"This isn't objective." Correct, and we are not pretending it is. We are not handing down a scientific verdict.
Actually, you are doing rational investigation in a fuzzy probabilistic new/emergent space, with open sharing to the world. I don’t understand why people downplay themselves and put on a pedestal others supposedly serious sciences.
If you write too many preconditions, postconditions, invariants etc. Then you cement your software and you will spend most of your time on the tests rather than on the actual useful software
I’m in the same boat, I don’t like Finder (better than Windows Explorer though), nor do I like default macOS files / folders dialogs, and I really dislike drag and drop behavior on macOS
I dont understand why we are stuck in stone’s age with filesystems GUI
I agree with that, and that’s why it’s better to ask an AI and improve your prompt, instead of hiring a human that will disappear and you will loose all institutional knowledge
I don’t think humans workers should be cogs in the machine, but from my experience unfortunately that’s how people want to be treated. One simple explanation is that there is no freedom / creativity without responsibility, and the latter is extremely expensive in brain resources
If AI is not beneficial, then those humans could be re-hired soon.
Big companies are always doing bad things, but not because they use AI, because they have legal protections which prevents small companies (which could be anyone like you and me) to compete. The same small companies who could also benefit from using AI.
Ask a SOTA LLM when Newton was born without any access to internet : the answer is Lossless for our shared culture understanding of this question. Not Near-lossless, lossless. Ask the same LLM when YOU were born, the answer is just wrong for almost anyone in the world, not lossy. Between the two there is a whole new field of Lossyness to study.
90% depends entirely on what the measure means here, do you understand what "Normalized Discounted Cumulative Gain at rank 10" means to the set of data that we are comparing ?
Sometimes coming up with new codecs (compressors decompressors) means coming up with new ways to interpret artifacts of the real world. And this is exactly why LLM are so powerful and they are like a giant Lossy (but Near-Lossless for various use cases) ZIP file / Database of the whole knowledge of the training data.
Nobody is trying to manipulate you here, humanity just has to find new explanations for complex topics.
Lossy-ness is binary
Lossless is binary in pure information theory. to quote my other comment :
Lossless is objective for information theory. To get from the real world to digital world you need an analog to digital converter, this process is by definition lossy. We are interested in the real world, and information is pure but never represents exactly reality.
Lossyness is baked into our problem statement here.
Using terms like near lossless means we think we are very close to reality for what we’re trying to do
Lossless is objective for information theory. To get from the real world to digital world you need an analog to digital converter, this process is by definition lossy. We are interested in the real world, and information is pure but never represents exactly reality.
Lossyness is baked into our problem statement here.
Using terms like near lossless means we think we are very close to reality for what we’re trying to do
There is, after you define what you’re ready to loose and understand the lossy space. That’s how we came up with mobile cellphones, audio and video codecs etc. Literally powering all modern devices we use.
192gb or 256gb of RAM would be enough ! We could run real time large MoE models, REAPed for our usage (e.g. english agentic coding), dynamic quant 2-4bits
Actually, you are doing rational investigation in a fuzzy probabilistic new/emergent space, with open sharing to the world. I don’t understand why people downplay themselves and put on a pedestal others supposedly serious sciences.