Degrading your product by filling it with ads or, God forbid, by making the LLM returns less accurate and influenced by advertisers, makes ZERO sense for any LLM company due to the ubiquity of LLMs. LLMs aren't something special that no one else can create and use. Google is a poor example because it had a monopoly on search for a long time due to its foothold and technology, but OpenAI has nothing like that at all. OpenAI has no moat or "secret sauce" that would enable it to be more successful than any other LLM company.
If I can use an open source highly effective LLM locally, and have it do all of the things ChatGPT can do (and more), then what motivation do I have to use ChatGPT? The only motivation people would have is ease of use but if one service becomes bogged down and compromised then people will just shift to one of the other hundred equally effective services with less ads. LLMs aren't hard to spin up and put out there for people to use.
Honestly, I don't think OpenAI is going to survive very long unless they get incredibly lucky or they come out with some banger of a new model or new app but even then, IDK...
Cool website. I don't understand enough about the various benchmarks or how they're done to judge whether or not anything is accurate, but I love the layout and features especially the spectator feature which is pretty cool. One thing, I saw the "Market simulator" spectator feature but didn't see a corresponding benchmark for that. Is it "Finance" or "Betting" or "Trading"?
One reason, beside basic altruism, is so you can put the projects on your resume. This is especially helpful if the project does very well or gets lots of stars.
Not really a bold claim. The scientific consensus is that intelligence is a highly polygenic and heritable trait, with genetic factors accounting for 50%-80% of the variations in intelligence. The rest is environmental (prenatal care, education, nutrition, lack of exposure to toxins like lead, etc.)
I think that writers may stop using "It's not x, it's y" analogies in their writings because every time they do, someone calls it out for being AI. Same with "Em Dashes" and such.
Total GDP increasing isn't a good measure of economic growth or productivity because it doesn't distinguish between productive value creation and money just changing hands. If I cut your hair and charge $50 and you cut my hair and charge $50 then $100 is added to the GDP. Sure, value is created by the transaction, but value would be created if no money changed hands at all thus not even affecting the GDP! GDP can also rise even as most people are doing poorly. A billionaire buying a superyacht adds the same amount to the GDP as 500 families buying their first home. You can have a growing GDP even in a society where the top .1% have 99% of the wealth, but that's not a society I'd want to live in.
I read the whole thing; it's definitely not AI slop. A few sentences taken out of an hour long read that are commonly used by AI doesn't mean the article was composed by AI. You'll find similar artifacts in any longer published work, including those published prior to AI.
In my limited experience, using LLMs to code up things unrelated to LLMs (robotics for instance) is significantly less productive than using LLMs to code up things related to LLMs. It works, just not very well and requires a lot more leg work on the user end than in other areas.
Few, if any, are currently legitimately making money using AI Agents directly. Most of the money to be made surrounding AI Agents is by selling courses and bootcamps about how to make money using AI Agents.
Right. If you are wealthy enough to go retire and live off interest, but STILL choose to work for a company you morally disagree with...then what sort of person are you?
I have attempted to replicate the "workflow" LLM process where several LLMs come up with different variations of a way to solve a problem and a "judge" LLM reviews them and the go through different verification processes to see if this workflow increased the accuracy of the LLM's ability to solve the problem. For me, in my experiments, it didn't really make much difference but at the time I was using LLMs significantly dumber than current frontier models. HOWEVER...When I enable "Thinking Mode" on frontier LLM's like ChatGPT it DOES tend to solve problems that the non-thinking mode isn't able to solve so perhaps it's just a matter of throwing enough iterations at it for the LLM to be able to solve a particular complex problem.
>The 40 year old who won't date a real girl because he is in love with a bot I'm more concerned with.
I think on the Venn diagram of 40-year-olds only willing to date bots and 40-year-olds capable of actually dating real women, the overlap is incredibly small at this point in time.
Alignment has a lot more to it than simply which answers an AI provides. In the future when agents are commonplace and when AI can do things in the physical world, alignment will be especially important because it will dictate how the AI chooses to accomplish the goals humans set out for it. Will it choose to accomplish them in a way that the human requestor does not want and did not anticipate, or will it choose to accomplish them in a way any human with common sense would choose?
Moreover, in the not so distant future if there is an AI that is acting totally autonomous and independent of human requests for long periods of time, weeks or months or longer, and it's doing good important things like medical research or environmental restoration, alignment will be incredibly important to ensure every single independent decision it makes is done in the way its designers would have intended.
Why should we assume that LLMs would be stagnant at current levels when, so far, they haven't stopped improving? I remember when simply using Copilot to just auto-complete 1 line of code was a groundbreaking unimaginable advancement. That was only a few years ago. New and improved models are being released nearly every week. The open-source models are nearly as advanced as the closed source models (if you have the hardware to run them at full capacity).
It depends on which application you're using. Applications like "RooCode", which is a free extension for VSCode, have several "modes" which allow the user to create an outline of the project using an "architect" LLM, followed by coding the project with a "Coding" LLM, followed by debugging the project with a "Debugging" LLM if there are bugs. There's also an LLM that answers questions about the project. Only the coding and the debugging LLMs do actual coding but you can set it so you have to approve each change it makes.
If I can use an open source highly effective LLM locally, and have it do all of the things ChatGPT can do (and more), then what motivation do I have to use ChatGPT? The only motivation people would have is ease of use but if one service becomes bogged down and compromised then people will just shift to one of the other hundred equally effective services with less ads. LLMs aren't hard to spin up and put out there for people to use.
Honestly, I don't think OpenAI is going to survive very long unless they get incredibly lucky or they come out with some banger of a new model or new app but even then, IDK...