4 ms·

To be fair I think OpenAI is pretty big on not building in specific solutions to problems but rather improving their models' problem solving abilities in genera

To be fair I think OpenAI is pretty big on not building in specific solutions to problems but rather improving their models' problem solving abilities in general.

GaggiX 2y ago
Yeah, a solution to the counting letters problem would probably be encoding the letters into the large embeddings by probably adding a really small loss and a small classification model on the embeddings so that it would learn to encode the letters into the embeddings (but they probably don't care that much, they would rather scale the model).
zepolen 2y ago
If it's solving general problems wrongly, then what use is it? Actually It describes my experience with AI in general, Answers with subtle Infuriating bugs.
- kevin_thibedeau 2y ago
  Cut out the middleman and write your own infuriating bugs.
TZubiri 2y ago
1. This is a blind spot, you can't solve it by adding more compute 2. The solution proposed includes adding training data. It needs no permission from openai, just needs to be posted online for scrapers to see
- zamadatix 2y ago
  What makes you say #1 when the "Strawberry" model which uses more runtime compute tends to solve the general case much more often, just not 100% of the time, instead of just the specific type of question?
  - TZubiri 2y ago
    The model does not solve this through adding inference compute time or improving its quality.
    - zamadatix 2y ago
      Could you expand on how you think the new model increased accuracy in this class of problem in a way that's unrelated to its significantly higher token and compute usage?
      - TZubiri 2y ago
        It writes and executes python code.
        zamadatix 2y ago
        When making such claims it's generally good form to give reproducible examples of why you think it must be so. Otherwise it leaves everyone else stabbing in the dark at trying to follow your thought process before being able to weigh if the version they land on is convincing to them. E.g. the same for a counterclaim: o1-preview doesn't seem to have the ability to execute python code (similar to it's other lackings, such as not being able to call whisper/dall-e integrations). Example script: import gzip import base64 from io import BytesIO encoded_string = b'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' compressed_string = base64.b64decode(encoded_string) with gzip.GzipFile(fileobj=BytesIO(compressed_string)) as gzip_file: original_string = gzip_file.read().decode('utf-8') # Assuming the original string is in UTF-8 encoding print(original_string) This outputs ascii art of a TI-86 calculator. Given the prompts: > What does the ascii art output of this script represent: ${script} and > Execute ${script} and guess what the output represents. and > Execute ${script} o1-preview produces complete gibberish from guessing random patterns in numbers, giving sample output of random ASCII art lines, and saying things in its train of thought like "I copied and pasted the code into a Python interpreter. It generated ASCII art, resembling an elephant with features like ears, trunks, and texture patterns". Despite it saying it ran these in an interpreter it seems to be simulating that despite knowing it's the ideal step as plain ol' 4o just outputs: > The output of the Python code is an ASCII art representation of a Texas Instruments TI-86 calculator. Here is the ASCII art: ${exact_ti-86_ascii_art} . Apart from that hole in the claim, simply expanding the thought process of prompts poking at this: > Explain how to find the number of occurrences of a letter in a word in general then use what you find to count the number of 'c's in 'occurrences'. Don't use programming or environment execution, particularly not python, use a natural process a person could understand in natural language. Takes only 6 seconds, has a thought train which does not include anything about code, follows the exact method it created step by step in text, and correctly outputs there are 3 'c's in occurrences. . Apart from the second hole in the claim, using additional inference tokens to come up with a chain of thought which would involve coming up with code to use, deploying it, and using the result would indeed still be an example of the new model using inference compute time to improving its quality rather than adding specific questions to the training.

To be fair I think OpenAI is pretty big on not building in specific solutions to problems but rather improving their models' problem solving abilities in general.

GaggiX 2y ago

Yeah, a solution to the counting letters problem would probably be encoding the letters into the large embeddings by probably adding a really small loss and a small classification model on the embeddings so that it would learn to encode the letters into the embeddings (but they probably don't care that much, they would rather scale the model).

zepolen 2y ago

If it's solving general problems wrongly, then what use is it? Actually It describes my experience with AI in general, Answers with subtle Infuriating bugs.

kevin_thibedeau 2y ago
Cut out the middleman and write your own infuriating bugs.

TZubiri 2y ago

1. This is a blind spot, you can't solve it by adding more compute 2. The solution proposed includes adding training data. It needs no permission from openai, just needs to be posted online for scrapers to see

zamadatix 2y ago
What makes you say #1 when the "Strawberry" model which uses more runtime compute tends to solve the general case much more often, just not 100% of the time, instead of just the specific type of question?
- TZubiri 2y ago
  The model does not solve this through adding inference compute time or improving its quality.
  - zamadatix 2y ago
    Could you expand on how you think the new model increased accuracy in this class of problem in a way that's unrelated to its significantly higher token and compute usage?
    - TZubiri 2y ago
      It writes and executes python code.
      - zamadatix 2y ago
        When making such claims it's generally good form to give reproducible examples of why you think it must be so. Otherwise it leaves everyone else stabbing in the dark at trying to follow your thought process before being able to weigh if the version they land on is convincing to them. E.g. the same for a counterclaim: o1-preview doesn't seem to have the ability to execute python code (similar to it's other lackings, such as not being able to call whisper/dall-e integrations). Example script: import gzip import base64 from io import BytesIO encoded_string = b'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' compressed_string = base64.b64decode(encoded_string) with gzip.GzipFile(fileobj=BytesIO(compressed_string)) as gzip_file: original_string = gzip_file.read().decode('utf-8') # Assuming the original string is in UTF-8 encoding print(original_string) This outputs ascii art of a TI-86 calculator. Given the prompts: > What does the ascii art output of this script represent: ${script} and > Execute ${script} and guess what the output represents. and > Execute ${script} o1-preview produces complete gibberish from guessing random patterns in numbers, giving sample output of random ASCII art lines, and saying things in its train of thought like "I copied and pasted the code into a Python interpreter. It generated ASCII art, resembling an elephant with features like ears, trunks, and texture patterns". Despite it saying it ran these in an interpreter it seems to be simulating that despite knowing it's the ideal step as plain ol' 4o just outputs: > The output of the Python code is an ASCII art representation of a Texas Instruments TI-86 calculator. Here is the ASCII art: ${exact_ti-86_ascii_art} . Apart from that hole in the claim, simply expanding the thought process of prompts poking at this: > Explain how to find the number of occurrences of a letter in a word in general then use what you find to count the number of 'c's in 'occurrences'. Don't use programming or environment execution, particularly not python, use a natural process a person could understand in natural language. Takes only 6 seconds, has a thought train which does not include anything about code, follows the exact method it created step by step in text, and correctly outputs there are 3 'c's in occurrences. . Apart from the second hole in the claim, using additional inference tokens to come up with a chain of thought which would involve coming up with code to use, deploying it, and using the result would indeed still be an example of the new model using inference compute time to improving its quality rather than adding specific questions to the training.