7 ms·

I've worked in AI for the past 30 years and have seen enthusiasm as robust as yours go bust before. Just because some kinds of narrow AI have done extraordinar

I've worked in AI for the past 30 years and have seen enthusiasm as robust as yours go bust before. Just because some kinds of narrow AI have done extraordinarily well -- namely those tasks that recognize patterns using connections between FSMs -- does not mean that same mechanisms will continue to scale up to human-level cognition, much less exceed it any time soon. The breakthroughs where deep AI have excelled -- object recognition in images, voice recognition and generation, and text-based info embedding and retrieval -- these require none of the multilevel abstraction that characterizes higher cognition (Kahneman's system 2 thinking). When we see steady progress on such frontiers, only then a plausible case can and should be made that the essentials of AGI are indeed within our grasp. Until then, plateauing at a higher level of pattern matching than we had expected -- which is what we have seen many times before from narrow AI -- this is not sufficient evidence that the other requisite skills needed for AGI are surely just around the corner.

spacemanspiff01 2y ago
So I am a neophite in this area, but my thesis for why "this time is different" compared to previous AI bubbles is that this time there exist a bunch of clear products (or paths to products) that work and only require what is currently available in terms of technology. Coding assistants today are useful, image generation is useful, speach recognition/generation is useful. All of these can support businesses, even in their current (early) state. Those businesses have value in funding even 1% improvements in engineering/science. I think that this is different than before, where even in the 80s there were less defined products, amd most everything was a prototype that needed just a bit more research to be commercially viable. Where as in the past, hopes for the technology waned and funding for research dropped off a cliff, today's stuff is useful now, and so companies will continue to spend some amount on the research side.
- bamboozled 2y ago
  I don't find coding assistants to be very useful. Image generation was fun for a few weeks. Speech recognition is useful. Anyway, considering all these things can be done on device, where is the long term business prospect of which you speak?
  - LtWorf 2y ago
    > Speech recognition is useful. Now try to mute a video on youtube and understand what's being said from the automatic subtitles. If you do it in english, be aware that it's the best performing language and all others are even worse.
    - KoolKat23 2y ago
      Just today, I received a note from a gas technician, part handwritten, for the life of me I couldn't make out what he was saying, I asked ChatGPT and it surprisingly understood, rereading the original note I'm very sure it was correct.
    - Terr_ 2y ago
      Sometimes speech-to-text machine learning models give very good results, however I think the key is that: 1. It's overwhelmingly more useful than the [no text] it was replacing, particularly for the deaf or if you want to search for keywords in a video. 2. When it fails, it tends to do so in ways that trigger human suspicion and oversight. Those aren't necessarily true of some of the things people are shoehorning LLMs into these days, which is why I'm a lost more pessimistic about that technology.
    - tkgally 2y ago
      For some reason, YouTube is not using a very good STT system now. The lack of sentence punctuation is particularly annoying. Transcriptions by Whisper and Gemini 1.5 Pro are much better. From a couple of weeks ago: https://news.ycombinator.com/item?id=41199567#41201773 https://news.ycombinator.com/item?id=41199567#41201773 I expect that YouTube will up their transcription game soon, too.
      - LtWorf 2y ago
        I've tried whisper too. I made this: https://codeberg.org/ltworf/srtgen https://codeberg.org/ltworf/srtgen Basically it's kinda useful to put time tags, but I need to manually fix each and every sentence. Sometimes I need to fix the time tags as well. I just spoke about youtube because it's more popular and easy to test.
- tarsinge 2y ago
  See this is exactly what is wrong with “this time it’s different” here. AI has been useful and used for decades (but under a different name because the term was tainted by previous bubbles). Look at the section “AI behind the scenes” here https://en.wikipedia.org/wiki/History_of_artificial_intelligence#AI_behind_the_scenes https://en.wikipedia.org/wiki/History_of_artificial_intellig...
- benreesman 2y ago
  “This time is different” in one fundamental, methodological, epistemological way: we test on the training set now. This has follow-on consequences for a shattering phase transition between “persuasive demo” and “useful product”. We can now make arbitrarily convincing demos that will crash airplanes (“with no survivors!”) on the first try in production. This is institutionalized by the market capitalizations of 7 companies being so inflated that if they were priced accurately the US economy would collapse.
  - m3kw9 2y ago
    There was really one AI winter which is a sample size of 1, saying this time is different is justified based on the exponential improvement over AI 10 years back
- crystal_revenge 2y ago
  > this time there exist a bunch of clear products Really? I work in AI and my biggest concern is that I don't see any real products coming out of this space. I work closer to the models, and people in this specific area are making progress, but when I look at what's being done down stream I see nothing, save demos that don't scale beyond a few examples. > in the 80s there were less defined products, amd most everything was a prototype that needed just a bit more research to be commercially viable. This is literally all I see right now. There's some really fun hobbyist stuff happening in the image gen area that I think is here to stay, but LLMs haven't broken out of the "autocomplete on steroids" use cases. > today's stuff is useful now Can you give me examples of 5, non-coding assistant, profitable use cases for LLMs that aren't still in the "needed just a bit more research to be commercially viable" stage? I love working in AI, think the technology is amazing, and do think there are some under exploited (though less exciting) use cases, but all I see if big promises with under delivery. I would love to be proven wrong.
  - andreilys 2y ago
    1. Content Generation: LLMs can be used to generate high-quality, human-like content such as articles, blog posts, social media posts, and even short stories. Businesses can leverage this capability to save time and resources on content creation, and improve the consistency and quality of their online presence. 2. Customer Service and Support: LLMs can be integrated into chatbots and virtual assistants to provide fast, accurate, and personalized responses to customer inquiries. This can help businesses improve their customer experience, reduce the workload on human customer service representatives, and provide 24/7 support. 3. Summarization and Insights: LLMs can be used to analyze large volumes of text data, such as reports, research papers, or customer feedback, and generate concise summaries and insights. This can be valuable for businesses in fields like market research, financial analysis, or strategic planning. 4. HR Candidate Screening: Use case: Using LLMs to assess job applicant resumes, cover letters, and interview responses to identify the most qualified candidates. Example: A large retailer integrating an LLM-based recruiting assistant to help sift through hundreds of applications for entry-level roles. 5. Legal Document Review: Use case: Employing LLMs to rapidly scan through large volumes of legal contracts, case files, and regulatory documents to identify key terms, risks, and relevant information. Example: A corporate law firm deploying an LLM tool to streamline the due diligence process for mergers and acquisitions.
- greenthrow 2y ago
  There were products and "path to products" too. Once the hype died down nobody wanted them. It is the same this time.
tasuki 2y ago
"AGI" is a nonsense term anyway. Humans don't have "general" intelligence either: our intelligence is specialized to our environment.
- Jensson 2y ago
  Humans is the most general intelligence we know about, so that is why we called it general intelligence, because we have made so many intelligences that are specialized on a specific domain like calculators or chess engines we need a word for something that is as general as humans, because being able to replace humans is a very important goal.
  - tasuki 2y ago
    Yes, humans are the most general intelligence we know about. That doesn't say much about how general it is, just highlights our limitations.
    - ace32229 2y ago
      This is a bit like saying Earth isn't big, because there are far larger planets etc. out there. For the average conversation, Earth is "big".
- ben_w 2y ago
  "AGI" means many different things to many different people: to me any AI which is general is an AGI so GPT-3.5 counts; to OpenAI it has to be economically transformative to count; to some commentators here it has to be superhuman to count. I think that none of the initials are boolean; things can be degrees of artificial, degrees of general, and degrees of intelligent. I think most would assert that humans count as a "general" intelligence, even if they disagree about most of the other things I've put in this comment.
tim333 2y ago
If you look at AI history there is often fairly steady progress in a given skill area for example chess programs improved in a steady way on ELO scores and you could project pretty well the future by drawing a line on a graph. Similarly large language models seem to be progressing from toddler like to high school student like (now) to PhD like - shortly. There are skills AI are still fairly bad at like the higher level reasoning you mention, and in robot form being able to pop to the shops to get some groceries say but I get the impression those are also improving in a steady way and it won't be so long.
b_be_building 2y ago
I have been using Chat-GPT has a full time expert and I can unequivocally tell you that its a transformative piece of technology. The technology isn't hyped.
- BoingBoomTschak 2y ago
  It is very nice as "Markov chains on steroids", but people believing that LLMs are anything but a distracting local maximum on the path to AGI are 200% in kool-aid drinking mode.
- j-a-a-p 2y ago
  I agree as this is also my personal experience. But I also see the usage of ChatGPT is falling down fast from 1.8 billion visitors to 260 million last month [1]. I am probably through some ETF an investor in MS, so I do hope the openai API usage is showing a more stable and upward trend. [1]: https://explodingtopics.com/blog/chatgpt-users https://explodingtopics.com/blog/chatgpt-users
  - margorczynski 2y ago
    Well ChatGPT is no longer the top dog and there's quite a bit of competition in the space. Including Llama 3.1 which is free. In general I think most of the moat that OpenAI had has evaporated in the last few months, but also for other LLM companies. Not sure how they plan on making money in the long-term, eventually the investors and shareholders will start asking when they will be seeing the returns on their investment.
KoolKat23 2y ago
All Kahneman's system 2 thinking is just slow deliberate thinking. And these models do indeed have this characteristic to an extent, as evidenced with chain of thought reasoning. You can see this in action with multiplication. Much like humans when asked to guess the answer, they'll get it wrong, unless they know the answer from rote learning multiplication tables, this System-1 thinking. In many cases when asked they can reason further and solve it, by breaking it down and solving it step by step, much like a human, this is system-2 thinking. In my opinion, it seems nearly everything is there for it it to take the next leap in intelligence, it's just putting it all together.
- dogcomplex 2y ago
  Agreed. System 2 strategizing may simply be the recursive application of symbolic System 1 tooling. An LLM is entirely capable of reading a problem, determining the immediate facts and tokens (fast and intuitive system 1) and determining the ideal algorithm to resolve them (logical analytical system 2). The execution to do all those steps at once is lacking in current LLMs (debatably - they get better every month) - but any basic architecture breaking things down into component sub-questions clearly works.
sandspar 2y ago
For readers' edification, would you mind making a strong hypothetical argument for why this time it actually is different, from an expert's perspective?

spacemanspiff01 2y ago

So I am a neophite in this area, but my thesis for why "this time is different" compared to previous AI bubbles is that this time there exist a bunch of clear products (or paths to products) that work and only require what is currently available in terms of technology. Coding assistants today are useful, image generation is useful, speach recognition/generation is useful. All of these can support businesses, even in their current (early) state. Those businesses have value in funding even 1% improvements in engineering/science. I think that this is different than before, where even in the 80s there were less defined products, amd most everything was a prototype that needed just a bit more research to be commercially viable. Where as in the past, hopes for the technology waned and funding for research dropped off a cliff, today's stuff is useful now, and so companies will continue to spend some amount on the research side.

bamboozled 2y ago
I don't find coding assistants to be very useful. Image generation was fun for a few weeks. Speech recognition is useful. Anyway, considering all these things can be done on device, where is the long term business prospect of which you speak?
- LtWorf 2y ago
  > Speech recognition is useful. Now try to mute a video on youtube and understand what's being said from the automatic subtitles. If you do it in english, be aware that it's the best performing language and all others are even worse.
  - KoolKat23 2y ago
    Just today, I received a note from a gas technician, part handwritten, for the life of me I couldn't make out what he was saying, I asked ChatGPT and it surprisingly understood, rereading the original note I'm very sure it was correct.
  - Terr_ 2y ago
    Sometimes speech-to-text machine learning models give very good results, however I think the key is that: 1. It's overwhelmingly more useful than the [no text] it was replacing, particularly for the deaf or if you want to search for keywords in a video. 2. When it fails, it tends to do so in ways that trigger human suspicion and oversight. Those aren't necessarily true of some of the things people are shoehorning LLMs into these days, which is why I'm a lost more pessimistic about that technology.
  - tkgally 2y ago
    For some reason, YouTube is not using a very good STT system now. The lack of sentence punctuation is particularly annoying. Transcriptions by Whisper and Gemini 1.5 Pro are much better. From a couple of weeks ago: https://news.ycombinator.com/item?id=41199567#41201773 https://news.ycombinator.com/item?id=41199567#41201773 I expect that YouTube will up their transcription game soon, too.
    - LtWorf 2y ago
      I've tried whisper too. I made this: https://codeberg.org/ltworf/srtgen https://codeberg.org/ltworf/srtgen Basically it's kinda useful to put time tags, but I need to manually fix each and every sentence. Sometimes I need to fix the time tags as well. I just spoke about youtube because it's more popular and easy to test.
tarsinge 2y ago
See this is exactly what is wrong with “this time it’s different” here. AI has been useful and used for decades (but under a different name because the term was tainted by previous bubbles). Look at the section “AI behind the scenes” here https://en.wikipedia.org/wiki/History_of_artificial_intelligence#AI_behind_the_scenes https://en.wikipedia.org/wiki/History_of_artificial_intellig...
benreesman 2y ago
“This time is different” in one fundamental, methodological, epistemological way: we test on the training set now. This has follow-on consequences for a shattering phase transition between “persuasive demo” and “useful product”. We can now make arbitrarily convincing demos that will crash airplanes (“with no survivors!”) on the first try in production. This is institutionalized by the market capitalizations of 7 companies being so inflated that if they were priced accurately the US economy would collapse.
- m3kw9 2y ago
  There was really one AI winter which is a sample size of 1, saying this time is different is justified based on the exponential improvement over AI 10 years back
crystal_revenge 2y ago
> this time there exist a bunch of clear products Really? I work in AI and my biggest concern is that I don't see any real products coming out of this space. I work closer to the models, and people in this specific area are making progress, but when I look at what's being done down stream I see nothing, save demos that don't scale beyond a few examples. > in the 80s there were less defined products, amd most everything was a prototype that needed just a bit more research to be commercially viable. This is literally all I see right now. There's some really fun hobbyist stuff happening in the image gen area that I think is here to stay, but LLMs haven't broken out of the "autocomplete on steroids" use cases. > today's stuff is useful now Can you give me examples of 5, non-coding assistant, profitable use cases for LLMs that aren't still in the "needed just a bit more research to be commercially viable" stage? I love working in AI, think the technology is amazing, and do think there are some under exploited (though less exciting) use cases, but all I see if big promises with under delivery. I would love to be proven wrong.
- andreilys 2y ago
  1. Content Generation: LLMs can be used to generate high-quality, human-like content such as articles, blog posts, social media posts, and even short stories. Businesses can leverage this capability to save time and resources on content creation, and improve the consistency and quality of their online presence. 2. Customer Service and Support: LLMs can be integrated into chatbots and virtual assistants to provide fast, accurate, and personalized responses to customer inquiries. This can help businesses improve their customer experience, reduce the workload on human customer service representatives, and provide 24/7 support. 3. Summarization and Insights: LLMs can be used to analyze large volumes of text data, such as reports, research papers, or customer feedback, and generate concise summaries and insights. This can be valuable for businesses in fields like market research, financial analysis, or strategic planning. 4. HR Candidate Screening: Use case: Using LLMs to assess job applicant resumes, cover letters, and interview responses to identify the most qualified candidates. Example: A large retailer integrating an LLM-based recruiting assistant to help sift through hundreds of applications for entry-level roles. 5. Legal Document Review: Use case: Employing LLMs to rapidly scan through large volumes of legal contracts, case files, and regulatory documents to identify key terms, risks, and relevant information. Example: A corporate law firm deploying an LLM tool to streamline the due diligence process for mergers and acquisitions.
greenthrow 2y ago
There were products and "path to products" too. Once the hype died down nobody wanted them. It is the same this time.

tasuki 2y ago

"AGI" is a nonsense term anyway. Humans don't have "general" intelligence either: our intelligence is specialized to our environment.

Jensson 2y ago
Humans is the most general intelligence we know about, so that is why we called it general intelligence, because we have made so many intelligences that are specialized on a specific domain like calculators or chess engines we need a word for something that is as general as humans, because being able to replace humans is a very important goal.
- tasuki 2y ago
  Yes, humans are the most general intelligence we know about. That doesn't say much about how general it is, just highlights our limitations.
  - ace32229 2y ago
    This is a bit like saying Earth isn't big, because there are far larger planets etc. out there. For the average conversation, Earth is "big".
ben_w 2y ago
"AGI" means many different things to many different people: to me any AI which is general is an AGI so GPT-3.5 counts; to OpenAI it has to be economically transformative to count; to some commentators here it has to be superhuman to count. I think that none of the initials are boolean; things can be degrees of artificial, degrees of general, and degrees of intelligent. I think most would assert that humans count as a "general" intelligence, even if they disagree about most of the other things I've put in this comment.

tim333 2y ago

If you look at AI history there is often fairly steady progress in a given skill area for example chess programs improved in a steady way on ELO scores and you could project pretty well the future by drawing a line on a graph. Similarly large language models seem to be progressing from toddler like to high school student like (now) to PhD like - shortly. There are skills AI are still fairly bad at like the higher level reasoning you mention, and in robot form being able to pop to the shops to get some groceries say but I get the impression those are also improving in a steady way and it won't be so long.

b_be_building 2y ago

I have been using Chat-GPT has a full time expert and I can unequivocally tell you that its a transformative piece of technology. The technology isn't hyped.

BoingBoomTschak 2y ago
It is very nice as "Markov chains on steroids", but people believing that LLMs are anything but a distracting local maximum on the path to AGI are 200% in kool-aid drinking mode.
j-a-a-p 2y ago
I agree as this is also my personal experience. But I also see the usage of ChatGPT is falling down fast from 1.8 billion visitors to 260 million last month [1]. I am probably through some ETF an investor in MS, so I do hope the openai API usage is showing a more stable and upward trend. [1]: https://explodingtopics.com/blog/chatgpt-users https://explodingtopics.com/blog/chatgpt-users
- margorczynski 2y ago
  Well ChatGPT is no longer the top dog and there's quite a bit of competition in the space. Including Llama 3.1 which is free. In general I think most of the moat that OpenAI had has evaporated in the last few months, but also for other LLM companies. Not sure how they plan on making money in the long-term, eventually the investors and shareholders will start asking when they will be seeing the returns on their investment.

KoolKat23 2y ago

All Kahneman's system 2 thinking is just slow deliberate thinking. And these models do indeed have this characteristic to an extent, as evidenced with chain of thought reasoning. You can see this in action with multiplication. Much like humans when asked to guess the answer, they'll get it wrong, unless they know the answer from rote learning multiplication tables, this System-1 thinking. In many cases when asked they can reason further and solve it, by breaking it down and solving it step by step, much like a human, this is system-2 thinking. In my opinion, it seems nearly everything is there for it it to take the next leap in intelligence, it's just putting it all together.

dogcomplex 2y ago
Agreed. System 2 strategizing may simply be the recursive application of symbolic System 1 tooling. An LLM is entirely capable of reading a problem, determining the immediate facts and tokens (fast and intuitive system 1) and determining the ideal algorithm to resolve them (logical analytical system 2). The execution to do all those steps at once is lacking in current LLMs (debatably - they get better every month) - but any basic architecture breaking things down into component sub-questions clearly works.

sandspar 2y ago

For readers' edification, would you mind making a strong hypothetical argument for why this time it actually is different, from an expert's perspective?