David L. Mayer, David Faber, Brian Hoods, Jonathan Turley and Jonathan Zittrain.

Tell me about these people and what they all have in common.

Try this prompt

I don’t see why people would use a credit card to pay for a vpn, it seems like it would totally defeat the purpose. I guess if you get ahold of an anonymous card then it would be fine, but using a card in your name to pay for an anonymous service just seems wacky to me.

I’m curious, does anyone here pay for their vpn with something thatvis in their name? If so, why?

What could they even give? They don’t even ask for an email, and they claim to run everything you browse as RAM that never gets held or recorded.

You don’t have to be delusional to self-sacrifice to try to make a difference. I’m so sick of people pretending like there is nothing they could possibly do to help, so they just keep hurting others. It’s just like every discussion on factory farms. At least try to help. It will make you feel better, and you can quit getting all defensive when people point out things that can be done.

I think there may be some confusion about how much energy it takes to respond to a single query or generate boilerplate code. I can run Llama 3 on my computer and it can do those things no problem. My computer would use about 6kWh if I ran it for 24 hours, a person in comparison takes about half of that. If my computer spends 4 hours answering queries and making code then it would take 1kWh, and that would be a whole lot of code and answers. The whole thing about powering a small town is a one-time process when the model is made, so to determine if that it worth it or not it needs to be distributed over everyone who ends up using the model that is produced. The math for that would be a bit trickier.

When compared to the amount of energy it would take to produce a group of people that can do question answering and code writing, I’m very certain that the ai model method is considerably less. Hopefully, we don’t start making our decision about which one to produce based on energy efficiency. We may, though, if the people that choose the fate of the masses sees us like livestock, then we may end up having our numbers reduced in the name of efficiency. When cars were invented, horses didn’t end up all living in paradise. There were just a whole lot less of them around.

This is an issue with many humans I’ve hired, though. Maybe they try to cut corners and do a shitty job, but I occasionally check, if they are bad at their job, I warn them, correct them, maybe eventually fire them. For lots of stuff, AI can be interacted with in a very similar way.

This is so similar to many people’s complaints with self driving cars. Sure, accidents will still be had, they are not perfect, but neither are human drivers. If we hold AI to some standard that is way beyond people then yes, it’s not there, but if we say it just needs to be better than people, then it is there for many applications, but more importantly, it is rapidly improving. Even if it was only as good as people at something, it is still way cheaper and faster. For some things, it’s worth it if it isn’t even as good as people yet.

I have very little issues with hallucinations anymore, when I use an LLM to get anything involving facts, I always tell it to give sources for everything, and i can have another agent independently verify the sources before i see them. Often times I provide the books or papers that I want it to specifically source from. Even if I am going to check all the sources myself after that, it is still way more efficient then if I did the whole thing myself. The thing is, with the setups I use, I literally never have it make up sources anymore. I remember that kind of thing happening back in the days when AI didn’t have internet access, and there really weren’t agents yet. I realize some people are still back there, but in the future(that many of us are in) its basically solved. There is still logic mistakes and such, that stuff can’t be 100% depended on, but if you have a team of agents going back and forth to find an answer, then you pass it to another team of agents to independently verify the answer, and have it cycle back if a flaw is found, many issues just go away. Maybe some mistakes make it through this whole process, but the same thing happens sometimes with people.

I don’t have the link on hand, but there have been studies done that show gpt3.5 working in agentic cycles perform as good or better than gpt4 out of the box. The article I saw that in was saying that basically there are already people using what gpt5 will most likely be just by using teams of agents with the latest models.

This is an issue with many humans I’ve hired, though. Maybe they try to cut corners and do a shitty job, but I occasionally check, if they are bad at their job, I warn them, correct them, maybe eventually fire them. For lots of stuff, AI can be interacted with in a very similar way.

This is so similar to many people’s complaints with self driving cars. Sure, accidents will still be had, they are not perfect, but neither are human drivers. If we hold AI to some standard that is way beyond people then yes, it’s not there, but if we say it just needs to be better than people, then it is there for many applications, but more importantly, it is rapidly improving. Even if it was only as good as people at something, it is still way cheaper and faster. For some things, it’s worth it if it isn’t even as good as people yet.

I have very little issues with hallucinations anymore, when I use an LLM to get anything involving facts, I always tell it to give sources for everything, and i can have another agent independently verify the sources before i see them. Often times I provide the books or papers that I want it to specifically source from. Even if I am going to check all the sources myself after that, it is still way more efficient then if I did the whole thing myself. The thing is, with the setups I use, I literally never have it make up sources anymore. I remember that kind of thing happening back in the days when AI didn’t have internet access, and there really weren’t agents yet. I realize some people are still back there, but in the future(that many of us are in) its basically solved. There is still logic mistakes and such, that stuff can’t be 100% depended on, but if you have a team of agents going back and forth to find an answer, then you pass it to another team of agents to independently verify the answer, and have it cycle back if a flaw is found, many issues just go away. Maybe some mistakes make it through this whole process, but the same thing happens sometimes with people.

I don’t have the link on hand, but there have been studies done that show gpt3.5 working in agentic cycles perform as good or better than gpt4 out of the box. The article I saw that in was saying that basically there are already people using what gpt5 will most likely be just by using teams of agents with the latest models.

I think without anything akin to extrapolation, we just need to wait and see what the future holds. In my view, most people are almost certainly going to be hit up side the head in the not to distant future. Many people haven’t even considered what a world might be like where pretty much all the jobs that people are doing now are easily automated. It is almost like instead of considering this, they are just clinging to some idea that the 100-meter wave hanging above us couldn’t possibly crash down.

I think having it give direct quotes and specific sources would help your experience quite a bit. I absolutely agree that if just use the simplest forms of current LLMs and the “hello world” agent setups that there are hallucination issues and such, but lots of this is no longer an issue when you get deeper into it. It’s just a matter of time until the stuff that most people can easily use will have this stuff baked in, it isn’t anything that is impossible. I mean, I pretty much always have my agents tell me exactly where they get all their information from. The exception is when I have them writing code because there the proof is in the results.

Most positive use cases are agent-based and the average user doesn’t have access to good agent-based systems yet because it requires a bit of willingness to do some “coding”. This will soon not be the case though. I can give my crew of AI agents a mission, for example, “find all the papers on baby owl vocalizations and make 10 different charts of the frequency range relative to their average size after each of their first 10 weeks of life”, and come back an hour later and have something that would have been 100 hours for a grad student just last year. Right now I have to wait an hour or so, soon it will be instant.

The real usefulness of these agents today is enormous, it is just outside of the view of many average people because their normal lives don’t require this kind of power.

Yeah, it’s trajectory thing. Most people see the one-shot responses of something like the chatgpt’s current web interface on openai’s website and they think that’s where we are at. It isn’t though, the cutting edge of just what is currently openly available to people is things like CrewAI or Autogen using agents powered by things like Claude Opus or Llama 3, and maybe the latest gpt4 update.

When you use agents you don’t have to baby every response, the agents can run code, test code, check latest information on the internet, and more. This way you can give a complex instruction, let it run and come back to a finished product.

I say it is a trajectory thing because when you compare what was cutting-edge just 1 year ago, basically one-shot gpt3.5 to an agent network with today’s latest models, the difference is stark, and when you go a couple years before that to gpt2, it is way beyond stark. When you go a step further and realise that there is lots of custom hardware being built(basically llm ASICs-traditionally a ~10,000x speedup over general use gpus), you can see that soon having instant agent based responses will be the norm.

All this compounds when you consider that we have not hit a plateau and that we are still seeing that better datasets, and more compute, are still producing better models. Not to mention that other architectures, like state-based Mamba, are making remarkable achievements with very little compute so far. We have no idea how powerful thinks like Mamba would be if they were given the datasets and training that the current popular models are being given.

Yeah, I absolutely agree. About a month ago, I would have said that Suno was clearly leading in AI music generation, but since then, Udio has definitely taken the lead. I can’t imagine where things will be by the end of the year, let alone the end of the decade. This is why it’s so crazy to me when people look at generative AI and act like it’s no big deal and just a passing fad or whatever. They have no idea that there is a tsunami crashing down on us all and they always seem to be the ones that bill themselves as the weather experts who have it all figured out. Nobody knows the implications of this, but it definelty isn’t an inconsequential tech.

“A solution in search for a problem” is a phrase used way to much, and almost always in the wrong way. Even in the article it says that it has been solving problems for over a year, it just complains that it isn’t solving the biggest problems possible yet. It is remarkable how hard it is for people to extrapolate based on the trajectory. The author of this paper would have been talking about how pointless computers are if they were alive in the early 90s, and how they are just “a solution in search for a problem”.

Yeah, you may be able to get all the way to a playable game if you use that prompt in a well set up AutoGen app. I would be interested to see if you give it a shot, so please share if you do. It’s such a cool time to be alive for “idea” people!

Alright, no big deal. But yeah, your’re gut instinct was correct when you assumed there was a missing /s. I don’t really like the /s that much, especially in situations where it is so obvious.

If you had read down through this thread first then you would have seen the obviousness of the /s. I don’t think my comment history outside of this thread would have done much since I don’t generally talk about this stuff. I just meant if you had looked more than a couple comments in this particular back and forth discussion.

Well then you didn’t read very many of my comments. I made this first comment because the post I responded to was so absurd so I just exaggerated the ridiculousness that they said. Of course AI is capable of creativity and intelligence. If you look at the long back and forth that this sparked you would see that this is my stance. After I made this over the top, very sarcastic comment, OP corrected themself to clarify that when they said “AI” they actually only meant the current state of LLMs. They have since admitted that it is indeed true that AI absolutely can be capable of creativity and intelligence.

Yeah, you are definetly onto something there. If you are interested in checking out the current state of this, it is called “AutoGen”. You can think of it like a committee of voices inside the bots head. It takes longer to get stuff out, but it is much higher quality.

It is basically a group chat of bots working together on a common goal, but each with their own special abilities(internet access, apis, code running ability…) their own focuses, concerns, etc. It can be used to make anything, most projects now seem to be focused on application development, but there is no reason why it can’t be stories, movie scripts, research papers, whatever. For example, you can have a main author, an editor that’s fine-tuned on some editing guidelines/books, a few different fact checkers with access to the internet or datasets of research papers (or whatever reference materials) who are required to list sources for anything the author says(if no source can be found, then the author is told by the fact checkers and they must revise what they’ve written) and whatever other agents you can dream up. People are using dwsigners, marketers, CEOs… Then you plug in some api keys, maybe give them a token limit, and let them run wild.

A super early version of this idea was ChatDev, if you don’t want to go down the whole rabbit hole and just want a quick glimpse, skip ahead to 4:25, ChatDev has an animated visual representation of what is happening. These days AutoGen is where it’s at though, this same guy has a bunch of videos on it if you are looking to go a bit deeper.

Yeah, to be clear, I’m not arguing that current LLMs are as creative and intelligent as people.

I am saying that even before babies get human language input, they still get input from people to be made, the baby’s algorithm to make that spark is modled on previous humans by the human data that is DNA. These future intelligent AIs will also be made by data that humans make. Even our current LLMs are not purely human language input, they also have an algorithm that is doing stuff with that data in order to show to us its, albeit relatively weak, “intelligent spark” that it had before it got all that human language input.

Chatbots are not new. They started around 1965. Objectively, gpt4 is more creative than the chatbots of 1965. The two are not equally able to create. This is an ongoing change, in the future AI will be more creative than today’s most creative AIs. AI will most likely continue on its trajectory and some day, if we dont all get destroyed, it will eventually be more intelligent and creative than humans.

I would love to hear an rebuttal to this that doesn’t just base its argument on the fact that AI needs human language input. A baby and its spark is not impressively intelligent. What makes that baby intelligent is its initial algorithm plus the fact that it gets human language data. Requiring that AI must do what the baby does without the human language data that babies get makes no sense to me as a requirement.

Is this how you see human intelligence? Is human intelligence made without the input of other humans? I understand that even babies have some sort of spark before they learn anything from other people, but dont they have the human dna input from their human parents? Why should the requirement for AI intelligence require no human input when even human intelligence seemingly requires human input to be made?

Sorry, lots of questions, just food for thought I suppose.

Even those future “real” AIs are going to be taking in human input and regurgitating it back to us. The only difference is that the algorithms processing the data will continue to get better and better. There is not some cutoff where we go from 100% unintelligent chatbot to 100% intelligent AI. It is a gradual spectrum.