A three-page paper that shook philosophy, with lessons for software engineers

We do know when some things are true, and when observing the world we can talk about truth under certain assumptions. As in “it’s True the sun didn’t explode yesterday, assuming we aren’t all living in a simulated reality run by aliens in a different solar system billions of years from now the exact day after the sun exploded, or similar possible scenarios”

And while everyone loves to run in circles around the argument “but how can you know with certainty” the fact is that I am as certain that this assumption holds as I am that it provides no value at all to continually question if reality is really real, you’d have to take that as an assumption to ever have any kind of value adding discussion.

The people who insist we can’t know if anything is definately true must agree that they can’t know if that assertion is definitely true, so they sort of kill their own argument axiomatically.

What you're saying is more or less exactly what the paper was getting at.

It's hard to say based on a short Internet comment, but it sounds like the spot where your disagreement comes from is that you're understanding the word "justified" in a slightly different way from how epistemologists were using it. For example, one of the responses to Gettier's paper was to suggest that maybe the definition of "justified" should be altered to include a provision that invalidating the justification would imply that the belief is false.

So, for example, under that modified definition, the visual evidence couldn't serve as a justification of the belief that there is a cow in the field, because it allows the possibility that it isn't a cow but there still is one in the field. On the other hand, it would work for justifying a belief like, "I can see a cow from here." (Yeah, there's another cow in the field, but it's not the one you think you see.) But, still, that wasn't quite the definition that the mid-century epistemologists who made up Gettier's audience were using.

(ETA: Also, the original paper didn't involve cattle at all. Wikipedia has what looks like a good summary: https://en.wikipedia.org/wiki/Gettier_problem#Gettier's_two_...)

As in, when u write "I can very easily think of examples in medicine, police work (e.g. regarding suspects), accounting, and so on..." but you don't give any, the natural tendency is not to believe you.

No, it doesn't. It relies on explicit rules (old school) or statistical inference (new school).

There's a difference between "breaking" unknown input - i.e. non-computable within the system as it stands - and "working" unknown input, which is within expected working parameters.

The latter is business as usual for computing systems.

The former should have a handler that tries to minimise the costs of making a mistake - either by ignoring the input, or failing safe, or with some other controlled response.

It may not do this perfectly, but not attempting to do at all it is a serious design failure.

I agree with your main points, but I think it's worth pointing out that even things like 2 + 2 = 4 and X + Y cows in fields A and B, all rest on certain definitions and sets of deductions, many of which go back down to basic axioms which have to be assumed (e.g. for arithmetic, see Peano axioms as an example: https://en.wikipedia.org/wiki/Peano_axioms).

- We have a definition of what a cow is, and we know that cows are discrete/physical objects, and have relatively fixed locations (i.e. that they are not like an electron cloud with a probabilistic location).

- We assume that fields A and B in your hypothetical have clear, non-overlapping boundaries.

- We assume that we are working in a fairly normal universe with a fairly standard model of physics, and that due to the way time works in this universe, a cow cannot simultaneously be located in both fields A and B.

- ...

- (this could get really pedantic and go on forever)

The point is, even the things "we can know with certainty", are only as certain as the framework of observations/deductions/axioms/etc. that they rest upon. Almost nothing is certain on its own, without any further layers of reasoning behind it.