81–90 of 139 posts
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Maybe GP is a non-native English speaker? This construct would be pretty common way for a native French speaker to say they are angry at something. Not sure if it's common in English as well.
This is a pretty common phrase in English as well, it is not meant to be taken literally.
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I've barely forgiven him for explaining genetic algorithms and acting like they have any relevance to contemporary ML research. The footnote video was an alright explanation of backprop. If that were part of the main video that would have been reasonable. I really like his history/geography videos but anything technical leave a lot to be desired. And don't get me started on Humans Need Not Apply.
> And don't get me started on Humans Need Not Apply. Well now you have to tell us. :) Many of the concrete examples in that video are exaggerated and/or misunderstood but the general question it asks - what to do when automation makes many people unemployable through no fault of their own - seems valid.
Unfortunately the video doesn't answer its own question directly.
The answer for the past 40 years or so seems to be "move them to lower-paying service jobs, or out of the job market entirely."
I have met people who think they understand a particular topic I am versed in, but actually don't. Similarly, I am often wary that I get superficial knowledge about a topic I don't know much about through "laymen" resources, and I doubt one can have an appropriate level of understanding mainly through analogies and metaphors. It's a kind of "epistemic anxiety". Of course, there are "laymen" books I stumbled upon whic…
Totally aware that this isn't a fully formal definition of deep learning, but one interesting takeaway for me is realizing that in a way, corporations with their formal and informal reporting structures are structured in a way similar to neural networks too. It seems like these sort of structures just regularly arise to help regulate the flow of information through a system.
There is research claiming the entire universe is a neural network: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7712105/
I have met people who think they understand a particular topic I am versed in, but actually don't. Similarly, I am often wary that I get superficial knowledge about a topic I don't know much about through "laymen" resources, and I doubt one can have an appropriate level of understanding mainly through analogies and metaphors. It's a kind of "epistemic anxiety". Of course, there are "laymen" books I stumbled upon whic…
> an appropriate level of understanding mainly through analogies and metaphors I think it's actually worse than that - somebody who doesn't know actually realizes that he doesn't know, but somebody who _thinks_ he understands through analogies and metaphors will confidently come to the incorrect conclusion and then argue with somebody who actually does understand the topic - often managing to convince innocent bystan…
On the one hand they're jargon used as short hand to technical concepts understood well by domain experts. And the concision they afford can lead to deeper understanding as they transcend their composite or adapted meanings and become base terminology in and of themselves (I think of e.g. Latin in English legal terminology. "Habeas corpus" has a literal meaning when translated, but the understood jargon has a deeper, and more specific meaning). At that point, they are powerful because of the precision of meaning and concision of expression they afford.
On the other hand, they lift intuitive terminology from a base language that is understood in vaguer terms by a broader audience. And this creates invisible disconnects because the abstraction created by these terms leaks like a sieve unless you know the precise semantics and have the model to use them.
By translating a discourse into a higher metaphoric level, we increase precision and efficiency amongst mutual understanders, but at the same time, we increase the level of ambiguity, the number of possible interpretations, and the availability of terms familiar to (and thus, handles to grab on to) non-understanders. And that latter situation allows non-understanders to string together what sound superficially like well-formed thoughts using jargon terms, but based on the base language semantics. But without the deeper knowledge required to understand whether a given utterance scans or not.
That's how I've been trying to wrap my head around it at least. I hope it doesn't sound like moralizing or condescension, I don't mean it to. I know I'm "guilty" of trying to manipulate metaphoric models that I don't actually understand, based on the lay-semantics of their jargon.
Nothing about LLMs?!
Yeah, I need something to explain me about those Transformers things. I know it was published by Google in 2017 and that it is 'magic'. End of knowledge. Maybe I should ask ChatGPT?
https://www.youtube.com/watch?v=kCc8FmEb1nY
(I haven't gotten around to watching it yet)
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I read this to see if it would be useful to share with my 9 year old. After reading it, I think it is not any more useful (alone) than watching the 3b1b video on this topic. The video is longer, but has more visualizations. I think that perhaps reading this description after watching the video might make the process more memorable. My guess is that if I had my daughter read this first, it wouldn't do much to make the…
You could just let your daughter see it. To what extent can you "protect" her exposure to the world?
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> And don't get me started on Humans Need Not Apply. Well now you have to tell us. :) Many of the concrete examples in that video are exaggerated and/or misunderstood but the general question it asks - what to do when automation makes many people unemployable through no fault of their own - seems valid.
> what to do when automation makes many people unemployable through no fault of their own - seems valid Unfortunately the video doesn't answer its own question directly. The answer for the past 40 years or so seems to be "move them to lower-paying service jobs, or out of the job market entirely."