4 ms·

It's correct often enough already to be useful. And it doesn't need to be more correct to disrupt industries already it just needs to be more correct than huma

It's correct often enough already to be useful. And it doesn't need to be more correct to disrupt industries already it just needs to be more correct than humans. The most crazy thing is that it's already so good and coherent that it makes totally sense to train a ml model like chatgpt instead of training humans! Because it scales. We never had this. And.i already mentor a few junior people I don't mind training an ai instead.

SamoyedFurFluff 4y ago
I struggle to believe we can train a LLM the same was as a junior eng, mostly in that I’ve tried to keep a LLM as a journal of sorts and it forgets things way more than a junior does.
- Hyption 4y ago
  We are not limited to LLM. And just an hour ago I asked chatgpt how to do something specific with docusaurus (Facebook static page generator) and it just told me the answer. You know how often I explain things in my current position? How slow some developers are? How often they forget things I told and explained them? How often they still get things wrong? It's a slow process and doesn't scale very well at all. If the table turns and we all teach one system instead ooohh boy. It will make experts faster and better and potentially removes a certain amount of people in every industry faster than we can imagine. You know the people who are adding some value but not that really but it's still better to have them than not having them? Cloud probably got rid of plenty of basic sysadmins. These new systems break through tasks were no one had an idea how we will break through.
pwinnski 4y ago
This is the same dangerous fallacy of thinking that causes "self-driving" cars to kill people. If it is "correct enough" to lull people into a false sense of security, the inevitable failures will be worse than if it people remained on guard. The particular way in which these LLMs fabricate information makes them incorrect enough to be dangerous, not "correct enough to be useful." People keep mistaking text completion for intelligence and understanding.
- alfor 4y ago
  Humans are not perfect either at driving, programming or writing. At some point it is a question of rate of error. The ML model will keep improving as the net get bigger and the dataset larger. But I agree they are good at having a very good surface understanding while having little of dept at the moment. Ex: a human making a mistake of visual interpretation while driving will examine in his mind the error he made and thing of the consequences in a more dangerous situation. The NN will not bother (at the moment)
  - pwinnski 4y ago
    Sigh. The models do not "have a very good surface understanding," they are capable of competing strings of words in a way that we interpret as a surface understanding. I believe time will show this is a category error, not just an error of degree. In much the same way that Teslas are still literally killing people when they encounter vehicles parked across roadways in 2023, just like they did back in 2016, the difference between the appearance of understanding and actual understanding is a much wider chasm than it first appears. The LLM will not "consider" anything. Even transcripts we've all seen in which LLMs seem to acknowledge errors (rather than doubling down and inventing false sources) are still just text-prediction, modeling what it would be like if someone acknowledged an error. When a particular LLM seems to take the corrections offered in a widely-publicized transcript on board and not repeat them in future transcripts, that's because someone acted to modify the model, adding filters or weights to ensure that the text prediction path goes differently in the future.
    - alfor 4y ago
      Text prediction is intelligence. To be able to code a program form a short description, combine different elements to make a solution, understanding is required. The understanding is in the weights and pattern detection capabilities. Sure, some part are missing, like the capacity we have to hold a few thought in our mind, correct ourselves, remember the mistakes we made, etc. The same is true of NN that can differentiate cats and dogs, they detect patterns is a similar ways that humans do. In the openAI api you can see the level of confidence the NN has on each words it output, it’s just not exposed on the chat interface. I think the larger missings parts are by design, to give the system memories, agency, access to the internet, to programming would be careless at this point, but it’s happening anyway. I feel it missing our capacity to examine our thought, to replay events in our head to reprogram itself when the results are not optimal.

SamoyedFurFluff 4y ago

I struggle to believe we can train a LLM the same was as a junior eng, mostly in that I’ve tried to keep a LLM as a journal of sorts and it forgets things way more than a junior does.

Hyption 4y ago
We are not limited to LLM. And just an hour ago I asked chatgpt how to do something specific with docusaurus (Facebook static page generator) and it just told me the answer. You know how often I explain things in my current position? How slow some developers are? How often they forget things I told and explained them? How often they still get things wrong? It's a slow process and doesn't scale very well at all. If the table turns and we all teach one system instead ooohh boy. It will make experts faster and better and potentially removes a certain amount of people in every industry faster than we can imagine. You know the people who are adding some value but not that really but it's still better to have them than not having them? Cloud probably got rid of plenty of basic sysadmins. These new systems break through tasks were no one had an idea how we will break through.

pwinnski 4y ago

This is the same dangerous fallacy of thinking that causes "self-driving" cars to kill people. If it is "correct enough" to lull people into a false sense of security, the inevitable failures will be worse than if it people remained on guard. The particular way in which these LLMs fabricate information makes them incorrect enough to be dangerous, not "correct enough to be useful." People keep mistaking text completion for intelligence and understanding.

alfor 4y ago
Humans are not perfect either at driving, programming or writing. At some point it is a question of rate of error. The ML model will keep improving as the net get bigger and the dataset larger. But I agree they are good at having a very good surface understanding while having little of dept at the moment. Ex: a human making a mistake of visual interpretation while driving will examine in his mind the error he made and thing of the consequences in a more dangerous situation. The NN will not bother (at the moment)
- pwinnski 4y ago
  Sigh. The models do not "have a very good surface understanding," they are capable of competing strings of words in a way that we interpret as a surface understanding. I believe time will show this is a category error, not just an error of degree. In much the same way that Teslas are still literally killing people when they encounter vehicles parked across roadways in 2023, just like they did back in 2016, the difference between the appearance of understanding and actual understanding is a much wider chasm than it first appears. The LLM will not "consider" anything. Even transcripts we've all seen in which LLMs seem to acknowledge errors (rather than doubling down and inventing false sources) are still just text-prediction, modeling what it would be like if someone acknowledged an error. When a particular LLM seems to take the corrections offered in a widely-publicized transcript on board and not repeat them in future transcripts, that's because someone acted to modify the model, adding filters or weights to ensure that the text prediction path goes differently in the future.
  - alfor 4y ago
    Text prediction is intelligence. To be able to code a program form a short description, combine different elements to make a solution, understanding is required. The understanding is in the weights and pattern detection capabilities. Sure, some part are missing, like the capacity we have to hold a few thought in our mind, correct ourselves, remember the mistakes we made, etc. The same is true of NN that can differentiate cats and dogs, they detect patterns is a similar ways that humans do. In the openAI api you can see the level of confidence the NN has on each words it output, it’s just not exposed on the chat interface. I think the larger missings parts are by design, to give the system memories, agency, access to the internet, to programming would be careless at this point, but it’s happening anyway. I feel it missing our capacity to examine our thought, to replay events in our head to reprogram itself when the results are not optimal.