[hwillis]

The sensor (the four gas sensors on the board) was created by a third person.

The artificial nose is the TinyML model which trains on the sensor data (CO, NO2, ethanol, VOCs) to detect arbitrary scents by their signatures in those four categories.

The fungal pneumonia detector wires up a whole API with azure etc. and trains the model specifically to recognize pneumonia, based on an actual science experiment which grew and measured the fungus in artificial lungs.

As far as I'm concerned, both Caleb and Benjamin had brilliant ideas, executed them fantastically, and created something that may be truly useful. A $40 sensor that can detect disease just by breathing on it is more of a tangible contribution to humanity than many software engineers make in their life and almost certainly more than 99% of us did before the age of 14.

[wesleywt]

It is great that the kid is involved and interested in these technologies. Whether it works is another issue. You are going to need metrics such a LODs, LOQs, sensitivity and specificity to determine if this beats the gold standard tests.

[amelius]

The reason why no engineer has made this yet, is because the medical data was not available to them.

The innovation is in the data, not in the ML.

[hwillis]

You are wrong. The kid GREW FUNGUS IN ARTIFICIAL LUNGS using a sterile field made out of a plastic bin with dish gloves cut into it. He didn't need medical data. Anyone could have done this- anyone with the intelligence and creativity that this kid has.

[dekhn]

is it possible you're overstating what the kid did a bit here? The history of sterile fields shows that even great scientists take decades to debug contamination that causes false positives and negatives. ASn experiment like this can be easily thrown off by any number of variables that weren't carefully controlled for.

[amelius]

You need medical data if you want to validate your results for a real medical disorder.

Fabrication of data is not very useful if you have to gather data for validation anyway.

[pmarreck]

> The innovation is in the data, not in the ML.

can't you kind of say this about all ML? That the main driver in ML is 99% the quality of the training data and 1% the specific details of the neural networks used?

[amelius]

For most real-world applications of ML, yes. Of course, what happens in ML-research is different (where e.g. new networks for new modalities are invented).

But back to the topic, I bet the kid didn't even invent their own neural network topology, but just pulled a predefined network from a library, perhaps without even knowing it. Which is ok, because that is how most people use ML.