If you have a llm, then you have something to have a conversation with.
If you give the llm the ability to have memory, then it gets much “smarter” (context window and encoded local knowlege base)
If you give the llm the ability to offload math problems to an orchestrator that composes math problems then it can give hard numbers based on real math.
If you give a llm the ability to use the internet to search then it has a way to update its knowledgebase before it answers (seems smarter)
If you give a llm a orchestrator that can use a credit card on the internet, it can deliver stuff via doordash or whatever. (I don’t trust it).
Definitely not general intelligence, but good at general conversation and brainstorming and able to get extended modularly while keeping the conversational interface
That conversation, that dataset, knowledge base gets too big?
Well the LLM now gets slower and less efficient, has to compare and contrast more and more contradictory data, to build its heuristics out of.
It has no ability to meta-cognate. It has no ability to discern, and disregard bullshit, both as raw data points, and bullshit processes for evaluating and formulating concepts and systems.
The problem is not that they know too little, but that they know so much that isn’t so is pointless contradictory garbage.
When people learn and grow and change and make breakthroughs, they do so by shifting to or inventing some kind of totally new mental framework for understanding themselves and/or the world.
You are right and I have seen some people try some clumsy solutions:
Have the llm summarize the chat context ( this loses information, but can make the llm appear to have a longer memory)
Have the llm repeat and update a todo list at the end of every prompt (this keeps it on task as it always has the last response in memory, BUT it can try to do 10 things and fails on step 1 but doesn’t realize jt)
Have a llm trained with really high quality data then have it judge the randomness of the internet. This is meta cognition by humans using the llm as a tool for itself. It definitely can’t do it by itself without becoming schizophrenic, but it can make some smart models from inconsistent and crappy/dirty datasets.
Again, you are right and I hate using the syncophantic clockwork-orange llms with no self awareness. I have some hope that they will get better.
They make a good virtual intelligence, and they do a very good impression of it when given all the tools. I don’t think they’ll get to proper intelligence without a self updating state/model, which will get into real questions about them being something that is being.
I’m now curious what you mean by self updating model? For a model to be made, it needs billions of data points for it to make the first. A second can be made with the first judging the quality of the input into the 2nd. Some models already do this sifting in prep for the next model creation.
I think of it like humans: we have billions of sensory signals each day, and we judge what is important based on genetics, culture, and our chosen interpretation of morality e.g. hedonism considers effort/discomfort. If a llm has a billion sensory signals each day and has application specific hardware like our genetics then would the hardware finally allow you to call it intelligent?
I am turning into a philosopher in this comment thread! Soo… when is a chair a chair and not a stool?
No more that you can have a conversation with a malfunctioning radio. Technically true, you’re saying words, and the radio sometimes is saying words in between all the static. But that’s not what we’re talking about.
Same for the rest of the points. “I’m feeling lucky” button on google that takes input from /dev/random is basically one step below what you’re talking about. With the same amount of utility, or intelligence for that matter.
It’s essentially a rubber duck. It doesn’t need to be intelligent, or even very good at pretending to be intelligent. Simply explaining an problem to an object is enough to help people see the problem from a different angle, the fact that it gibbers back at you is either irrelevant or maybe a slight upgrade from your standard rubber duck.
Still a lot of resources to expend for what can be done with a much lower tech solution.
Talking about rubber duck intelligence, there is a two step “thinking then respond” that recent iterations of llms have started using. It is literally a rubber duck during the thinking phase. I downloaded a local llm with this feature and had it run and the cli did not hide the “thinking” once done. The end product was better quality than if it had tried to spit an answer immediately (I toggled thinking off and it definitely was dumber, so I think you are right for the generation of llms before “thinking”
That’s why I’m saying this might be an upgrade from a rubber duck. I’ll wait for some empirical evidence before I accept that it definitely is better than a rubber duck, though, because even with “thinking” it might actually cause tunnel vision for people who use it to bounce ideas. As long as the LLM is telling you that you’re inventing a new type of math you won’t stop to think of something else.
I think a more apt comparison would be a complicated magic 8 Ball, since it actually gives answers that seem to be relevant to the question, but your interpretation does the actualy mental work.
-The Barnum effect, also called the Forer effect or, less commonly, the Barnum–Forer effect, is a common psychological phenomenon whereby individuals give high accuracy ratings to descriptions of their personality that supposedly are tailored specifically to them, yet which are in fact vague and general enough to apply to a broad range of people.[1] This effect can provide a partial explanation for the widespread acceptance of some paranormal beliefs and practices, such as astrology, fortune telling, aura reading, and some types of personality tests.[1]
Well a local model responding to a prompt on less than 20gb of vram (gaming computer) costs less power than booting up any recent AAA high fps game. The primary cost in power is r&d. Training the next model to be “the best” is an Arms race. 90% of power consumption is trying to train the next model in 100 different ways. China was able to build off of chat cpt tech and built a model that has similar abilities and smartness for only 5 million. I think I won’t update my local model until there is actually more abilities in the next one.
And there is also a cost in the brain atrophy that these are causing in people who use them regularly. LLMs will make a huge segment of the population mentally and emotionally stunted. Who knows what these will do to this generation of children (many of whom will virtually raised by LLMs). Television and smartphones have done similar brain damage, particularly to attention spans, but these things will really wreak havoc on humanity on another level entirely. Like potentially a Fermi paradox/great filter solution level of harm.
When you say “And there is also a cost in the brain atrophy that these are causing in people who use them regularly. LLMs will make a huge segment of the population mentally and emotionally stunted.” I have difficulty believing it.
A mit professor did a study where she asked students to use llms to write a paper in one prompt then copy and paste and submit, she then checked and found none of them remembered the next week. Everyone else who wrote down the essay actually remembered and had good brain scans. This is bad evidence because there are other examples of people trying to learn a language who learn faster and with better articulation with llm as a tailoring tutor. There are examples of people feeling isolated at work using llms as a “rubber duck and / or inexperienced intern from a foreign country” and the daily collaborator increases creativity and productivity and mental health of the human. As such, I don’t see conclusive evidence either way.
Generally I find using llms for brainstorming has led me to think outside of my tunnel vision. I have found using a llm for vibe coding ( I am not a programmer) is a lot of frustration. They are too verbose for their own “time savings”.
I think social media and short form videos like tick tok do far more harm to attention spans than the rounding error of a llm. I think attention spans are more closely linked with regular handling one’s own boredom.
Thanks for your engagement, I look forward to conversations like this. Also if you have a source for your claim of brain atrophy, please give it!
I don’t know why you’re getting downvoted. i think you hit on an interesting observation.
If all a person had was broca’s and wernicke’s areas of the brain, their abilities would be pretty limited. you need the rest: cerebellum for coordination, prefrontal cortex for planning, hippocampus for memory regulation or whatever (i am not a neurologist), etc.
LLMs might be a part of AGI some day, but they are far from sufficient on their own
If you have a llm, then you have something to have a conversation with.
If you give the llm the ability to have memory, then it gets much “smarter” (context window and encoded local knowlege base)
If you give the llm the ability to offload math problems to an orchestrator that composes math problems then it can give hard numbers based on real math.
If you give a llm the ability to use the internet to search then it has a way to update its knowledgebase before it answers (seems smarter)
If you give a llm a orchestrator that can use a credit card on the internet, it can deliver stuff via doordash or whatever. (I don’t trust it).
Definitely not general intelligence, but good at general conversation and brainstorming and able to get extended modularly while keeping the conversational interface
Heres the main problem:
LLMs don’t forget things.
They do not disregard false data, false concepts.
That conversation, that dataset, knowledge base gets too big?
Well the LLM now gets slower and less efficient, has to compare and contrast more and more contradictory data, to build its heuristics out of.
It has no ability to meta-cognate. It has no ability to discern, and disregard bullshit, both as raw data points, and bullshit processes for evaluating and formulating concepts and systems.
The problem is not that they know too little, but that they know so much that
isn’t sois pointless contradictory garbage.When people learn and grow and change and make breakthroughs, they do so by shifting to or inventing some kind of totally new mental framework for understanding themselves and/or the world.
LLMs cannot do this.
You are right and I have seen some people try some clumsy solutions:
Have the llm summarize the chat context ( this loses information, but can make the llm appear to have a longer memory)
Have the llm repeat and update a todo list at the end of every prompt (this keeps it on task as it always has the last response in memory, BUT it can try to do 10 things and fails on step 1 but doesn’t realize jt)
Have a llm trained with really high quality data then have it judge the randomness of the internet. This is meta cognition by humans using the llm as a tool for itself. It definitely can’t do it by itself without becoming schizophrenic, but it can make some smart models from inconsistent and crappy/dirty datasets.
Again, you are right and I hate using the syncophantic clockwork-orange llms with no self awareness. I have some hope that they will get better.
I also like to have a conversation with a magic 8 ball
They make a good virtual intelligence, and they do a very good impression of it when given all the tools. I don’t think they’ll get to proper intelligence without a self updating state/model, which will get into real questions about them being something that is being.
I’m not sure the world is quite ready for that.
I’m now curious what you mean by self updating model? For a model to be made, it needs billions of data points for it to make the first. A second can be made with the first judging the quality of the input into the 2nd. Some models already do this sifting in prep for the next model creation.
I think of it like humans: we have billions of sensory signals each day, and we judge what is important based on genetics, culture, and our chosen interpretation of morality e.g. hedonism considers effort/discomfort. If a llm has a billion sensory signals each day and has application specific hardware like our genetics then would the hardware finally allow you to call it intelligent?
I am turning into a philosopher in this comment thread! Soo… when is a chair a chair and not a stool?
No more that you can have a conversation with a malfunctioning radio. Technically true, you’re saying words, and the radio sometimes is saying words in between all the static. But that’s not what we’re talking about.
Same for the rest of the points. “I’m feeling lucky” button on google that takes input from /dev/random is basically one step below what you’re talking about. With the same amount of utility, or intelligence for that matter.
It’s essentially a rubber duck. It doesn’t need to be intelligent, or even very good at pretending to be intelligent. Simply explaining an problem to an object is enough to help people see the problem from a different angle, the fact that it gibbers back at you is either irrelevant or maybe a slight upgrade from your standard rubber duck.
Still a lot of resources to expend for what can be done with a much lower tech solution.
Talking about rubber duck intelligence, there is a two step “thinking then respond” that recent iterations of llms have started using. It is literally a rubber duck during the thinking phase. I downloaded a local llm with this feature and had it run and the cli did not hide the “thinking” once done. The end product was better quality than if it had tried to spit an answer immediately (I toggled thinking off and it definitely was dumber, so I think you are right for the generation of llms before “thinking”
That’s why I’m saying this might be an upgrade from a rubber duck. I’ll wait for some empirical evidence before I accept that it definitely is better than a rubber duck, though, because even with “thinking” it might actually cause tunnel vision for people who use it to bounce ideas. As long as the LLM is telling you that you’re inventing a new type of math you won’t stop to think of something else.
I think a more apt comparison would be a complicated magic 8 Ball, since it actually gives answers that seem to be relevant to the question, but your interpretation does the actualy mental work.
https://en.wikipedia.org/wiki/Barnum_effect
-The Barnum effect, also called the Forer effect or, less commonly, the Barnum–Forer effect, is a common psychological phenomenon whereby individuals give high accuracy ratings to descriptions of their personality that supposedly are tailored specifically to them, yet which are in fact vague and general enough to apply to a broad range of people.[1] This effect can provide a partial explanation for the widespread acceptance of some paranormal beliefs and practices, such as astrology, fortune telling, aura reading, and some types of personality tests.[1]
Nice. We can also reductively claim LLM as just an expensive magic-8-ball and make LLM-bros mad. :-)
okay, and how much does this high tech journal cost to build and operate?
Well a local model responding to a prompt on less than 20gb of vram (gaming computer) costs less power than booting up any recent AAA high fps game. The primary cost in power is r&d. Training the next model to be “the best” is an Arms race. 90% of power consumption is trying to train the next model in 100 different ways. China was able to build off of chat cpt tech and built a model that has similar abilities and smartness for only 5 million. I think I won’t update my local model until there is actually more abilities in the next one.
And there is also a cost in the brain atrophy that these are causing in people who use them regularly. LLMs will make a huge segment of the population mentally and emotionally stunted. Who knows what these will do to this generation of children (many of whom will virtually raised by LLMs). Television and smartphones have done similar brain damage, particularly to attention spans, but these things will really wreak havoc on humanity on another level entirely. Like potentially a Fermi paradox/great filter solution level of harm.
When you say “And there is also a cost in the brain atrophy that these are causing in people who use them regularly. LLMs will make a huge segment of the population mentally and emotionally stunted.” I have difficulty believing it.
A mit professor did a study where she asked students to use llms to write a paper in one prompt then copy and paste and submit, she then checked and found none of them remembered the next week. Everyone else who wrote down the essay actually remembered and had good brain scans. This is bad evidence because there are other examples of people trying to learn a language who learn faster and with better articulation with llm as a tailoring tutor. There are examples of people feeling isolated at work using llms as a “rubber duck and / or inexperienced intern from a foreign country” and the daily collaborator increases creativity and productivity and mental health of the human. As such, I don’t see conclusive evidence either way.
Generally I find using llms for brainstorming has led me to think outside of my tunnel vision. I have found using a llm for vibe coding ( I am not a programmer) is a lot of frustration. They are too verbose for their own “time savings”.
I think social media and short form videos like tick tok do far more harm to attention spans than the rounding error of a llm. I think attention spans are more closely linked with regular handling one’s own boredom.
Thanks for your engagement, I look forward to conversations like this. Also if you have a source for your claim of brain atrophy, please give it!
lol and how much does does 20gb of vram cost right now?
About 600$ before tax and shipping
I don’t know why you’re getting downvoted. i think you hit on an interesting observation.
If all a person had was broca’s and wernicke’s areas of the brain, their abilities would be pretty limited. you need the rest: cerebellum for coordination, prefrontal cortex for planning, hippocampus for memory regulation or whatever (i am not a neurologist), etc.
LLMs might be a part of AGI some day, but they are far from sufficient on their own
LLMs are a completely separate technology from what AGI will be. There is no path from LLM to AGI that isn’t a complete redo.