[webmaven]

> how expensive it would be to build out these datasets?

Assuming you have the expertise necessary to design and run the process in-house, the major expense is going to be compensating the humans in the loop, which can add up quickly.

This is why organizations that already have access to large datasets have such a huge advantage.

I think that one of the reasons we're seeing such a rush to deploy chatbots is that even a minimally-useful bot will quickly start accumulating extremely useful (and very clean) training data.

There is a lot of noise being made about "democratizing AI", but as long as the best results require a lot of training and huge amounts of training data it will remain the bottleneck.

Look for progress on 1-shot and 0-shot learning to get a better feel for how much progress is made on real democratization.

[roymurdock]

Thanks for the pointers - I'm looking forward to seeing how AI/ML is brought to market, as we hear a lot about research but not as much on the product side just yet. Sounds like MSFT will be pushing in this direction as well with the Maluuba team:

Last fall, we formed the Artificial Intelligence and Research organization, bringing engineering and research closer together to accelerate the pipeline from cutting-edge research to product development. Maluuba, too, has closely aligned its research and engineering teams, and we’re looking forward to learning from their experiences as well.