19 ms·

Generative AI and The Future of Work

106 points by jbcranshaw 4y ago

d4rkp4ttern 4y ago
Their product MaestroAI is marketed as “for teams” (and of course with the obligatory fading-color call-to-action buttons) presumably to attract VC $$$ but I would love something like this (powered by LLMs) to extract info from all my documents. Maybe something like this exists? Please no DEVONThink suggestions :)
deleted 4y ago
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zabzonk 4y ago
> The results are often wildly creative and spookily accurate, giving these models a human-like feel. or wildly inaccurate, particularly in fields such as programming
- toss1 4y ago
  Yup. What seems to be largely missed is that these models have zero understanding, and are actually destroyers of information, not creators. In classic Information Theory, information is basically surprise value — how much unexpected info is in the message? — yet these "AI" systems put out the most expected subset in each instance. This highly averaged output is very recognizable and so very striking, but it is not actually very informative (perhaps except in cases where it is specifically used as a verbose search engine, where the query takes advantage of the breadth of the AI's training).
  - Gh0stRAT 4y ago
    > In classic Information Theory, information is basically surprise value — how much unexpected info is in the message? — yet these "AI" systems put out the most expected subset in each instance. Forgive me, but isn't this kind of moving-the-goalposts? Information is the surprise value from the recipient's point of view, which meas the recipient's bayesian prior probability is "expected". Saying "these "AI" systems put out the most expected subset in each instance" assumes that the recipient's priors exactly equal those of the model which would only be the case when the model is talking to itself. (or I suppose to an even more complex model with perfect knowledge of ChatGPT's weights) The fact that no information is transferred when the model talks to itself should not be surprising and would apply to any AI. (even including a superhuman post-singularity god-like AI)
    - toss1 4y ago
      Yes, the AI's output could be surprising to the point of view of many recipients. This does not mean anything more than that the AI has a greater breadth of training background, which is likely. We get the output most likely expected from any of (or the average of) the humans whose writing/drawing/whatever was included in the input set. What we will not be getting from the AIs is any creative output based on unique understanding, as we would from an intelligent, creative human. Many of hte humans in the input set would see the same prompt and produce an actual novel and meaningful output, not simply a cut-and-paste from prior works. (& yes, seme novel output may come from some randomizing algo, but if it is correct, it is no more correct than the broken clock that is correct twice every day.) Or, another example, I was involved in a legal deposition where an "AI" transcription system was used instead of a skilled court reporter. The output LOOKED fantastic, until I actually read it, and it was absolute garbage. The standard errata sheet has room for the deponent to put in about a dozen corrections, and most are less than a handful. My errata list was multiple pages. These errors often reversed the meaning of sentences, substitutin "I have ..." for "You have...", dropping or adding "not", or substituting in common names for unusual names (e.g., "Jack Kennedy" for "John Kemeny". note human transcribers always ask for correct spellings of names in the next break, this crap just inserted it like it had a clue). So, even though the total "experience" or training set of the may go beyond the experience of the reader, so some of the output is surprising, this is no more so than a search engine produces surprise. In fact, I think this is the best use of the AIs, to have them trained on an enormous data set, and provide possibly better results, defined as more on-point, but likely less thorough.
      - Gh0stRAT 4y ago
        >This does not mean anything more than that the AI has a greater breadth of training background, which is likely. This has nothing to do with Shannon's definition of information. The light from a star going supernova or a tsunami hitting shore convey information without any kind of agency nor intent being involved. >What we will not be getting from the AIs is any creative output based on unique understanding, as we would from an intelligent, creative human. This is a really big assumption here that I don't think is justified. Are you assuming that humans have some non-physical soul which makes us somehow different from any other deterministic information-processing system?
- aero142 4y ago
  I've been asking friends in non-programming engineering fields how ChatGPT does in their area of expertise, and I believe programming is the area that ChatGPT is the most accurate. Finding solution to general engineering problems seems blatantly wrong in almost all cases, whereas in programming, it seems to be able to generate mostly correct code for simple, boiler-plate like tasks.
  - zabzonk 4y ago
    why is "mostly correct" ok for programming? also, i don't believe that good programmers want to have boiler-plate in their code.
    - fooker 4y ago
      Because that's what most programmers achieve too. You can iterate from there by taking advantage of the last 50 years of software engineering wisdom.
  - ccozan 4y ago
    yes, but why? Why is GPT so much better at programming than other tasks? can it be that programming itself can be so easily predicted in a generative way, while others require more ingenuity and real world model to be solved? In this case I would totally offload programming to a GPT /LLM AI, while my job is simply to specify largely the business case.
    - aero142 4y ago
      It seems the improvement in the models is the size of the training set. Hence the "Large" in large language models. I am assuming that it is better at programming because there a is a ton of training data available on Github, etc. There isn't a similar set of data on solving a variety of physics problems.
    - lancesells 4y ago
      Is it because programming is a more limited and specific language than the ones people speak? There's less room for double-meanings, slang, meaning, or even sentence structure.
- blablablerg 4y ago
  Or worse, subtly inaccurate. The problem I have with generative AI right now, its product looks like it makes sense and sometimes it does, but there is always the risk of total nonsense hidden somewhere in the middle. So you still need someone capable to check and correct for most professional work, and sometimes that is harder or more time consuming than making the product itself. The same sort of problem with self driving cars, they are often correct but not often enough, and staying alert to correct the AI is worse than driving yourself which is more work, paradoxically enough. AI might manage to push through these barriers, but I remain skeptical with the technology in the current state: statistical machines that are good in the common cases but sketchy at the edges.
  - riskable 4y ago
    > there is always the risk of total nonsense hidden somewhere in the middle This is true of human-generated code as well. Trust me: Reviewing other people's code is my day job. It's exceptionally rare that a malicious actor is trying to sneak something into the code. The common scenario is the developer who's new to the project not fully understanding how everything works so they copy & paste something they think is necessary but ultimately isn't and could in fact be very wrong. Just like how ChatGPT works.
  - xenonite 4y ago
    Which is similar to correcting subpar human results: Rather than meticulously correcting the works of a subpar programmer it is much more efficient to let a proficient developer produce the code. Or even better, engage a 10x developer.
    - oceanplexian 4y ago
      Most problems don’t require efficient code. If an inefficient programmer can solve a real world business problem in a fraction of the time, but it requires slightly more computing resources, I’d still pay for it. Efficiency can be measured in a lot of dimensions and often times spending time and money writing optimized code is inefficient as well.
smoldesu 4y ago
> Widespread adoption of generative AI will act as a lubricant between systems, I largely agree with this article, but I feel like you have to be careful with these general predictions. Many technologies have purported themselves to be this "business lubricant" tech (ever since the spreadsheet), but the actual number of novel spreadsheet applications remains small. It feels like the same can be said for generative AI, too. Almost every day I feel the need to explain that "generation" and "abstract thought" are distinct concepts, because conflating the two leads to so much misconception around AI. Stable Diffusion has no concept of artistic significance, just art. Similarly, ChatGPT can only predict what happens next, which doesn't bestow it heuristic thought. Our collective awe-struck-ness has left us vulnerable to the fact that AI generation is, generally speaking, hollow and indirect. AI will certainly change the future, and along with it the future of work, but we've all heard idyllic interpretations of benign tech before. Framing the topic around content rather than capability is a good start, but you easily get lost in the weeds again when you start claiming it will change everything.
- epistemer 4y ago
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- jbcranshaw 4y ago
  > Our collective awe-struck-ness has left us vulnerable to the fact that AI generation is, generally speaking, hollow and indirect. This totally resonates with me. This is absolutely correct. Thinking about the future of work, there's much of what I do every day in my job that is hollow and indirect. And I would be totally okay if I could have something like ChatGPT do it for me.
  - deleted 4y ago
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- teknopaul 4y ago
  "but the actual number of novel spreadsheet applications remains small." That's not my experience, I am continuously amazed by the amount of tasks worker bees manage to do in excel. I kind of wish MS access was more of a thing, because when eventually it doesn't scale and you need a "proper" system, it takes a rewrite.
d_burfoot 4y ago
I don't have a problem with the main point of the article, but there is a huge terminology confusion that is rapidly gathering force to confuse people. The key breakthroughs of GPT3 et al are not primarily about generative AI. People had been building generative models long before GPT3, and it was generally found that discriminative models had better performance. They key to the power of GPT3 is that it has billions of parameters, AND those parameters are well-justified because it was trained on billions of documents. So the term should be something like "gigaparam AI" or something like that. Maybe GIGAI as a parallel to GOFAI. If you could somehow build a gigaparam discrimative model, you would get better performance on the task it was trained on than GPT3.
- jbcranshaw 4y ago
  Good point on the terminology. What do you think the right terminology should be? LLMs is too much of a mouthful and is not as informative for the general public, imo. People are also using Foundation Models, which I rather like.
  - zone411 4y ago
    I don't like "Foundation Models" because it's a term invented by Stanford and they're pushing it hard while not really doing all that much in the field.
  - d_burfoot 4y ago
    +1 to Foundation Models. I don't share your concerns about LLMs, though, and often refer to the future involving LXMs where X could be images, audio, bioinformatics data, etc
    - jbcranshaw 4y ago
      Ooh, I like LXMs. Hadn't heard that before.
29athrowaway 4y ago
If social media resulted in a deluge of low quality crap, now you can expect that same phenomenon to the power of infinity.
- resource0x 4y ago
  We are at the point where any further increase in the amount of low quality crap will not make much difference.
impalallama 4y ago
ChatGPT help me solve a refactoring bug today. I had spent hours messing around trying to figure out what the issue was until I realized, via asking ChatGPT, that I had misunderstood a piece of the code and the docs. It was able to answer and provide examples (until it had error and crashed) in a way a senior engineer might have been able to. The funny thing is I had tried just pasting in code and saying "find the bug" and it wasn't helpful at all, but when I posted in a portion and asked it to explain what the code was doing I was able to work backwards and solve the issue. Its nice anecdote where the AI felt additive instead of existentially destructive which has been a overbearing anxiety for me this last month.
- ape4 4y ago
  That works for a really small amount of code (like 100 lines).
- teddyh 4y ago
  Sounds a lot like “rubber duck” debugging.
  - oneoff786 4y ago
    If you’re willing to overlook the small detail that it is the duck explaining to you how your code works rather than you explaining to the duck then sure
    - teddyh 4y ago
      Except that the duck is an unreliable confabulator, and will cheerfully make up plausible-sounding fake information when “explaining”.
      - oneoff786 4y ago
        Shrug. Precision seems like a low importance metric when suggesting interpretations of code for the purpose of debugging.
k__ 4y ago
I used ChatGPT for my work as a writer, and it's pretty nice. I wouldn't let it write a whole article, but it can really save time at research. Just needs a bit of fact checking in the end.
- astockwell 4y ago
  Can you elaborate more on your process, and the venue/focus of the writing?
  - k__ 4y ago
    I write educational technical articles for a living. Dev tools, frameworks, security, APIs, infrastructure, web3, etc. I talk to the AI as if I would interview an expert on a subject matter. This usually gives a good starting point for an article, if the subject is general enough, and not too new. It's also good at structuring and rewriting texts. If you already have all the correct data, you can use it to write an outline or something like that. The problems I saw were that it can't follow a coherent thought for more than a few paragraphs, and the writing style is generally a bit boring. Also, the system uses sampling of results to sound more interesting and to prevent overfitting, it happens regularly that it tells you crap. One time you get a good answer, then you change one word in your prompt and the results isn't accurate anymore. But I worked for years as a developer, so I usually notice when things are off, and I also fact check manually with Google when I want to be sure.
    - samvher 4y ago
      No offense but this approach worries me - it seems like a novel mechanism to (perhaps inadvertently) generate and spread false information. It takes a lot of fact checking to make sure everything is right, and if you do the research yourself that's a natural part of the process. It seems way too easy to minimize that effort in a process like this. I was already worried about ChatGPT-like systems generating mass-produced nonsense and polluting the internet, but if people are also going to edit ChatGPT output just enough to make it seem right (a mechanism I hadn't thought of so far), that might make the nonsense a lot harder to detect. I totally understand the reasoning though, it sounds like a productive workflow.
commitpizza 4y ago
I paid for Tabnine pro since it was 50% off for a year but I won't renew it unless it massively improves. I mean, it does give good completions sometimes but the time saved isn't that great imho. Maybe chatgpt is better but it feels like AI still have some way to go to actually be so useful you would be less sucessful without it.
RyanShook 4y ago
Does anyone else feel like the crypto crowd just migrated to AI?
- greedo 4y ago
  And they were joined by the "self-driving" car crowd.
- oliveshell 4y ago
  No, but I do think that both of these things are revealing to us just how bad highly technical people and highly intuitive and empathetic people are at properly communicating with one another. (It is a broad generalization to assume that these traits are mutually exclusive. They do, of course, co-exist in many people. However, it seems to me that the number of people in whom they coexist robustly are few. However, if is these few from whence come true once-in-a-generation geniuses.)
  - oliveshell 4y ago
    Replying to myself to apologize to all future readers for the absolute hash I made of the above comment. I distractedly typed it out while doing something else and didn't realize how unintelligible I made it sound until it was too late to edit!
- kobalsky 4y ago
  what a weird moment to take a stab at AI, right now when all the projects are starting to bear fruit and we can see advances that remind me of moore's law in the 90s. these projects have direct commercial applications right now.
  - oliveshell 4y ago
    Honestly? I get it. What you say about AI is true; however, from where I'm standing, it seems there is still too much greed-driven, mindless "no problem; we'll simply brute-force the problem of inventing a machine that does human-level cognition, which is a thing we admit we do not understand at all"-type enthusiasm, and not enough openness, humility, and critical thinking in the field.
a13o 4y ago
On the topic of Content is King, I have a different view than the author. I think in the case of these trained AIs, 'content' refers to the training datasets and not the generated outputs. Trained AIs are in something like the early digital streaming days where there was only one provider in town, so that provider aggregated All The Content. Over the next decade we would see the content owners claw their content back from Netflix, and onto competitor platforms -- which takes us to where we are today. Netflix's third party content has dwindled and forced them to focus on creating their own first party content which can not be clawed away. When these generative AIs start to produce income, it will be at the expense of the artists whose art was in the training dataset nonconsensually. This triggers the same content clawback we saw in digital streaming. Training datasets will be heavily scrutinized and monetized because the algorithms powering generative AIs aren't actually carrying much water. What is DALL-E without its dataset? Content is King.
mlfia 4y ago
GPT3 has shown how ML can be trained on multiple unstructured data sources to produce structured information on demand. Iterate a few more versions from here, so that the models are stronger at producing the correct structured data, and the impact on every office job will be profound. I.e. instead of training a generative model on text from the internet, train it on every single excel file, sql database, word document and email your company stores. Then query this model asking it to generate Report X showing Y and Z. When you step back and consider it, 99% of office jobs are about producing structured data from unstructured data sources. The implications of this are being hugely underestimated.
- lmarcos 4y ago
  Nah. When AI is able to do all what you have said, requirements will just get harder and humans will still have to put hours to make something done. Just like 30 years ago it wasn't feasible to implement streaming music over the internet in a weekend, and now any teenager can do so by just 'npm install'ing... AI will only open the door to even more complex problems to solve.
  - shishy 4y ago
    Both of those things can be true... it's not just one or the other. There are plenty of jobs right now that are more menial tasks which will definitely be replaced by this, and I feel for those older folks in those positions because they'll most likely lose their jobs / be impacted by this.
    - jhbadger 4y ago
      Exactly. People forget that there used to be (up to about thirty years ago) a large number of people employed as "file clerks". Their job was to file and retrieve on demand paper documents in filing cabinets. When relational databases became practical, this information was stored and retrieved electronically and the entire job of "file clerk" was made obsolete.
- eldaisfish 4y ago
kylehotchkiss 4y ago
Unexpected way: I can’t get my work done because ChatGPT is overloaded
hooande 4y ago
ChatGPT and generative AI will not who write these things for a living will definitely have fewer clients. But this is a small percentage of paid writing, and not the most lucrative or desirable. I do not think that the world is changing because of large language models. That seems to be a controversial opinion so I won't get into it here. But these are powerful new tools, no question. The way I work has changed and I'm very glad to have ChatGPT. I do believe that in the coming years knowing how to use ChatGPT or similar products will be as important as knowing how to use Google is now. People that know how to leverage LLMs going forward will simply have an advantage over those who don't. It won't be long before it isn't optional for executives and knowledge workers. This will be a big change for many people. But we adapted to Google in the early 2000s and people will adapt to this as well.
revskill 4y ago
The article is purely "common sense" for the purpose of marketing new service ? Nothing new at all.
ChildOfChaos 4y ago
So this article doesn't go into 'Unexpected ways generative AI will change how you work forever' instead is just an advert.
aaron695 4y ago
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devinprater 4y ago
Report generator, yes please. Have it hook into Salesforce and Moodle, and I'll be set.

106 points by jbcranshaw 4y ago

Their product MaestroAI is marketed as “for teams” (and of course with the obligatory fading-color call-to-action buttons) presumably to attract VC $$$ but I would love something like this (powered by LLMs) to extract info from all my documents. Maybe something like this exists? Please no DEVONThink suggestions :)

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zabzonk 4y ago

> The results are often wildly creative and spookily accurate, giving these models a human-like feel. or wildly inaccurate, particularly in fields such as programming

toss1 4y ago
Yup. What seems to be largely missed is that these models have zero understanding, and are actually destroyers of information, not creators. In classic Information Theory, information is basically surprise value — how much unexpected info is in the message? — yet these "AI" systems put out the most expected subset in each instance. This highly averaged output is very recognizable and so very striking, but it is not actually very informative (perhaps except in cases where it is specifically used as a verbose search engine, where the query takes advantage of the breadth of the AI's training).
- Gh0stRAT 4y ago
  > In classic Information Theory, information is basically surprise value — how much unexpected info is in the message? — yet these "AI" systems put out the most expected subset in each instance. Forgive me, but isn't this kind of moving-the-goalposts? Information is the surprise value from the recipient's point of view, which meas the recipient's bayesian prior probability is "expected". Saying "these "AI" systems put out the most expected subset in each instance" assumes that the recipient's priors exactly equal those of the model which would only be the case when the model is talking to itself. (or I suppose to an even more complex model with perfect knowledge of ChatGPT's weights) The fact that no information is transferred when the model talks to itself should not be surprising and would apply to any AI. (even including a superhuman post-singularity god-like AI)
  - toss1 4y ago
    Yes, the AI's output could be surprising to the point of view of many recipients. This does not mean anything more than that the AI has a greater breadth of training background, which is likely. We get the output most likely expected from any of (or the average of) the humans whose writing/drawing/whatever was included in the input set. What we will not be getting from the AIs is any creative output based on unique understanding, as we would from an intelligent, creative human. Many of hte humans in the input set would see the same prompt and produce an actual novel and meaningful output, not simply a cut-and-paste from prior works. (& yes, seme novel output may come from some randomizing algo, but if it is correct, it is no more correct than the broken clock that is correct twice every day.) Or, another example, I was involved in a legal deposition where an "AI" transcription system was used instead of a skilled court reporter. The output LOOKED fantastic, until I actually read it, and it was absolute garbage. The standard errata sheet has room for the deponent to put in about a dozen corrections, and most are less than a handful. My errata list was multiple pages. These errors often reversed the meaning of sentences, substitutin "I have ..." for "You have...", dropping or adding "not", or substituting in common names for unusual names (e.g., "Jack Kennedy" for "John Kemeny". note human transcribers always ask for correct spellings of names in the next break, this crap just inserted it like it had a clue). So, even though the total "experience" or training set of the may go beyond the experience of the reader, so some of the output is surprising, this is no more so than a search engine produces surprise. In fact, I think this is the best use of the AIs, to have them trained on an enormous data set, and provide possibly better results, defined as more on-point, but likely less thorough.
    - Gh0stRAT 4y ago
      >This does not mean anything more than that the AI has a greater breadth of training background, which is likely. This has nothing to do with Shannon's definition of information. The light from a star going supernova or a tsunami hitting shore convey information without any kind of agency nor intent being involved. >What we will not be getting from the AIs is any creative output based on unique understanding, as we would from an intelligent, creative human. This is a really big assumption here that I don't think is justified. Are you assuming that humans have some non-physical soul which makes us somehow different from any other deterministic information-processing system?
aero142 4y ago
I've been asking friends in non-programming engineering fields how ChatGPT does in their area of expertise, and I believe programming is the area that ChatGPT is the most accurate. Finding solution to general engineering problems seems blatantly wrong in almost all cases, whereas in programming, it seems to be able to generate mostly correct code for simple, boiler-plate like tasks.
- zabzonk 4y ago
  why is "mostly correct" ok for programming? also, i don't believe that good programmers want to have boiler-plate in their code.
  - fooker 4y ago
    Because that's what most programmers achieve too. You can iterate from there by taking advantage of the last 50 years of software engineering wisdom.
- ccozan 4y ago
  yes, but why? Why is GPT so much better at programming than other tasks? can it be that programming itself can be so easily predicted in a generative way, while others require more ingenuity and real world model to be solved? In this case I would totally offload programming to a GPT /LLM AI, while my job is simply to specify largely the business case.
  - aero142 4y ago
    It seems the improvement in the models is the size of the training set. Hence the "Large" in large language models. I am assuming that it is better at programming because there a is a ton of training data available on Github, etc. There isn't a similar set of data on solving a variety of physics problems.
  - lancesells 4y ago
    Is it because programming is a more limited and specific language than the ones people speak? There's less room for double-meanings, slang, meaning, or even sentence structure.
blablablerg 4y ago
Or worse, subtly inaccurate. The problem I have with generative AI right now, its product looks like it makes sense and sometimes it does, but there is always the risk of total nonsense hidden somewhere in the middle. So you still need someone capable to check and correct for most professional work, and sometimes that is harder or more time consuming than making the product itself. The same sort of problem with self driving cars, they are often correct but not often enough, and staying alert to correct the AI is worse than driving yourself which is more work, paradoxically enough. AI might manage to push through these barriers, but I remain skeptical with the technology in the current state: statistical machines that are good in the common cases but sketchy at the edges.
- riskable 4y ago
  > there is always the risk of total nonsense hidden somewhere in the middle This is true of human-generated code as well. Trust me: Reviewing other people's code is my day job. It's exceptionally rare that a malicious actor is trying to sneak something into the code. The common scenario is the developer who's new to the project not fully understanding how everything works so they copy & paste something they think is necessary but ultimately isn't and could in fact be very wrong. Just like how ChatGPT works.
- xenonite 4y ago
  Which is similar to correcting subpar human results: Rather than meticulously correcting the works of a subpar programmer it is much more efficient to let a proficient developer produce the code. Or even better, engage a 10x developer.
  - oceanplexian 4y ago
    Most problems don’t require efficient code. If an inefficient programmer can solve a real world business problem in a fraction of the time, but it requires slightly more computing resources, I’d still pay for it. Efficiency can be measured in a lot of dimensions and often times spending time and money writing optimized code is inefficient as well.

smoldesu 4y ago

> Widespread adoption of generative AI will act as a lubricant between systems, I largely agree with this article, but I feel like you have to be careful with these general predictions. Many technologies have purported themselves to be this "business lubricant" tech (ever since the spreadsheet), but the actual number of novel spreadsheet applications remains small. It feels like the same can be said for generative AI, too. Almost every day I feel the need to explain that "generation" and "abstract thought" are distinct concepts, because conflating the two leads to so much misconception around AI. Stable Diffusion has no concept of artistic significance, just art. Similarly, ChatGPT can only predict what happens next, which doesn't bestow it heuristic thought. Our collective awe-struck-ness has left us vulnerable to the fact that AI generation is, generally speaking, hollow and indirect. AI will certainly change the future, and along with it the future of work, but we've all heard idyllic interpretations of benign tech before. Framing the topic around content rather than capability is a good start, but you easily get lost in the weeds again when you start claiming it will change everything.

epistemer 4y ago
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jbcranshaw 4y ago
> Our collective awe-struck-ness has left us vulnerable to the fact that AI generation is, generally speaking, hollow and indirect. This totally resonates with me. This is absolutely correct. Thinking about the future of work, there's much of what I do every day in my job that is hollow and indirect. And I would be totally okay if I could have something like ChatGPT do it for me.
- deleted 4y ago
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teknopaul 4y ago
"but the actual number of novel spreadsheet applications remains small." That's not my experience, I am continuously amazed by the amount of tasks worker bees manage to do in excel. I kind of wish MS access was more of a thing, because when eventually it doesn't scale and you need a "proper" system, it takes a rewrite.

d_burfoot 4y ago

I don't have a problem with the main point of the article, but there is a huge terminology confusion that is rapidly gathering force to confuse people. The key breakthroughs of GPT3 et al are not primarily about generative AI. People had been building generative models long before GPT3, and it was generally found that discriminative models had better performance. They key to the power of GPT3 is that it has billions of parameters, AND those parameters are well-justified because it was trained on billions of documents. So the term should be something like "gigaparam AI" or something like that. Maybe GIGAI as a parallel to GOFAI. If you could somehow build a gigaparam discrimative model, you would get better performance on the task it was trained on than GPT3.

jbcranshaw 4y ago
Good point on the terminology. What do you think the right terminology should be? LLMs is too much of a mouthful and is not as informative for the general public, imo. People are also using Foundation Models, which I rather like.
- zone411 4y ago
  I don't like "Foundation Models" because it's a term invented by Stanford and they're pushing it hard while not really doing all that much in the field.
- d_burfoot 4y ago
  +1 to Foundation Models. I don't share your concerns about LLMs, though, and often refer to the future involving LXMs where X could be images, audio, bioinformatics data, etc
  - jbcranshaw 4y ago
    Ooh, I like LXMs. Hadn't heard that before.

29athrowaway 4y ago

If social media resulted in a deluge of low quality crap, now you can expect that same phenomenon to the power of infinity.

resource0x 4y ago
We are at the point where any further increase in the amount of low quality crap will not make much difference.

impalallama 4y ago

ChatGPT help me solve a refactoring bug today. I had spent hours messing around trying to figure out what the issue was until I realized, via asking ChatGPT, that I had misunderstood a piece of the code and the docs. It was able to answer and provide examples (until it had error and crashed) in a way a senior engineer might have been able to. The funny thing is I had tried just pasting in code and saying "find the bug" and it wasn't helpful at all, but when I posted in a portion and asked it to explain what the code was doing I was able to work backwards and solve the issue. Its nice anecdote where the AI felt additive instead of existentially destructive which has been a overbearing anxiety for me this last month.

ape4 4y ago
That works for a really small amount of code (like 100 lines).
teddyh 4y ago
Sounds a lot like “rubber duck” debugging.
- oneoff786 4y ago
  If you’re willing to overlook the small detail that it is the duck explaining to you how your code works rather than you explaining to the duck then sure
  - teddyh 4y ago
    Except that the duck is an unreliable confabulator, and will cheerfully make up plausible-sounding fake information when “explaining”.
    - oneoff786 4y ago
      Shrug. Precision seems like a low importance metric when suggesting interpretations of code for the purpose of debugging.

k__ 4y ago

I used ChatGPT for my work as a writer, and it's pretty nice. I wouldn't let it write a whole article, but it can really save time at research. Just needs a bit of fact checking in the end.

astockwell 4y ago
Can you elaborate more on your process, and the venue/focus of the writing?
- k__ 4y ago
  I write educational technical articles for a living. Dev tools, frameworks, security, APIs, infrastructure, web3, etc. I talk to the AI as if I would interview an expert on a subject matter. This usually gives a good starting point for an article, if the subject is general enough, and not too new. It's also good at structuring and rewriting texts. If you already have all the correct data, you can use it to write an outline or something like that. The problems I saw were that it can't follow a coherent thought for more than a few paragraphs, and the writing style is generally a bit boring. Also, the system uses sampling of results to sound more interesting and to prevent overfitting, it happens regularly that it tells you crap. One time you get a good answer, then you change one word in your prompt and the results isn't accurate anymore. But I worked for years as a developer, so I usually notice when things are off, and I also fact check manually with Google when I want to be sure.
  - samvher 4y ago
    No offense but this approach worries me - it seems like a novel mechanism to (perhaps inadvertently) generate and spread false information. It takes a lot of fact checking to make sure everything is right, and if you do the research yourself that's a natural part of the process. It seems way too easy to minimize that effort in a process like this. I was already worried about ChatGPT-like systems generating mass-produced nonsense and polluting the internet, but if people are also going to edit ChatGPT output just enough to make it seem right (a mechanism I hadn't thought of so far), that might make the nonsense a lot harder to detect. I totally understand the reasoning though, it sounds like a productive workflow.

commitpizza 4y ago

I paid for Tabnine pro since it was 50% off for a year but I won't renew it unless it massively improves. I mean, it does give good completions sometimes but the time saved isn't that great imho. Maybe chatgpt is better but it feels like AI still have some way to go to actually be so useful you would be less sucessful without it.

RyanShook 4y ago

Does anyone else feel like the crypto crowd just migrated to AI?

greedo 4y ago
And they were joined by the "self-driving" car crowd.
oliveshell 4y ago
No, but I do think that both of these things are revealing to us just how bad highly technical people and highly intuitive and empathetic people are at properly communicating with one another. (It is a broad generalization to assume that these traits are mutually exclusive. They do, of course, co-exist in many people. However, it seems to me that the number of people in whom they coexist robustly are few. However, if is these few from whence come true once-in-a-generation geniuses.)
- oliveshell 4y ago
  Replying to myself to apologize to all future readers for the absolute hash I made of the above comment. I distractedly typed it out while doing something else and didn't realize how unintelligible I made it sound until it was too late to edit!
kobalsky 4y ago
what a weird moment to take a stab at AI, right now when all the projects are starting to bear fruit and we can see advances that remind me of moore's law in the 90s. these projects have direct commercial applications right now.
- oliveshell 4y ago
  Honestly? I get it. What you say about AI is true; however, from where I'm standing, it seems there is still too much greed-driven, mindless "no problem; we'll simply brute-force the problem of inventing a machine that does human-level cognition, which is a thing we admit we do not understand at all"-type enthusiasm, and not enough openness, humility, and critical thinking in the field.

a13o 4y ago

On the topic of Content is King, I have a different view than the author. I think in the case of these trained AIs, 'content' refers to the training datasets and not the generated outputs. Trained AIs are in something like the early digital streaming days where there was only one provider in town, so that provider aggregated All The Content. Over the next decade we would see the content owners claw their content back from Netflix, and onto competitor platforms -- which takes us to where we are today. Netflix's third party content has dwindled and forced them to focus on creating their own first party content which can not be clawed away. When these generative AIs start to produce income, it will be at the expense of the artists whose art was in the training dataset nonconsensually. This triggers the same content clawback we saw in digital streaming. Training datasets will be heavily scrutinized and monetized because the algorithms powering generative AIs aren't actually carrying much water. What is DALL-E without its dataset? Content is King.

mlfia 4y ago

GPT3 has shown how ML can be trained on multiple unstructured data sources to produce structured information on demand. Iterate a few more versions from here, so that the models are stronger at producing the correct structured data, and the impact on every office job will be profound. I.e. instead of training a generative model on text from the internet, train it on every single excel file, sql database, word document and email your company stores. Then query this model asking it to generate Report X showing Y and Z. When you step back and consider it, 99% of office jobs are about producing structured data from unstructured data sources. The implications of this are being hugely underestimated.

lmarcos 4y ago
Nah. When AI is able to do all what you have said, requirements will just get harder and humans will still have to put hours to make something done. Just like 30 years ago it wasn't feasible to implement streaming music over the internet in a weekend, and now any teenager can do so by just 'npm install'ing... AI will only open the door to even more complex problems to solve.
- shishy 4y ago
  Both of those things can be true... it's not just one or the other. There are plenty of jobs right now that are more menial tasks which will definitely be replaced by this, and I feel for those older folks in those positions because they'll most likely lose their jobs / be impacted by this.
  - jhbadger 4y ago
    Exactly. People forget that there used to be (up to about thirty years ago) a large number of people employed as "file clerks". Their job was to file and retrieve on demand paper documents in filing cabinets. When relational databases became practical, this information was stored and retrieved electronically and the entire job of "file clerk" was made obsolete.
eldaisfish 4y ago

kylehotchkiss 4y ago

Unexpected way: I can’t get my work done because ChatGPT is overloaded

hooande 4y ago

ChatGPT and generative AI will not who write these things for a living will definitely have fewer clients. But this is a small percentage of paid writing, and not the most lucrative or desirable. I do not think that the world is changing because of large language models. That seems to be a controversial opinion so I won't get into it here. But these are powerful new tools, no question. The way I work has changed and I'm very glad to have ChatGPT. I do believe that in the coming years knowing how to use ChatGPT or similar products will be as important as knowing how to use Google is now. People that know how to leverage LLMs going forward will simply have an advantage over those who don't. It won't be long before it isn't optional for executives and knowledge workers. This will be a big change for many people. But we adapted to Google in the early 2000s and people will adapt to this as well.

revskill 4y ago

The article is purely "common sense" for the purpose of marketing new service ? Nothing new at all.

ChildOfChaos 4y ago

So this article doesn't go into 'Unexpected ways generative AI will change how you work forever' instead is just an advert.

aaron695 4y ago

[dead]

devinprater 4y ago

Report generator, yes please. Have it hook into Salesforce and Moodle, and I'll be set.