Hacker News Reader

Comment by exp1orer | Hacker News Reader

Guesstimate [1] (cited in this paper in footnote 6 and previously discussed on HN [2]) is a really nice implementation of some of these ideas.

Has anyone come across other good implementations?

As a side note, I've been doing more probabilistic programming with pymc3 recently, and it's pretty incredible how leaky the abstractions can be. I'm not saying there's a way to do better, just that at present there's a huge gap between the beautiful vision of "the inference button" and the current tools.

[1] https://www.getguesstimate.com/

[2] https://news.ycombinator.com/item?id=18785371

View on HN

We're working on something like this: https://causal.app :)

Guesstimate is awesome, but their team sadly stopped working on it a while back. It's definitely early days for automated inference, but I think giving people the tools to build "static" (non-learning) models that can account for uncertainty is hugely valuable in itself. You need serious gymnastics to do this in spreadsheets right now, and I wouldn't wish Excel's probabilistic plugins (Palisade @RISK, Oracle Crystal Ball) on anyone.

But progress towards the "inference button" dream is starting to accelerate:

- Tensorflow recently got its own PPL [1]

- The first international conference on probabilistic programming was held (PROBPROG 2018) [2]

- Lots of PPL development going on in tech companies: Uber, FB, Google, Microsoft, Stripe, Improbable, etc.

[1]: https://www.tensorflow.org/probability

[2]: https://probprog.cc/

Microsoft recently released a probabilistic programming library for .NET named Infer.NET [1]

[1] https://github.com/dotnet/infer

They recently open sourced it. It was previewed in lectures from Christopher Bishop more than 10 years ago.

1) https://www.microsoft.com/en-us/research/project/infernet/ 2) http://videolectures.net/mlss09uk_bishop_ibi/

>As a side note, I've been doing more probabilistic programming with pymc3 recently, and it's pretty incredible how leaky the abstractions can be. I'm not saying there's a way to do better, just that at present there's a huge gap between the beautiful vision of "the inference button" and the current tools.

Yeah, there's a pretty active debate in the probabilistic programming R&D community over whether it's a bug to sell people on "the inference button", then deliver a leaky abstraction, or a feature to offer richly programmable inference. Our lab has been working on some ideas to get "basic" and "advanced" inference techniques and generative models to compose together nicely to try and build a bridge between the two options[1]

[1] https://drive.google.com/file/d/1bv8g7KTgpgRLsx3ZcaPzIlhGzSa..., https://arxiv.org/abs/1811.05965

Paper's abstract looks cool, I'd love to take a look at this! Rather than selling "the inference button", I always wondered why more probabilistic frameworks don't play up the "programming" portion of it. Most of us who look into probabilistic programming aren't that afraid of getting our hands wet.

Hopefully we should have the actual framework open-sourced and a longer-form paper on arxiv (and under review) within a few months. This has been over a year in development by now, but we're getting there.

Moving from point estimates to distributions is great progress.

It reminds me of this great paper that highlights how much information we're losing when we're only looking at means or assume everything is normally distributed. https://arxiv.org/pdf/1806.02404.pdf

Abstract link: Sandberg, Drexler, and Ord - Dissolving the Fermi paradox (https://arxiv.org/abs/1806.02404).

Reply on news.ycombinator.com