Maybe that is part of the nostalgia for the 8-bit days: the practice of "slow programming"? It was relaxing, sort of like those "slow tv" shows.

I did a tour of Adelaide airport with Engineers Australia in the 1990s. We visited their obsolete IBM main frame, still running ATC in their computer room. They were tied to it by the certification of the software it was running. Just as you described.

https://openlibrary.org/works/OL6322148W/High_Tech?edition=k...

https://www.ebay.com.au/itm/334312859146

https://en.wikipedia.org/wiki/HDMI#HEC

Any chance the TV can phone home via that? One would have to make sure the HDMI cable physically doesn't have the Ethernet pairs, or set the trusted box to filter the traffic.

Comments URL: https://news.ycombinator.com/item?id=50218126

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Australia is doing that with tobacco. It worked in that it has drastically cut smoking rates and deaths, but hasn't worked it has also made the market more lucrative for smugglers/organised crime. Illegal tobacco avoids the tax, so it can be sold at a lower price whilst still raking in the profits. Today 80% of tobacco in Australia is illegal, with a consequent 80% hit to tax revenue.

https://www.abc.net.au/news/2026-08-29/expensive-cigarettes-...

Comments URL: https://news.ycombinator.com/item?id=50206515

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https://www.accc.gov.au/business/selling-products-and-servic...

Bring it on Meta...

Comments URL: https://news.ycombinator.com/item?id=50099493

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My gut feeling is that the AI field is eventually going to discover how much overlap it has with information and communications theory.

Question to any AI researchers out there: Can a layer in a trained AI be treated as a form of "matched filter"? Is that a concept that is used in the understanding AI: to view it as a cascade of matched filters? The next question is "what is it matching?"

The symbol rate was 1Msymbol/s and there were 66 taps. The circuit (in an FPGA) ran at a clock rate of 66MHz. In each symbol period the error was calculated based on the output symbol, then an iteration was done over all 66 coefficients, using the error to update them.

Edit: I also remember that to make most efficient use of the FPGA I figured out the minimum precision required in the maths whilst preserving convergence. It was a 4-bit mantissa plus an exponent (3-bit or 4-bit exponent, I can't remember). It's interesting that today's AIs seem to have also figured out that things still work if 4-bit floating point is used, though that is 4-bits including the exponent?

I recently started trying to understand how AI works. It turns out that I wrote an "AI" in the late 1990s without realising it. The "AI" was an MMSE (minimum mean square error) radio receiver for CDMA.

This receiver consisted of an FIR filter (mathematically a linear combination) with trainable coefficients (weights). The coefficients started with random values. A sequence of known training symbols (tokens) was fed in and a gradient search was used to adjust the coefficients to achieve MMSE. The unknown data to be received (classified) followed the training sequence, at which point the training was turned off and the system ran as an FIR filter. Once trained, the FIR was a matched filter.

In AI terminology the above is now called a "layer". Current AI cascades multiple layers, in which case you need to pass the output of each FIR/layer though a non-linear function. If you don't have the non-linear functions at the end of each layer the multi-layer system is mathematically equivalent to a linear combination, which could be implemented with a single layer, losing degrees of freedom.

I write this because many electrical/communications engineers probably know how AI works, but don't realise it.

My still incomplete understanding is that LLMs are "just" a graph of connected layers. More complex that the above MMSE receiver, but trained and operated in the same way. The smarts in AI are:

1) Arranging the layers and the connections between them.

2) Having access to the right training data

3) Having the computing power to perform the gradient search (aka training)

My point is that most professional engineers already have the knowledge to understand AI/LLMs, and potentially already do know how they work but don't realise it due to a difference in terminology.

https://www.unisuper.com.au/investments/our-investment-optio...

It's diversified, but the biggest holdings tend to be in Australian Banks, BHP and the "big" US tech stocks.

Recently read books by Anthony Doerr (Shell Collector, About Grace, Memory Wall, All the Light We Cannot See) and Simon Singh (Code Book, Fermat's Last Theorem, Trick or Treatment, The Simpsons and Their Mathematical Secrets).

Yep. I was on a fixed term contract, which had expired, even though I was still working on the project well after its expiration. It would have been interesting to argue that any IP assignment expired with the contract. As you said: messy. As I judged: not worth it.

I don't know your situation in life, but if your experience is anything like mine I reckon you've probably made the right decision.

https://www.gutenberg.org/cache/epub/72890/pg72890-images.ht...

https://en.wikipedia.org/wiki/The_Machine_Stops