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What I didn't predict is so many AI boosters being so miffed at regular folks pointing out the obvious: a half-working system may be tremendously exciting to AI

What I didn't predict is so many AI boosters being so miffed at regular folks pointing out the obvious: a half-working system may be tremendously exciting to AI enthusiasts, but it's not that useful to society. Recently we've had all these people complaining of moving goalposts, when in fact most people never had goalposts for AI in the first place, and certainly never had goalposts that remotely resembled those that AI experts use to evaluate their own progress. My mental model seems to clash with that of the boosters. They appear to have little doubt that reliability will emerge from scaling the models up. I think reliability is the lion's share of the problem and probably won't be solved by current architectures. It will be interesting to see who is right.

gerdesj 4y ago
It's a bloody calculator and a damn good one (but it can and does go mad). Use it like that and you'll be fine. Have you actually engaged with it in any way? Its free to sign up. By the way, what on earth is an AI booster? Do you really mean "enthusiast"? "My mental model seems to clash with that of the boosters." Please tell us about your mental model, we can't read your mind.
- Turskarama 4y ago
  It is very nearly the opposite to a calculator. A calculator gives you exact results for a very small subset of problems (equations). Chat-GPT will give you approximate (and occasionally incorrect) solutions to an extremely wide range of problems. The main problem with AI is that there are only so many problems where an approximate solution is fine, and an incorrect solution is easy to work around. In order for AI to be broadly useful, it has to get to the point where it's correct more often than an expert human. It may well get there within the next couple of years, but this isn't a space LLMs are designed to be able to solve so it's not as guaranteed as a lot of people seem to think.
  - MVissers 4y ago
    Disagree. For AI to be useful it needs to be better at the job than how the job currently being done. We humans rely on thousands of things where an approximate solution is fine. Especially when it's about interacting with other humans. Programming is an exception, but what % of the population are programmers and engineers?
    - Turskarama 4y ago
      Programmers, engineers, accountants, lawyers, doctors, nurses, builders, farmers, manufacturers, drivers (including pilots and ships captains), and scientists all need to be very precise in their work and mistakes will cause deaths (I have almost certainly forgotten some). This may not be the majority of workers by number, but these are the jobs civilization is _built_ on, they are the jobs that need to keep going no matter what or the whole house of cards collapses. The majority of jobs that require less precision falls under the umbrella of artists (I will include advertisers here), people managers, and service workers.
      - noobermin 4y ago
        I would put cooks into that too, allergic reactions can kill, badly cooked food can kill, and in fact people can be penalized for it too.
        pharmakom 4y ago
        Ture but does it matter if the chicken is cooked for 10 minutes, 10 minutes 5 seconds, 10 minutes 10 seconds,... programming requires far more precision.
        Turskarama 4y ago
        It does matter if you put some peanut in when the customer is allergic to peanuts.
        eternityforest 4y ago
  - PeterisP 4y ago
    In order for a system to be useful it doesn't need to match an expert human, it's sufficient if it can do most of the job. If companies can replace an entry-level person with something that's slightly worse but much cheaper, they're going to be widely used. In any profession where you have some sort of supervisor handing off tasks to unskilled humans and verifying their work, it will be broadly useful to hand those tasks to some AI model instead even if it needs a bit more handholding than humans, since it's going to be much cheaper than those humans and that is what makes something economically useful.
- AnimalMuppet 4y ago
  A "booster" is one who promotes something, not just an enthusiast. See "civic booster" or "athletic booster" for similar uses.
- MilStdJunkie 4y ago
  "Booster" can also now mean technologists who have correctly predicted the number and size of executive erections once teased with the idea of automating most of their office. Put on something black and tight, go up on stage and twirl around that notion until balled-up Jacksons grease the creaking floorboards. You know you'll be riding in a Cayenne tonight. Maybe a Boxster. It actually sounds pretty familiar for those of us in the side shows for other Miracle Technologies That'll Fix Your Business, but it's a hell of a lot easier to set up a convincing model than something like SAP. Also, unlike ERP, GPT is a real thing, and it does real stuff, so that will make it easier to take the money. Real science is happening, which is always more fun, even if Homo Executivus uses it as a marital aid. I guess this makes me a Booster, but one of those old wrinkled Boosters who sounds like a carton of Newports.
  - ryanwaggoner 4y ago
    Upvoted for being the kind of comment AI can’t yet write.
lisper 4y ago
> a half-working system may be tremendously exciting to AI enthusiasts, but it's not that useful to society. A half-working system might actually be worse than useless if people believe it 100% of the time.
- textninja 4y ago
  There’s a simple solution to that problem.
- 3np 4y ago
  And a 98% working system even worse due to it being treated as 100%.
  - kelnos 4y ago
    Well that depends. If a hypothetical emergency room doctor is right about patient diagnoses 90% of the time, then treating the even more hypothetical 98%-correct AI diagnoses as 100% will still probably give you better outcomes. We're still far off from that, and I'm skeptical we'll get there anytime soon, but humans aren't infallible either.
    - b112 4y ago
      The real problem is how humans react. Imagine a doc comes in, and is very wrong about a diagnosis. Say... you have a broken arm, doc says it is sprained. You say No! and point out why. AI says this, you say no, and the minumum wage person who works with the AI, shrugs and says the computer says so.
  - justeleblanc 4y ago
    Can you give a single example of a "system" that works 100% well, 100% of the time?
dougmwne 4y ago
This is the self driving car argument applied to things it doesn’t fit. Sure, unless a self driving car is several 9’s reliable, it’s not terribly useful for society. That’s because there’s no way for a human to be safely in the loop at highway speed. But a LLM assisting a wide rage of professionals at even a low accuracy could be part of an efficient workflow, allowing for be doctor or lawyer to give quality service to more people.
- chezelenkoooo 4y ago
  The author touches on this in the very article linked. His point is that the low accuracy answers provided by ChatGPT risks reinforcing the doctor's biases and then not thinking outside the box. In other words it risks the doctor blindly trusting the AI too much.
  - hollandays 4y ago
    The inherent problem with chat bots is you can't investigate why they're telling you something. When I search Google, I'm critically evaluating the information, and it's frustrating when the results it gives are low-accuracy. If I ask a chat bot a question, what would I do with the answer if it might be wrong? There's no context for me to evaluate.
    - pulvinar 4y ago
      You can ask it to explain its reasoning. And it's even more useful if you ask it to show its reasoning when you ask a question, as it improves the answer.
      - hollandays 4y ago
        True, though I said investigate, not interrogate. In the context of the pre-bias example I was responding to, it's fine to integrate it into a search engine to assist with guiding you to sources of information, but it shouldn't manipulate the information by describing it to you, even if it also provided source links.
jay_kyburz 4y ago
I my mind, the AI only needs to be more reliable than the human expert, and doctors and lawyers and other experts are "only human". There is only so much a human can read and remember. There is only so much they can be convinced to change their mind in the face of new evidence. They get tired. They get cranky. They have biases, and fears. Some doctors are just terrible. I think the AI is probably more reliable than them already. I don't think we are very far away from being able to correct an AI, and for it to remember that correction forever.
- dgb23 4y ago
  Apparently much of the usefulness of ChatGPT comes from human guidance via reinforcement learning. The base model seems to be quite useless, but with only a little bit of human input it becomes deceptively smart.
  - PeterisP 4y ago
    The key purpose for the reinforcement learning part (RLHF) is so that it would be socially acceptable to provide the model to the general public without getting into a PR nightmare like Microsoft Tay; that post-training does not make it "deceptively smart", it trades off a bit of smartness in order to ensure some alignment with certain restrictions. It decreases the performance on some tasks and e.g. the GPT-4 paper, which is very light on other details, provides some experimental evidence that this post-training significantly hurts the model's confidence calibration, which decreases its usability for tasks where you want to know how certain the model is.
- recursive_loops 4y ago
  I totally agree, but we're going to need to figure out better ways to verify correctness of what the AI tells us. This will be pretty hard since we can't hardly do that with human-produced stuff either. And further if the AI is trained on human-produced stuff that contains lots of errors... I don't really know how we're going to do it.
  - jay_kyburz 4y ago
danielrpa 4y ago
The goalposts people usually talk about were just the relatively low expectations of the past - low at least by the standards set by ChatGPT. Before ChatGPT there wasn't anything quite like it, so I think that the goalposts were mostly about passing the Turing test, or at least "not sucking" after 4 or 5 phrases. The leap from whatever existed before it was enormous, and I think comparable to the first iPhone versus, let's say, the Blackberry or Windows CE. Maybe even more than that. Go back to the first iPhone, it had a lot of issues but it offered so much as it was that people wouldn't look back. It's too much to expect ChatGPT 3 to be better an experienced emergency doctor or neurosurgeon. But the fact that it can be a competent assistant to a doctor instead of a toy assistant - that by itself is amazing.
- saurik 4y ago
  > ...I think that the goalposts were mostly about passing the Turing test... I mean, if that was ever the goalpost, ChatGPT does not clear it: the Turing test isn't about merely tricking someone who isn't paying attention, or making someone seriously doubt their belief system... it involved a careful discussion with a chat agent by someone who actively was trying to determine whether or not it/they were human. If you sit down with ChatGPT--even 4--and start discussing complex topics about the world with it, you absolutely can quickly get it starting to adamantly defend things about the world which a human would never assert... and, even more damning, if you just ask it "are you human?" it will reply "no", because we actively told it to fail the Turing test. If we instead merely re-define Turing's test to be "it is able to converse with a human and manages to occasionally trick a human into thinking it is also human" then I had a friend (Roy Keene) who managed to pull that off years ago with a chatbot that was literally just "every time someone messages it, wait a small random period of time and then reply with a random response from the following set: Yes, No, Maybe, I dunno, :), :(, ;P (and like one other thing I forget)". He had it set up to reply when he was not available and when I finally caught him (I asked about a task I gave him the day before which he claimed to have no memory of, so he admitted to me he had this bot) he gave me a log file and OMG I had had long conversations about personal topics with this thing and had never noticed... but only because I wasn't trying.
  - 4y ago
dreamcompiler 4y ago
A half-working system is cute and useful when it recommends a pizza place you might like. But the same system is a menace to society when you tell it to drive a car or prescribe a drug to treat an illness or decide a person's prison sentence or protect a perimeter with lethal force. It's absolutely amazing that so many AI scientists don't seem to understand this distinction.
- singularity2001 4y ago
  A half working system is good enough for real life tasks when combined and used by and in conjunction with attentive half working human collaborators.
  - deleted 4y ago
    [deleted]
  - DangitBobby 4y ago
    What does it mean to be attentive and half working?
    - tidenly 4y ago
      Have you never corrected your phones autocorrect before? That's what they're referring to.
    - rpozarickij 4y ago
      You can be attentive while not giving your 100% to the task. For example while writing a piece of code you might know what you want to accomplish but you aren't straining your brain to type out the actual code. So you type the name of the method and GitHub Copilot gives you an implementation of that method.
  - TazeTSchnitzel 4y ago
    Translators who've been picking up after machine translation for the last decade will tell you that it is not, in fact, useful to them. They get paid half as much as before, but correcting the machine output is as much work as translating from scratch, and sometimes more, since the machine can give results that look good at first glance but are fundamentally incorrect.
namaria 4y ago
People pushing the curve get to the middle of the sigmoid and want to believe it's gonna be all vertical now. People observing from the outside heavily discount the hype because they know limits will be reached soon. It happens every time.
- lerchmo 4y ago
  Not sure why the outside view is so wise? Predicting the future in complex systems is hard, regardless of your prediction.
  - namaria 4y ago
    It's unwise to hype up technology for profit. Those who can see it should point it out. Forecasting is hard and pricing is an useful signalling system for allocating resources socially. Hype creates noise to seek rent.
dzdt 4y ago
The half-working AI is like 95% of the progress to a better-than-the-best-human AI.
- salawat 4y ago
  And then what? Do you just think that humans are just going to cede that part of existence to that AI, in a fit of self-fulfilling prophecy? Cutting off the source of novel training data, thereby stagnating any progress of humanity at "we made a 1% better than the best human on a bad day AI once"? This is the central conceit of AI maximalists; and tech maximalists as a whole imo. Someone wants something that fits in a box that everyone else must eventually delegate to. Given capitalism's focus on creating owned systems, and the tendency to centralize and maintain asymmetry of access to rent extract, this is a most concerning direction to keep pushing development in. We should be building better people; but we've abandoned that in favor of making a better mantrap.
vidarh 4y ago
A half-working system that hasn't been trained specifically for this purpose is a strong indicator that a system that can handle this use-case far better is within reach. That is what is exciting about it. I'm constantly mystified by those who expect a system trained only on publicly accessible data pulled from the web to be an expert in everything rather than seeing it as an indication of what can be possible if enough specialised training data is added.
tomrod 4y ago
As an AI booster that falls into the regular folks view -- YES! THIS exactly! People will use and fund AI tools that have usefulness to them. AI boosters love to see the capabilities because it unlocks a whole new verdant valley in the continent of useful ideas, but until a street is laid down and a general store is available it remains the domain of early adopters. LLMs are genuinely impressive. They aren't yet AGI, but they are fantastic tools. More exciting to me as a practitioner is the Segment Anything foundational Model (SAM) since I can put that into immediate use for computer vision in a variety of domains. LLMs still require some baking for broad usefulness (though there are many places they are already useful, its not quite broad enough). When folks use AI focused on augmenting capabilities rather than replacing people, you get a great return. Until AGI is clear (and we'll know, the AI will tell us without being prompted I reckon), let's treat AI as an exoskeleton rather than a new species.

gerdesj 4y ago

It's a bloody calculator and a damn good one (but it can and does go mad). Use it like that and you'll be fine. Have you actually engaged with it in any way? Its free to sign up. By the way, what on earth is an AI booster? Do you really mean "enthusiast"? "My mental model seems to clash with that of the boosters." Please tell us about your mental model, we can't read your mind.

Turskarama 4y ago
It is very nearly the opposite to a calculator. A calculator gives you exact results for a very small subset of problems (equations). Chat-GPT will give you approximate (and occasionally incorrect) solutions to an extremely wide range of problems. The main problem with AI is that there are only so many problems where an approximate solution is fine, and an incorrect solution is easy to work around. In order for AI to be broadly useful, it has to get to the point where it's correct more often than an expert human. It may well get there within the next couple of years, but this isn't a space LLMs are designed to be able to solve so it's not as guaranteed as a lot of people seem to think.
- MVissers 4y ago
  Disagree. For AI to be useful it needs to be better at the job than how the job currently being done. We humans rely on thousands of things where an approximate solution is fine. Especially when it's about interacting with other humans. Programming is an exception, but what % of the population are programmers and engineers?
  - Turskarama 4y ago
    Programmers, engineers, accountants, lawyers, doctors, nurses, builders, farmers, manufacturers, drivers (including pilots and ships captains), and scientists all need to be very precise in their work and mistakes will cause deaths (I have almost certainly forgotten some). This may not be the majority of workers by number, but these are the jobs civilization is _built_ on, they are the jobs that need to keep going no matter what or the whole house of cards collapses. The majority of jobs that require less precision falls under the umbrella of artists (I will include advertisers here), people managers, and service workers.
    - noobermin 4y ago
      I would put cooks into that too, allergic reactions can kill, badly cooked food can kill, and in fact people can be penalized for it too.
      - pharmakom 4y ago
        Ture but does it matter if the chicken is cooked for 10 minutes, 10 minutes 5 seconds, 10 minutes 10 seconds,... programming requires far more precision.
        Turskarama 4y ago
        It does matter if you put some peanut in when the customer is allergic to peanuts.
        eternityforest 4y ago
- PeterisP 4y ago
  In order for a system to be useful it doesn't need to match an expert human, it's sufficient if it can do most of the job. If companies can replace an entry-level person with something that's slightly worse but much cheaper, they're going to be widely used. In any profession where you have some sort of supervisor handing off tasks to unskilled humans and verifying their work, it will be broadly useful to hand those tasks to some AI model instead even if it needs a bit more handholding than humans, since it's going to be much cheaper than those humans and that is what makes something economically useful.
AnimalMuppet 4y ago
A "booster" is one who promotes something, not just an enthusiast. See "civic booster" or "athletic booster" for similar uses.
MilStdJunkie 4y ago
"Booster" can also now mean technologists who have correctly predicted the number and size of executive erections once teased with the idea of automating most of their office. Put on something black and tight, go up on stage and twirl around that notion until balled-up Jacksons grease the creaking floorboards. You know you'll be riding in a Cayenne tonight. Maybe a Boxster. It actually sounds pretty familiar for those of us in the side shows for other Miracle Technologies That'll Fix Your Business, but it's a hell of a lot easier to set up a convincing model than something like SAP. Also, unlike ERP, GPT is a real thing, and it does real stuff, so that will make it easier to take the money. Real science is happening, which is always more fun, even if Homo Executivus uses it as a marital aid. I guess this makes me a Booster, but one of those old wrinkled Boosters who sounds like a carton of Newports.
- ryanwaggoner 4y ago
  Upvoted for being the kind of comment AI can’t yet write.

lisper 4y ago

> a half-working system may be tremendously exciting to AI enthusiasts, but it's not that useful to society. A half-working system might actually be worse than useless if people believe it 100% of the time.

textninja 4y ago
There’s a simple solution to that problem.
3np 4y ago
And a 98% working system even worse due to it being treated as 100%.
- kelnos 4y ago
  Well that depends. If a hypothetical emergency room doctor is right about patient diagnoses 90% of the time, then treating the even more hypothetical 98%-correct AI diagnoses as 100% will still probably give you better outcomes. We're still far off from that, and I'm skeptical we'll get there anytime soon, but humans aren't infallible either.
  - b112 4y ago
    The real problem is how humans react. Imagine a doc comes in, and is very wrong about a diagnosis. Say... you have a broken arm, doc says it is sprained. You say No! and point out why. AI says this, you say no, and the minumum wage person who works with the AI, shrugs and says the computer says so.
- justeleblanc 4y ago
  Can you give a single example of a "system" that works 100% well, 100% of the time?

dougmwne 4y ago

This is the self driving car argument applied to things it doesn’t fit. Sure, unless a self driving car is several 9’s reliable, it’s not terribly useful for society. That’s because there’s no way for a human to be safely in the loop at highway speed. But a LLM assisting a wide rage of professionals at even a low accuracy could be part of an efficient workflow, allowing for be doctor or lawyer to give quality service to more people.

chezelenkoooo 4y ago
The author touches on this in the very article linked. His point is that the low accuracy answers provided by ChatGPT risks reinforcing the doctor's biases and then not thinking outside the box. In other words it risks the doctor blindly trusting the AI too much.
- hollandays 4y ago
  The inherent problem with chat bots is you can't investigate why they're telling you something. When I search Google, I'm critically evaluating the information, and it's frustrating when the results it gives are low-accuracy. If I ask a chat bot a question, what would I do with the answer if it might be wrong? There's no context for me to evaluate.
  - pulvinar 4y ago
    You can ask it to explain its reasoning. And it's even more useful if you ask it to show its reasoning when you ask a question, as it improves the answer.
    - hollandays 4y ago
      True, though I said investigate, not interrogate. In the context of the pre-bias example I was responding to, it's fine to integrate it into a search engine to assist with guiding you to sources of information, but it shouldn't manipulate the information by describing it to you, even if it also provided source links.

jay_kyburz 4y ago

I my mind, the AI only needs to be more reliable than the human expert, and doctors and lawyers and other experts are "only human". There is only so much a human can read and remember. There is only so much they can be convinced to change their mind in the face of new evidence. They get tired. They get cranky. They have biases, and fears. Some doctors are just terrible. I think the AI is probably more reliable than them already. I don't think we are very far away from being able to correct an AI, and for it to remember that correction forever.

dgb23 4y ago
Apparently much of the usefulness of ChatGPT comes from human guidance via reinforcement learning. The base model seems to be quite useless, but with only a little bit of human input it becomes deceptively smart.
- PeterisP 4y ago
  The key purpose for the reinforcement learning part (RLHF) is so that it would be socially acceptable to provide the model to the general public without getting into a PR nightmare like Microsoft Tay; that post-training does not make it "deceptively smart", it trades off a bit of smartness in order to ensure some alignment with certain restrictions. It decreases the performance on some tasks and e.g. the GPT-4 paper, which is very light on other details, provides some experimental evidence that this post-training significantly hurts the model's confidence calibration, which decreases its usability for tasks where you want to know how certain the model is.
recursive_loops 4y ago
I totally agree, but we're going to need to figure out better ways to verify correctness of what the AI tells us. This will be pretty hard since we can't hardly do that with human-produced stuff either. And further if the AI is trained on human-produced stuff that contains lots of errors... I don't really know how we're going to do it.
- jay_kyburz 4y ago

danielrpa 4y ago

The goalposts people usually talk about were just the relatively low expectations of the past - low at least by the standards set by ChatGPT. Before ChatGPT there wasn't anything quite like it, so I think that the goalposts were mostly about passing the Turing test, or at least "not sucking" after 4 or 5 phrases. The leap from whatever existed before it was enormous, and I think comparable to the first iPhone versus, let's say, the Blackberry or Windows CE. Maybe even more than that. Go back to the first iPhone, it had a lot of issues but it offered so much as it was that people wouldn't look back. It's too much to expect ChatGPT 3 to be better an experienced emergency doctor or neurosurgeon. But the fact that it can be a competent assistant to a doctor instead of a toy assistant - that by itself is amazing.

saurik 4y ago
> ...I think that the goalposts were mostly about passing the Turing test... I mean, if that was ever the goalpost, ChatGPT does not clear it: the Turing test isn't about merely tricking someone who isn't paying attention, or making someone seriously doubt their belief system... it involved a careful discussion with a chat agent by someone who actively was trying to determine whether or not it/they were human. If you sit down with ChatGPT--even 4--and start discussing complex topics about the world with it, you absolutely can quickly get it starting to adamantly defend things about the world which a human would never assert... and, even more damning, if you just ask it "are you human?" it will reply "no", because we actively told it to fail the Turing test. If we instead merely re-define Turing's test to be "it is able to converse with a human and manages to occasionally trick a human into thinking it is also human" then I had a friend (Roy Keene) who managed to pull that off years ago with a chatbot that was literally just "every time someone messages it, wait a small random period of time and then reply with a random response from the following set: Yes, No, Maybe, I dunno, :), :(, ;P (and like one other thing I forget)". He had it set up to reply when he was not available and when I finally caught him (I asked about a task I gave him the day before which he claimed to have no memory of, so he admitted to me he had this bot) he gave me a log file and OMG I had had long conversations about personal topics with this thing and had never noticed... but only because I wasn't trying.
- 4y ago

dreamcompiler 4y ago

A half-working system is cute and useful when it recommends a pizza place you might like. But the same system is a menace to society when you tell it to drive a car or prescribe a drug to treat an illness or decide a person's prison sentence or protect a perimeter with lethal force. It's absolutely amazing that so many AI scientists don't seem to understand this distinction.

singularity2001 4y ago
A half working system is good enough for real life tasks when combined and used by and in conjunction with attentive half working human collaborators.
- deleted 4y ago
  [deleted]
- DangitBobby 4y ago
  What does it mean to be attentive and half working?
  - tidenly 4y ago
    Have you never corrected your phones autocorrect before? That's what they're referring to.
  - rpozarickij 4y ago
    You can be attentive while not giving your 100% to the task. For example while writing a piece of code you might know what you want to accomplish but you aren't straining your brain to type out the actual code. So you type the name of the method and GitHub Copilot gives you an implementation of that method.
- TazeTSchnitzel 4y ago
  Translators who've been picking up after machine translation for the last decade will tell you that it is not, in fact, useful to them. They get paid half as much as before, but correcting the machine output is as much work as translating from scratch, and sometimes more, since the machine can give results that look good at first glance but are fundamentally incorrect.

namaria 4y ago

People pushing the curve get to the middle of the sigmoid and want to believe it's gonna be all vertical now. People observing from the outside heavily discount the hype because they know limits will be reached soon. It happens every time.

lerchmo 4y ago
Not sure why the outside view is so wise? Predicting the future in complex systems is hard, regardless of your prediction.
- namaria 4y ago
  It's unwise to hype up technology for profit. Those who can see it should point it out. Forecasting is hard and pricing is an useful signalling system for allocating resources socially. Hype creates noise to seek rent.

dzdt 4y ago

The half-working AI is like 95% of the progress to a better-than-the-best-human AI.

salawat 4y ago
And then what? Do you just think that humans are just going to cede that part of existence to that AI, in a fit of self-fulfilling prophecy? Cutting off the source of novel training data, thereby stagnating any progress of humanity at "we made a 1% better than the best human on a bad day AI once"? This is the central conceit of AI maximalists; and tech maximalists as a whole imo. Someone wants something that fits in a box that everyone else must eventually delegate to. Given capitalism's focus on creating owned systems, and the tendency to centralize and maintain asymmetry of access to rent extract, this is a most concerning direction to keep pushing development in. We should be building better people; but we've abandoned that in favor of making a better mantrap.

vidarh 4y ago

A half-working system that hasn't been trained specifically for this purpose is a strong indicator that a system that can handle this use-case far better is within reach. That is what is exciting about it. I'm constantly mystified by those who expect a system trained only on publicly accessible data pulled from the web to be an expert in everything rather than seeing it as an indication of what can be possible if enough specialised training data is added.

tomrod 4y ago

As an AI booster that falls into the regular folks view -- YES! THIS exactly! People will use and fund AI tools that have usefulness to them. AI boosters love to see the capabilities because it unlocks a whole new verdant valley in the continent of useful ideas, but until a street is laid down and a general store is available it remains the domain of early adopters. LLMs are genuinely impressive. They aren't yet AGI, but they are fantastic tools. More exciting to me as a practitioner is the Segment Anything foundational Model (SAM) since I can put that into immediate use for computer vision in a variety of domains. LLMs still require some baking for broad usefulness (though there are many places they are already useful, its not quite broad enough). When folks use AI focused on augmenting capabilities rather than replacing people, you get a great return. Until AGI is clear (and we'll know, the AI will tell us without being prompted I reckon), let's treat AI as an exoskeleton rather than a new species.