7 ms·

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 p

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.
- WalterSear 2y ago
  > I don't find coding assistants to be very useful. I've come to notice a correlation between contemporary AI optimism and having effectively made the jump to coding with AI assistants.
  - crystal_revenge 2y ago
    > effectively made the jump to coding with AI assistants I think this depend heavily on what type of coding your doing. The more your job could be replaced by copy/pasting from Stack Overflow, the more useful you find coding assistants. For that past few years most of the code I've written has been solving fairly niche quantitative problems with novel approaches and I've found AI coding assistants to range from useless to harmful. But on a recent webdev project, they were much more useful. The vast majority of problems in webdev are fundamentally not unique so a searchable pattern library (which is what an LLM coding assistant basically is) should be pretty effective. For other areas of software, they're not nearly as useful.
    - mhuffman 2y ago
      >I think this depend heavily on what type of coding your doing. The more your job could be replaced by copy/pasting from Stack Overflow, the more useful you find coding assistants. I think this is true and also why you see some "older devs just don't like AI" type comments. AI assistants seem to be great at simple webdev tasks, which also happens to be the type of work that more junior developers do day to day. I have also found them useful with that and I keep one active for those types of projects because of the speed up, although I still have to keep a close eye on what it wants to inject. They also seem to excel at generating tests if you have already developed the functions. Then there are more difficult (usually not webdev) projects. In those cases, it really only shines if I need to ask it a question that I would previously have searched on SO or some obscure board for an answer. And even then, it really has to be scrutinized, because if it was simple, I wouldn't be asking the question. There is def. something there for specific types of development, but it has not "changed my life" or anything like that. It might have if I was just starting out or if I only did webdev type projects.
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.

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.
WalterSear 2y ago
> I don't find coding assistants to be very useful. I've come to notice a correlation between contemporary AI optimism and having effectively made the jump to coding with AI assistants.
- crystal_revenge 2y ago
  > effectively made the jump to coding with AI assistants I think this depend heavily on what type of coding your doing. The more your job could be replaced by copy/pasting from Stack Overflow, the more useful you find coding assistants. For that past few years most of the code I've written has been solving fairly niche quantitative problems with novel approaches and I've found AI coding assistants to range from useless to harmful. But on a recent webdev project, they were much more useful. The vast majority of problems in webdev are fundamentally not unique so a searchable pattern library (which is what an LLM coding assistant basically is) should be pretty effective. For other areas of software, they're not nearly as useful.
  - mhuffman 2y ago
    >I think this depend heavily on what type of coding your doing. The more your job could be replaced by copy/pasting from Stack Overflow, the more useful you find coding assistants. I think this is true and also why you see some "older devs just don't like AI" type comments. AI assistants seem to be great at simple webdev tasks, which also happens to be the type of work that more junior developers do day to day. I have also found them useful with that and I keep one active for those types of projects because of the speed up, although I still have to keep a close eye on what it wants to inject. They also seem to excel at generating tests if you have already developed the functions. Then there are more difficult (usually not webdev) projects. In those cases, it really only shines if I need to ask it a question that I would previously have searched on SO or some obscure board for an answer. And even then, it really has to be scrutinized, because if it was simple, I wouldn't be asking the question. There is def. something there for specific types of development, but it has not "changed my life" or anything like that. It might have if I was just starting out or if I only did webdev type projects.

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.