Using Neural Networks to Find Answers in Tables

Yeah, we think we run the machines but the machines really run us.

Google could resolve this will well thought out UX.

Each answer could have a "show working" link which would display extracts of the tables used to derive the result, with the specific data used highlighted.

They could also have a "show a different result" link which would display the next most likely result with a substantially different answer. The user can then compare both methods and decide if Google probably has it correct.

We already have this problem, and Google's "search" widgets (e.g. dictionary, snippets, Q&A) are probably a big offender. Falsehoods are everywhere, but people tend to arrive at the truth, and once they do, the truth tends to go viral and flood fill over the falsehoods. What scares me is how easy it would be to create higher-order falsehoods. For example, X, Y, and Z are all false, but corroborate each other, making all of them more believable. These "regions" of falseness already trap millions of people today into throwing away their money -- think pyramid schemes, cults, etc.

I think you're absolutely right that this only going to be exacerbated.

Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning (https://arxiv.org/abs/1709.00103)