beaucronin

Knowing authorship has long been an important signal for judging work. Sometimes we specifically want authorship to be anonymous -- in peer review or contest judging, for example. But in many cases knowing authorship is useful. We are now in an era where that signal is (deliberately or not) obscured.

Here is a simple set of labels that I find useful for distinguishing how much AI contributed to a document or code file.

My pet peeve is that the IPCC reports are balled up in these massive PDFs. So I exploded the most recent one into an Airtable, with sections linked from the summary and so on. Still some cleanup, but it's mostly there. Interested if this is useful for anyone.

All kind of things, but mostly what you'd expect: monitors, speakers. Pretty much anything that can generate a stimulus with fine control.

That's fair pushback re how "standard" deep learning is. That said, those methods are rapidly establishing themselves as the go-to for applications in speech, vision, and text. At least for those outfits that can afford the substantial dev costs.

Blogspam? I demand satisfaction!

Prior Knowledge (SF), Full Time, Distributed Systems Developers and Machine Learning Experts

P(K) is a small team in downtown San Francisco pushing the frontiers of probabilistic machine learning to the masses. Our first product is Veritable, a predictive database. We like to think of it as a database for things you don't (yet) know.

We're passionate about discovering the hidden causes behind data, and are currently split pretty evenly between machine learning experts, scalable systems engineers, and people used to working with horribly messy, complex, and sparse data. We're backed by Peter Thiel's grou, Founders Fund, who share our commitment to long-term technical innovation.

http://priorknowledge.com/join-us describes a bit more about what we're looking for, and you can always e-mail me (jonas@priorknowledge.com) for more info! Or swing by our offices at the intersection of 3rd and Market to meet the team.

Prior Knowledge (SF), Full Time, Distributed Systems Developers and Machine Learning Experts

P(K) is a small team in downtown San Francisco pushing the frontiers of probabilistic machine learning to the masses. Our first product is Veritable, a predictive database. We like to think of it as a database for things you don't (yet) know.

We're passionate about discovering the hidden causes behind data, and are currently split pretty evenly between machine learning experts, scalable systems engineers, and people used to working with horribly messy, complex, and sparse data. We're backed by Peter Thiel's grou, Founders Fund, who share our commitment to long-term technical innovation.

http://priorknowledge.com/join-us describes a bit more about what we're looking for, and you can always e-mail me (jonas@priorknowledge.com) for more info! Or swing by our offices at the intersection of 3rd and Market to meet the team.

Templating is definitely not required - states are run through Jinja by default, but for most simple setups you won't need to template anything.

Just submit your email and we'll get you into the beta very quickly:

https://dev.priorknowledge.com/login?next=/