4 ms·

I came to the same conclusion a while back. LLMs are very useful when user expertise level is medium to high, and task complexity is low to medium. Why ? becaus

by fsndz 2y ago

I came to the same conclusion a while back. LLMs are very useful when user expertise level is medium to high, and task complexity is low to medium. Why ? because it those scenarios, the user can use the LLM as a tool for brainstorming on drawing the first sketch before improving it. Human in the loop is the key and will stay key for the forceable future no matter what the autonomous AI agent gurus are saying. https://www.lycee.ai/blog/mistral-ai-strategy-openai https://www.lycee.ai/blog/mistral-ai-strategy-openai

euroderf 2y ago
So it's not so much an "artificial intelligence" as it is an "intelligence amplifier", with the usual amplifier feedback loop.
- fsndz 2y ago
  exactly or one can say that all artificial intelligences are and will be human intelligence amplifiers
  - teleforce 2y ago
    Yes, amplification is really apt and suitable analogy comparison. Just treat the hallucinations as the non-linear distortion and harmonics phenomena that come from amplification process. You can just filter the unwanted signals and noises judiciously if you're well informed. Taking this analogy further you need to have an appropriate and proper impedance matching to maximize the accuracy, and impedance matching source or load-pull (close-loop or open-loop) and for LLM it can be in the form of RAG for example.
    - shanusmagnus 2y ago
      I love a good analogy. Can you expand on this one?
- emporas 2y ago
  Habitual Artificial Intelligence contrasts nicely with Artifial General Intelligence. It parses data and forms habits based on that data. When you want to discover something new, you have to break out of a habit and think. It forms some habits better than others also. When i saw how Alphazero played chess back in 2017, different than other engines, that's what i described it usually, as a habit forming machine.
ianbicking 2y ago
"when user expertise level is medium to high, and task complexity is low to medium" – this reminds me of Python itself. Python isn't the best at anything, it's slow, sometimes vague in its formalisms, etc. But it keeps being super popular because most work is low to medium complexity. In everyone's work, from a simple webdev to an AI researcher, there are moments of complexity in the work but most of the work is getting through the relatively simple stuff. Or maybe in general we can say that to do something really hard and complex you must and should put a lot of effort into getting all the not-hard not-complex pieces in place, making yourself comfortable with them so they don't distract, and setting the stage for that hard part. And when you look back you'll find it odd how the hard part wasn't where you spent most of the time, and yet that's how we actually do hard stuff. Like we have to spend time knolling our code to be ready for the creative part.

by fsndz 2y ago

euroderf 2y ago

So it's not so much an "artificial intelligence" as it is an "intelligence amplifier", with the usual amplifier feedback loop.

fsndz 2y ago
exactly or one can say that all artificial intelligences are and will be human intelligence amplifiers
- teleforce 2y ago
  Yes, amplification is really apt and suitable analogy comparison. Just treat the hallucinations as the non-linear distortion and harmonics phenomena that come from amplification process. You can just filter the unwanted signals and noises judiciously if you're well informed. Taking this analogy further you need to have an appropriate and proper impedance matching to maximize the accuracy, and impedance matching source or load-pull (close-loop or open-loop) and for LLM it can be in the form of RAG for example.
  - shanusmagnus 2y ago
    I love a good analogy. Can you expand on this one?
emporas 2y ago
Habitual Artificial Intelligence contrasts nicely with Artifial General Intelligence. It parses data and forms habits based on that data. When you want to discover something new, you have to break out of a habit and think. It forms some habits better than others also. When i saw how Alphazero played chess back in 2017, different than other engines, that's what i described it usually, as a habit forming machine.

ianbicking 2y ago

"when user expertise level is medium to high, and task complexity is low to medium" – this reminds me of Python itself. Python isn't the best at anything, it's slow, sometimes vague in its formalisms, etc. But it keeps being super popular because most work is low to medium complexity. In everyone's work, from a simple webdev to an AI researcher, there are moments of complexity in the work but most of the work is getting through the relatively simple stuff. Or maybe in general we can say that to do something really hard and complex you must and should put a lot of effort into getting all the not-hard not-complex pieces in place, making yourself comfortable with them so they don't distract, and setting the stage for that hard part. And when you look back you'll find it odd how the hard part wasn't where you spent most of the time, and yet that's how we actually do hard stuff. Like we have to spend time knolling our code to be ready for the creative part.