chartpath

TBF this is an academic article though. Why should total accessibility of language to non-experts be a goal in that venue exactly? Isn't it reasonable to expect that other educated people will check the citations?

Also in this case it looks like the authors are from business schools. Business buzzwords are a well known and common absurdity. On the other hand, I might be freaked out if random names for stuff started coming out of logic or maths circles.

Same, except that you still can't execute expressions in the debugger. It only reads existing values. I think it's something with Delve.

And yet they don't pay living wages to their non-engineering employees. Greedy scumbags.

Glad to see some action in NB! Hi from Montreal.

Happy user of Prefect here. I prefer it for being more programmable and able to run on Dask. If you just want dynamic distributable DAGs and not necessarily an "ops" appliance feel (like Airflow), check them out: https://docs.prefect.io/core/getting_started/why-not-airflow...

Not knocking Airflow, it is great. Luigi too.

I've been through similar "searching" efforts and concluded the same.

What about working in legal or medical software, since those industries will never go anywhere? Or any other industry that is boring.

Hoodies are also comfy.

CS grads should be required to take a year of liberal arts at least.

What kind of oversight could even work though? We have the Queen in my neck of the woods but that is not exactly accountable and never does anything to check poor governance and only rips off taxpayers. We also have non-confidence votes which can bring down a Prime Minister, and it seems to work (in minority governments at least).

How can a separation of powers approach still check itself? Like different term limits, VP powers, congressional army? Banning factions or breaking up parties that get too big, banning private donors? Rooting for the American experiment to get sorted!

If you want to integrate with the data science ecosystem, would you use modern Python for APIs as well, or do Go services with minimal Python bits?

EDIT: coming from someone who has had great experiences with FastAPI and Uvicorn combined with Dask and Redis.

This is especially useful for those of us who have grown up thinking of ourselves as smart and becoming very identified with intelligence as personality. It can lead to anxiety disorders because sometimes rumination and thinking harder just can't help explain things. Listening to nature and letting it happen to us is such a sweeter ride than trying to carry around a simulation of everything.

I used to love Stoic thinking and read the usual primary sources a few years ago. I still do love it and practise it.

However, I became more and more turned off by the modern company of success porn bros who use it as a kind of macho way to justify not caring about others. This is not the truth of it, as Stoic ethics is about concentric circles of concern emanating outward (family, friends, community, country). I wish there was more focus on that.

I use PyKnow which is based on CLIPS!!

Is the resurgence of expert systems finally a thing after statistics-all-the-things fatigue?!

Would anyone recommend Clojure if you run an engineering org and get to make decisions about languages?

I've been running node shops for the last several years, and love Clojure since having hosted a couple meetups a while back.

That and being most influenced by lispy/ai culture from SICP, CTM, PAIP, AIMA books. Node is getting tiresome due to not being able to trust the ecosystem quality (which takes away some of the common justification of huge ecosystem).

Not worried about hiring since not open to fresh grads from universities or bootcamps without lots of training anyway (which can include a new lang). Are there crappy/charlatan Clojure devs out there or is it more of an elite/experienced culture?

With stuff like Fulcro, could we replace our basic REST APIs and business logic without having to re-invent too much?

I am currently using PyKnow (https://github.com/buguroo/pyknow/) as a rules engine for dialog management (to drive topic stack frame selection). It's just a prototype so far and I have barely scratched the surface of expert systems, GOFAI and logic programming in general.

It is based on CLIPS [1] which is a knowledge-oriented expert systems thing developed at NASA. It allows encoding logic as "gates" that solve for constraints with left-hand-side facts triggering right-hand-side consequences. I like to tell the business people it's like "IFTTT for X".

Can someone with experience contrast this kind of thing with real logic programming? Is it just the level of formality and provability? Are there many logics like we have in philosophy (symbolic, predicate etc.) that are used in programming?

[1] http://clipsrules.sourceforge.net/documentation/v640/ug.pdf

Yeah. I'm coming from the same background, but with some experience orchestrating these systems in production. AFAIK a bunch of the submissions to the various "AGI" awards like Alexa prize use ensembles of models and some way of weighing each one based on a context in order to choose which classifier to trust in a particular scenario. E.g. MILABOT.

There is so much more than one monolithic NN since those are easy to saturate in terms of precision and recall with enough training data and features, but are not enough to provide good UX in any complex domain/ontology. So it makes more sense to have many different models trained on each subdomain/taxonomy so that each can be specialized and then combined orthogonally.

Then the question becomes "how do we orchestrate them?" Well, there is a lot of research from the 80s and 90s that kinda got left by the wayside due to hype cycles (see the last "AI winter"). My faves are Collagen and Ravenclaw. And there is a lot of literature around topic frame stack modelling, which can be combined with various expert systems or other logics. I am currently using CLIPS (PyKnow) with custom Ravenclaw implementation. I believe b4.ai is doing something similar without the logic/rules engine, and actually applying ML to topic selection as well. My systems are goal oriented so I like to give them a teleology for business reasons, which would not suffice for AGI ambitions.

TLDR data science isn't enough on its own. We need engineers to architect things properly to solve problems.

It's probably in New Brunswick, the only officially bilingual province in Canada, or Ottawa, for show. I live in Montreal and there are a few hidden-away places that still have English-only stop signs, but the vast majority were converted to French-only after bill 101.

https://en.wikipedia.org/wiki/Road_signs_in_Canada#Stop_sign