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> Lots of feature engineering based on domain expertise Exactly. This is what is required to make machine learning work well. For most people, this issue with

by kmax12 8y ago

> Lots of feature engineering based on domain expertise Exactly. This is what is required to make machine learning work well. For most people, this issue with machine learning isn’t that it doesn’t work but that it’s hard to use. I suspect that if we gave domain experts who often don’t know how to code more power to do feature engineering than we’d see a lot more applied machine learning research like this.

DrNuke 8y ago
Ultimately, yes, more power means time aka money to pursue a target freely while messing with feature engineering. Brute force a la full DL stack is not there yet for two reasons: on one side, the space domain to search for novel materials is immense; on the other side, novel materials found through ML methods must be stable somewhere in their physics state diagram, synthesizable to be manufactured properly and cheap enough to be worth engineering deployment. The x10 process acceleration (from 20-30 years to 2-3 years) is actually in the space domain search thanks to ML methods working through several thousand experiments like in the linked article, not in the engineering readiness protocol for a candidate novel material from the lab confirmation to the real application. Outsiders can help as well by implementing their own pipeline after collecting their niche-specific datasets through journal papers, conference contributions and meeting minutes. I for example am interested in novel alloys or steels for Gen IV nuclear and now creating my own dataset for a first shot, having got a benchmark already from a known, valdated and successfully deployed material.
Barrin92 8y ago
>I suspect that if we gave domain experts who often don’t know how to code more power to do feature engineering than we’d see a lot more applied machine learning research like this. With a lot of talk about high paying AI whiz kids recently I wonder whether it is not much more promising to try to bring basic ML techniques into a really wide field of day-to-day business, given how many small businesses are still completely left out.
- petra 8y ago
  Do small businesses have enough data to do something useful with ML/DL ? what ?
  - Barrin92 8y ago
    https://cloud.google.com/blog/big-data/2016/08/how-a-japanese-cucumber-farmer-is-using-deep-learning-and-tensorflow https://cloud.google.com/blog/big-data/2016/08/how-a-japanes... I liked this example very much. A small family business of a handful of people used standard ML to automate their process of classifying cucumbers for their business. Just imagine how many people we could free from manual labour to seek higher education if even only a fraction of family businesses had a use case like this and every one of those farmers or small shop owners who is bogged down by repetitive classification tasks could free up the time of a family member or two. That must be tens of millions if not more people on the whole planet.

by kmax12 8y ago

DrNuke 8y ago

Ultimately, yes, more power means time aka money to pursue a target freely while messing with feature engineering. Brute force a la full DL stack is not there yet for two reasons: on one side, the space domain to search for novel materials is immense; on the other side, novel materials found through ML methods must be stable somewhere in their physics state diagram, synthesizable to be manufactured properly and cheap enough to be worth engineering deployment. The x10 process acceleration (from 20-30 years to 2-3 years) is actually in the space domain search thanks to ML methods working through several thousand experiments like in the linked article, not in the engineering readiness protocol for a candidate novel material from the lab confirmation to the real application. Outsiders can help as well by implementing their own pipeline after collecting their niche-specific datasets through journal papers, conference contributions and meeting minutes. I for example am interested in novel alloys or steels for Gen IV nuclear and now creating my own dataset for a first shot, having got a benchmark already from a known, valdated and successfully deployed material.

Barrin92 8y ago

>I suspect that if we gave domain experts who often don’t know how to code more power to do feature engineering than we’d see a lot more applied machine learning research like this. With a lot of talk about high paying AI whiz kids recently I wonder whether it is not much more promising to try to bring basic ML techniques into a really wide field of day-to-day business, given how many small businesses are still completely left out.

petra 8y ago
Do small businesses have enough data to do something useful with ML/DL ? what ?
- Barrin92 8y ago
  https://cloud.google.com/blog/big-data/2016/08/how-a-japanese-cucumber-farmer-is-using-deep-learning-and-tensorflow https://cloud.google.com/blog/big-data/2016/08/how-a-japanes... I liked this example very much. A small family business of a handful of people used standard ML to automate their process of classifying cucumbers for their business. Just imagine how many people we could free from manual labour to seek higher education if even only a fraction of family businesses had a use case like this and every one of those farmers or small shop owners who is bogged down by repetitive classification tasks could free up the time of a family member or two. That must be tens of millions if not more people on the whole planet.