r-zip

7 ms·

1.
by r-zip 2y ago
How stupid do these CEOs think people are?
2.
by r-zip 2y ago
They’re not wrong though. The frequency with which these things still just make shit up is astonishingly bad. Very dismissive of a legitimate criticism.
3.
by r-zip 2y ago
Unwillingness to adapt to the destruction of the middle class and knowledge work is pretty reasonable tbh.
4.
by r-zip 2y ago
What mathematical guarantees? I don't see any mathematical results in the paper.
5.
by r-zip 2y ago
They are NOT as unaware of things as we are. That’s like someone seeing a software developer googling stuff and saying “see, they don’t know much more than me”. An expert refreshing their knowledge on Google is not the same as a layman lear
6.
by r-zip 2y ago
> in no universe is that a legitimate ML interview question Why not? This seems like the ML equivalent of FizzBuzz. If you don't know how matrix multiplication works well enough to implement it, I would argue that you don't kno
7.
by r-zip 2y ago
But they likely did have their wives do a lot of the domestic heavy lifting.
8.
by r-zip 3y ago
Damn, really?
9.
by r-zip 3y ago
I think the time estimates depend heavily on the field. A mathematician recently told him that it would take him 1-2 weeks (with nothing else to do in that time) to digest a paper outside his main area.
10.
by r-zip 3y ago
Did you read the abstract?
11.
by r-zip 3y ago
What are you talking about? Did we read the same abstract?
12.
by r-zip 3y ago
Even with an accuracy score, it’s meaningless. Accuracy isn’t the right metric.
13.
by r-zip 3y ago
This was also a problem a decade ago. I asked a professor for research opportunities and ended up segmenting a bunch of images. That was it. No credit for that work, either (or pay).
14.
by r-zip 3y ago
Self-supervised training doesn’t involve human labelers, and that’s what’s responsible for most of the recent gains in LLM performance.
15.
by r-zip 3y ago
Deep nets are notoriously overconfident in their predictions. So why do you expect this approach to succeed?
16.
by r-zip 3y ago
Would you say the same about golf?
17.
by r-zip 3y ago
There is an Apple Pay option. I just used it!
18.
by r-zip 4y ago
Wouldn't ">=" be "not <"?
19.
by r-zip 4y ago
You’re joking, right?
20.
by r-zip 4y ago
If you seriously don’t think this will be widely abused, then you’re extremely optimistic. Spreading propaganda is the most obvious use case, assuming the resource requirement remains high. I don’t think it’s far-fetched either. If these mo
21.
by r-zip 4y ago
Serious question: do the people who work on this topic think they’re doing a good thing?
22.
by r-zip 4y ago
I guess that depends on your definition of “world.” For me, that would be a planet, not a moon.
23.
by r-zip 4y ago
Just ask it the definition of a mathematical term and watch it get it wrong. For example, I asked it to define the Kronecker product and it gave me a lot of correct information, but the actual equations were wrong.
24.
by r-zip 4y ago
The funny thing is that it shouldn't be. There is plenty of potential, but all sorts of (mostly non-technical) factors are getting in the way.
25.
by r-zip 4y ago
Can you recommend a few such places? :)
26.
by r-zip 4y ago
I think a big part of the problem is that most PMs are non-technical, and at some point up the chain so are most managers. Data science, when done thoughtfully, requires you to appreciate minute issues from data collection and management al
27.
by r-zip 4y ago
Computer science...
28.
by r-zip 4y ago
> where's the scientific method? Where's the experiments and rigor? No time for that, buddy. CXOs need results, ASAP! (This was basically the attitude of my managers at the last place I worked).
29.
by r-zip 4y ago
Why do math on paper when you can write code and look cool (and not like a useless academic)?
30.
by r-zip 4y ago
That's all fine, as long as you still understand the underlying assumptions and pitfalls. Many people who skim documentation and throw things together haphazardly do not.

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