wow, is there nothing it cannot do?
2 times 3 can sometimes equal 7 with Android's Neural Network API
81–90 of 92 posts
Re: 2 times 3 can sometimes equal 7 with Android's Neural Network API
#82Earlier quoted context omitted.
It's a test, you shouldn't have to sugar-coat explanations to an instructor (unless you're acting out some other scenario as part of the test).
In a test you have to prove your knowledge by transmitting symbols through language. If you don't "sugar coat" it, how do you expect that the right symbols will be interpreted by the receiver? It is part of the test to use the appropriate language to ensure the best understanding of what you are saying. Nonviolent communication tries to do exactly that and is essential to this end. You can argue that since the profes…
And the contrary view is that the professor has a pronounced responsibility to see past unfortunate framing and phrasing of intricate subject matter details. Both are worthwhile goals, I think.
Re: 2 times 3 can sometimes equal 7 with Android's Neural Network API
#83Earlier quoted context omitted.
In a test you have to prove your knowledge by transmitting symbols through language. If you don't "sugar coat" it, how do you expect that the right symbols will be interpreted by the receiver? It is part of the test to use the appropriate language to ensure the best understanding of what you are saying. Nonviolent communication tries to do exactly that and is essential to this end. You can argue that since the profes…
And the contrary view is that the professor has a pronounced responsibility to see past unfortunate framing and phrasing of intricate subject matter details. Both are worthwhile goals, I think.
In my university there's a professor that has an awful attitude towards students when taking exam, outright making them feel stupid (without saying anything, just from gestures and body language)... The plot twist is he does this on purpose. Many times he'll just give a passing grade, but the experience of taking an exam with him is terrible.
Re: 2 times 3 can sometimes equal 7 with Android's Neural Network API
#84Using a neural network for things that have clear cut rules is wrong. When you know the exact rules, implement them as such, instead of bruteforcing a guesstimation. This is also why I'm sceptical of the usr of GPT-3 for all sorts of purposes where accuracy is important. Think of the code generation case. Bugs may be very subtle and may go unnoticed.
This is not like we are asking GPT what is 2 times 3. It is not even a bug. Just that the author is using quantized multiplication which takes subset of floating point space and just makes all the value closest to nearest point. So 2 can be approximated as say 2.5 if there are few points. And that is not deterministic. Also it is a known thing that something like that could occur. It is just that neural network still seem to learn.
Re: 2 times 3 can sometimes equal 7 with Android's Neural Network API
#85Earlier quoted context omitted.
For code, I could see it being super useful for a beefed up auto-complete. There are many times I find myself searching for things like "how do I do X in Y language" to copy a snippet that I'm sure has been written 10000x times before. I can review the code and verify its correctness by writing tests.
Either OpenAI or Microsoft demoed something similar to this some time in theist 12-18 months. The fact that it’s not released (and also the fact that GPT-3 etc are still not publicly available) makes me suspect that these models are far too unstable for actual production use. It’s also why I’m getting a bit tired of these overhyped cherry-picked samples with seemingly nothing solid to ever back it up.
> I’m getting a bit tired of these overhyped cherry-picked samples with seemingly nothing solid to ever back it up.
Most of the times a "fantastic GPT-3 result" is shown, you have to dig a bit and then you'll find out how it was primed[0] and how many different texts they had it generate. Then the one(s) carrying out the experiment go on and pick the most shocking writings. If you read all of the outputs (there are a few articles around that show you 5 or 6 different outputs) you'll see the variations that it took duing those. I understand that 5 or 6 is actually small, to get shocking results they usually go into de dozens of tries.
[0] usually the priming phrases are given, but depending on how much of a snake-oil-salesman the person writing/giving a talk is, they may even hide this part
Re: 2 times 3 can sometimes equal 7 with Android's Neural Network API
#86Earlier quoted context omitted.
Either OpenAI or Microsoft demoed something similar to this some time in theist 12-18 months. The fact that it’s not released (and also the fact that GPT-3 etc are still not publicly available) makes me suspect that these models are far too unstable for actual production use. It’s also why I’m getting a bit tired of these overhyped cherry-picked samples with seemingly nothing solid to ever back it up.
They are too expensive to run - hundreds of GB of GPU memory - so they can't be deployed for the public at large yet, kind of like the SGI workstations from 20 years ago. You can do that and more for cheap today, but not then. I think we can get models about 1/100th the size for general use. That's also the main reason Google is developing TPUs.
> They are too expensive to run - hundreds of GB of GPU memory - so they can't be deployed for the public at large yet, kind of like the SGI workstations from 20 years ago. You can do that and more for cheap today, but not then.
I don't buy this. OpenAI literally released pricing for GPT-3, so either they grossly miscalculated their cost base (unlikely) or there's some scaling/instability/resourcing issue preventing them from doing so (much more likely).
I think it's telling that they spent the last 6 months on yet another flashy demo (DALL-E) rather than actually productionizing GPT-3. It just feels like constant smoke and mirrors.
Re: 2 times 3 can sometimes equal 7 with Android's Neural Network API
#87Earlier quoted context omitted.
In a test you have to prove your knowledge by transmitting symbols through language. If you don't "sugar coat" it, how do you expect that the right symbols will be interpreted by the receiver? It is part of the test to use the appropriate language to ensure the best understanding of what you are saying. Nonviolent communication tries to do exactly that and is essential to this end. You can argue that since the profes…
And the contrary view is that the professor has a pronounced responsibility to see past unfortunate framing and phrasing of intricate subject matter details. Both are worthwhile goals, I think.
Don't get me wrong, I hate how university handles exposition and evaluation of subjects. It encourages exploitation of memorization and test solving techniques instead of focusing on the quality of the subject.
However, life is very rarely optimal or fair. The only effect we can productively have on the world is through how we approach it, instead of focusing energy on how our actions are interpreted. It is unfortunate, but that is the most logical outcome. Focus on yourself, not on others.
Re: 2 times 3 can sometimes equal 7 with Android's Neural Network API
#88As someone who builds neural networks routinely, this sort of non-reproducibility sounds troubling to me. We expect small differences for floating point arithmetic between platforms, but integer math is typically exact. This is all the more concerning for 8-bit quantized arithmetic, where off-by-one means a relative error of about half a percent. If a individual layers in a quantized neural net have off-by-one errors…
Regarding bias: This is exactly true especially with the authors method, as the learned quantization ranges are fixed and accumulating biases would lead to the entire batch being clipped to 0 or 255, depending on the direction of the biases. Luckily the bias parameters are kept in int32, so the overall bias produced by them will be much smaller than 2 pct. The arithmetic errors of the int8 matmults are summed within matmul, and are therefore an unbiased estimate of the true entry in the result matrix.
Re: 2 times 3 can sometimes equal 7 with Android's Neural Network API
#89Holy shit I just spent weeks building a tool ontop of this paper for work and now its on HN this is cool
Re: 2 times 3 can sometimes equal 7 with Android's Neural Network API
#90Never forget there's a reason why they call it Artificial intelligence. Sometimes nothing but the real thing can put you on the correct path.
That has nothing to do with it's “artificiality”. Some intelligence is simply less intelligent than others.
>Some intelligence is simply less intelligent than others.
I completely agree with you there, you're preaching to the choir.
To compare apples & oranges I could say how would you feel if you were surrounded on a dangerous freeway with nothing but noticeably below-average drivers including the vehicle you were in.
Natually I expect many passengers have become familiar with that particular traffic situation a time or two.
IOW not just below average but below ordinary expectations, and as mentioned dangerously so.
Natural intelligence, or lack of enough in the case of many who are performing noticeably below average, can only take you so far and it has always been a limitation.
OTOH would you feel more comfortable with all automated drivers instead having noticeably below-average performance due to their less intelligent below-average automaton behavior?
What if you noticed something your driver did not?
What could you do to alert a driver that truly needs a little advice from the back seat for instance, whether for navigation, safety, or far more elusively a sense of danger or even courtesy, in either case?
Would your observations as a passenger have any possibility of ever being helpful in either situation?
Would the relative artificiality of the intelligence or lack of it involved be a factor?
What if it was not just below-average drivers but some of the traditionally worst who are barely acceptable and realistically for them it's only under ideal conditions?
Seems to me risks increase exponentially the further from ideal, and the deviation between natural and artificial types of risks could result in a valley having its own kind of uncanniness.
Personally speaking as the strongest advocate toward ML & automation most people have met over the last 50 years.