I also disagree about whether there are major obstacles left before achieving AGI. There are important test datasets on which computers do poorly compared to humans.
2022-Feb 2023 should update our AGI timeline expectations in three ways:
There is no longer any doubt as to the commercial viability of AI startups after image generation models (Dall-E 2, Stable Diffusion, Midjourney) and ChatGPT. They have captured people’s imagination and caused AGI to become a topic that the general public thinks about as a possibility, not just sci-fi. They were released at the same time as crypto crashed, freeing VC money to chase the next hot thing. Unlike with crypto, tech people are on the same page. OpenAI got a $10B investment and the recent YC batch is very AI-oriented. This accelerates the AGI timelines.
The same commercial viability might cause big labs like DeepMind to stop openly publishing their research (as I expected would happen). If this happens, it will slow down the AGI timelines.
ChatGPT/Bing Chat were aggressively misaligned. While people disagree with me that the best thing for AI alignment would be to connect the current not-so-bright language models to network services and let them do malicious script kiddie things in order to show real-life instances of harm and draw regulators’ attention before smarter models are available, even the chat model should show them that there are serious issues involved. NYT readers are freaked out. With lesser significance, we got Stable Diffusion reproducing images from its training set because they did a poor job of de-duplication, bringing on the ire of visual artists who will also push for AI-related legislation. This adds uncertainty to the AGI timelines in the US and could slow down things. But Chinese regulators would also have to get involved for a major effect.
The same commercial viability might cause big labs like DeepMind to stop openly publishing their research (as I expected would happen). If this happens, it will slow down the AGI timelines.
I also disagree about whether there are major obstacles left before achieving AGI. There are important test datasets on which computers do poorly compared to humans.
2022-Feb 2023 should update our AGI timeline expectations in three ways:
There is no longer any doubt as to the commercial viability of AI startups after image generation models (Dall-E 2, Stable Diffusion, Midjourney) and ChatGPT. They have captured people’s imagination and caused AGI to become a topic that the general public thinks about as a possibility, not just sci-fi. They were released at the same time as crypto crashed, freeing VC money to chase the next hot thing. Unlike with crypto, tech people are on the same page. OpenAI got a $10B investment and the recent YC batch is very AI-oriented. This accelerates the AGI timelines.
The same commercial viability might cause big labs like DeepMind to stop openly publishing their research (as I expected would happen). If this happens, it will slow down the AGI timelines.
ChatGPT/Bing Chat were aggressively misaligned. While people disagree with me that the best thing for AI alignment would be to connect the current not-so-bright language models to network services and let them do malicious script kiddie things in order to show real-life instances of harm and draw regulators’ attention before smarter models are available, even the chat model should show them that there are serious issues involved. NYT readers are freaked out. With lesser significance, we got Stable Diffusion reproducing images from its training set because they did a poor job of de-duplication, bringing on the ire of visual artists who will also push for AI-related legislation. This adds uncertainty to the AGI timelines in the US and could slow down things. But Chinese regulators would also have to get involved for a major effect.
Looks like this indeed happened: https://www.businessinsider.com/google-publishing-less-confidential-ai-research-to-compete-with-openai-2023-4 .