I think it’s sort of like asking if autocomplete is the end of writing. It’s a writing tool. If you care about how you sound and take the time to edit it so it says what you want to say, maybe the...
I think it’s sort of like asking if autocomplete is the end of writing. It’s a writing tool. If you care about how you sound and take the time to edit it so it says what you want to say, maybe the process doesn’t matter so much?
It seems like there are similar issues with ghostwriting? Editorial assistance can go too far sometimes. If your oversight of the process is too casual, it doesn’t sound like you.
Language is a bridge. If we disconnect one side of the bridge, the bridge falls. If one listener or speaker, writer, or reader stops feeling what is said, the bridge crumbles. Language without a body is senseless, meaningless, void. As the machines start outdoing us technically, let’s not neglect that understanding can be real and that, at its best, it can be mutual. The end of writing is to understand each other through time and space, to feel what others have felt and to make others feel through time and space how we feel now.
This is one use of writing, but there’s an ambiguity: writing has many uses. It’s also used to report routine transactions, and this has been automated for a long time using form letters.
For a more family-oriented example, how about the common use of birthday and Christmas cards? They have writing in them, professionally written, but it’s only approximately what the sender meant. The message is more in selecting an appropriate card, and the act of sending it.
I'm unfamiliar with the nuances of the publishing industry. How involved is the nominal author in a ghostwritten book? This is how I feel about sharing raw AI images. If the spender dumps...
ghostwriting
I'm unfamiliar with the nuances of the publishing industry. How involved is the nominal author in a ghostwritten book?
it’s only approximately what the sender meant. The message is more in selecting an appropriate card, and the act of sending it.
This is how I feel about sharing raw AI images. If the spender dumps everything they find interesting, they're a spammer; when they're limited to 2 a day, then there is some curation involved in the selection.
Thanks for posting this, it's more interesting that I expected, not like most articles I've seen lately which either dismiss AI as a boondoggle or spread fear about it. Personally, I keep noticing...
Thanks for posting this, it's more interesting that I expected, not like most articles I've seen lately which either dismiss AI as a boondoggle or spread fear about it.
Personally, I keep noticing machines that have been around for years that parallel AI quite a bit.
A dishwasher machine doesn't know what dishes are, or what you do with them. It (usually) doesn't know what clean means. It could sort of know what "dry" means if it has a humidity sensor.
Newer clothes washers don't know what clothes are, but maybe they have a sensor that weighs how many you put in so it can run a little longer if it's a lot. But it doesn't know if you are putting in wearable clothes are paper towels or something else.
A bread machine does not know what bread is. It never tasted bread. You put some flour in a machine and it jostles it around for a while and then heats it up. It just happens to work but it can't recover if you forget to put water or yeast in.
I've used Copilot a few times for programming tasks. Sometimes it is helpful in that it is faster than checking stackoverflow or something. But just as often it confidently tells me a lot of wrong things and it really isn't as good as promised (yet) of doing mundane work for me. Unfortunately a lot of managers probably think it is better than it is.
Anyway, like the machines I mentioned, the thing that is called "AI" can be a useful tool in the right hands, but applications are pretty narrow.
I think this is a good way of framing it. Whatever aspects of AI that we use, whether it's LLMs or something else, can be looked at in this way. Eventually if it ever reaches AGI then possibly it...
I think this is a good way of framing it. Whatever aspects of AI that we use, whether it's LLMs or something else, can be looked at in this way. Eventually if it ever reaches AGI then possibly it will surpass these basic machines in that way.
Sometimes it is helpful in that it is faster than checking stackoverflow or something. But just as often it confidently tells me a lot of wrong things and it really isn't as good as promised (yet) of doing mundane work for me. Unfortunately a lot of managers probably think it is better than it is.
This is very much my experience with them too. If you ask them one thing and leave it be, you'll come away thinking you got the right answer. If you start probing at all, you realize it is just making things up and it will constantly apologize saying it told you the wrong thing before. The "hallucination" aspect to them that people talk about, I think it's even wrong to call it that. I get the impression that these things give bad information >90% of the time. Calling it a hallucination has the impression of making it seem like it's just a side effect or something when in reality that's the primary thing it does is make things up. I get that the output is very input dependent, but that's kind of my point, you can lead the thing any which way without realizing you did because it doesn't know any better. If I ask it leading questions, it generally seems to answer as to reassure my leading question, and then when it inevitably responds with some logical fallacy and I point it out, it apologizes and says it was wrong before.
I really dislike the term "hallucination". The thing is just wrong. Yeah it's wrong for very specific reasons, but so am I when I'm wrong. It sounds like corporate speak made by people trying to...
I really dislike the term "hallucination". The thing is just wrong. Yeah it's wrong for very specific reasons, but so am I when I'm wrong. It sounds like corporate speak made by people trying to sell a product.
Yeah, people who use words like “hallucination” want to make it sound like these text generation algorithms are conscious when they are nothing of the sort.
Yeah, people who use words like “hallucination” want to make it sound like these text generation algorithms are conscious when they are nothing of the sort.
Bullshitting fits what it's doing so well though and I can't think of a closer word for talking out of your ass. Lying just doesn't sound right and hallucinating doesn't make sense.
Bullshitting fits what it's doing so well though and I can't think of a closer word for talking out of your ass.
Lying just doesn't sound right and hallucinating doesn't make sense.
Lying implies that you know the truth and choose to say something else; hallucinating implies that you genuinely believe what you said was true; bullshtting means truth is not a consideration—only...
Lying implies that you know the truth and choose to say something else; hallucinating implies that you genuinely believe what you said was true; bullshtting means truth is not a consideration—only whether the arrangement of words is pleasing or not matters. If BS were not an option, I'd say that hallucinations are a better description than lies: the computer's functions decided that it's probably good.
That logic does make a lot more sense when put that way. :) Still not liking it though, there must be some condition where someone does something similar... oh nice, did some searching and learned...
That logic does make a lot more sense when put that way. :)
Still not liking it though, there must be some condition where someone does something similar... oh nice, did some searching and learned a new word, confabulate! Basically when your brain fills in the gaps with false info and you believe it to be the truth. Confabulate sounds weird but I feel like the description fits what's going on.
I believe this use of “hallucination” became popular with image generation, and the images from DeepDream in 2015 really were pretty trippy. Earlier text generation often had a surreal aspect to...
I believe this use of “hallucination” became popular with image generation, and the images from DeepDream in 2015 really were pretty trippy. Earlier text generation often had a surreal aspect to it too. Here is the first example from the GPT 2 announcement in 2019.
Prompt:
In a shocking finding, scientist discovered a herd of unicorns living in a remote, previously unexplored valley, in the Andes Mountains. Even more surprising to the researchers was the fact that the unicorns spoke perfect English.
Continuation:
The scientist named the population, after their distinctive horn, Ovid’s Unicorn. These four-horned, silver-white unicorns were previously unknown to science.
Now, after almost two centuries, the mystery of what sparked this odd phenomenon is finally solved.
Dr. Jorge Pérez, an evolutionary biologist from the University of La Paz, and several companions, were exploring the Andes Mountains when they found a small valley, with no other animals or humans. Pérez noticed that the valley had what appeared to be a natural fountain, surrounded by two peaks of rock and silver snow.
Pérez and the others then ventured further into the valley. “By the time we reached the top of one peak, the water looked blue, with some crystals on top,” said Pérez.
Pérez and his friends were astonished to see the unicorn herd. These creatures could be seen from the air without having to move too much to see them – they were so close they could touch their horns.
When I was a kid, one things we did for fun with our primitive computers was to write silly sentence generators. They work essentially like a form letter generator, taking sentence templates and filling in random words. This was the computerized version of Mad Libs, a popular game where you would do a similar thing on paper.
Their first example of what you could do with GPT-2 was definitely leaning into that, showing that it was a much better nonsense generator. At the time, it seemed astonishing how, if you weren’t paying attention, it could almost sound real.
Since then, with more training, they’ve found more mundane uses, but with suitable adjustments to generation settings, one can reveal an LLM’s dream-like nonsense generator again. Seems more fun?
I think that as a metaphor, dreaming works pretty well for what an LLM does, because it’s something you do unconsciously. You’re not responsible for the content of your dreams. Neither is an LLM. People are trying pretty hard to wish the nonsense generator into a real boy, though.
That does highly depend on the subject and the sort of questions you do ask them. But, in the context of complex tasks, you are probably right. I still think LLM's are really great tools. I use...
I get the impression that these things give bad information >90% of the time.
That does highly depend on the subject and the sort of questions you do ask them. But, in the context of complex tasks, you are probably right.
I still think LLM's are really great tools. I use ChatGPT and the OpenAI api on a daily basis. But mostly as a tool in my tool belt where I am fully aware of the limitation and make sure to only ask it for things I have enough domain knowledge myself to validate the outcome. More detailed comment I wrote a while ago about it.
I largely agree. First impression is that it's magic, but it wears off. I imagine that using a dishwasher seemed like magic at first? I've been using Copilot for months now, for autocomplete only....
I largely agree. First impression is that it's magic, but it wears off. I imagine that using a dishwasher seemed like magic at first?
I've been using Copilot for months now, for autocomplete only. I tried Cody instead for a few days, and I found its autocomplete broadly similar in quality - often making things up, but good at repetitive code.
Then Cody's limited free version ran out. I tried coding without it, and wow do I miss autocomplete. I type something, wait... and nothing happens. It feels sort of like typing on a phone without autocomplete.
So, I turned Copilot back on again. But I'll probably switch to Cody after my Copilot subscription runs out, just to try it out more.
I've been testing a few Copilot alternatives recently. My goal was 1) free, because coding is still mostly an unpaid hobby for me, and 2) local, because my internet is not super reliable and I...
I've been testing a few Copilot alternatives recently. My goal was 1) free, because coding is still mostly an unpaid hobby for me, and 2) local, because my internet is not super reliable and I like the idea of local hosting.
The best options I found were Continue and twinny. Both support local models via ollama, which is where I keep my instruct and FIM models. I used Deepseek Coder (7B) for instruct, and alternated between codegemma and StarCoder2 for FIM (2B).
I found Cody's local model support is currently quite poor, and they also require an account to use the VS Code extension, so I discounted them at this time. However they also recently became open-source, and seems to be moving to support local models in the future, so I'll likely revisit them in the future.
I also tested a non-local alternative, Codeium. It's nice and offers a free tier, though does require an account and enabling telemetry in VS Code. My FIM results were a bit better, though still not perfect.
I expect Copilot probably beats each of these in performance. I haven't had a chance to test it though, and probably won't for some time.
I still mostly see these as auto-complete features. It is useful for saving time, and sometimes offers something even better than I'd considered. That's the biggest benefit so far to me.
I will say that sometimes the auto-complete disrupts my current train of thought though by offering something completely different than what I was expecting. So I'm still trying to find the right rhythm with these tools.
I don't really find the "explain this code" feature too useful, since I'm mostly working in my own codebases. Maybe I'll come across something truly arcane or unfamiliar in the future. I might ask a basic question if I think it's faster than searching the docs. eg. "Are sets one-dimensional in JavaScript? Can they only contain unique elements?). Due to the nature of LLMs though, I still feel the need to double check my answers if it's anything sufficiently complex or niche.
I don't much trust them for refactoring either. Maybe on a small scale: "Can this be a one liner?, or "Add null checks here", but never a major restructuring.
Still, I think these coding assistants can offer a lot of value. They're probably especially helpful in learning a new language by offering appropriate syntax. And for a novice - while they may need to be careful about trusting the suggestions too much, I think the explain features could be especially helpful in understanding a function line-by-line.
CodeGemma sounds promising? I'll wait for people to start talking about their experiences with it, though. I've been using GPT 4 directly for generic how-to questions about how to do things in...
CodeGemma sounds promising? I'll wait for people to start talking about their experiences with it, though.
I've been using GPT 4 directly for generic how-to questions about how to do things in TypeScript - the sort of thing I might have previously done a Google search to look for StackOverflow articles. It's become less useful as I've gotten up to speed, though. Asking on r/typescript helped one time.
The following phase (emphasis from the original) is what inspired me to share this article. We all know the joke about business communication: I use Grammarly to write a 5-paragraph essay as a...
The following phase (emphasis from the original) is what inspired me to share this article.
AI has made clear that, to a large degree, we all simulate knowledge and meaning. AI is so good at simulating school and business language because a lot of our own understanding in both spheres is largely simulated.
We all know the joke about business communication: I use Grammarly to write a 5-paragraph essay as a follow-up report of the meeting; you use ChatGPT to summarize it into three sentence-length bullet points that fit on a PowerPoint slide. A lot of white-collar office work seems to be putting forth the effort to pretend our college degrees mean something instead of admitting the reality that most of us would be just as effective with a point and a grunt (at least before the rise of remote work).
This article is a trip, but a good one. I like it. We have so many questions on how things will go, but I think we should all just take AI as it comes and embrace it. It'll work out if we stay...
This article is a trip, but a good one. I like it. We have so many questions on how things will go, but I think we should all just take AI as it comes and embrace it. It'll work out if we stay vigilant. There's a future out there where we don't have to work and have the time to find ourselves, figure out what we enjoy doing and do it.. if we feel like it. That's assuming we work for that future NOW... some potential futures need to be avoided at all costs.
We could move to a post-capitalism society where working is a choice, be stuck in a society similar to now where we're all basically corporate slaves despite tech being at a sufficient level to allow us to work FAR less, or all become impoverished as the rich no longer need us... maybe we'll be kept like cattle to ensure the genetic integrity among the rich or they'll just off us all as an eyesore.
I really don't understand point of some of these articles. End of writing? Hardly when AI writing lacks creativity and flair. A to B fine. In between? Questionable.
I really don't understand point of some of these articles.
End of writing? Hardly when AI writing lacks creativity and flair. A to B fine. In between? Questionable.
I think it’s sort of like asking if autocomplete is the end of writing. It’s a writing tool. If you care about how you sound and take the time to edit it so it says what you want to say, maybe the process doesn’t matter so much?
It seems like there are similar issues with ghostwriting? Editorial assistance can go too far sometimes. If your oversight of the process is too casual, it doesn’t sound like you.
This is one use of writing, but there’s an ambiguity: writing has many uses. It’s also used to report routine transactions, and this has been automated for a long time using form letters.
For a more family-oriented example, how about the common use of birthday and Christmas cards? They have writing in them, professionally written, but it’s only approximately what the sender meant. The message is more in selecting an appropriate card, and the act of sending it.
I'm unfamiliar with the nuances of the publishing industry. How involved is the nominal author in a ghostwritten book?
This is how I feel about sharing raw AI images. If the spender dumps everything they find interesting, they're a spammer; when they're limited to 2 a day, then there is some curation involved in the selection.
I've read articles about ghostwriters before, but I don't remember that much. I assume it varies.
Thanks for posting this, it's more interesting that I expected, not like most articles I've seen lately which either dismiss AI as a boondoggle or spread fear about it.
Personally, I keep noticing machines that have been around for years that parallel AI quite a bit.
A dishwasher machine doesn't know what dishes are, or what you do with them. It (usually) doesn't know what clean means. It could sort of know what "dry" means if it has a humidity sensor.
Newer clothes washers don't know what clothes are, but maybe they have a sensor that weighs how many you put in so it can run a little longer if it's a lot. But it doesn't know if you are putting in wearable clothes are paper towels or something else.
A bread machine does not know what bread is. It never tasted bread. You put some flour in a machine and it jostles it around for a while and then heats it up. It just happens to work but it can't recover if you forget to put water or yeast in.
I've used Copilot a few times for programming tasks. Sometimes it is helpful in that it is faster than checking stackoverflow or something. But just as often it confidently tells me a lot of wrong things and it really isn't as good as promised (yet) of doing mundane work for me. Unfortunately a lot of managers probably think it is better than it is.
Anyway, like the machines I mentioned, the thing that is called "AI" can be a useful tool in the right hands, but applications are pretty narrow.
I think this is a good way of framing it. Whatever aspects of AI that we use, whether it's LLMs or something else, can be looked at in this way. Eventually if it ever reaches AGI then possibly it will surpass these basic machines in that way.
This is very much my experience with them too. If you ask them one thing and leave it be, you'll come away thinking you got the right answer. If you start probing at all, you realize it is just making things up and it will constantly apologize saying it told you the wrong thing before. The "hallucination" aspect to them that people talk about, I think it's even wrong to call it that. I get the impression that these things give bad information >90% of the time. Calling it a hallucination has the impression of making it seem like it's just a side effect or something when in reality that's the primary thing it does is make things up. I get that the output is very input dependent, but that's kind of my point, you can lead the thing any which way without realizing you did because it doesn't know any better. If I ask it leading questions, it generally seems to answer as to reassure my leading question, and then when it inevitably responds with some logical fallacy and I point it out, it apologizes and says it was wrong before.
I really dislike the term "hallucination". The thing is just wrong. Yeah it's wrong for very specific reasons, but so am I when I'm wrong. It sounds like corporate speak made by people trying to sell a product.
Yeah, people who use words like “hallucination” want to make it sound like these text generation algorithms are conscious when they are nothing of the sort.
"bullshitting" is a tad too direct for the messiah complex-laden individuals within that particular area of research
Bullshitting fits what it's doing so well though and I can't think of a closer word for talking out of your ass.
Lying just doesn't sound right and hallucinating doesn't make sense.
Lying implies that you know the truth and choose to say something else; hallucinating implies that you genuinely believe what you said was true; bullshtting means truth is not a consideration—only whether the arrangement of words is pleasing or not matters. If BS were not an option, I'd say that hallucinations are a better description than lies: the computer's functions decided that it's probably good.
That logic does make a lot more sense when put that way. :)
Still not liking it though, there must be some condition where someone does something similar... oh nice, did some searching and learned a new word, confabulate! Basically when your brain fills in the gaps with false info and you believe it to be the truth. Confabulate sounds weird but I feel like the description fits what's going on.
I believe this use of “hallucination” became popular with image generation, and the images from DeepDream in 2015 really were pretty trippy. Earlier text generation often had a surreal aspect to it too. Here is the first example from the GPT 2 announcement in 2019.
Prompt:
Continuation:
When I was a kid, one things we did for fun with our primitive computers was to write silly sentence generators. They work essentially like a form letter generator, taking sentence templates and filling in random words. This was the computerized version of Mad Libs, a popular game where you would do a similar thing on paper.
Their first example of what you could do with GPT-2 was definitely leaning into that, showing that it was a much better nonsense generator. At the time, it seemed astonishing how, if you weren’t paying attention, it could almost sound real.
Since then, with more training, they’ve found more mundane uses, but with suitable adjustments to generation settings, one can reveal an LLM’s dream-like nonsense generator again. Seems more fun?
I think that as a metaphor, dreaming works pretty well for what an LLM does, because it’s something you do unconsciously. You’re not responsible for the content of your dreams. Neither is an LLM. People are trying pretty hard to wish the nonsense generator into a real boy, though.
That does highly depend on the subject and the sort of questions you do ask them. But, in the context of complex tasks, you are probably right.
I still think LLM's are really great tools. I use ChatGPT and the OpenAI api on a daily basis. But mostly as a tool in my tool belt where I am fully aware of the limitation and make sure to only ask it for things I have enough domain knowledge myself to validate the outcome. More detailed comment I wrote a while ago about it.
I largely agree. First impression is that it's magic, but it wears off. I imagine that using a dishwasher seemed like magic at first?
I've been using Copilot for months now, for autocomplete only. I tried Cody instead for a few days, and I found its autocomplete broadly similar in quality - often making things up, but good at repetitive code.
Then Cody's limited free version ran out. I tried coding without it, and wow do I miss autocomplete. I type something, wait... and nothing happens. It feels sort of like typing on a phone without autocomplete.
So, I turned Copilot back on again. But I'll probably switch to Cody after my Copilot subscription runs out, just to try it out more.
I've been testing a few Copilot alternatives recently. My goal was 1) free, because coding is still mostly an unpaid hobby for me, and 2) local, because my internet is not super reliable and I like the idea of local hosting.
The best options I found were Continue and twinny. Both support local models via ollama, which is where I keep my instruct and FIM models. I used Deepseek Coder (7B) for instruct, and alternated between codegemma and StarCoder2 for FIM (2B).
I found Cody's local model support is currently quite poor, and they also require an account to use the VS Code extension, so I discounted them at this time. However they also recently became open-source, and seems to be moving to support local models in the future, so I'll likely revisit them in the future.
I also tested a non-local alternative, Codeium. It's nice and offers a free tier, though does require an account and enabling telemetry in VS Code. My FIM results were a bit better, though still not perfect.
I expect Copilot probably beats each of these in performance. I haven't had a chance to test it though, and probably won't for some time.
I still mostly see these as auto-complete features. It is useful for saving time, and sometimes offers something even better than I'd considered. That's the biggest benefit so far to me.
I will say that sometimes the auto-complete disrupts my current train of thought though by offering something completely different than what I was expecting. So I'm still trying to find the right rhythm with these tools.
I don't really find the "explain this code" feature too useful, since I'm mostly working in my own codebases. Maybe I'll come across something truly arcane or unfamiliar in the future. I might ask a basic question if I think it's faster than searching the docs. eg. "Are sets one-dimensional in JavaScript? Can they only contain unique elements?). Due to the nature of LLMs though, I still feel the need to double check my answers if it's anything sufficiently complex or niche.
I don't much trust them for refactoring either. Maybe on a small scale: "Can this be a one liner?, or "Add null checks here", but never a major restructuring.
Still, I think these coding assistants can offer a lot of value. They're probably especially helpful in learning a new language by offering appropriate syntax. And for a novice - while they may need to be careful about trusting the suggestions too much, I think the explain features could be especially helpful in understanding a function line-by-line.
CodeGemma sounds promising? I'll wait for people to start talking about their experiences with it, though.
I've been using GPT 4 directly for generic how-to questions about how to do things in TypeScript - the sort of thing I might have previously done a Google search to look for StackOverflow articles. It's become less useful as I've gotten up to speed, though. Asking on r/typescript helped one time.
The following phase (emphasis from the original) is what inspired me to share this article.
We all know the joke about business communication: I use Grammarly to write a 5-paragraph essay as a follow-up report of the meeting; you use ChatGPT to summarize it into three sentence-length bullet points that fit on a PowerPoint slide. A lot of white-collar office work seems to be putting forth the effort to pretend our college degrees mean something instead of admitting the reality that most of us would be just as effective with a point and a grunt (at least before the rise of remote work).
This article is a trip, but a good one. I like it. We have so many questions on how things will go, but I think we should all just take AI as it comes and embrace it. It'll work out if we stay vigilant. There's a future out there where we don't have to work and have the time to find ourselves, figure out what we enjoy doing and do it.. if we feel like it. That's assuming we work for that future NOW... some potential futures need to be avoided at all costs.
We could move to a post-capitalism society where working is a choice, be stuck in a society similar to now where we're all basically corporate slaves despite tech being at a sufficient level to allow us to work FAR less, or all become impoverished as the rich no longer need us... maybe we'll be kept like cattle to ensure the genetic integrity among the rich or they'll just off us all as an eyesore.
I really don't understand point of some of these articles.
End of writing? Hardly when AI writing lacks creativity and flair. A to B fine. In between? Questionable.