New research explains you’ll get more right- or left-wing answers, depending on which AI model you ask.
Chiyoko Yoshida's insight:
From Cris Ippolite LinkedIn "According to new research from the University of Washington, Carnegie Mellon University, and Xi’an Jiaotong University. Researchers conducted tests on 14 large language models and found that OpenAI’s ChatGPT and GPT-4 were the most left-wing libertarian, while Meta’s LLaMA was the most right-wing authoritarian."
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Regular Expression to limit all letters less than 100 characters Ask Question Asked 11 years, 1 month ago Modified 5 years, 2 months ago Viewed 24k times 8 I need the regular expression that would limit the number of characters to 100 and allow the use of 0-9, !
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Recent advances in deep learning have relied heavily on the use of large Transformers due to their ability to learn at scale. However, the core building block of Transformers, the attention operator, exhibits quadratic cost in sequence length, limiting the amount of context accessible. Existing subquadratic methods based on low-rank and sparse approximations need to be combined with dense attention layers to match Transformers, indicating a gap in capability. In this work, we propose Hyena, a subquadratic drop-in replacement for attention constructed by interleaving implicitly parametrized long convolutions and data-controlled gating. In recall and reasoning tasks on sequences of thousands to hundreds of thousands of tokens, Hyena improves accuracy by more than 50 points over operators relying on state-spaces and other implicit and explicit methods, matching attention-based models. We set a new state-of-the-art for dense-attention-free architectures on language modeling in standard datasets (WikiText103 and The Pile), reaching Transformer quality with a 20% reduction in training compute required at sequence length 2K. Hyena operators are twice as fast as highly optimized attention at sequence length 8K, and 100x faster at sequence length 64K.
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Just as mistakes and the unexpected are part of life, bugs are part of software development. In general, the longer the time between when a bug was first introduced and when the bug is identified and fixed the more expensive it is in both time and money. It might go something like this: If you spot a bug as you're writing a new feature everything is fresh in your mind and it can sometimes take just a moment to fix. If a bug turns up later or perhaps soon after it's deployed you might have an idea of where it might be and track it down fairly quickly. If a lot of time has passed since a feature was worked on and a bug is spotted or tackled then it might take a fair bit of time to figure out how everything works again before you can fix it. And if a really long time has passed then, aside from the cost of interrupting what you are otherwise working on, it may not even be clear what was intended by the original code, probably written by others, and there's a fair chance more has been built on top of the buggy code making it more complex and a bigger task to tackle. The only way to solve it may be stepping through and figuring out behaviour slowly and steadily line-by-line. You could probably replace 'bugs' with 'code' 'problems' or 'mistakes' in most scenarios. Aside from it matching my experience, Joel Spolsky gives a nice explanation in his classic article The Joel Test.
Converting HEIF/HEIF image formats to PNG/GIF/JPEG in the browser - GitHub - alexcorvi/heic2any: Converting HEIF/HEIF image formats to PNG/GIF/JPEG in the browser
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