A problem in P can be solved in polynomial time - the computation required grows relatively slowly as the input size increases. Like sorting a list of numbers.
A problem in NP requires exponential time or greater, but a proposed solution can be verified quickly. For example, checking a completed Sudoku puzzle.
It is believed but unproven that all problems in NP are NOT in P.
The danger of anthropomorphism is not we elevate the machines, it's that we debase humanity.
I also think different ideas get conflated. It may be possible to build a machine that is super-human in the sense it can outperform the human brain in all kinds of measurable ways. Does not imply it possesses all the same qualities of the brain.
I respect a number of things Anthropic has published about the ethical issues at stake. But, having an in-house philosopher does invite you to make all kinds of unfalsifiable claims.
Everybody always wants to talk about job losses. It's only part of the larger imperative of preserving human dignity.
We don't really have leaders with the maturity and perspective (and lack of self interest) certainly in Government and questionably in tech that can be trusted to advocate for human dignity, so the release of this document from the Pope is a remarkable event.
Hello. Interesting project! Haven't gone through it yet, but want to consider using this in my CS master's capstone. While you have benchmarks I may create my own specific scenarios and comparisons vis-a-vis hosted inference to highlight specific economic benefit. Any suggestions?
Don’t quite agree with the implication that if the answer to a problem is readily available (from an LLM) there is no use in the struggle to find the solution.
However I think it’s very important to approach such questions objectively, or at least self uninterested, and not as one who’s worried about one’s job or sense of self worth threatened by LLM technology.
The value is in the development of one’s own mental faculties. In math classes they tell you you have to work the problems. Even if LLMs become capable of solving entire classes of problems that that set expands over time, the value in developing one’s ability never goes out of style.
I wouldn't underestimate this as a good business decision either.
When the mass surveillance scandal, or first time a building with 100 innocent people get destroyed by autonomous AI, the company that built is gonna get blamed.
Made a quick bot app (OC clone). For me I just want to iMessage it - but do not want to give Full Disk rights to terminal (to read the imessage db).
Uses Mlx for local llm on apple silicon. Performance has been pretty good for a basic spec M4 mini.
Nor install the little apps that I don't know what they're doing and reading my chat history and mac system folders.
What I did was create a shortcut on my iphone to write imessages to an iCloud file, which syncs to my mac mini (quick) - and the script loop on the mini to process my messages. It works.
Wonder if others have ideas so I can iMessage the bot, im in iMessage and don't really want to use another app.
Compilation is transforming one computing model to another. LLMs aren't great at everything, but seem particularly well suited for this purpose.
One of the first things I tried to have an llm do is transpile. These days that works really well. You find an interesting project in python, i'm a js guy, boom js version. Very helpful.
I disagree I think we always need new languages. Every language over time becomes more and more unnecessarily complex.
It's just part of the software lifecycle. People think their job is to "write code" and that means everything becomes more and more features, more abstractions, more complex, more "five different ways to do one thing".
Many many examples, C++, Java esp circa 2000-2010 and on and on and on.
There's no hope for older languages. We need simpler languages.
We don't understand the brain. We fully understand what LLM are doing, humans built them. The idea we don't understand what LLMs are doing is magical. Magical is good for clicks and fundraising.
I dont think you could stop upvote fakery. Maybe like highway speeding just try to control it enough to keep things somewhat safe.
Old, established accts on all the sites can be, and are, sold to people for purposes of fake upvoting. Maybe limit the number of upvotes one can do in a day. Doesn't seem a major inconvienence and makes the upvotes more valuable.
Appropriate to say that hateful comments are not welcome in the workplace. Not appropriate to bring politics into it with Trump. Employees should not be made to feel unwelcome because of their political affiliation.
Quick example, as awful as sexual harrassment is - the code of conduct at your workplace - it doesn't say - Sexual harassment (like the kind Trump does) will not be tolerated. They manage to get a point across regardless.
On management, it's tough to find good info - some is trite leaderhip strategies. I tend to favor the order writings cause they've stood the test of time. I keep this one saved and re-read it pretty regularly.
My team is remote, a couple of us been together 10 years and most of us at least 5 years. My managing style I picked up from my mgrs in the company. Pretty hands off. Stars are stars and don't need me meddling - so the general style - let them do their thing.
As far as the average or underperforming employee, I view my job not as trying to "improve people". I need to empower people. As a leader you have some influence on your team, but I think that's a bit overblown. A person's performance is really a result of their mindset.
Their IQ as well. That's important, but some people are just motivated for excellence, others not. I think the key is - understanding where you have opportunities to influence this in your team members' and when not.
One thing I did notice - moving to a mgmt position. It is very temptimg the idea to just get rid of them. It's tough but easier then getting someone truly hopeless to make the necessary effort to improve.
A problem in P can be solved in polynomial time - the computation required grows relatively slowly as the input size increases. Like sorting a list of numbers.
A problem in NP requires exponential time or greater, but a proposed solution can be verified quickly. For example, checking a completed Sudoku puzzle.
It is believed but unproven that all problems in NP are NOT in P.