femto

HDMI has an Ethernet channel

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.

In the One Electron Theory the positron is just the One Electron moving backwards in time. No separate positron!

video: https://www.youtube.com/watch?v=Sui0zDX5mvQ

> more heavily tax voluntary vices

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-...

"interference-to-signal ratio", "capacity", ... These terms, and their context, are things a communications/electrical engineer would understand. I encourage comms engineers to start learning how AI works under the hood. Chances are, a lot of what you know about maximising channel capacity and throughput is applicable to AI (can be seen as a channel with memory?).

More to the point, Australia has laws against unfair contract terms for small businesses and consumers.

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

Bring it on Meta...

As used in LDPC decoders?

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?"

From memory the entire filter was updated in one symbol period (ie. global)

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?

Most professional engineers, especially those in electrical or communications, have the tools to understand AI.

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.

About 0.1% (Woodside)

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.

Deep Learning With Python, Third Edition by Francois Chollet and Matthew Watson

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).

> These things can get messy fast.

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 was in on the ground floor of WiFi, in that around 1995 I did a significant chuck of the work that proved that WiFi works, and my work at a university lead to the formation of a company that put WiFi into the market (Radiata). The IP situation was murky. A patent holder made about $1b and the company sold for $560m (in 2000). I gather the university complained and got a good chunk of "go away" money. I could have thrown my hat in the ring: maybe I would have got something, maybe I wouldn't have. Either way, I walked away, as I judged it wasn't worth the non-financial cost. 30 years layer I still think I made the right decision. I might have been living in a nicer house, but I wouldn't have the life I have today, which I am happy with.

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.

Published in 1909:

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

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

> ...wait for Australia to join first.

A possibility.

https://www.reuters.com/business/australia-on-same-page-cana...

Same technique as Australia's bush turkey.

https://en.wikipedia.org/wiki/Megapode#/media/File:Brushturk...

A more direct link than a crocodile, as birds came from dinosaurs.

Pictures in this article:

https://www.thehistoryblog.com/archives/76901

I'm designing and building a rack to house 5 ukuleles and a guitar.

Best to use a termination, to avoid the power that would have gone into the antenna from reflecting back into the transmitter output and damaging the output driver transistors. Damage is no bad thing if you do want to permanently disable the transmitter, but not so good if you want it to be reversible. Chances of damage due to reflections is probably low given the relatively low power being transmitted, but best not to take the risk if it can easily be avoided with a termination.

There's probably a reason for that.

> Current ITU regulations ignore unintended satellite hardware leakage, leaving astronomers without legal protection.

Oh, what a coincidence.

It should be 100% liability on the self-driving car. Only by doing that will there be enough incentive for the manufacturers to drive the accident rate down to zero (or in practice a low enough number that the cost to manufacturer of the remaining accidents is negligible). Less than 100% liability provides wiggle room, which car manufacturers will exploit to maximise profits over lives. With 100% liability manufacturers can still make a pile of money, but only if they have a low accident rate.

Because you perceive a readily available result to be worth less? Why do something whose value has gone down?

It's "their" fault, at least according to Andrew "Twiggy" Forrest, who is one of "them". His view is that if a specific group, of about 1000 powerful people, chose to address climate change, it would be fixed.

https://www.smh.com.au/environment/climate-change/inside-and...

It's geofenced.

https://blog.google/products-and-platforms/products/maps/uni...

In that regard, it's not too different from mobile telephony. Mobile phones drove the electronics industry 20 years ago, but there is limited demand for people who really know how to build a phone (ie. build the hardware and write all the signal processing from scratch), as there aren't that many companies that do phones at the lowest level. A few of the engineers got rich (eg. Viterbi), but most 'just' made a good living. Most people who got rich off phones didn't do it by knowing how phones work.

Incidentally, the skills for the lowest levels of LLMs aren't that far removed from those needed for mobile telephony, in that both are based on maths, computation and information theory.

1) Does applying a copyright message warrant that the content has not been generated by an AI?

2) If one applies a copyright message to AI generated output, is that fraudulent?