I worked at a chair for 12 years - in that time I've seen a lot of PhD students go through this.
If it helps anything at all: It's normal. At this point, you've already proven you're smart and knowledgeable. Now, the universe wants to see if you can also finish what you've started. That's the main thing a PhD proves: That you can take an incredibly interesting topic and then do all the boring stuff that they need you to do to be formally compliant with arbitrary rules.
Focus on finishing. Reduce the scope as much as possible again. Down to your core message (or 3-4 core messages, I guess, for paper-based dissertations).
This is spot on. My dad was a professor and had dozens of PhDs. The only thing differentiating them (as I remember him telling me) was the resolve to keep work as /tiny/ as possible. Who is remember for his/her PhD? Only the smallest cream of the crop. He even made good fun of worthless thesis by (then) well known professors. It’s not about your PhD.
When I did my MSc thesis he told me it was a pretty good PhD. (Before giving me a months work in corrections.) I didn’t understand back then, but I understand now. It was small, replicatable and novel (still is)! Just replicate three times and be done with it. You’ve proven your mastery. Now start something serious.
> This is spot on. My dad was a professor and had dozens of PhDs. The only thing differentiating them (as I remember him telling me) was the resolve to keep work as /tiny/ as possible. Who is remember for his/her PhD? Only the smallest cream of the crop. He even made good fun of worthless thesis by (then) well known professors. It’s not about your PhD.
My professor once told me he presented at a small conference, the whole audience everybody had PhD in mathematics and maybe 2 of the 50 or so people in the audience could follow along. The point he was trying to make is at some point the people in the audience were not really interested in what was being presented because it is difficult to just follow along some really niche topic.
There was a book I read a couple years back called "Mathematica: A Secret World of Intuition and Curiosity", by David Bessis.
He discussed this topic and how generally it's left to those who are more notable in a field to ask the 'dumb' questions everyone else is afraid to ask. And such questions often need to be asked to get the audience on board and open the floodgates with areas of niche research - the speaker themself is often too far into the rabbit hole to discern the difference between opaque and obvious.
So it stands to reason, at smaller conferences this would be a big problem, with fewer thought leaders in attendance whose reputations are intact enough that they wouldn't mind looking foolish.
If you’ve spent a significant amount of time widening the scope as far as possible to include everything interesting about your original question, and there is nothing in that whole widened scope that the audience will give a shit about, your topic is unsaveable and your advisor is a failure.
If there is something interesting enough to qualify, then reduce the scope as much as possible. It should go without saying that you shouldn’t throw out the interesting bit.
The problem that occurs in practice is “focus on finishing” leads people to finish without actually doing anything meaningful. Advisors may or may not encourage this depending on where they are in their career.
When you get on the industry job market nobody cares if it took you 3 years or 7 years to do the work, they only care if it’s meaningful.
It's been a long long time since I was the academic research world - but isn't 3 published papers pretty much the expectation for a PhD quantity of research?
Really depends on the field. Computer science research usually has pretty short cycle times. If you're working on, say, biology or anthropology, collecting data can take substantially longer.
Technical feedback yes, but always reject any career feedback from your advisor since the data shows it's unlikely a good model for future career success
I totally agree. I had to choose and chose a macbook air. Love that little machine! Then, when I had saved up enough, I got myself the ipad (13") for reading ttrpg pdfs.
The two machines solve totally different problems. I never bothered to get the keyboard for the ipad - because typing is something i do on the macbook air. The ipad is incredible for reading pdfs that are meant to be letter/a4 sized.
Man I'm so disappointed: I thought I'd be able to use this tool to learn different British accents but apparently the tool "British" is already an accent...
I'm not totally sold on the idea itself, but... you don't need to get a 100 ton railcar there. Ship empty railcars and fill it with rocks / sand / water or whatever you find at the destination.
Sure. But neither do you. So are you really thinking or are you just autocompleting?
When was the last time you sat down and solved an original problem for which you had no prior context to "complete" an approximated solution with? When has that ever happened in human history? All the great invention-moment stories that come to mind seem to have exactly that going on in the background: Prior context being auto-completed in an Eureka! moment.
I think (hah) you're understimating what goes on when living things (even small animals) think. We use auto-compleition for some tasks, but it is a component of what we do.
Let's say your visual system auto-completes some pattern and detects a snake while you're walking, that part is auto-completion. You will probably react by freezing or panicing, that part is not auto-compleition, it is a deterministic algorithm. But then you process the detected object, auto-compleiting again to identify it as just a long cucumber. But again, the classification part is auto-completion. What will you do next? "Hmm, free cucumber, i can cook with it for a meal" and you pick it up. auto-completion is all over that simple decision, but you're using results of auto-completion to derive association (food), check your hunger level (not auto-completion), determine that the food is desirable and safe to eat (some auto-compleition), evalute what other options you have for food (evaluate auto-complete outputs), and then instruct your nervous system to pick it up.
We use auto-compleition all the time as an input, we don't reason using auto-compleition in other words. You can argue that if all your input is from auto-completion (it isn't) then it makes no difference. But we have deterministic reasoning logical systems that evaluate auto-completion outputs. if your cucumber detection identified it as rotten cucumber, then decision that it is not safe to eat is not done by auto-completion but a reasoning logic that is using auto-completion output. You can approximate the level of rot but once you recognize it as rotten, you make decision based on that information. You're not approximating a decision, you're evaluating a simple logic of: if(safe()){eat();}.
Now amp that up to solving very complex problems. what experiments will you run, what theories will you develop, what R&D is required for a solution,etc.. these too are not auto-completions. an LLM would auto complete these and might arrive at the same conclusion most of the time. but our brains are following algorithms we developed and learned over time where an LLM is just expanding on auto-completion but with a lot more data. In contrast, our brains are not trained on all the knowledge available on the public internet, we retain a tiny miniscule of that. we can arrive at similar conclusions as the LLM because we are reasoning and following algorithms matured and perfected over time.
The big take away should be that, as powerful as LLMs are now, if they could reason like we do, they'd dominate us and become unstoppable. Because their auto-completion is many magnitudes better than ours, if they can write new and original code based on an understanding of problem solving algorithms, that would be gen ai.
We can not just add 1 + 1 but prove that the addition operation is correct mathematically. and understand that when you add to a set one more object, the addition operation always increments. We don't approximate that, we always, every single time , increment because we are following an algorithm instead of choosing the most likely correct answer.
Fun fact, but also fake news. Emmethaler cheese has holes even in Switzerland. It's the only part of that cheese that tastes any good, so why remove them?
As a Swiss, confusing Emmenthaler and Gruyere is wild - they're soooo different in just about any property except both being called cheese. And I personally believe Emmenthaler to be the worst cheese produced in Switzerland. The only thing it has going for it are the iconic holes. Gruyere on the other hand is up there with the best of Swiss cheeses.
If it helps anything at all: It's normal. At this point, you've already proven you're smart and knowledgeable. Now, the universe wants to see if you can also finish what you've started. That's the main thing a PhD proves: That you can take an incredibly interesting topic and then do all the boring stuff that they need you to do to be formally compliant with arbitrary rules.
Focus on finishing. Reduce the scope as much as possible again. Down to your core message (or 3-4 core messages, I guess, for paper-based dissertations).
Listen to the feedback you get from your advisor.
You got this!