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I mildly disagree with the author, but would be happy arguing his side also on some of his points: Last September I used ChatGPT, Gemini, and Claude in combina

I mildly disagree with the author, but would be happy arguing his side also on some of his points: Last September I used ChatGPT, Gemini, and Claude in combination to write a complex piece of code from scratch. It took four hours and I had to be very actively involved. A week ago o3 solved it on its own, at least the Python version ran as-is, but the Common Lisp version needed some tweaking (maybe 5 minutes of my time). This is exponential improvement and it is not so much the base LLMs getting better, rather it is: familiarity with me (chat history) and much better tool use. I may be be incorrect, but I think improvements in very long user event and interaction context, increasingly intelligent tool use, perhaps some form of RL to develop per-user policies for improving incorrect tool use, and increasingly good base LLMs will get us to a place that in the domain of digital knowledge work where we will have personal agents that are AGI for a huge range of use cases.

13years 2y ago
> where we will have personal agents that are AGI for a huge range of use cases We are already there for internet social media bots. I think the issue here is being able to discern the correct use cases. What is your error tolerance? For social media bots, it really doesn't matter so much. However, mission critical business automation is another story. We need to better understand the nature of these tools. The most difficult problem is that there is no clear line for the point of failure. You don't know when you have drifted outside of the training set competency. The tool can't tell you what it is not good at. It can't tell you what it does not know. This limits its applicability for hands-off automation tasks. If you have a task that must always succeed, there must be human review for whatever is assigned to the LLM.
xandrius 2y ago
Depends on what AGI means to you. If writing a program that you find complicated counts then it is. I do think that writing code is a specific type of task that a statistical machine can do well without actually bringing us closer to AGI.

13years 2y ago

> where we will have personal agents that are AGI for a huge range of use cases We are already there for internet social media bots. I think the issue here is being able to discern the correct use cases. What is your error tolerance? For social media bots, it really doesn't matter so much. However, mission critical business automation is another story. We need to better understand the nature of these tools. The most difficult problem is that there is no clear line for the point of failure. You don't know when you have drifted outside of the training set competency. The tool can't tell you what it is not good at. It can't tell you what it does not know. This limits its applicability for hands-off automation tasks. If you have a task that must always succeed, there must be human review for whatever is assigned to the LLM.

xandrius 2y ago

Depends on what AGI means to you. If writing a program that you find complicated counts then it is. I do think that writing code is a specific type of task that a statistical machine can do well without actually bringing us closer to AGI.