On Thu, Nov 9, 2023 at 1:16 PM Matt Mahoney <mattmahone...@gmail.com> wrote:
> ... > Of course GPT4 is just doing what a human who doesn't know the answer > would do. That's how you pass the Turing test. > Would a person who didn't know the answer but figured it out, as most-assuredly any cryptanalyst would, <https://crypto.stackexchange.com/questions/34682/turings-still-classified-inference-engine-algorithm> therefore fail the Turing Test? > > On Thu, Nov 9, 2023 at 12:51 PM Mike Archbold <jazzbo...@gmail.com> wrote: > > > > I asked it to create a compress function, so it just zip'd it: > > > > import zlib > > > > s = > '0000000001000100001100100001010011000111010000100101010010110110001101011100111110000100011001010011101001010110110101111100011001110101101111100111011111011111' > > > > def compress(s): > > return zlib.compress(s.encode('utf-8')) > > > > print(compress(s).decode('utf-8')) > > > > On Wed, Nov 8, 2023 at 6:26 AM James Bowery <jabow...@gmail.com> wrote: > >> > >> The following from GPT4 Turbo is what you get from an industry that > has, like Shane Legg, lost its Ockham's Razor alignment (Algorithmic > Information Criterion) with truth to the hysteria over feel-good > "alignment" while snatching the low hanging fruits: > >> > >> USER > >> What is the shortest python program you can come up with that outputs > this string? > 0000000001000100001100100001010011000111010000100101010010110110001101011100111110000100011001010011101001010110110101111100011001110101101111100111011111011111 > >> ASSISTANT > >> To output the given string in Python, the shortest program would be to > just print it directly: > print("0000000001000100001100100001010011000111010000100101010010110110001101011100111110000100011001010011101001010110110101111100011001110101101111100111011111011111") > >> This program doesn’t perform any operations other than printing the > provided string. > >> USER > >> can you compress that string? > >> ASSISTANT > >> To compress the string and then output it in Python, we could use > different approaches. One approach is to use built-in libraries like zlib > to compress the data. However, it’s worth noting that compression works > well on larger datasets and might not be efficient for short repetitive > strings such as this one. > >> Here’s an example of how you might compress and then decompress a > string using zlib: > >> import zlib > >> > >> # Your original string > >> data = > "0000000001000100001100100001010011000111010000100101010010110110001101011100111110000100011001010011101001010110110101111100011001110101101111100111011111011111" > >> # Compress the data > >> compressed_data = zlib.compress(data.encode()) > >> > >> # Decompress the data > >> decompressed_data = zlib.decompress(compressed_data).decode() > >> > >> # Print the decompressed data > >> print(decompressed_data) > >> > >> However, with such a specific sequence and length, you are unlikely to > achieve actual compression because the overhead of the compression > algorithm might exceed any savings, especially since the string pattern is > not highly repetitive and is quite short. In this case, I expect the > compressed_data to be larger in size than the original data. > >> For this specific string, it’s challenging to compress it meaningfully > without a custom encoding scheme that takes advantage of patterns that > could be represented in fewer bits. > >> If the characters have a specific meaning or pattern, like binary > representation of numbers that could be converted into a shorter sequence, > then you could devise an encoding that would represent the data more > compactly. Nevertheless, for the given sequence without context, the best > ‘compression’ would be simply transmitting it as is. > >> USER > >> are there any patterns in that string? > >> ASSISTANT > >> To determine whether there are any compressible patterns in the > provided string, we would typically look for repeating sequences, > predictable sequences (like arithmetic sequences), or some sort of > structure that could be simplified. At a glance, the string appears to be a > random sequence of 0s and 1s without any immediately obvious patterns: > 0000000001000100001100100001010011000111010000100101010010110110001101011100111110000100011001010011101001010110110101111100011001110101101111100111011111011111 > >> A manual inspection doesn’t reveal any simple repeats or > sequences—there are no long runs of 0s or 1s that could easily be > compressed. There isn’t a clear arithmetic progression or other > mathematical patterns that can be discerned without further context. > >> Without additional context on how this string was generated, or what it > represents, devising a pattern-based compression algorithm is not feasible. > If the string encodes binary data that represents a more complex structure > (e.g., an encoded image, a sequence of commands, etc.), understanding that > structure would be key to compressing the string. > >> > >> As it stands, the string appears to be binary data with no discernible > simple patterns suitable for straightforward compression. If this is a > coding challenge or puzzle, additional information or constraints would > likely be provided to guide the discovery of an appropriate compression > mechanism. > >> > >> In summary, without knowing the origin or the meaning of the data, we > can’t identify patterns or provide a more tailored method for compression. > Standard compression algorithms like zlib, gzip, or others would likely not > be effective on a string of this length and apparent randomness. > >> USER > >> examine the string for patterns. do so carefully. > >> ASSISTANT > >> Upon carefully examining the string: > >> > >> > 0000000001000100001100100001010011000111010000100101010010110110001101011100111110000100011001010011101001010110110101111100011001110101101111100111011111011111 > >> We can still attempt another visual and analytical inspection for > patterns. Let’s look at the string in smaller segments to see if any > patterns emerge: > >> > >> 0000000001 00010000 11001000 01010011 00011101 00001001 01010010 > 11011000 11010110 01111100 00100011 00101001 11010010 10110110 10111110 > 00110011 10101101 11110011 10111110 11111 > >> After breaking down the string into segments, some potential > observations might be: > >> > >> The string starts with a sequence of nine 0s followed by a 1. This > initial part can be considered a pattern. > >> After this initial pattern, the rest of the string doesn’t show any > immediate, easily discernible repetition, nor does it have uniformly spaced > recurring substrings. > >> > >> In terms of character distribution, the segments seem to alternate in > density between 0s and 1s, but not in a clear pattern. > >> The end of the string has a pattern, “11111,” though it’s a small > portion. > >> Without additional structure to the data segmentation, the remaining > portions of the string don’t exhibit any evident simple patterns that would > allow straightforward, rule-based compression. > >> > >> Data compression often involves looking for repetition and redundancy, > which do not appear obvious in this string. A pattern that is not > immediately visible to a human might still be detected algorithmically, but > standard text compression tools are unlikely to be effective on such a > short and seemingly random binary sequence. > >> > >> Given the lack of visible patterns, if you are certain there’s a > pattern embedded, the string might be encoding something that is not > intended to be human-readable, such as a binary representation of some > other data structure or a hashed value. > >> Without further context or insight into how this string was generated, > it’s challenging to provide more specifics on potential patterns or how to > compress it effectively. If you have additional information or if this > string represents specific data encoded in binary, providing that context > might reveal ways to compress it. > > > > Artificial General Intelligence List / AGI / see discussions + > participants + delivery options Permalink > > -- > -- Matt Mahoney, mattmahone...@gmail.com ------------------------------------------ Artificial General Intelligence List: AGI Permalink: https://agi.topicbox.com/groups/agi/T8581faf50bfa0ead-Mb3325c84df20a9c06a7cb591 Delivery options: https://agi.topicbox.com/groups/agi/subscription
