It’s not mandatory. You have to explicitly enable participation. But most people probably do, as you yourself can’t use the location tracking, unless you opt in yourself.
The AirPods location as shown on the map is not the actual location, it is the location of the phone that last reported “hearing” the signal from the AirPods.
When you are close to them and connected to them, the map will show your own location not the AirPods.
This explains why the AirPods tracked the authors own location at times. Possibly also why the map showed the boys location. He was using Bluetooth so his phone was actively listening, and actively connected to the internet and due to this, the boys iPhone was quick to post it’s location when it heard the authors AirPods. Where as other people in the museum was not using their iPhones so their phones would not be actively listening or if they where would not upload the position in real-time.
True, LLMs definetly has something that is "thought-like".
But todays networks lacks the recursion(feedback where the output can go directly to the input) that is needed for the type of internalized thoughts that humans have. I guess this is one thing you are pointing at by mentioning the continuousnes of the internals of LLMs.
The text output of a llm is the thought process. In this context the main difference between humans and llms, is that llms can’t have internalized thoughts. There are of course other differences to, like the fact that humans have a wider gamut of input: visuals, sound, input from other bodily functions. And the fact that we have live training.
The stronger your immune system is the higher are your chances to survival. Until your immune system is so strong it attacks it’s own host.
Evolutionary pressures will make sure that organisms are very close to the edge between to weak and to strong.
One naive way of interpreting this is that 50% of the population will have a immune response that is to strong (above the edge) and 50% to weak (under the edge). But it is naive to say that, as this is a topic that has much more complexities than the one dimensional model that I have described here.
At what percentage the threshold of the statistics for what we call autoimmune disease would be located would be interesting to know.
If you search for patterns in your "input data" you will find them. If you search for the patterns enough, your "mind" will automate the process. Then you have those "patterns" in your "normal" perception.
An example: if focus on the noice floor of you hearing for a few days you will begin to perceive tinnitus.
My hypothesis would be that the man from the article took the drug, became scared cause he perceived scan lines in is visual perception, then placed loots of attention on the scan lines to determine if they would go away. The attention he placed on the scan lines strengthened the perceptions and now he can’t undo the conditioning he did to his visual processing system.
An intressting thing with that, is that the side of the brain that controls speech might be completly accurate. It could have said showel cause that side of the brain thoght of its neighbors shovel. The only thing that it would be missing would be that it was influenced to think of that shovel event by input given to the other brain half.
On a societal level “work” does not generate money.
Money exists as is and is only generated as a consequence of inequality these days. Banks inject money to compensate for the rich who won’t or can’t spend their money.
Yeah I agree.
My take on it is boils down to the following: Truth cant be context free.
Truth, is only valid within the model that it is defined in. True means something like, one thing being in accordance with some other thing according to some model. Example: 1=1 is true according to the rules of math.
There is always a context/model that describes what it means for something to be true, even if you can't see it. If two people seam to be disagreeing about something being true they are just miscommunicating and they are evaluating the things they think they are talking about in different ways.
With this as a background the original header of the article
> Is it my fault if you can't handle the truth?...
Is nonsense, it is a weak way of blaming once own insufficient communication skills on the receiver.
All models have a validity-property, validity is a property that describes how well the model manages to model the thing that it is modeling.
The less validity a model has, the more "wrong" it is.
A description of the earth as flat, seamingly has a lower validity than a description as the earth as a perfect sphere. But ultimately it depends on how one measures validity.
The correction prompt is very important, it will definitely determine the outcome of the process, a bad correction prompt will obviously lead to a garbage result.
Training in steps with different prompts might be of value. First step might be to fix contradictions, then factual errors if that is an issue. This is an idea that I got when viewing the he output of LLaMA, it often contains contradictions (eg. an example I have seen is "Peter is a boy and he is part of the Gama sorority"). Asking it to fix those types of issues should be a first good step.
But I suspect that this type of training would need to be mixed with original training data. Otherwise the restructuring in the model caused by the new training would most likely garble the rest of the model.
Now, use this library to "bootstrapp the smarts of LLaMA from its own smartness" like this:
1. Ask it things. Let it answer.
2. Ask it to find errors in the answer it outputted and for it to correct the answer.
3. Use the original prompt and the corrected output as training data.
This should, with each iteration make the model less and less likely to output statements that are self contradictions or obviously wrong, until the model can no longer spot its own faults.