3 ms·

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

by 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.
- Hyption 4y ago
  There is a YouTube video from someone talking to chatgpt to play SQL server. It can reproduce the right output of 3 insert statements with a select *. It can interpretate an SQL procedure too. Whatever the architecture is behind this, is slightly more than just text prediction. I don't think 'text prediction ' does this type of state management justice. But hey let's see. Perhaps this is just an emerging feature from a tremendously huge text/language model.

by alfor 4y ago

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.
Hyption 4y ago
There is a YouTube video from someone talking to chatgpt to play SQL server. It can reproduce the right output of 3 insert statements with a select *. It can interpretate an SQL procedure too. Whatever the architecture is behind this, is slightly more than just text prediction. I don't think 'text prediction ' does this type of state management justice. But hey let's see. Perhaps this is just an emerging feature from a tremendously huge text/language model.