[HN Gopher] AutoML-Zero: Evolving Machine Learning Algorithms fr...
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AutoML-Zero: Evolving Machine Learning Algorithms from Scratch
Author : lainon
Score : 69 points
Date : 2020-03-10 20:08 UTC (2 hours ago)
(HTM) web link (github.com)
(TXT) w3m dump (github.com)
| joe_the_user wrote:
| _AutoML-Zero aims to automatically discover computer programs
| that can solve machine learning tasks, starting from empty or
| random programs and using only basic math operations._
|
| If this system is not using human bias, who is it choosing what
| good program is? Surely, human labeling data involves humans
| adding their bias to the data?
|
| It seems like AlphaGoZero was able to do just end-to-end ML
| because it was able to use a very clear and "objective" standard,
| whether a program wins or loses at the game of Go.
|
| Would this approach only deal with similarly unambiguous
| problems?
|
| Edit: also, AlphaGoZero was one of the most ML ever created (at
| least at the time of its creation). How much computing resources
| would this require for more fully general learning? Will there be
| a limit to such an approach?
| darawk wrote:
| > It seems like AlphaGoZero was able to do just end-to-end ML
| because it was able to use a very clear and "objective"
| standard, whether a program wins or loses at the game of Go.
|
| Just a fun note: winning or losing at the game of Go is
| actually surprisingly subjective:
|
| https://en.wikipedia.org/wiki/Go_(game)#Scoring_rules
| jxcole wrote:
| Interesting, but how does it perform on standard benchmarks like
| image net and MNIST?
| p1esk wrote:
| They have some cifar10 results in the paper, but only very
| small networks.
| lokimedes wrote:
| Reminds me of https://www.nutonian.com/products/eureqa/ which I
| used quite productively to model multivariate distributions from
| data back in the 2000's. Funny how everything stays the same, but
| with a new set of players on the bandwagon.
| TaylorAlexander wrote:
| Shouldn't this link directly to the Readme?
|
| https://github.com/google-research/google-research/blob/mast...
| manually wrote:
| Next:
|
| - Autosuggest database tables to use
|
| - Automatically reserve parallel computing resources
|
| - Autodetect data health issues and auto fix them
|
| - Autodetect concept drift and auto fix it
|
| - Auto engineer features and interactions
|
| - Autodetect leakage and fix it
|
| - Autodetect unfairness and auto fix it
|
| - Autocreate more weakly-labelled training data
|
| - Autocreate descriptive statistics and model eval stats
|
| - Autocreate monitoring
|
| - Autocreate regulations reports
|
| - Autocreate a data infra pipeline
|
| - Autocreate a prediction serving endpoint
|
| - Auto setup a meeting with relevant stakeholders on Google
| Calendar
|
| - Auto deploy on Google Cloud
|
| - Automatically buy carbon offset
|
| - Auto fire your in-house data scientists
| neximo64 wrote:
| Would be funny but most of those things are already on AutoML
| Tables, including the carbon offset
|
| https://cloud.google.com/automl-tables
| otagekki wrote:
| Poor data scientists, now whose heads get cut when things go
| wrong and companies lose billions?
| manually wrote:
| In the days when Sussman was a novice, Minsky once came to
| him as he sat hacking at the PDP-6.
|
| "What are you doing?", asked Minsky.
|
| "I am training a randomly wired neural net to play Tic-Tac-
| Toe" Sussman replied.
|
| "Why is the net wired randomly?", asked Minsky.
|
| "I do not want it to have any preconceptions of how to play",
| Sussman said.
|
| Minsky then shut his eyes.
|
| "Why do you close your eyes?", Sussman asked his teacher.
|
| "So that the room will be empty."
|
| At that moment, Sussman was enlightened.
| xiaodai wrote:
| Autodetect data health issues and auto fix them
|
| Funy you say that cos my company is actually developing
| something along those lines
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(page generated 2020-03-10 23:00 UTC)