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Comment by codingslave | Hacker News Reader

This is a really good article. One company that I always think of when I think machine learning is the computer vision "startup" Clarifai. At one point in time they were cutting edge, filling the need for large scale image classification that enterprises had. This was when computer vision neural network architectures were rudimentary and hard to train (they still kind of are). Then in-house data science teams sprang up, tooling got better, better network architectures came out, and Clarifai essentially lost all of their edge over night. Machine Learning in itself is basically never the edge, it has to be a unique data set or sticky user base, something else that builds a moat.

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I was one of the first few employees at Clarifai so I can add to this.

When Matt and Adam started the company there was no Tensorflow and outside of Hinton/Bengio/LeCun triangle nobody was doing deep learning yet. Matt just beat Google on ImageNet (around the time when he was doing an internship at Google Brain under Jeff Dean) and was one of the few experts in the field as he was lucky enough to have Hinton as his masters thesis advisor at UofT and did a PhD at NYU under Rob Fergus and Yann LeCun.

We had a clear technological advantage for about 2 years but thanks to the open nature of deep learning research (arxiv and willingness to open source code) the whole field caught up. Google giving out tensorflow and pretrained models for free made a bit of a dent as well.

A big problem with the "machine learning model as a service" business model is that each customer has slightly different needs and a different source of data so an off the shelf solution is usually not good enough. Because of that you end up being forced into doing consulting for large companies that don't know how to hire machine learning engineers. You end up spending months building them a model that they won't even know how to use and move on to the next customer.

IMHO there are only two viable AI business models right now:

1. Spin out your research lab into a company and keep churning out papers without ever thinking about having real customers and make enough noise to get acquired by FAANG, ala Deepmind, Metamind, etc.

2. Find a problem with a lot of repetitive manual labor and slowly wedge a machine learning model into the process. Design a good feedback loop by first augmenting the workers and use their feedback to keep improving the model until it's good enough to replace them. Doing so requires you to actually build a real product in that domain so you'll need much more diverse team than a paper mill. Most companies doing this shouldn't even call themselves "AI" companies since their customers don't care how the solution works as long as it solves their problem.

I believe that companies pursuing option 2 will take over a lot of large established players because building machine learning driven products requires buy in from the whole organization and it's not something that's easy to do at large enterprises. You need to be able to design the product in a way that helps you collect feedback, build data infrastructure to collect it and be willing to accept a solution that won't always be right.

Thanks for the info.

What do you think about the auto ml business model?

I.e. give each customer the ability to create its own models (using thier own data), but automating the model training and deployment?

I think it's a great way to bill enterprise customers for a ton of compute.

The intersection of people who can't train a machine learning model but can properly use and evaluate one is really small. Doing a brute force architecture search to squeeze out an extra percentage point of accuracy is not that useful.

Few shot learning is a much more interesting proposition and it's something that Clarifai offers.

Awesome to hear from somebody close to the source. There's a third option. There are entire industries where automation has not been practical because of the necessity for many hardcoded rules - problems outside of bland image recognition, including those solved by novel architectures like GANs and encoders and such. ML has finally gotten to the point where complex heuristics can be learned to automate those tasks which were impractical previously. Now you can legitimately write and sell ML services which meaningfully analyze, catalogue, and search data in ways that are currently human intensive, but just unique enough that regular programming won't cut it. There will be a proliferation of such businesses in the near future. The first wave is in development now. I've said it repeatedly, and I'll say it again, we are on the verge of an internet-like change in society. ML is poised to take human endeavors to new heights in the next decade...and if innovation and hardware continue to progress, I think the recent proliferation of the ML zoo and associated theory has given us the foundational tools for true AI which we may see in our [distant] lifetimes.

Would you share any examples of research or companies that are doing this?

I am not sure what the difference between this and the "option 2" in the post you replied to. Some examples would be helpful. It seems to me both options replace human intensive tasks.

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