Today’s most advanced AI models have many flaws, but decades from now, they will be recognized as the first true examples of artificial general intelligence.
(Wow. That’s really a bad article. And even though the author managed to ramble on for quite some pages, they somehow completely failed to address the interesting and well discussed arguments.)
[Edit: I disagree -strongly- with the article]
We’ve discussed this in June 2022 after the Google engineer Blake Lemoine claimed his company’s artificial intelligence chatbot LaMDA was a self-aware person. We’ve discussed both intelligence and conciousness.
And my -personal- impression is: If you use ChatGPT for the first time, it blows you away. It’s been a ‘living in the future’ moment for me. And I see how you’d write an excited article about it.
But once you used it for a few days, you’ll see every 6th grade teacher can distinguish if homework assignments were done by a sentient being or an LLM. And ChatGPT isn’t really useful for too many tasks. Drafting things, coming up with creative ideas or giving something the final touch, yes. But defenitely limited and not something ‘general’. I’d say it does some of my tasks so badly, it’s going to be years before we can talk about ‘general’ intelligence.
While large language models (LLMs) have demonstrated impressive performance on a range of decision-making tasks, they rely on simple acting processes and fall short of broad deployment as autonomous agents. We introduce LATS (Language Agent Tree Search), a general framework that synergizes the capabilities of LLMs in planning, acting, and reasoning. Drawing inspiration from Monte Carlo tree search in model-based reinforcement learning, LATS employs LLMs as agents, value functions, and optimizers, repurposing their latent strengths for enhanced decision-making. What is crucial in this method is the use of an environment for external feedback, which offers a more deliberate and adaptive problem-solving mechanism that moves beyond the limitations of existing techniques. Our experimental evaluation across diverse domains, such as programming, HotPotQA, and WebShop, illustrates the applicability of LATS for both reasoning and acting. In particular, LATS achieves 94.4% for programming on HumanEval with GPT-4 and an average score of 75.9 for web browsing on WebShop with GPT-3.5, demonstrating the effectiveness and generality of our method.
Graphs:
I think we can’t really get the most out of current LLMs because of how much they cost to run. Once we can get speeds up and costs down, they’ll be able to do more impressive things.
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(Wow. That’s really a bad article. And even though the author managed to ramble on for quite some pages, they somehow completely failed to address the interesting and well discussed arguments.)
[Edit: I disagree -strongly- with the article]
We’ve discussed this in June 2022 after the Google engineer Blake Lemoine claimed his company’s artificial intelligence chatbot LaMDA was a self-aware person. We’ve discussed both intelligence and conciousness.
And my -personal- impression is: If you use ChatGPT for the first time, it blows you away. It’s been a ‘living in the future’ moment for me. And I see how you’d write an excited article about it. But once you used it for a few days, you’ll see every 6th grade teacher can distinguish if homework assignments were done by a sentient being or an LLM. And ChatGPT isn’t really useful for too many tasks. Drafting things, coming up with creative ideas or giving something the final touch, yes. But defenitely limited and not something ‘general’. I’d say it does some of my tasks so badly, it’s going to be years before we can talk about ‘general’ intelligence.
deleted by creator
Have you seen this paper?
Abstract:
Graphs:
I think we can’t really get the most out of current LLMs because of how much they cost to run. Once we can get speeds up and costs down, they’ll be able to do more impressive things.
https://www.youtube.com/watch?v=Zlgkzjndpak
https://www.youtube.com/watch?v=NfGcWGaO1E4
My standard for agi is that its able to do a low-level human work from home job.
If it needs me to pre-chew and check every single step then it can still be a smart tool but its definitely not intelligent.
“You can use them for all kinds of tasks” - so would you say they’re generally intelligent? As in they aren’t an expert system?