Muscosal immunity (secreted anitbodies) peak within three months after infection/ vaccination.

âoeAccording to research and medical reviews on âVaccines for the common cold, vaccine development is largely hindered by both rapid mutation and the biology of mucosal immunity, compounded by the sheer multitude of causative viruses. Furthermore, because the illness is generally mild and self-limiting, the immense public health and economic resources required to develop an effective multi-strain vaccine have historically lacked urgency.â

Interestingly, the benchmark questions are not available to the public for âoesafety and licensingâ reasons. I donâ(TM)t t see how benchmarking questions about answering science questions could be a public risk unless the questions are asking how to build atomic weapons or biological warfare agents, and the scoring matrix contains useful /secret information â¦

A severe reaction to a tick bite is probably not his powerful âvenomâ(TM), but rather an allergic reaction to the tick regurgitating part of his last meal, probably a mammal. This strong reaction to a tick bite is a sign that you are at risk of mammalian meat allergy (MMA) or alpha gal syndrome. This can lead to life threatening anaphylaxis. https://www.news.com.au/lifest...?

Yes, the idea put forward by the Supreme Court in Florida vs Riley is that it must be visible from the street with the naked eye or from a plane above 400 feet. However, the recent ruling in the Michigan Supreme Court pointed out that the US Supreme Court has ruled multiple times that the exclusionary rule applies only in criminal cases, not civil cases. https://law.justia.com/cases/m...

Yes, the idea put forward by the Supreme Court in Florida vs Riley is that it must be visible from the street with the naked eye or from a plane above 400 feet. However, the recent ruling in the Michigan Supreme Court pointed out that the US Supreme Court has ruled multiple times that the exclusionary rule applies only in criminal cases, not civil cases. https://law.justia.com/cases/m...

If you're using Office 365 Personal, one user reported a method to opt out:
"If you're using O365 Personal it looks like you need to 'cancel' your subscription - when you hit cancel you have the option to change to 'classic (without AI)' - at your next renewal you will lose all AI features for Office and save about $50"8.

Scroll down to the bottom of this link:
https://bgr.com/tech/microsoft...
or
the bottom of this link:
https://www.reddit.com/r/Offic...

The price increase is optional, but hidden by a dark pattern:
https://www.consumer.org.nz/ar...

Yes, good points. I had not thought this through yet. I will get a Raspberry Pi AI camera when they are generally available.

One weak point of the IMX500: it only has 8 Mb of memory, limiting what models you can run.
https://developer.aitrios.sony...

Sipeed had modules like this two or three years ago. They have moved on to make more useful cameras like the MaixII and MaixCam. The MaixCam has 256 Mb ram and costs ~$70 with screen and camera, and ~$40 with only camera. The 500 mA (max) power is not too bad, similar to running the IMX on a Pi Zero. The Maix modules can run PyTorch and Tensorflow models, have a model zoo, and an IDE for running and testing models on the board.

If you are prototyping as a professional enginneer, then Aitrios and the Sony sensor may be useful.
https://developer.aitrios.sony...

If you are just a hobbyist running modes from the zoo, the Maix camera with the builtin screen for troubleshooting (or the ability to monitor the image from the IDE when troubleshooting) may be more useful.

Or the Jetson Nano, at 40 TOPS:
https://developer.nvidia.com/e...

The SONY IMX500 is about 1 TOPS. The existing RPi kit with Hailo-8L NPU found in the Raspberry Pi AI Kit is 13 TOPS.
https://www.raspberrypi.com/pr...
Price? $70 versus $120 plus RPI5 cost?
Power consumption?
This is a competitor to the Sipeed MaixCam: https://wiki.sipeed.com/hardwa...
Sipeeed have been making this stuff for a while... nice little modules, but the English documentation is a little bit thin sometimes....

I think we need to conceptually seperate "synthetic data" from the "model collapse" mentioned above.

Synthetic data does not add more information: it helps solve the problem of overfitting the model to the training data, so that it performs poorly on a separate test data set.

With image classification, it is clear from research that "data augmentation" by zooming, adding noise, streching, rotating, shearing and changing the color space can increase the effective size of your annotated image dataset and thus reduce overfitting and help generalize the data:
https://innotescus.medium.com/...

Another technique is to take your 100 cat imges and paste them randomly over say 1000 random images, genereating tens of thousands of different images.

Synthetic data has several advantages besides reducing overfitting: even up class distributions, reducing bias, and potentially, improving privacy.
https://syntheticus.ai/blog/th...

The big problem is when you use synthetic data generated by a generative model to train the same generative model (rather than a classification model).
https://www.techtarget.com/wha...

You end up with a death spiral of "model collapse" that cannot be recovered from.

Generative AI on a phone (Youtube): Stable diffussion creates an image of a cat in 15 seconds
https://www.zdnet.com/article/...
https://youtu.be/B-uojHRr7HE

Phones with NPU or TPU (Tensor processing units):
https://www.zdnet.com/article/...
https://www.samnews24.com/2022...

Depends on what you want to do: Image classification or generative language... In any case, a ten-fold improvement is economically useful.

For example: with specialized processor you get about a ten fold reduction in the amount of power needed for image classification at the edge:

With the K210, a specialized processor optimized to process convolutional neural networks, you can do image classification with about 300 milliamps. Roughly comparable results on a Raspberry Pi take about 3 amps.
https://wiki.sipeed.com/soft/m...
https://wiki.sipeed.com/hardwa...
https://wiki.sipeed.com/hardwa...

I got my Maix Nanos for about $15 each, compared to roughly $100 for a RPi4, so big cost advantage, no heat sink required, longer battery life, etc.

The disadvantage is that your model is limited to 2mb (or maybe 4 mb if you use C instead of Micropython). Your image stream is limited to 320 by 240. If you need to classify HD streams, you need the next step up, the Maix II.

Remember: training on the GPU with big memory is expensive and slow, but running the classification model can be quite lightweight and quick.

Google earns its money in ways that don't depend on using the newspapers snippets on news.google.com

1) Ads on searches
2) Ads on other company's web sites, throught Adsense's targeted advertising service

This (plus the loss of classified revenue to realestate.com.au and carsguide.com.au) cuts into the pool of advertising money that would otherwise go to newspapers.

Most copyright protection focuses on 'cease and desist'. Why is it that the Australian law blocked Google from delisting the newspapers? Because Googles fair use of snippets drove increased traffic to the newspaper websites and the newspapers did not want to loose the revenue from traffic via Google. One of the key questions in a copyright lawsuit is does the use decrease the market value of (or compete with) the copied material. In this case, obviously, not.

The newspapers are going broke because of loss of revenue to internet advertising in general, and the specific loss of classified revenue to carsguide.com.au and realestate.com.au, not the loss of income they could derived from selling the snippets they give away for free on their web front pages.