121–130 of 132 posts
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Today's perfections are yesterday's "good enoughs". Don't be sad for the trend. In 10 years, you will have new perfections to enjoy.
Like the probabilistic bank account balance. "You have between $10 and $1000 with the greatest likelihood being $537 (52% chance)."
This trend seems to be everwhere
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99% of the time it works all of the time. Like humans. How do we deal with problematic humans? Retraining or replacement.
No, we deal with 99% accuracy in humans by designing all our human-based algorithms to be resilient against mistake (and still mistakes in the end result happen very frequently). This is completely different from the way we have designed our computer systems, because it's way easier to do this with flexible agents like humans than with inflexible agents like computers.
That’s... equivocation.
Both humans and ML algorithms are flexible. That is the point of “learning”.
Adaptation.
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99% of the time it works all of the time. Like humans. How do we deal with problematic humans? Retraining or replacement.
But the problems where we apply human labour are vastly different from the ones where we apply machine labour. In (most) tasks where we apply human labour a few errors are tolerated.
Neither humans nor ML make zero errors.
Ceteris paribus, if an ML algorithm makes fewer errors at a task which with low error tolerance - you would use the algorithm instead of the human, no?
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99% of the time it works all of the time. Like humans. How do we deal with problematic humans? Retraining or replacement.
That is not practical when you expect the system to be correct 100% of the time and make decisions based on that. There are many situations where this is critical. You would never want your car's safety system to be correct only 99% of the time.
Perfect is the enemy of good enough.
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> I am not sure I will ever trust an ML algorithm trained on inputs/outputs only It’s doing TDD!
Imagine a world where all programmers do is write the tests and the generalized learning algos write all the implementations.
Its just that algorithms like the Prolog’s WAM or the reet used in rules are replaced with neural nets and the like.
Rules use the same When Then format acting on a Given fact base. Prologs Horn Clauses can easily be translated into that form.
BDD writes all tests in the format Given When Then.
It’s already here.
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But the problems where we apply human labour are vastly different from the ones where we apply machine labour. In (most) tasks where we apply human labour a few errors are tolerated.
This seems irrelevant. Neither humans nor ML make zero errors. Ceteris paribus, if an ML algorithm makes fewer errors at a task which with low error tolerance - you would use the algorithm instead of the human, no?
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That is not practical when you expect the system to be correct 100% of the time and make decisions based on that. There are many situations where this is critical. You would never want your car's safety system to be correct only 99% of the time.
If you expect 100% correctness you are not a very practical man. Perfect is the enemy of good enough.
I suppose it depends on your end goals.
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> I am not sure I will ever trust an ML algorithm trained on inputs/outputs only It’s doing TDD!
Imagine a world where all programmers do is write the tests and the generalized learning algos write all the implementations.
It's really hard to write correct code. Sorting was broken in java and nobody noticed for a long time: http://envisage-project.eu/wp-content/uploads/2015/02/sortin... The same was true for java's binary search: https://ai.googleblog.com/2006/06/extra-extra-read-all-about... So I am not sure I will ever trust an ML algorithm trained on inputs/outputs only (which is what I think "neural program induction" means). The a…
> The same was true for java's binary search I used to ask "implement binary search" as an interview question and gave up on using it because not a single candidate could do it correctly in 45 minutes. This was at a FAANG.