It did more than SQL. It could generate programs in the syntax of an arbitrary programming language (with enough pretraining examples) as well. What powers it is a tree-to-tree transducer, which is a kind of recursive neural network (not recurrent, which is what LSTMs are).
It's been 5 years and I've been thinking a lot on this. This is a product with no good market fit. If you break it down by "kind" of sales, your basic branches are B2B and B2C.
B2C is mostly out because the person on the omnibus have no general need for a programming language, SQL or not (plus, outside of emacs, nothing consumers use are inherently "programmable"). So this program simply becomes a utility program that you reach out to occasionally like `cut` or `sed`.
We mostly targeted businesses and other startups. We wanted to empower the common employee to be able to query data on their own. That itself came with a huge amount of challenge. Turns out most employers don't like the idea of any Tom Dick and Harry having query access to databases. So we branched out, tried to allow querying spreadsheets and presentations (yes, the advertising arm of one big media corporation stored their data in tables in a .pptx file on a sharepoint server). The integrations are finnicky and break often.
Maybe we're not smart enough to figure this out. But one day, one day I shall be back.
But in the meantime, the failure of the startup spawned the Gorgonia family of deep learning libraries (https://github.com/gorgonia/gorgonia). Check it out if you want.
Don't wait too long, the market's for it's already here, and huge. "Data scientist" is one of the fastest growing programming careers. Companies will pay good money to make their data scientists more effective. Look at Redshift, BigQuery, Snowflake, Tableau, etc. Look at the Looker acquisition by Google. Also look at Splunk as a query language.
It's not remotely easy a business though. Read the uphill battle just in the HN comment thread on the Looker acquisition by Google.
You've probably already thought of and discarded this idea, but what about shipping^Hdumping the current version online so people can poke at it through a web form and a heavily ratelimited API? (Gate API keys through a sane login system that uses Google or GitHub for identity)
My thinking would be to explicitly *not* ship a supported "product" of any kind, but rather to provide a way to put the raw materials in their current state "out there" from a research perspective and provide users a way to exercise the system and reason about its limits, without letting everyone run off with it. (For example pretraining would happen on the server from uploaded datasets.)
In this scenario, the idea would be to let the market come to you, by just putting this absolutely everywhere in its current raw form and *letting the market materialize/emerge*. Rationale being, you want to know what the correct next step is from where you stand right now. If the program actually runs, for an interesting definition of "runs", ie it can actually handle real-world work in a novel way, well, I say that directly translates to it being worth trying because you might get novel answers/ideas for where to turn next. IOW, I'm theorizing that the quality of the answers you would get may correlate with the irreducible complexity of how well the program stands up in practice.
Reviewing uploaded data and executed commands may also prove insightful and inspire new ideas for real-world integrations. (Something something view-but-not-share TOS clause)
Depending on resource usage, charging for certain API actions, as a way to further ratelimit and not necessarily to make money, may be reasonable - for example doing extensive training (or lots of iterations) or performing tasks that take a long time to complete or scan a lot of input data. (And of course there would be research exceptions to this as well...)
Also, tables-in-pptx is now filed in my head a few rows down from "email of a TIFF of a scan of a photocopy of a printout of a fax of another email" :) - that's terrible, haha
It's been 5 years and I've been thinking a lot on this. This is a product with no good market fit. If you break it down by "kind" of sales, your basic branches are B2B and B2C.
B2C is mostly out because the person on the omnibus have no general need for a programming language, SQL or not (plus, outside of emacs, nothing consumers use are inherently "programmable"). So this program simply becomes a utility program that you reach out to occasionally like `cut` or `sed`.
We mostly targeted businesses and other startups. We wanted to empower the common employee to be able to query data on their own. That itself came with a huge amount of challenge. Turns out most employers don't like the idea of any Tom Dick and Harry having query access to databases. So we branched out, tried to allow querying spreadsheets and presentations (yes, the advertising arm of one big media corporation stored their data in tables in a .pptx file on a sharepoint server). The integrations are finnicky and break often.
Maybe we're not smart enough to figure this out. But one day, one day I shall be back.
But in the meantime, the failure of the startup spawned the Gorgonia family of deep learning libraries (https://github.com/gorgonia/gorgonia). Check it out if you want.