Words change meaning as they are used. Especially negative words that may start rather specific tend to get used more generally until the specificity is lost.
how about we put some effort into actually picking the correct words and not just handwaving everything? Especially since the whole topic of discussion here is 'internet research is increasingly less reliable because people just wrote/publish any old BS for clicks.'
I don’t think it’s necessarily handwaving. I don’t think anyone has a monopoly on the way language is used and broadening terms is a very natural thing that happens as language evolves
I think the fact that it’s primarily an Internet related term that gets used a lot on the Internet, has something to do with the acceleration in the broadening of its meaning
The government having the power to curate access to information seems bad. You could try to separate it as an independent agency, but as the current US administration is showing, that’s not really a thing.
The idea is that the government is biased towards hiding certain information and private companies are biased towards hiding a different set.
While unlikely, the ideal would be for the government to provide a foundational open search infrastructure that would allow people to build on it and expand it to fit their needs in a way that is hard to do when a private companies eschews competition and hides its techniques.
Perhaps it would be better for there to be a sanctioned crawler funded by the government, that then sells the unfiltered information to third parties like google. This would ensure IP rights are protected while ensuring open access to information.
And in a world where running a Google-like search engine is just one of the many jobs the US federal government has, why shouldn't how the government runs that search engine be a national-level political question decided by elections, just like the management of all the other things the US federal government does is? Regardless of how the government curated access to information, a huge chunk of the US electorate would be mad about how they were doing it, reflecting very real polarization among the population.
That, and also I specifically loathe the way AI has been created.
In my heart I’m a code “artist,” and like all artists that have been more directly attacked, I also feel personally attacked by all of my stolen “art” that is now monetized by big corporations that do not give a single f*ck about beauty in the code, or whatever.
This may be a strange position, idk. Anyways, that’s the reason why.
Enough curiosity to have read and understood the papers, formalize some of the more grand theorems (they’re not that impressive once restated this way), and listen to the experiences of developers who’ve adopted it.
That’s more than 3 minutes. I’m not being glib when I say I’ve given it serious thought.
It seems to me like someone would have at least _tried_ to have a CEO agent talk to a board of directors of agents, have a marketing department of agents try to find market fit for a product, etc. has this been done?
I am not a corporate lawyer but AFAIK a CEO and CFO which can be the same person must be a real human for numerous legal responsibilities including those related to the board. The board members also have to be humans. I agree with jalapenos that it would be a fun movie. Perhaps M3GAN 3.1
Because it's still relatively new. Gambling's been around forever, and so has addiction. What hasn't been around is gambling your life away on the same device(s) you do everything else in today's modern society on. If you had an unlimited supply of whatever monkey is on your back, right at your fingertips, you'd be dead before the week is out from an overdose. It's the normalization of this level of access to gambling which gives me great fear for the future. Giving drugs to minors is a bigger crime than to adults for a reason. Without regulation and strong cultural push back, it's gonna get way worse, unless we make huge leaps in addiction treatment (which I am hopeful for. GLP-1s aren't yet scientifically proven to help with that, but there's a large body of anecdotal evidence to suggest it does.
That’s the thing here. Software engineering is an intelligence-complete problem. If AI can solve it, then it can solve any sort of knowledge work like accounting, financial analysis, etc
Only if by "solving it", you mean being able to write any program to do anything.
Software engineering is a hubris-complete problem. Somehow, being able to do so much seems to make us all assume that everyone else is capable of so little. But just because we can write 1000 programs to do 1000 different things, and because AI can write 1000 programs to do 1000 different things, it doesn't mean that we can write the million other programs that do a million other things. That would be like assuming that because someone is a writer and has written 1 book, that they are fully capable of writing both War & Peace and an exhaustive manual on tractor repair.
Financial analysis is not easier than programming. You don't feed in numbers, turn a crank, and get out correct answers. Some people do only that, and yeah, AI can probably replace them.
"Computing" as a field only made sense when computers were new. We're going to have to go back to actually accomplishing things, not depending on the fact that computers are involved and making them do anything is hard so anyone who can make them do things is automatically valuable. (Which sucks for me, because I'm pretty good at making computers do things but not so good at much of anything else with economic value.) "What do you do?" "I use computers to do X." "Why didn't you just say you do X, then?" is already kind of a thing; now it's going to move on to "I use AI to do X."
Then again: the AI-dependent generation is losing the ability to think, as a result of leaning on AI to do it for them. So while my generation stuck the previous generation with maintaining COBOL programs, the next generation will stick mine with thinking. I can deal with that. I like thinking.
> Financial analysis is not easier than programming. You don't feed in numbers, turn a crank, and get out correct answers
It’s not, but if software engineering is solved then of course so is financial analysis, because a program could be written to do it. If the program is not good enough, then software engineering is not solved.
I think this what you were getting at with this part, but it’s not clear to me, because it seems like you were disagreeing with my thesis: “ because AI can write 1000 programs to do 1000 different things, it doesn't mean that we can write the million other programs that do a million other things”
I’m not sure if you’re saying that people weren’t using computers to solve problems before, but that’s pretty much everything they do. Some people were specifically trained to make computers solve problems, but if computers can solve X problem without a programmer, then both the computer programmer and the X problem solver are replaced.
I don't think software engineering is ever going to be solved, but financial analysis will definitely never be solved. It's impossible, the nature of it dictates that, whatever changes happen will further change the results. Financial analysis requires novel thinking, and even if you have AGI that can engage in novel thought they will just be another input into the system.
This is the crux of it. The digital world doesn't produce value except when it eases the production of real goods. Software Development as a field is strange: it can only produce value when it is used to make production of real goods more efficient. We can use AI to cut out bureaucratic work, which then means that all that is left is real work: craftsmanship, relationship building, design, leadership.
There are plenty of "human in the loop" jobs still left. I certainly don't want furniture designed by AI, because there is no possible way for an AI to understand my particular fleshly requirements (AI simply doesn't have the wetware required to understand human tactile needs). But the bureaucratic jobs will mostly be automated away, and good riddance. They were killing the human spirit.
> Software Development as a field is strange: it can only produce value when it is used to make production of real goods more efficient. We can use AI to cut out bureaucratic work, which then means that all that is left is real work: craftsmanship, relationship building, design, leadership.
Thats a really odd take. Software is merely a way of ingesting data and producing information. And information often has intrinsic value. This can scale from simple things like minor annoyances of forgetting your umbrella, to avoiding deaths/millions of dollars in losses due to ships sinking in storms.
Now the long term value of software does approach zero, because it can usually be duplicated quite easily.
Extraction and manufacturing are considered the primary and secondary economic sectors. In a closed loop system, tertiary and onward sectors, like services and technology, cannot exist without the primary and secondary.
I value your weird rant. Yes it did go on as a thought stream, but there's sense in there.
I've been thinking a lot around a kind of smart-people paradox: very intellectual arguments all basically plotting a line toward some inevitable conclusion like super intelligence or consciousness. Everything is a raw compute problem.
While at the same time all scientific progress gives us more and more evidence that reality is non-computable, non linear.
> non computable, non-linear as in given known input parameters you can determine the output parameters.
These two words do not mean the same thing.
Non-linear functions do not mean you cannot determine the output for a given input.
All non-linear means is that the condition f(x+y) = f(x) + f(y) and f(kx) = kf(x) do not hold for arbitrary x,y,k
For example f(x) = x^2 is a non-linear function. Can you determine what f(x) for arbitrary x?
Perhaps you meant what used to be called "chaotic systems", those which were highly sensitive to initial conditions. Yes, they are non-linear but they are completely deterministic. A classic example would be the n-body problem in physics under most conditions.
And I'm not sure what you understand what non-computable means. It means that the computation will not halt in a finite amount of time for a general input. For a particular input, it may indeed halt in a finite amount of time.
Most real numbers are non-computable, such as the square root of 2 or Pi.
Practically speaking however, we can get approximations as close as we want. In other cases, such as the Busy Beaver function, we can set bounds
You're correct. I only have a very casual understanding of these things. For the non-linear thing, I just mean that for any advanced system there are say trillions of parameters, like cellular systems, and even if you mapped them in you couldn't be sure what the output would be.
> And I'm not sure what you understand what non-computable means. It means that the computation will not halt in a finite amount of time for a general input. For a particular input, it may indeed halt in a finite amount of time.
Sounds familiar, the "halting problem"? I suppose I'm too loosely tying concepts together. Particular vs general input is same as simple vs complex input above, given a complex enough input, the compute involved approaches boundless/infinite.
In practice, yes, as I understand it, modern science is all about stochastic approximations and for all intents and purposes it's quite reliable.
I probably should stop using "non-linear" terminology. I really just mean that it's not 1:1. You mention how systems can be deterministic and I looked it up and yes wave function collapse specifically says:
> The observable acts as a linear function on the states of the system
We can compute the possible states, but not the exact state. We can't predict the future.
Thanks for the reply, this is much more interesting to me as it approaches philosophy, so admittedly I too loosely throw words-that-mean-things around.
You are right, but I think at the moment, a lot of people are confusing "software engineering" with "set up my react boilerplate with tailwind and unit tests", and AI just is way better for that sort of rote thing.
I've never felt comfortable with the devs who just want some Jira ticket with exactly what to do. That's basically what AI/LLMs can do pretty well.
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