Every now and then, I'll actually sort of believe an article like this. Then I go and test the current models on things like semantic search.

For instance -

The Hough transform detects patterns with certain structure in images, e.g. circles or lines.

So I'm looking for academic research papers which apply the Hough transform to audio spectra, to recognize the harmonic structure of tonal audio and thus determine the fundamental pitch. (i.e. the Hough space would be a 1D space over fundamental frequency).

I've spent probably 90 minutes, over the several times I've read an optimistic post like this, asking various LLMs (mostly GPT-4o, though my early tests predate GPT-4o, and I've also tried Gemini and Claude), prompts along the lines of

> The Hough transform detects patterns with certain structure in images, e.g. circles or lines. > I'm looking for academic research papers (please link them or provide a DOI.org link at least) which apply the Hough transform to audio spectra, to identify the harmonic structure of audio and thus determine the fundamental pitch. > Make sure to provide only papers that actually exist. If you can't find anything particularly relevant, say so as a disclaimer & just provide the most relevant papers you can.

This is a reliable "fake paper generator", unfortunately - it'll just make up plausible garbage like

> Here are some academic papers related to applying the Hough transform to audio spectra for identifying harmonic structures and fundamental pitch:

> "An Audio Pitch Detection Algorithm Based on the Hough Transform" > Authors: Mark W. and John D. > Published In: IEEE Transactions on Audio, Speech, and Language Processing > DOI: 10.1109/TASL.2008.2000773 > Abstract: This paper proposes an audio pitch detection algorithm that utilizes the Hough transform to analyze the harmonic structure of audio spectra and determine the fundamental pitch.

This paper does not exist. Complete waste of my time. And again, this behavior persists over the >1 year period I've been trying this query.

And it's not just search-like tasks. I've tried asking for code and gotten stuff that's outright dangerous (try asking for code to do safe overflow-checked addition on int64_t in C- you have about an 80% chance of getting code that triggers UB in one way or another). I've asked for floating-point calling conventions on RISC-V for 32-bit vs 64-bit (would have been faster than going through the extension docs), and been told that RV64 has 64 floating-point registers (hey, it's got a 64 in the name!). I've asked if Satya Nadella ever had COVID-19 and been told- after GPT-4o "searched the web"- that he got it in March of 2023.

As far as I can tell, LLMs might conceivably be useful when all of the following conditions are true:

1. You don't really need the output to be good or correct, and 2. You don't have confidentiality concerns (sending data off to a cloud service), and, 3. You don't, yourself, want to learn anything or get hands-on - you want it done for you, and 4. You don't need the output to be in "your voice" (this is mostly for prose writing, for code this doesn't really matter); you're okay with the "LLM dialect" (it's crucial to delve!), and 5. The concerns about environmental impact and the ethics of the training set aren't a blocker for you.

For me, pretty much everything I do professionally fails condition number 1 and 2, and anything I do for fun fails number 3. And so, despite a fair bit of effort on my part trying to make these tools work for me, they just haven't found a place in my toolset- before I even get to 4 or 5. Local LLMs, if you're able to get a beefy enough GPU to run them at usable speed, solve 2 but make 1 even worse...

If this means alexa isn't a pet rock that can only reliably set timers, good for them.

Better aesthetics and even 18% reduction in file sizes refuses to move the sleepy elephant (h265 is likely still fringe stage). Even a trivial codec licensing fee of $2.50 for Pi users was not very successful for many media formats (i.e. 10% of retail price kills most of the market). However, h264 support was important enough to wrap into the pi4 retail price, and even at 11m/pcs a month there is still no stock available.

https://www.youtube.com/watch?v=ygU2bCx2Z3g

In general, the legacy hardware codec deployments are more important than what some ambitious software vendors think is "better". The primary inertia of media publishing markets, is content that will deliver properly on all platforms with legacy compatibility.

Initially, a new software codec will grind the cpu and battery-life like its on a 20 year old phone. Then often becomes pipelined into premium GPUs for fringe users, and finally mainstreamed by mobile publishers to save quality/bandwidth when the market is viable (i.e. above 80% of users).

If anyone thinks they can shortcut this process, or repeat a lock-down of the market with 1990s licensing models... than it will end badly for the project. There are decades of media content and free codecs keeping the distribution standards firmly anchored in compatibility mode. These popular choices become entrenched as old Patents expire on "good-enough" popular formats.

Best of luck, =)

We used them for real time array signal processing and beam-forming. They worked great.

Crazy idea:

Pressure plates in the streets which are pressed when cars drive over them - pushing fluids through your coils, but connected to multiple units on either side.

Harvest the kinetic energy of cars passing through the streets to apply pressure to pumps that feed fluids through the system, capturing that energy in a dynamo way?

Put these plates in every high trafficked area. Piping the pumping action from parking garages to freeway exits and shipping ports which roll off weight from water to street and pump a f-ton of fluid based on vehicle traffick and weight.

Make smaller installations... make an adapter interface to railway. heavy as cars on trains constantly hitting the pump valves. (yes we still need to deal with the bureau assholes in that industry... Im talking engineering)