- 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
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.
https://cloud.google.com/blog/products/ai-machine-learning/u...
Funy you say that cos my company is actually developing something along those lines
“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.
https://github.com/google-research/google-research/blob/mast...
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?
Just a fun note: winning or losing at the game of Go is actually surprisingly subjective:
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.
"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."
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.
AI finds the equation to add two numbers.
AI outputs the equation as an executable program.
AI discards itself.
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.
Once we've got one we can then presumably train specific models in a more targeted way.
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)
https://twitter.com/no_identd/status/1238565087675330560
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. ;)
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."
This is in the site guidelines: https://news.ycombinator.com/newsguidelines.html.
