5 ms·

> I really dont see any reason why this could not have been done 10 or even 20 years ago. The advancements in tooling, infrastructure and accessibility of ML i

by scep12 8y ago

> I really dont see any reason why this could not have been done 10 or even 20 years ago. The advancements in tooling, infrastructure and accessibility of ML in the last 3 years alone have made the difference. That's seems obvious. Maybe your point is that the underlying techniques haven't changed, and thus it would have been possible to have made this discovery decades ago. But isn't that true of even the greatest inventions? Much of what's created or discovered is a function of the environment and conditions surrounding it. In other words, it's not surprising to see a halo effect in other sectors as a result of tech investment in ML.

kmax12 8y ago
I agree that it is exactly this. New tooling has made machine learning easier to use. As a result, people with deep domain knowledge but less machine learning expertise are starting to apply ML to the problems they understand that best. One of the biggest roadblocks to this happening more today is that people don't know how to perform feature engineering to prepare raw data for existing machine learning algorithms. If we could automate this step, it would be a lot easier for subject matter experts to use ML. For example, I work on an open source python library called featuretools (https://github.com/featuretools/featuretools/ https://github.com/featuretools/featuretools/) that aims automated feature engineering for relational datasets. We've seen a lot of non-ml people use it make their first machine learning models. We also have demos for people interested in trying it themselves: https://www.featuretools.com/demos https://www.featuretools.com/demos. I expect to see a lot more work in the automated feature engineering space going forward.
- petra 8y ago
  That's interesting! So could we see a gui based machine learning for non-programmers becoming a reality soon ? And how close in performance this could get vs code based solutions ?
  - kmax12 8y ago
    Yes, I think so. Featuretools is actually the core of my company's commercial product. Performance is tricky thing to answer. If you care about machine learning performance such as AUC, RSME, F1, then I think the answer would be 80%-90% of coding. If you care about building a first solution, then I think the automation would be 5-10x better.
- yazr 8y ago
  +1 for this tool.
ajross 8y ago
Yeah, the grandparent is hung up on the theory vs. application delay. By the same logic, nothing in modern CMOS logic or its production process requires physics or chemistry of a vintage later than the 1940's to explain, so why did it take us three quarters of a century to get where we are? Because it's hard. Knowing how it works and figuring out how to do it are two different things.
kingkongjaffa 8y ago
Traditional engineering has been using machine learning for years for condition monitoring... http://infolab.stanford.edu/~maluf/papers/flairs97.pdf http://infolab.stanford.edu/~maluf/papers/flairs97.pdf

by scep12 8y ago

kmax12 8y ago

I agree that it is exactly this. New tooling has made machine learning easier to use. As a result, people with deep domain knowledge but less machine learning expertise are starting to apply ML to the problems they understand that best. One of the biggest roadblocks to this happening more today is that people don't know how to perform feature engineering to prepare raw data for existing machine learning algorithms. If we could automate this step, it would be a lot easier for subject matter experts to use ML. For example, I work on an open source python library called featuretools (https://github.com/featuretools/featuretools/ https://github.com/featuretools/featuretools/) that aims automated feature engineering for relational datasets. We've seen a lot of non-ml people use it make their first machine learning models. We also have demos for people interested in trying it themselves: https://www.featuretools.com/demos https://www.featuretools.com/demos. I expect to see a lot more work in the automated feature engineering space going forward.

petra 8y ago
That's interesting! So could we see a gui based machine learning for non-programmers becoming a reality soon ? And how close in performance this could get vs code based solutions ?
- kmax12 8y ago
  Yes, I think so. Featuretools is actually the core of my company's commercial product. Performance is tricky thing to answer. If you care about machine learning performance such as AUC, RSME, F1, then I think the answer would be 80%-90% of coding. If you care about building a first solution, then I think the automation would be 5-10x better.
yazr 8y ago
+1 for this tool.

ajross 8y ago

Yeah, the grandparent is hung up on the theory vs. application delay. By the same logic, nothing in modern CMOS logic or its production process requires physics or chemistry of a vintage later than the 1940's to explain, so why did it take us three quarters of a century to get where we are? Because it's hard. Knowing how it works and figuring out how to do it are two different things.

kingkongjaffa 8y ago

Traditional engineering has been using machine learning for years for condition monitoring... http://infolab.stanford.edu/~maluf/papers/flairs97.pdf http://infolab.stanford.edu/~maluf/papers/flairs97.pdf