This just kind of sounds like a random idea that sounds good in your head but not based in reality; the point of a PhD has always been one thing, and one thing alone: train someone who can publish influential papers in top-tier venues.
Anyone who says otherwise is just either uninformed or selling a dream.
I've seen the ins-and-outs of academia within an R1 research institution from about as top-level as one can while remaining a student, and my idea is based on extensive interface with the reality of academia.
Your observation that the doctoral degree system has always been that way is precisely my point: the world has changed, and new forms of training are needed to complement the paper-publishers. The PhD system is broken in part because it's catering to multiple audiences when it should regain its focus on its core mission. That being said, many people want to do research but don't want to work in academia; in fact, I think their numbers are far greater than the academia-oriented. My idea caters to those people, and I think all parties (students, schools, industry, government, the general public) will benefit in this arrangement with almost no drawbacks.
From a degree-focused perspective, it's somewhat unusual that U.S. universities almost exclusively assign PhDs, save for the professional degrees (e.g., MD, PharmD, JD). Multiple types of bachelors and masters degrees exist, and those degrees are certainly differentiated from one another. In some European countries, the ScD is a terminal degree higher than a PhD.
> The US used to be the most desirable place for immigrants and the US higher education system used to be the envy of the world and now for both of these it is not any more.
That is a nice sound bite for TV--the reality is that the government has been systematically rejecting visas in a bid to kill universities. Yes, it's true that universities counted too much on rich MS students and the like. But the reason for the failures isn't that "it is not any more," as you say--it's a very calculated move by the current administration to eat universities' bottom line.
This is very cool to hear. Please get in touch with me, I would love to learn more. By the way, I am recruiting participants for an upcoming seminar in which I am soliciting industrial participation: https://kmicinski.com/minnowbrook-26. Please get in contact with me if this is relevant to anyone at your company.
I am the author of this paper, and I do not agree with Dr. Smaradgakis' comments. As far as I can tell, the root of his concern is that that paper did not target Souffle Datalog, a specific Datalog language in which his group writes. The criticism is totally fair in a sense, but I do not agree with you that these are "pretty big caveats" in our paper, for the reasons I address in my rebuttal to his comment. I will say however, that his very engaging comments have pushed us to do significant follow-on work, which has now pushed our engines to scale to the kind of code he writes in Datalog, yielding very exciting results, and I am hoping that he will be satisfies when he sees it :-)
I will also mention that our group has follow-on work from this (I cannot share this widely due to reviewing reasons but a preprint is available if you would like to search) which significantly addresses Yiannais' concerns. In the engine cited here, we scale to small programs (tens of lines): our engine does not support large, tricky queries for interesting, asymptotic reasons (which are also shared by other Datalog engines based upon binary joins, not unique to our engines). Our new engines port a significantly more complex class of join algorithms to the GPU, and we have used these new algorithms (and our novel GPU-based implementation) to run 500-1000-line Datalog programs which beat all existing state-of-the-art program analysis engines by 20-50x.
In sum, I strongly disagree with the "pretty big caveats" remark. Dr. Smaradgakis' comments are quite firm in nature and I very much respect them. But I encourage you to check out my rebuttal and also (regarding scaling to larger subsets of Datalog and "real" programs) our recent follow-on work.
If you would like proof, please email me, we are happy to help you evaluate for yourself. My email is always open: kkmicins@syr.edu.
I think the basic issue is that ADTs are simply not indexed--so to the degree that you write a query that would necessitate an index on a subtree of an ADT, you will face asymptotic blowup, as the way ADTs work will force you to scan-then-test across all ADTs (associated with that top-level tag). The issue is discussed in Section 5.2 of this paper here: https://arxiv.org/pdf/2411.14330
Ah, yes, but I think Ascent also doesn't index ADTs. In this case, based on some other information, it seems like Soufflé _can_ plan the queries better if it has profiling data. It seems like Ascent just happened to pick a better query plan in my case without the profiling data.
It's true that Ascent does not index ADTs either, but there are some tricks that you can use when you control the container type to get similar performance by, e.g., storing a pre-computed hash. I believe Arash, the main author of Ascent, was exploiting this trick for Rc<...> members and seeing good performance gains. It is a bit nuanced, you're right that Ascent doesn't pervasively index ADTs out of the box for sure.
Seems potentially interesting to explore what would be required to store durable continuations. Feels very related to incrementalization and provenance, as you can see materializing a continuation to disk (whatever storage backend) requiring dependence tracking to do anything other than simply snapshotting the whole program state. I am just spitballing though, not sure if anyone has actually tried this.
I agree with you--that's a topic I will definitely cover in my blog, too. You make a good point: I know some folks who worked at big financial orgs, writing hundreds of thousands of lines of code, and never wrote general-recursive functions (only used simple recursors like foldl).
Anyone who says otherwise is just either uninformed or selling a dream.