> Would be funny but most of those things are already on AutoML Tables, including the carbon offset
GCP datacenters are 100% offset with PPAs. Are you referring to different functionality for costing AutoML instructions in terms of carbon?
...
I'd add:
- Setup a Jupyter Notebook environment
> Jupyter Notebooks are one of the most popular development tools for data scientists. They enable you to create interactive, shareable notebooks with code snippets and markdown for explanations. Without leaving Google Cloud's hosted notebook environment, AI Platform Notebooks, you can leverage the power of AutoML technology.
> There are several benefits of using AutoML technology from a notebook. Each step and setting can be codified so that it runs the same every time by everyone. Also, it's common, even with AutoML, to need to manipulate the source data before training the model with it. By using a notebook, you can use common tools like pandas and numpy to preprocess the data in the same workflow. Finally, you have the option of creating a model with another framework, and ensemble that together with the AutoML model, for potentially better results.
This sounds like the sort of thing that would be useful outside of data science. Which leads to the question of whether it needs to be generalized, or redone differently for different specializations. Which in turn seems like the sort of question that it's tricky to answer with AI.
> This sounds like the sort of thing that would be useful outside of data science.
The instruction/operation costing or the computational essay/notebook environment setup?
Ethereum ("gas") and EOS have per-instruction costing. SingularityNET is a marketplace for AI solutions hosted on a blockchain, where you pay for AI/ML services with the SingularityNET AGI token. E.g. GridCoin and CureCoin compensate compute resource donations with their own tokens; which also have a floating exchange rate.
TLJH: "The Littlest JupyterHub" describes how to setup multi-user JupyterHub with e.g. Docker spawners that isolate workloads running with shared resources like GPUs and TPUs: http://tljh.jupyter.org/en/latest/
The notebook/procedure thing. Like, doesn't everybody everywhere operate on a basis of mixed manual/automated procedures, where it needs to fluidly transition from one to another, yet be controlled and recorded and verified and structured?
REES is one solution to reproducibility of the computational environment.
> BinderHub ( https://mybinder.org/ ) creates docker containers from {git repos, Zenodo, FigShare,} and launches them in free cloud instances also running JupyterLab by building containers with repo2docker (with REES (Reproducible Execution Environment Specification)). This means that all I have to do is add an environment.yml to my git repo in order to get Binder support so that people can just click on the badge in the README to launch JupyterLab with all of the dependencies installed.
> REES supports a number of dependency specifications: requirements.txt, Pipfile.lock, environment.yml, aptSources, postBuild. With an environment.yml, I can install the necessary CPython/PyPy version and everything else.
Storing a container built with repo2docker in a container registry is one way to increase the likelihood that it'll be possible to run the same analysis pipeline with the same data and get the same results years later.
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Pachyderm ( https://pachyderm.io/platform/ ) does Data Versioning, Data Pipelines (with commands that each run in a container), and Data Lineage (~ "data provenance"). What other platforms are there for versioning data and recording data provenance?
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Recording manual procedures is an area where we've somewhat departed from the "write in a lab notebook with a pen" practice. CoCalc records all (collaborative) inputs to the notebook with a timeslider for review.
In practice, people use notebooks for displaying generated charts, manual exploratory analyses (which does introduce bias), for demonstrating APIs, and for teaching.
Is JupyterLab an ideal IDE? Nope, not by a longshot. nbdev makes it easier to write a function in a notebook, sync it to a module, edit it with a more complete data-science IDE (like RStudio, VSCode, Spyder, etc), and then copy it back into the notebook. https://github.com/fastai/nbdev
With all due respect to Minsky, I find this zen style story a little silly. If Minsky want to say something informative why don't he use formal concepts like Jeffreys priors, mixing time, high dimensional varieties, minimun description length, entropy, etc. Is that style of telling stories a projection from a high dimensional mind to a zero dimensional dumb style space?, is that a PCA reduction from ideas to cliches? I apologize in advance from being harsh, but I am entitle to speak from my heart and I reiterate my appreciation for Minsky's work.
It should be nice using a more informative language for giving advice. If this story is tagged as "popular story for dummies" I would feel we are making real progress.
Just one of Minsky great ideas related to reinforcement learning: The credit assignment problem:How do you distribute credit for success among the many decisions that may have been involved in producing it?, in "Steps Toward Artificial Intelligence" (Minsky, 1961): All of the methods we discuss in this book are, in a sense, directed toward solving this problem.
That book is linked from HN and it has just one comment, so I think that NDNS, no dumb nerd stories, will never become popular.
More from (2) Minsky in 1951 built the world's first “randomly wired neural network learning machine,” called the stochastic neural-analog reinforcement computer (snarc)
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.
Actually it is somewhat similar as both find the model, and obliviously fits the data to that model in the process. My use was finding a parameterization that could be reused through regular regression fitting.
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?
> 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:
The game ends by agreement of the players. If they don't agree on the result ("those stones are alive!") they must keep playing. Chinese rules are much better at this than Japanese ones especially (IMHO) the old ones with the group tax. There are no ambiguities there. Unfortunately the group tax is unpleasant and Chinese rules are a pain to score manually. Japanese rules are full of flaws but are such a nice shortcut that almost everybody except China use them or some variant of them.
Btw, if any Chinese player is reading this, how do you count the score while playing? Do you count territory and remember the number of captured stones or do you count both stones and territory? Thanks.
> The game ends by agreement of the players. If they don't agree on the result ("those stones are alive!") they must keep playing. Chinese rules are much better at this than Japanese ones especially (IMHO) the old ones with the group tax. There are no ambiguities there. Unfortunately the group tax is unpleasant and Chinese rules are a pain to score manually. Japanese rules are full of flaws but are such a nice shortcut that almost everybody except China use them or some variant of them.
No, not really. Under Chinese rules, eventually it will reach a clean, objectively scored state. Of course, human players will agree on the score before this point.
Is the question "Does AutoML-Zero minimize or maximize a cost function with error as a primary component, instead of using a binary win/lose classifier like AlphaGoZero?"
My reaction too. They've reinvented genetic/evolutionary programming. They should probably read some of the decades of work that have already been done on it.
The paper [1] cites Koza among a total of 102 citations.
"An early example of a symbolically discovered optimizer
is that of Bengio et al. [8], who represent F as a tree: the
leaves are the possible inputs to the optimizer (i.e. the xi
above) and the nodes are one of {+, −, ×, ÷}. F is then
evolved, making this an example of genetic programming
[36]. Our search method is similar to genetic programming
but we choose to represent the program as a sequence of
instructions—like a programmer would type it—rather than
a tree. "
"[36]" is "Koza, J. R. and Koza, J. R. Genetic programming: on
the programming of computers by means of natural
selection. MIT press, 1992."
Same here. When I studied genetic programming, I was hoping that's where problem solving evolve from as it was flawless. But recent events prove otherwise which made me believe we are using the wrong tool for the wrong problem. Here is why.
When AI gets to 100% accuracy, the equation to find the answer becomes 100% accurate. We no longer have to run the AI with heavy resources and equation can be converted to an executable program. This modal of AI will save computing power, and uses resources smartly.
Example.
AI tries to find right equation to add two numbers.
You might not be familiar with how neural networks work. When training they do use a lot of computing power. But when running they don't. Yes, they still require some external boilerplate code to multiply the matrices, but you already have it and it's not heavy. So yes there is some convenience in program synthesis in a human programming language, but it is a small convenience, not a game changer.
If the solution program is sufficiently complex (as one would imagine it to be in non-trivial cases where we use AI, e.g. computer vision, speech synthesis, etc.), what makes you think the solution program is going to be more lightweight than running inference on an "AI model"? Futhermore, what guarantee do you have that the discovered solution is going to be efficient w.r.t. computation at all?
For those interested AutoML-Zero cites "Evolving neural networks through augmenting topologies" (2002) among other "learning to learn" papers and is worth a read if you have time and inclination.
For those with more background and time, would any mind bridging the 18 year gap succinctly? A quick look at the paper reveals solution space constraints (assuming for speed), discovering better optimizers, and specific to the AutoML-Zero paper: symbolic discovery.
Now, can we evolve a ML algorithm that would in turn produce a better AutoML? Ladies and gentlemen, the Singularity Toolkit v[quickly changing digits here].
I am way out of my depth so maybe this is pure nonsense, but presumably the goals aren't so much to evolve better performing models directly for some dataset specifically, but to see what kinds of model families it evolves and whether we've thought of all of them?
Once we've got one we can then presumably train specific models in a more targeted way.
if AutoML-Zero is going to be more than a grid-like method then I think it should try to learn a probabilistic distribution over (method, problem, efficiency) and use it to discover features for problems using an auto-encoder in which the loss function is a metric over the (method,efficiency) space. That means using transfer-learning from related problems in which the similarity of problems is based of the (method,efficiency) differency.
Problem P1 is locally similar to P2 if (method,efficiency,P1) meassured in computation time is similar to (method,efficiency,P2) for method in a local space of methods. The method should learn to classify both problem and methods, that's similar to learning words and context words in NLP or matrix factorization in recommendation systems. To sample the (space,method,efficiency) space one need huge resources.
Added: To compare a pair of (method,problem) some stardardization should be used, for linear problems related to solving linear systems the condition number of the coefficiency matrix should be used as a feature for standardization and, for example in SAT an heuristic using the number of clauses and variables should be used for estimating the complexity and normalization of problems. So the preprocessing step should use the best known heuristic for solving the problem and estimating its complexity as both a feature and a method for normalization. Heuristic and DL for TSP is approaching SOTA (but concord is better yet).
Finally perhaps some encoding about how the heuristic was obtained could be used as a feature of the problem (heuristic from minimum spanning tree, branch and bound, dynamic programming, recurrence, memoization, hill climbing, ...) as an enumerative type.
So some problems for preprocessing are:
1) What is a good heuristic for solving this problem.
2) What is a good heuristic for bounding or estimating its complexity.
3) How can you use those heuristics to standardize or normalize its complexity.
4) How big should be the problem so that the assymptotic complexity takes over the noise of small problems.
5) How do you encode the different types of heuristics.
6) How do you value the sequential versus parallel method for solving the problem.
Finally, I wonder if once a problem is autoencoded then if some kind of curvature could be defined, that curvature should be related to the average complexity of a local space of problems, also transitions like in graph problems should be feautured. The idea is using gems of features to allow the system to combine those or discover new better features. Curvature could be used for clustering problem that is for classification of types of problems. For example all preprocessed problems for solving a linear system should be normalize to have similar efficiency when using the family F of learning methods otherwise a feature is introduced for further normalization. For example some problems could require to estimate the number of local extrema and the flat (zero curvature extend of those zones)
Very insightful comment, thank you. There's one other related thing I also find worthy of exploring further namely the population based training used by AutoML-Zero at the moment seems extremely simplistic, and there exist a lot of bleeding edge methods in that area which can tremendously improve outcomes of evolutionary algorithms, I've tweeted about them here (and at the AutoML-Zero people):
And it doesn't seem unlikely that tweaking these would tremendously improve the outcomes. Combining that with what you've just described would… well, I'll leave that to the readers imagination. ;)
Basic Tech Bros still don't get it. This is cool but real problem is finding/defining the problem. And you don't get a million guesses.
Here is a simple test: get me data to predict the future. Can an algo like this learn to read APIs, build scripts, sign up and pay fees, collect data (laying down a lineage for causal prediction), set up accounts, figure out how account actions work and then take actions profitably without going bust?
If it can even do the first part of this I am in. But I doubt it. This is still just at the level of "cool! Your dog can play mini golf."
- 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