[HN Gopher] Why can 2 times 3 sometimes equal 7 with Android's N...
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Why can 2 times 3 sometimes equal 7 with Android's Neural Network
API?
Author : aga_ml
Score : 32 points
Date : 2021-01-23 19:58 UTC (3 hours ago)
(HTM) web link (alexanderganderson.github.io)
(TXT) w3m dump (alexanderganderson.github.io)
| kristjansson wrote:
| It's an interesting observation, and a shocking title, but the
| applicable lesson seems to be "don't use an aggressively
| quantized network if your application is sensitive to
| quantization errors"
| throwaway2245 wrote:
| So, computers are getting closer to human-like mistakes.
| Hallucinaut wrote:
| Reminds me of this classic
|
| https://joelgrus.com/2016/05/23/fizz-buzz-in-tensorflow/
| fuzzfactor wrote:
| Never forget there's a reason why they call it Artificial
| intelligence.
|
| Sometimes nothing but the real thing can put you on the correct
| path.
| Blikkentrekker wrote:
| That has nothing to do with it's "artificiality".
|
| Some intelligence is simply less intelligent than others.
| oso2k wrote:
| Because of "The Secret Number" (https://youtu.be/qXnFr1d7B9w)?
| bluejay2387 wrote:
| Don't use an function approximator if you need the exact output
| of a function?
| Avalaxy wrote:
| Using 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.
| Grimm1 wrote:
| Code generation only needs to generate code with n bugs where n
| is less than the number of bugs a human developer generates for
| it to have usefulness, and maybe some other factor of severity
| where they are generally less severe than human developers. I
| think it'll make neat autopilot functionality for developers
| but not replace the need to have someone look over and
| understand the code.
| yuliyp wrote:
| This is a very simplistic of what code is and the role it
| plays in a system.
|
| There are many implementations that can fulfill a set of
| requirements. Not all of them are created equal. The ways in
| which they behave as the system changes can be wildly
| different. Well-written code will be able to handle those
| changes gracefully. Poorly-written code may end up proving
| brittle and bug-prone. Generated code will be completely
| unpredictable.
|
| Imagine you're trying to build a street network for a city.
| Some designs are much more predictable than others. If you've
| played Factorio, the distinction between a spaghetti base and
| one that has some design is abundant. Even if they currently
| fulfill the same requirements now, the ability to improve
| upon and reason about how it will behave after changes is
| vastly different.
| danfang wrote:
| This is naive. The point is that code is a well defined
| system with clear rules that can be expressed through logic
| and mathematics. GPT is suited to approximate systems where
| the rules are not well defined. Until AI can actually learn
| the principles of logic, it may not be useful for code
| generation on a meaningful scale, other than things just like
| simple auto-completions.
|
| Not only that, AI would also have to learn the principles of
| system design, performance, security, readability,
| maintainability. That's what makes "good" software. It's a
| far stretch to say that AI could achieve anything of the sort
| based on current abilities.
| kulig wrote:
| Its not that simple.
|
| People are understanding when car crashes happen in busy
| roads amongst other cars.
|
| They are _not_ understanding if a self-driving car swerves
| into the sidewalk and kills a group of children.
| ben_w wrote:
| I disagree that that is enough to be useful. To give a
| deliberately extreme example: if it produces code which has
| half the number of bugs as a human, but it only outputs
| Malbolge source code, nobody else will be able to fix those
| bugs which remain.
| perl4ever wrote:
| This is a perfect satire of the logic people use to advocate
| self driving cars being rushed into production.
|
| Only every time I read something similar, I think "surely no
| programmer could think this". Are you a programmer?
| Grimm1 wrote:
| I sure am, and if I can code gen 90% of the boiler plate
| away I'll do it happily. Besides attacking me, do you have
| any point you'd like to make?
| bobthebuilders wrote:
| Do you want to die when your self driving car crashes?
| Debug issues when your app des at 12am? Same concept.
| Grimm1 wrote:
| I don't want to die when I crash my own car, and I
| already debug my own apps at 12am. If your argument is
| that things need to be perfect than my god you must never
| leave your home! I'd trust a machine to drive more
| accurately than most people I see on the highway.
|
| Humans aren't special, in fact more often than not we're
| sloppy, subject to fatigue, and a whole bunch of other
| negative things.
|
| That considered, I had a pretty strict qualifier in my
| above post which means the machine must perform better
| than the average human in the respective task and
| therefore I'd be more likely to die driving my own car
| than a machine meeting my prerequisites.
| Judgmentality wrote:
| > I'd trust a machine to drive more accurately than most
| people I see on the highway. Humans aren't special, in
| fact more often than not we're sloppy, subject to
| fatigue, and a whole bunch of other negative things.
|
| Humans are much, much, much more capable than the
| absolute state-of-the-art robots when it comes to doing
| things in an uncontrolled environment.
|
| https://www.youtube.com/watch?v=g0TaYhjpOfo
| Hasnep wrote:
| One of the advantages of an autonomous driver is that its
| superhuman reflexes, never driving while tired, never
| getting road rage, etc., will make it less likely to get
| into an uncontrolled environment.
|
| Would you prefer your pilots to fly your plane with no AI
| assistance?
| Judgmentality wrote:
| > One of the advantages of an autonomous driver is that
| its superhuman reflexes, never driving while tired, never
| getting road rage, etc.
|
| First of all, when you actually understand a self-driving
| car stack, you'll realize those super-human reflexes are
| more human than you think. The stack is complicated and
| not only are there delays to be expected, some hardware
| syncing requirements guarantee certain delays in the
| perception pipeline. It's still better than a person, but
| it's nothing close to approaching instantaneous.
| Likewise, sensors can get dirty, and blah blah blah there
| are other weaknesses robots have that humans don't. My
| point is simple: robots aren't perfect. In fact, they are
| almost always much worse than most people realize.
|
| > will make it less likely to get into an uncontrolled
| environment
|
| You're misunderstanding me. I'm not saying less likely to
| get into an accident. I'm saying the world, where cars
| drive, is an uncontrolled environment - and the current
| state of robotics is such that humans are better for
| doing things in the real world. There is no "less likely
| to get into an uncontrolled environment" because by
| definition you are always putting it into that situation.
|
| > Would you prefer your pilots to fly your plane with no
| AI assistance?
|
| AI assistance is fine. AI replacement is not.
| dealforager wrote:
| 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.
| [deleted]
| [deleted]
| jzer0cool wrote:
| Why would use use a neural net to approximate 2 x 3 when there is
| a clear definition of the result. Or as a fun side affect, neural
| nets are prone to off by one errors too :)
| unnouinceput wrote:
| Famous Pentium F-DIV 20 years later, the sequel?
| segfaultbuserr wrote:
| It's a neural network. It gives approximate results. Here's a
| newbie question that asks basically the same question, with
| some interesting answers.
|
| > codesternews: Any deeplearning expert here. Why Neural
| network can't compute a linear function Celsius to Fahrenheit
| 100% accurately. Is it data or is it something can be
| optimised. print(model.predict([100.0]))
| // it results 211.874 which is not 100% accurate
| (100x1.8+32=212)
|
| https://news.ycombinator.com/item?id=19708787
| techbio wrote:
| Baker's (half-)dozen?
| moonbug wrote:
| if only there was some way of doing computatiin without
| Tensorflow.
| YarickR2 wrote:
| Well, every tool has it's own range of use cases; doing integer
| math is not a use case for a guesstimate engine .
| justicezyx wrote:
| Or one can claim that it's entirely obvious when relating that
| with human beings making mistakes, where not only 2*3 can be 7,
| millions can die of some obscure disctators whim, without much
| conacusoly realized the insanity...
| vmception wrote:
| Did someone just train a GAN on HN comments?
| justicezyx wrote:
| "The real question is not whether machines think but
| whether men do. The mystery which surrounds a thinking
| machine already surrounds a thinking man."
|
| -- B F Skinner
| MayeulC wrote:
| It would be fairly interesting to try, and take votes as
| feedback. That's what we all do here, anyway...
|
| ...Although you can reach a point where you have enough
| karma not to care and troll a bit/speak more freely, which
| if you only look at the vote outcome, can net you big in
| both directions (though there is a lower bound). In the
| end, it's exactly like an optimization problem, if you're
| "farming" karma: a lot of safe bets, and a few more risky
| ones to maybe discover a new class of safe ones.
|
| Reddit is full of safe gamblers who are farmink karma by
| repeating canned patterns.
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(page generated 2021-01-23 23:00 UTC)