Stopped reading when the author proposed I do so, thank you for the notice.
ZankerH
Karma: 560
Modern datacenter GPUs are basically the optimal compromise between this and still retaining enough general capacity to work with different architectures, training procedures, etc. The benefits of locking in a specific model at the hardware level would be extremely marginal compared to the downsides.
My inferences, in descending order of confidence:
(source: it was revealed to me by a neural net)
84559, 79685, 87081, 99819, 37309, 44746, 88815, 58152, 55500, 50377, 69067, 53130.
ofcourse you have to define what deceptions means in it’s programming.
That’s categorically impossible with the class of models that are currently being worked on, as they have no inherent representation of “X is true”. Therefore, they never engage in deliberate deception.
>in order to mistreat 2, 3, or 4, you would have to first mistreat 1
What about deleting all evidence of 1 ever having happened, after it was recorded? 1 hasn’t been mistreated, but depending on your assumptions re:consciousness, 2, 3 and 4 may have.
That’s Security Through Obscurity. Also, even if we decided we’re suddenly ok with that, it obviously doesn’t scale well to superhuman agents.
Any insufficiently human-supremacist AI is an S-risk for humanity. Non-human entities are only valued inasmuch as individual humans value them concretely. No abstract preferences over them should be permitted.
We have no idea how to make a useful, agent-like general AI that wouldn’t want to disable its off switch or otherwise prevent people from using it.
Global crackdown on the tech industry?
)
>The aliens sent their message using a continuous transmission channel, like the frequency shift of a pulsar relative to its average or something like that. NASA measured this continuous value and stored the result as floating point data.
Then it makes no sense for them to publish it in binary without mentioning the encoding, or making it part of the puzzle to begin with.
Your result is virtually identical to the first-ranking unambiguously permutation-invariant method (MLP 256-128-100). HOG+SVM does even better, but it’s unclear to me whether that meets your criteria.
Could you be more precise about what kinds of algorithms you consider it fair to compare against, and why?
The issue with MNIST is that everything works on MNIST, even algorithms that utterly fail on a marginally more complicated task. It’s a solved problem, and the fact that this algorithm solves it tells you nothing about it.
If the code is too rigid or poorly performant to be tested on larger or different tasks, I suggest F-MNIST (fashion MNIST), which uses the exact same data format, has the same number of categories and amount of data points, but is known to be far more indicative of the true performance of modern machine learning approaches.
Square error has been used instead of absolute error in many diverse optimization problems in part because its derivative is proportional to the magnitude of the error, whereas the derivative of the absolute error is constant. When you’re trying to solve a smooth optimization problem with gradient methods, you generally benefit from loss functions with a smooth gradient than tends towards zero along with the error.
Sounds like you need to work on that time preference. Have you considered setting up an accountability system or self-blackmailing to make sure you’re not having too much fun?
This is why anti-semitism exists.
Yes, with the possible exception of moral patients with a reasonable likelihood of becoming moral agents in the future.
Meat tastes nice, and I don’t view animals as moral agents.
From what I was allowed to read, I think you’re deliberately obfuscating and misrepresenting the active and passive choices. If that was unintentional, you need to work on good faith argumentation.