re: Creator of node.js says the era of human computer coding is OVER

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I don’t see that as an apt comparison. I’ve yet to see anyone so much as attempt to explain how we go from LLM to AGI. As far as I can tell, one doesn’t evolve into the other.

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Yeah its not a good comparison.

Bayesian statistics and artificial intelligence research has been around for awhile with several intractable problems. LLMs have a long way to go. But by their own nature will grow but still controlled by inputs from humans. And review by humans.

John Henry Vs the Steam Drill.

Paul Bunyan vs the chain saw


AI has the relentless engine but humans still have the heart
The A.I Polka

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AI will help cover more ground and open up avenues for skilled labor that’s not yet fully understood.

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This is what I think is most likely to happen; it's what happened with the rise of the personal computer and the internet.

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there is a certain economics to adoption of AI that hopefully will course correct these fears

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When I see the reported earnings for the AI companies, I wonder how they are going to make money long term. If OpenAI is resorting to ads, that doesn't seem like a good sign. Supposedly Anthropic is projected to turn a profit this year.

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Or tech workers are screwed in 10 years or less and we’ll end up “working in the fields” instead.

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Whether it's tech, politics, the economy, or the weather, fear and doomcasting sell and get clicks.

If tech goes away, I'll go get a real job.

Learn to coal, bitches!

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I don’t see that as an apt comparison. I’ve yet to see anyone so much as attempt to explain how we go from LLM to AGI. As far as I can tell, one doesn’t evolve into the other.

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Part of the problem is that people can’t agree on the definition of AGI in the first place.

That being said.. if we can create models that match or surpass human performance across an array of individual tasks, we should be able to combine those models via the mixture-of-experts approach, with a core LLM likely acting as the human-machine interface.

I don’t see LLM’s as “evolving” into AGI so much as the glue that makes AGI functionally possible. They’ll have to be combined with other types of models (for example speech recognition, computer vision, data analysis, etc.) to extend the capabilities. We are already seeing steps in that direction with the public models available today.

Ultimately I think the technical barriers are:
1. Identifying cognitive tasks/functions that are not adequately addressed by current AI models, and developing new models to attack them.
2. Creating the core model that handles goal setting, planning, and execution. Right now it looks (to me) like this is something LLM’s might be able to accomplish, but this could end up being something novel.
3. Connecting everything together and giving it the freedom to step out “into the real world.”

I don’t think any of these are insurmountable with the trajectory we are on right now. I think the real (practical) question is scalability. Building AGI is one thing. Scaling it is another, and I wouldn’t be surprised if we hit a major bottleneck in compute power before it’s all said and done.