and able to execute on that agenda.
The practical reality here is that charisma isn't just a way of gaming the "getting elected" part of the job, it's also a requirement to be effective at the job.
I find that I have to steer the AI a lot, but I do have optimism that better prompting will lead to better agents.
To take an example from the article: code re-use. When I'm writing code, I subconsciously have a mental inventory of what code is already there, and I'm subconsciously asking myself "hey, is this new task super similar to something that we already have working (and tested!) code for?". I haven't looked into the details of the initial prompt that a coding agent gets, but my intuition is that an addition to the prompt instructing the agent to keep an inventory of what's in the codebase, and when planning out a new batch of code, check the requirements of the new tasks against what's already there.
Yes, this adds a bunch of compute cycles to the planning process, but we should be honest and say "that's just the price of an agent writing code". Better planning > ability to fix things.
Totally agree here. I tried the following and had a very different experience:
"Answer as if you're a senior software engineer giving advice to a less experienced software engineer. I'm looking for a Rust crate to access PostgreSQL with Apache Arrow support. How should I proceed? What are the pluses and minuses of my various options?"
call me old-fasahioned, but two spaces after a period will solve this problem if people insist on all-lower-case. this also helps distinguish between abbreviations such as st. martin's and the ends of sentences.
i'll bet that the linguistics experimentalists have metrics that quantify reading speed measurements as determined by eye tracking experiments, and can verify this.
If your prompt instructs the model to ask such questions along the way, the model will, in fact, do so!
But yes, it would be nice if the model were smart enough to realize when it's in a situation where it should ask the user a few questions, and when it should just get on with things.
My personal view is that the roadmap to AGI requires an LLM acting as a prefrontal cortex: something designed to think about thinking.
It would decide what circumstances call for double-checking facts for accuracy, which would hopefully catch hallucinations. It would write its own acceptance criteria for its answers, etc.
It's not clear to me how to train each of the sub-models required, or how big (or small!) they need to be, or what architecture works best. But I think that complex architectures are going to win out over the "just scale up with more data and more compute" approach.
Perhaps better stated as "adult human height is approximately Gaussian for a given biological sex", with an asterisk that environmental factors stretch the distribution.
I love the anecdote that people born in the American colonies came back to England to visit family, and were remarkably taller compared to their cousins due to environmental factors.
Interestingly enough, I remember in my younger days being inspired by Rand Corp's 1950's era game theory work on e.g. mutually assured destruction. It later occurred to me that I don't need to be employed by a think tank to write think pieces!
That being said, I like to think that startups growing into large corporations have an opportunity to be better when it comes to things like performance management.
My take on the press release is that they're announcing a collaboration between two companies.
Lithoz uses photopolymerization to 3-D print a variety of ceramics, and is in the business of selling 3-D printers.
Glassomer makes the "ink" - they've got a few patents on silica + binder dating back to 2016.
All of this is similar to many things that have been done in the scientific literature (e.g. Nature Materials volume 20, pages 1506–1511 (2021)). They've put it into production and made it purchasable.
I'm not up on state-of-the-art, so I'm not sure if this has any features that differentiate it from competitors. I'm not seeing any surprises.