For "us", having such a level of intelligence available as an assistant throughout the day is a massive life upgrade, if we can just afford more tokens.
Then I see contrarians claiming that LLMs are literally never useful for anyone, and I get "don't believe your lying eyes" vibes. At this point, such sentiments feel either willfully ignorant, or said in bad faith. It's wild.
1) is all about experimenting, which is what Tao is doing.
Having a playful and open minded attitude is like 80% of the game
While I don't doubt that there's at least one person that has said this, what you're saying doesn't conflict with the things I and many others in the "skeptic" camp have said. LLMs are useful for a very specific set of tasks. The tasks you've listed are a tiny sliver of all the tasks that AI could potentially be doing. Would it be a good idea to consult an LLM if your mother is passed out on the floor? Probably not. The problem I have is with extrapolating from the current successes to conclude that many more tasks will be done by AI in five years.
I personally did find some use cases for it and it does a decent job of cutting out minor gruntwork for me. But the experience itself screams to me that whatever gains I'm feeling I'm getting are all in my head.
Yes, to me LLM is exactly like this: from nano to vim.
It's just that every time I use nano it's (a) unintentional, as it's opened via EDITOR; (b) sort-of coerced, because most distros installing it by default also think it's somehow too much to install Vim or Emacs alongside it; and (c) extremely painfully awkward, because all other editors I use, I've invested at least as couple years of practice into.
If I spent a year using nano every day, and if I evolved a config file and read the manual during that time, I might eventually reach a place where using nano didn't feel cumbersome and irritating, but why would I do that if I already use Emacs and Vim every day? If I learn a 'new' editor it's going to be something extensible that I could see myself programming in every day: Emacs without evil; or one of the newer modal editors with a reversed sentence order, like kakoune and Helix; or, hell, VSCode.
So nano is likely doomed to remain forever cumbersome and irritating for me, somewhere on the level of typing on a touchscreen instead of a real keyboard.
I feel exactly the same, but in the opposite direction.
As someone who’s been programming for 17 years and working professionally for 10, I’m unable to get any huge productivity boosts from AI tools. They’re better than Google+stack overflow for asking random questions, but in a specific context and they’re good for repetitive, but not identical, syntax. That’s about where the gains end for me.
Maybe at this point I’m just so fast about looking up documentation. Maybe the languages/problems I’m facing aren’t well represented in the training data, but I just don’t see this amazing advancement.
I’d really love to see, live, someone programming who really gets these big productivity gains.
It kept generating annoyingly wrong code. Things with subtly wrong misleading names, missing edge cases, ignoring immediate same file context etc. I found that it slowed me down so i turned it off.
For rust it failed spectacularly. So bad that its not worth discussing lol
Makes me wonder if people who don't like Copilot output will not like my natural output as well.
Could you share any code on GitHub (or pastebin or whatever) that you wrote with the help of AI?
Or could you share what kind of experience you have with programming (how many years, what domain you work in, etc)
I have around 10+ years of professional experience although I did on/off hobby coding before that since 15 years ago.
It's mostly API endpoints, calling a database, third party APIs, data transformation, aggregation type of things.
Then either UI according to what designers provide or whatever I want to do for my side projects.
I think it's of course wildly more productive multiplier for side projects, since then it's mostly about typing things out since you know exactly what you want to do and being a little off doesn't matter.
I don't want to share any of my actual code right now, but I think one example for example is a React component that needs to fetch some sort of data, e.g. using @tanstack/react-query, then it does loading handling, error handling boilerplate things for me, which some of I change to what I specifically need for that situation, but I need very few keystrokes myself to get the initial boilerplate out that I then edit, and during edits it of course also gives me decent suggestions. And it will create the component prop types based on the args I pass to the component etc.
Then with backend, it's really good at data transformations. E.g. combining different datasets, reducing etc.
How well it picks the correct libraries and patterns depends on the project and I think how much I've navigated around, I'm not fully sure how the context is exactly passed, so usually I will feel it out and adapt code where necessary.
At my job we have this pretty clean SOA type architecture backed by a mongo db. Copilot has trouble building the more complicated, domain specific queries on its own, I’ve found.
I do occasionally ask chatgpt how to write a certain query in a general case and apply that to what I’m writing. I also don’t really like mongosh’s docs.
It’s autocomplete++, except without knowledge of the rest of my codebase.
I would speculate it's a productivity boost for programmers specifically working in areas that they are new to (or haven't really mastered yet). One question I have is whether overly relying on LLMs will reduce the ability to master a domain, and thus hurt your long-term skill. It might seem silly, like complaining that no one knows assembly anymore because of compilers, but I think it's different than just another layer of abstraction.
They just don't have the background, and probably lack the interest to dedicate studying for a few years to get to that level.
Intelligence is probably a distant third.
Incorrect. University graduates shows a good work ethic, a certain character and a ability to manage time. It's not a measure of being better than the rest of humanity. Also, it's not a good measure of intelligence. If you only want to view the world through credentials. Academics don't consider your intelligence until you have a Ph.D and X years of work in your field. Industry only uses graduates as a entry requirement for junior roles and then favors and cares only about your years of experience after that. Given that statement I can only assume you haven't been to University. You are mistaken to think, especially in time we are in now that the elite class are any more knowledgeable then you are.
Misinterpretation of the Original Point:
Intelligence vs. Moral Superiority: Noch discusses the intelligence level of a mediocre graduate science student compared to the general population. Thewanderer1983 misreads this as a claim of moral or inherent superiority over "the rest of humanity," which was not implied.
Conflation of Educational Levels:
University Graduates vs. Graduate Students: The response conflates undergraduate university graduates with graduate science students. Noch specifically refers to graduate students who have pursued advanced degrees, which typically require higher levels of specialization and intellectual rigor.
Incorrect Assessment of Intelligence Measures:
Graduate Studies as a Measure of Intelligence: Successfully completing graduate studies, especially in science, often requires significant intellectual capability. Dismissing this as "not a good measure of intelligence" overlooks the challenges inherent in advanced academic work.
Irrelevant Focus on Credentials and Industry Practices:
Credentials vs. Intelligence Discussion: Noch's comment centers on intelligence levels, not merely on holding credentials. Bringing up how industry values experience over degrees shifts the focus away from the original discussion about intelligence.
Unfounded Assumptions About Noch's Background:
Ad Hominem Attack: Suggesting that Noch hasn't been to university is an unfounded personal assumption that does not contribute to the argument and detracts from a respectful discourse.
Introduction of the 'Elite Class' Notion:
Straw Man Argument: Thewanderer1983 introduces the concept of an "elite class," which Noch did not mention. This misrepresents the original comment and argues against a point that wasn't made.
Overgeneralizations About Academia and Industry:
Academia's Recognition of Intelligence: Claiming that academics don't consider intelligence until one has a Ph.D. and years of work is an overgeneralization. Intelligence is recognized and valued at various academic levels.
Industry's View on Graduates: Stating that industry only uses graduates as an entry requirement ignores the significant roles that advanced degree holders often play in innovation and leadership within industries.
Ignoring the Core Benefit Highlighted:
AI as a Life Upgrade: Noch emphasizes how access to AI with the intelligence level of a graduate student is a substantial benefit for most people. Thewanderer1983 fails to address this key point, instead focusing on unrelated issues.
Misunderstanding of the Value of Graduate Education:
Work Ethic vs. Intellectual Achievement: While a good work ethic is important, graduate education in science also demands high intellectual capability, critical thinking, and problem-solving skills.
Logical Fallacies:
Red Herring: The discussion about industry preferences and academic credentials diverts from the main argument about the intelligence level of graduate students.
Ad Hominem: Attacking Noch's presumed lack of university experience instead of addressing the argument presented.LLMs are good for mediocre poems and presidential speeches that have no shame.
Let’s evaluate the correctness of Thewanderer’s argument in detail:
Distinction Between Credentials and Intelligence:
Correctness: Thewanderer is correct in stating that a university degree is not a definitive measure of intelligence. Intelligence is a complex trait that encompasses various cognitive abilities, problem-solving skills, creativity, and emotional intelligence. Academic credentials primarily reflect one’s ability to succeed in a structured educational environment, which is just one aspect of intelligence.
Value of Real-World Experience:
Correctness: The argument that real-world experience is crucial is accurate. Many industries value practical experience and skills over formal education. For example, in technology and business sectors, hands-on experience, problem-solving abilities, and adaptability are often more important than academic qualifications alone. This is supported by numerous studies and industry practices that prioritize experience and performance over degrees.
Critique of Credentialism:
Correctness: Thewanderer’s critique of credentialism is valid. Over-reliance on academic credentials can overlook the diverse talents and skills that individuals without formal degrees may possess. This perspective is supported by the growing recognition of alternative education paths, such as vocational training, apprenticeships, and self-directed learning, which can also lead to successful careers.
Inclusivity and Egalitarianism:
Correctness: Promoting inclusivity and valuing diverse forms of knowledge is a correct and progressive stance. Intelligence and capability are not confined to those with advanced degrees. Many successful individuals in various fields do not have formal academic credentials but have achieved significant accomplishments through experience, self-learning, and practical skills.
Encouragement of Self-Worth:
Correctness: Encouraging individuals to value their own experiences and knowledge is a positive and correct approach. It fosters confidence and self-worth, which are important for personal and professional growth. Recognizing the value of diverse experiences and perspectives contributes to a more inclusive and equitable society.
It accurately distinguishes between academic credentials and broader measures of intelligence.
It correctly emphasizes the importance of real-world experience.
It validly critiques the overemphasis on academic credentials.
It promotes an inclusive and egalitarian view of intelligence.
It encourages self-worth and confidence in one’s abilities.
Please could you share your prompt or a link to the conversation?
I'm genuinely puzzled that you're more interested in doubling down and justifying yourself and making new points (different from what I initially presented) than understanding the other person's point of view.
If you share your prompt, I'll have a better understanding of your motivations and whether you are arguing in good faith.
As far as silly games go: if you honestly believe a game is silly, you shouldn't play it, unless you want to win silly prizes.