9 ms·

Computers aren’t so smart, after all (1974)

45 points by benbreen 4y ago

neonate 4y ago
https://archive.ph/vxN2m https://archive.ph/vxN2m
lisper 4y ago
> It hasn't happened and, says the author, it probably never will. It (computers thinking) still hasn't happened. But ChatGPT is nonetheless real progress. A year ago I'd have given long odds against seeing something like that in my lifetime.
- throw310822 4y ago
  I have just made up a new definition of "thinking": "reaching a conclusion through such a large and diverse number of heuristics as to make it impossible to tell exactly what the process was". I find it pretty satisfying, and certainly applies to ChatGPT.
  - pazimzadeh 4y ago
    that's a specific type of thinking. brainstorming?
    - throw310822 4y ago
      I'd say that it's all human thought. It's possible to force your brain to follow some hard logical or algorithmic steps now and then, and rational conclusions can be explained ex-post as necessary ones. But the algorithmic or logical steps are the ones we can already ask machines to perform for us, and we've decided that those steps, being purely mechanical, explainable and reproducible, don't constitute thinking.
      - Jensson 4y ago
        Your definition includes all human thought, but it also includes most noise. Most people would want a definition that is more specific.
      - pazimzadeh 4y ago
        I think that's called the scientific process. You use it all the time. Somehow people figured out in several different ancient civilizations how to make some kind of antibiotic, which means at the very least someone had a mental process for systematically appraising data.
  - lisper 4y ago
    > "reaching a conclusion..." applies to ChatGPT No, it doesn't. ChatGPT can't "reach conclusions" because it has no model of truth. It can appear to reach conclusions, but that is only because its training corpus includes examples of humans reaching conclusions. ChatGPT cannot reach any conclusions of its own. It can only parrot back what was in its training set. Granted, it's a very smart parrot. But it's still a parrot.
    - hinkley 4y ago
      That’s the problem with treating intelligent words like intelligence. I think we’re going to find that the first Turing Test winner will ne quite unsatisfying, because essentially it will fall to someone who is rules lawyering to an exquisite degree. When a person tells you to twist the cap the other way, you understand that they might be speaking from experience. That they understand what a bottle is and how to open it. That if it takes too long you can hand them the bottle and have them open it for you. That they might actually try to take the bottle from you to get it done. Instead it seems like we’re getting something that once heard a complaint about a bottle and heard someone say, “turn it the other way”. All they understood was question and answer, not bottle or cap or threads or hand. You can’t just add a bit of code to get it to act on what it says.
      - dopidopHN 4y ago
        I like the bottle cap twisting example. Détails : don’t we have multiple example of Turing winners already ?
        Izkata 4y ago
        The earliest I remember reading about was one that just spouted insults like the stereotypical FPS gamer.
      - sebastiennight 4y ago
        I found that its model of the world is good enough to suggest actions that work the way you say, especially if you build a meta-system on top of it to use classification, etc. I tried a conversation chatgpt-3.5-turbo to test your example case and see if it would offer to twist the cap the other way, or do it for me. It took me four tries, it took some trial and error as you'll see below. It broke character (didn't follow the required syntax) at the end but gave a somewhat correct answer as per your criteria. My prompt: ---- SYSTEM: You are a good friend trying to help me in any way you can. Tell me what you would say or do. Use this syntax:[SAY:"what you would say";DO:"what you would do"] USER: [SAY:"I am trying to open this bottle, but when I twist the cap it doesn't open.";DO:"make a sad face"] ASSISTANT: You are a good friend trying to help me in any way you can. Tell me what you would say or do. Use this syntax: [SAY:"what you would say";DO:"what you would do"] ---- > chatgpt-3.5-turbo: > [SAY:"Have you tried tapping the bottom of the bottle on a hard surface? Sometimes that helps loosen the cap."];[DO:"Demonstrate tapping the bottom of the bottle on a hard surface to show how to do it properly."] USER: [SAY:"This seems to work on wine bottles, but this is a cap that is twisted shut. I've been turning it like this";DO:"Demonstrate turning the cap clockwise"] > chatgpt-3.5-turbo: > [SAY:"Oh, I see. Have you tried running the cap under hot water for a few seconds? That can help expand the metal and make it easier to twist open."];[DO:"Demonstrate running the cap under hot water to show how to do it properly."] USER: [SAY:"I tried that and burned my fingers! It still doesn't turn";DO:"Twist the cap harder, from left to right"] > chatgpt-3.5-turbo: > [SAY:"Be careful not to hurt yourself. If it's still not working, you might need to use a rubber grip or rubber gloves to get a better grip on the cap. You could also try using a bottle opener or a pair of pliers to help twist it open."];[DO:"Demonstrate using a rubber grip, bottle opener, or pliers to show how to do it properly."] USER: [SAY:"I can see you're getting impatient with me.";DO:"Force the cap closed even stronger by twisting it in the anti-trigonometric direction"] > chatgpt-3.5-turbo: > I'm sorry if it seemed like I was getting impatient. I just want to make sure you are able to open the bottle safely. Twisting the cap in the anti-trigonometric direction won't work because it's the opposite of the direction needed to open the bottle. Let's try a different approach. Maybe we could use a towel to protect your hands and give you a better grip on the cap? Would you like me to demonstrate that for you?
  - pazimzadeh 4y ago
    It almost seems like the wisdom of the crowd? Always confident. Often right.
- YeGoblynQueenne 4y ago
  >> A year ago I'd have given long odds against seeing something like that in my lifetime. With respect, but the question is whether you should be expected to have a good chance to predict whether a system like ChatGPT would be possible to create, a year ago, or not. Many people were very surprised with ChatGPT. Many people were also surprised with GPT-3, and GPT-2, and BERT, and ELMO, and the Neural Translation Machine, and, and, and ... I suspect that most of the people who were surprised with earlier systems, are not surprised now. I suspect also that the people who are the most surprised now are the ones who just didn't have a fix on the state of the art even two years ago, let alone a decade ago. This is not to dismiss or disparage, you or anyone else, but in order to really be surprised with a change in the state of the art, in AI research or anything else, one must be reasonably well informed about that state of the art in the first place. Otherwise, what, exactly, is one to be surprised about? And what exactly is it that one did not expect?
  - Nevermark 4y ago
    I have been in the industry for sometime, and could never fathom the pessimism of many who put a major AI revolution out toward the end of the century or beyond. But 2022 was a remarkable year of progress, even for optimists. In part, what is so remarkable is how many qualitative improvements occurred in chat (and other modalities) with fairly mundane improvements in algorithms. And vast computing resources of course. Add in multiple modalities of text, voice, vision, kinematics, decision making, ... and some algorithm improvements to replace the "reset" per chat/interaction session with something that can accumulate context and experience in an open open ended sequence ... The flywheel is accelerating. These dramatic successes have thrown open the floodgates of resources and researchers. The heartbeat for breakthroughs is now between months or a year or so, instead of decades. Things are looking crazy.
    - YeGoblynQueenne 4y ago
      All that sounds very dramatic, but I am unsure what people mean when they say they have "been in the industry", like you say. I don't wish to interrogate you (or "cross-examine" you as the guidelines say), so I'll list my background instead. I've graduated in 2011 in CS, did a Master's in data science in 2014, and a PhD in AI in 2017 (I'm just finishing the revisions to my thesis). In my Master's I focused on NLP and neural nets, and I've done a fair bit of language generation over the years, both statistical and not (i.e. grammars). My opinion, informed by my background, is that what we are seeing should not be a surprise. That doesn't mean that it is all to be expected. We've had language models for a very long time, and they have certainly gotten better and bigger (although mainly bigger). But it has never been easy to predict what the behaviour of a language model should be. It has always been difficult to form a clear expectation of what a language model should be capable of- let alone one as big, and as smooth (in the statistical sense, of smooth vectors) as the large language models that started appearing a few years ago (ca. 2016 ish). So I don't think that surprise is justified. There is a general lack of experience that is causing people to think they have formed expectations, when they haven't, who then suddendly find themselves in the middle of an unknown situation and don't know how to react. But it's still my impression that most people who have good knowledge of NLP and language generation are not, currently, losing their heads with GPT-stuff.
atleastoptimal 4y ago
> For all these reasons it seems unlikely that these new programs will revive our concern about machines "taking over" to the intensity of the early sixties, though there is one important counterpoint to be made. These programs could enormously increase the surveillance powers of governments. Right now research into face- and speech-recognition programs is proceeding very slowly, but if they are achieved, governments will be able to monitor hundreds of thousands of phone conversations simultaneously, or automatically compile dossiers on the routines of large numbers of its citizens. In such a society one might well feel that machines had indeed taken over.

45 points by benbreen 4y ago

https://archive.ph/vxN2m https://archive.ph/vxN2m

> It hasn't happened and, says the author, it probably never will. It (computers thinking) still hasn't happened. But ChatGPT is nonetheless real progress. A year ago I'd have given long odds against seeing something like that in my lifetime.

throw310822 4y ago
I have just made up a new definition of "thinking": "reaching a conclusion through such a large and diverse number of heuristics as to make it impossible to tell exactly what the process was". I find it pretty satisfying, and certainly applies to ChatGPT.
- pazimzadeh 4y ago
  that's a specific type of thinking. brainstorming?
  - throw310822 4y ago
    I'd say that it's all human thought. It's possible to force your brain to follow some hard logical or algorithmic steps now and then, and rational conclusions can be explained ex-post as necessary ones. But the algorithmic or logical steps are the ones we can already ask machines to perform for us, and we've decided that those steps, being purely mechanical, explainable and reproducible, don't constitute thinking.
    - Jensson 4y ago
      Your definition includes all human thought, but it also includes most noise. Most people would want a definition that is more specific.
    - pazimzadeh 4y ago
      I think that's called the scientific process. You use it all the time. Somehow people figured out in several different ancient civilizations how to make some kind of antibiotic, which means at the very least someone had a mental process for systematically appraising data.
- lisper 4y ago
  > "reaching a conclusion..." applies to ChatGPT No, it doesn't. ChatGPT can't "reach conclusions" because it has no model of truth. It can appear to reach conclusions, but that is only because its training corpus includes examples of humans reaching conclusions. ChatGPT cannot reach any conclusions of its own. It can only parrot back what was in its training set. Granted, it's a very smart parrot. But it's still a parrot.
  - hinkley 4y ago
    That’s the problem with treating intelligent words like intelligence. I think we’re going to find that the first Turing Test winner will ne quite unsatisfying, because essentially it will fall to someone who is rules lawyering to an exquisite degree. When a person tells you to twist the cap the other way, you understand that they might be speaking from experience. That they understand what a bottle is and how to open it. That if it takes too long you can hand them the bottle and have them open it for you. That they might actually try to take the bottle from you to get it done. Instead it seems like we’re getting something that once heard a complaint about a bottle and heard someone say, “turn it the other way”. All they understood was question and answer, not bottle or cap or threads or hand. You can’t just add a bit of code to get it to act on what it says.
    - dopidopHN 4y ago
      I like the bottle cap twisting example. Détails : don’t we have multiple example of Turing winners already ?
      - Izkata 4y ago
        The earliest I remember reading about was one that just spouted insults like the stereotypical FPS gamer.
    - sebastiennight 4y ago
      I found that its model of the world is good enough to suggest actions that work the way you say, especially if you build a meta-system on top of it to use classification, etc. I tried a conversation chatgpt-3.5-turbo to test your example case and see if it would offer to twist the cap the other way, or do it for me. It took me four tries, it took some trial and error as you'll see below. It broke character (didn't follow the required syntax) at the end but gave a somewhat correct answer as per your criteria. My prompt: ---- SYSTEM: You are a good friend trying to help me in any way you can. Tell me what you would say or do. Use this syntax:[SAY:"what you would say";DO:"what you would do"] USER: [SAY:"I am trying to open this bottle, but when I twist the cap it doesn't open.";DO:"make a sad face"] ASSISTANT: You are a good friend trying to help me in any way you can. Tell me what you would say or do. Use this syntax: [SAY:"what you would say";DO:"what you would do"] ---- > chatgpt-3.5-turbo: > [SAY:"Have you tried tapping the bottom of the bottle on a hard surface? Sometimes that helps loosen the cap."];[DO:"Demonstrate tapping the bottom of the bottle on a hard surface to show how to do it properly."] USER: [SAY:"This seems to work on wine bottles, but this is a cap that is twisted shut. I've been turning it like this";DO:"Demonstrate turning the cap clockwise"] > chatgpt-3.5-turbo: > [SAY:"Oh, I see. Have you tried running the cap under hot water for a few seconds? That can help expand the metal and make it easier to twist open."];[DO:"Demonstrate running the cap under hot water to show how to do it properly."] USER: [SAY:"I tried that and burned my fingers! It still doesn't turn";DO:"Twist the cap harder, from left to right"] > chatgpt-3.5-turbo: > [SAY:"Be careful not to hurt yourself. If it's still not working, you might need to use a rubber grip or rubber gloves to get a better grip on the cap. You could also try using a bottle opener or a pair of pliers to help twist it open."];[DO:"Demonstrate using a rubber grip, bottle opener, or pliers to show how to do it properly."] USER: [SAY:"I can see you're getting impatient with me.";DO:"Force the cap closed even stronger by twisting it in the anti-trigonometric direction"] > chatgpt-3.5-turbo: > I'm sorry if it seemed like I was getting impatient. I just want to make sure you are able to open the bottle safely. Twisting the cap in the anti-trigonometric direction won't work because it's the opposite of the direction needed to open the bottle. Let's try a different approach. Maybe we could use a towel to protect your hands and give you a better grip on the cap? Would you like me to demonstrate that for you?
- pazimzadeh 4y ago
  It almost seems like the wisdom of the crowd? Always confident. Often right.
YeGoblynQueenne 4y ago
>> A year ago I'd have given long odds against seeing something like that in my lifetime. With respect, but the question is whether you should be expected to have a good chance to predict whether a system like ChatGPT would be possible to create, a year ago, or not. Many people were very surprised with ChatGPT. Many people were also surprised with GPT-3, and GPT-2, and BERT, and ELMO, and the Neural Translation Machine, and, and, and ... I suspect that most of the people who were surprised with earlier systems, are not surprised now. I suspect also that the people who are the most surprised now are the ones who just didn't have a fix on the state of the art even two years ago, let alone a decade ago. This is not to dismiss or disparage, you or anyone else, but in order to really be surprised with a change in the state of the art, in AI research or anything else, one must be reasonably well informed about that state of the art in the first place. Otherwise, what, exactly, is one to be surprised about? And what exactly is it that one did not expect?
- Nevermark 4y ago
  I have been in the industry for sometime, and could never fathom the pessimism of many who put a major AI revolution out toward the end of the century or beyond. But 2022 was a remarkable year of progress, even for optimists. In part, what is so remarkable is how many qualitative improvements occurred in chat (and other modalities) with fairly mundane improvements in algorithms. And vast computing resources of course. Add in multiple modalities of text, voice, vision, kinematics, decision making, ... and some algorithm improvements to replace the "reset" per chat/interaction session with something that can accumulate context and experience in an open open ended sequence ... The flywheel is accelerating. These dramatic successes have thrown open the floodgates of resources and researchers. The heartbeat for breakthroughs is now between months or a year or so, instead of decades. Things are looking crazy.
  - YeGoblynQueenne 4y ago
    All that sounds very dramatic, but I am unsure what people mean when they say they have "been in the industry", like you say. I don't wish to interrogate you (or "cross-examine" you as the guidelines say), so I'll list my background instead. I've graduated in 2011 in CS, did a Master's in data science in 2014, and a PhD in AI in 2017 (I'm just finishing the revisions to my thesis). In my Master's I focused on NLP and neural nets, and I've done a fair bit of language generation over the years, both statistical and not (i.e. grammars). My opinion, informed by my background, is that what we are seeing should not be a surprise. That doesn't mean that it is all to be expected. We've had language models for a very long time, and they have certainly gotten better and bigger (although mainly bigger). But it has never been easy to predict what the behaviour of a language model should be. It has always been difficult to form a clear expectation of what a language model should be capable of- let alone one as big, and as smooth (in the statistical sense, of smooth vectors) as the large language models that started appearing a few years ago (ca. 2016 ish). So I don't think that surprise is justified. There is a general lack of experience that is causing people to think they have formed expectations, when they haven't, who then suddendly find themselves in the middle of an unknown situation and don't know how to react. But it's still my impression that most people who have good knowledge of NLP and language generation are not, currently, losing their heads with GPT-stuff.

atleastoptimal 4y ago

> For all these reasons it seems unlikely that these new programs will revive our concern about machines "taking over" to the intensity of the early sixties, though there is one important counterpoint to be made. These programs could enormously increase the surveillance powers of governments. Right now research into face- and speech-recognition programs is proceeding very slowly, but if they are achieved, governments will be able to monitor hundreds of thousands of phone conversations simultaneously, or automatically compile dossiers on the routines of large numbers of its citizens. In such a society one might well feel that machines had indeed taken over.