Comment by kmax12 | Hacker News Reader

Comment by kmax12 | Hacker News Reader

Despite the claim to make AI accessible to every business, this release is fairly limited in that it only applies to images. We will have to see how they extend it going forward. Given the technology it's based on, I'd expect things like text, audio, videos to come next.

However, I'm curious if they plan to support structured/relational datasets which are definitely something every business needs. In Kaggle's 2017 State of Data Science [0] survey, data scientists said they spent 65% of their time using relational datasets vs 18% for images. Given that Kaggle is owned by Google, this must be something on their radar.

For those data scientists, I maintain an open source library for automated feature engineering called Featuretools (https://github.com/featuretools/featuretools). For people interested in trying it out, we have demos (https://www.featuretools.com/demos) to help you get started.

[0] https://www.kaggle.com/surveys/2017

Disclosure: I work at Google on Kubeflow

We're just getting started! Stay tuned for lots more AutoML goodness.

Recently YouTube started pulling off kids eating Tide pods video. Can AutoML figure this out or do they use manual labor to do it? Couldn’t this have been done before? I mean can it detect stupid/dangerous videos automatically and pull it when it threatens to become an epidemic?

> data scientists said they spent 65% of their time using relational datasets vs 18% for images

It will be interesting to see the trend over years. One year doesn't say anything about the trend in the industry.

> data scientists said they spent 65% of their time using relational datasets vs 18% for images

Part of the reason is that use cases are driven by limited definitions of ML or "AI" -- AutoML for example note they are "Making AI accessible to every business..." but they are just managing a small part of one type of ML (images with conv nets and res nets.) A senior exec who reads this might develop a narrow view of what AI is.

A general annoyance is how obvious techniques like regression, tree models, Bayesian models, etc on tabular data are so ignored while everyone gets hyper-obsessed over GANs or whatever. Almost 90% of low-hanging-fruit I see can be captured with simple classic ML applied to tabular data.

90% of the time companies apply regression to problems because is the tool they know and works well for a lot of problems. But I think that has changed with deep learning. Deep learning works well for a lot of complex problems (as images). So now companies can solve problems that before couldn't solve. That is why I think it is relevant to see the trend.

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