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Yes - I'm happy you found that. I don't think it's really related to this thread, but I maintain we're firmly in the "demo" phase, and it's going to fizzle out

Yes - I'm happy you found that. I don't think it's really related to this thread, but I maintain we're firmly in the "demo" phase, and it's going to fizzle out when the rubber hits the road and companies actually try and find a way to justify their investments. Not that I'd rule out there being some crappy customer service chatbots being fielded. Goalposts (including mine surely) will get moved around to claim successes / no successes, but the balance is going to be well towards indifference to the tech as opposed to any real changes resulting from it.

olalonde 4y ago
Apologies for the tangent, it wasn't related to this thread. I've been in the habit of bookmarking predictions on HN to revisit them later, and your username caught my eye. I'm a bit surprised you maintain it, but the final verdict remains to be seen.
- version_five 4y ago
  What have you seen since the new year that makes you surprised I'd change an opinion I had then?
  - olalonde 4y ago
    OpenAI's GPT4. Bing's AI chat. Google's bard. Midjourney v5. GitHub copilot X. etc.
    - version_five 4y ago
      I was deeply involved in the last AI hype cycle. I'd want to see lasting applications giving clear benefits before I change my opinion. More foundation models don't change that, nor do the startup launches based around llms I've seen so far. It all has the same 2016 "deep learning" vibe, lots of apparent potential but no pathway to actual use cases.
      - Karrot_Kream 4y ago
        I don't necessarily agree with your prediction, but I agree that we're still in a demo-ish phase for this new round of AI and it's likely that this hype cycle might fizzle out. However I think there are a few key differences this hype cycle around: 1. The generality of this current crop of models is much higher than before which makes it really easy for non-experts to get started. 2016 deep learning still required lots of data cleaning, training, hyperparameter tuning, retraining, etc. This can lead to a lot more experimentation with lower barrier to entry. 2. The cost of using these models has become cheap enough where a lot of tasks that 2016 AI would have worked well on but were too expensive (due to labor required to build the models or compute required to train the model) are now within reach for a lot of businesses. 3. The internet of 2023 is a lot more varied than the internet of 2016. Using ML models to generate content wouldn't have made sense in 2016 where traditional media still captured a lot of people's interests but now with the rise of influencers and medium-sized fandoms, there's large markets to generate content associated with medium-sized brands.

olalonde 4y ago

Apologies for the tangent, it wasn't related to this thread. I've been in the habit of bookmarking predictions on HN to revisit them later, and your username caught my eye. I'm a bit surprised you maintain it, but the final verdict remains to be seen.

version_five 4y ago
What have you seen since the new year that makes you surprised I'd change an opinion I had then?
- olalonde 4y ago
  OpenAI's GPT4. Bing's AI chat. Google's bard. Midjourney v5. GitHub copilot X. etc.
  - version_five 4y ago
    I was deeply involved in the last AI hype cycle. I'd want to see lasting applications giving clear benefits before I change my opinion. More foundation models don't change that, nor do the startup launches based around llms I've seen so far. It all has the same 2016 "deep learning" vibe, lots of apparent potential but no pathway to actual use cases.
    - Karrot_Kream 4y ago
      I don't necessarily agree with your prediction, but I agree that we're still in a demo-ish phase for this new round of AI and it's likely that this hype cycle might fizzle out. However I think there are a few key differences this hype cycle around: 1. The generality of this current crop of models is much higher than before which makes it really easy for non-experts to get started. 2016 deep learning still required lots of data cleaning, training, hyperparameter tuning, retraining, etc. This can lead to a lot more experimentation with lower barrier to entry. 2. The cost of using these models has become cheap enough where a lot of tasks that 2016 AI would have worked well on but were too expensive (due to labor required to build the models or compute required to train the model) are now within reach for a lot of businesses. 3. The internet of 2023 is a lot more varied than the internet of 2016. Using ML models to generate content wouldn't have made sense in 2016 where traditional media still captured a lot of people's interests but now with the rise of influencers and medium-sized fandoms, there's large markets to generate content associated with medium-sized brands.