Religious traditions tend to hold that certain claims or parts of their canon are infallible, whereas academic philosophy is more welcoming of the critical and argumentative process.
For the skimmer/TL;DR'er, note that this article is by an advocacy group presenting their analysis of a situation, and then advocating and taking action on it: "Next Steps: Commission must repeal EU-US deal. noyb ..."
It is not reporting on an opinion of a representative or proxy of the European Commission.
I find this a hilarious reversal of what you typically see in journalism; here the headline and the "key takeaways" are very neutral language and the article itself is dramatic
I don't think Geohot has a good idea about LeCun and Hutter's views on the limitations of LLMs. I think that on abstract, textual domains, LLMs perform superbly, and they would agree. I am not too well-informed about LeCun and Hutter's views either, but I think that:
LeCun thinks that LLMs are a bad fit for AI that understands the physical, dynamical systems that we inhabit, and that understanding this is necessary for AGI/ASI.
I don't know that Hutter is bearish on LLMs, but Hutter is interested in AI that can reason exceptionally well given infinite compute, and approximations of such a reasoning AI. I think he is open to the idea that LLMs can be such an approximation.
> Google now pulls the rug on Android which is a whole different story because it used to be open. The whole idea of Android was to be open.
This is the narrative for us in developed nations, but the majority of users today are people who were in developing countries and got a mid-tier smartphone to chat with friends and do banking with the same values as Apple users.
My intuitive understanding about double descent is that
1. Older ML models encoded in their architecture and lack of expressivity a bias to simplicity; which aided interpolation.
2. Overparameterized models instead use regularization to nudge parameters to simpler and more robust representations, while still memorizing the noise. In this manner, we still achieve generalization performance OOD. Moreover, the softer nudging and fundamental architectural expressivity allows for "data-specific" generalizations and representations that may be impossible to represent in small models.
3. At the critical point between the two regimes, the model is expressive enough to memorize; but not expressive enough to simultaneously both do that and encode general patterns.
I wonder how this understanding translates to these researchers' models of deep learning.
By that same argument, taken naively, film and video are dehumanizing, but not deplorably so: certainly the intensity of emotion and experience through film is far less present than say immersive theater, but we may be more comfortable with this modality, and also, benefit from the economies of scale.
Similarly, a call center worker may not care about having their accent being heard, but wants to get their numbers up, without struggling with a customer that isn't familiar with their accent, and enjoys the ease of speaking in their own accent than having to use one that distant customers are accustomed to. Likewise a customer probably just wants their problem fixed, without the effort of getting accustomed to an accent that they rarely encounter. This meets your definition of deplorable, but analogous to the former scenario, perhaps not deplorably so.
> But accent and pronunciation are different things [...]
This isn't true in the way you are thinking of. An accent can pronounce words the same way that another accent distinguishes. An accent can pronounce word x that another accent pronounces word y. What comes to mind immediately: in Indian English accents, RP/GA fricative "th" is pronounced as the aspirate, while the RP/GA aspirated "t" is pronounced retroflex, so naively, "three" can be misheard as "tree".
The working-class accent that I use where I'm from (not India) is syllable-timed (stress does not lengthen the duration of a syllable), and uses pitch lexical stress, rather than intensity/loudness for it, and stress itself is frequently very differently located compared to RP or GA. For "th" as well, we collapse it into t/d.
All in all, for someone who has heard it for the first time or rarely, it can be extremely disorienting to listen to a very distant foreign accent.
I don't understand the locus of the arrangement/decision that you find dehumanizing. There are several distinct ways I perceive how someone might find aspects of such an arrangement and change of arrangement dehumanizing, and I shall list them out, though I may or may not subscribe to them (for the purpose of this comment, I am assuming Filipino call center contractors, though one may substitute in any other country where the population knows English and jobs are outsourced to):
- Is it dehumanizing to Filipinos that Filipinos probably now do their job more efficiently without having to learn an accent that they are not exposed to?
- Is it dehumanizing to Filipinos that they no longer enjoy having their accent heard as a externality of a counterfactual arrangement?
- Is it dehumanizing to the customers that the company does not expect their customers to be cosmopolitan enough to understand a foreign accent with ease?
- Is it dehumanizing to the customers that the customers are now more sensorily shielded from a current-day reality regarding globalized providers of service?
- Is it dehumanizing, not due to this decision itself; but the globalized arrangement, to Canadians that they cannot expect to hold such a job and get by in Canada? Or perhaps to Filipinos, that such a job might be low-paying in their own country (or in respect to non-domestic goods that need to be purchased from outside their polity)?
- Is it dehumanizing, regarding not this decision, but the offshoring decision, that such decisions can be made without consent by employees and contractors?
My position regarding devices is that only 2 out of 3 should be satisfied:
1. Used as a proof of identity (for banks, govt services, etc.)
2. Is distributed to laypeople who have more pressing concerns in their lives than security.
3. Is an open platform where you can download apps arbitrarily from the Internet that can read your data and exfiltrate them to a malicious actor.
The mainstream today chooses 1&2. Novelty, underpowered devices choose 2&3. Hobbyists have option 3 (and those who like to live dangerously 1&3) with some inconvenience. You can still run GrapheneOS... and the mainstream apps that expect your device to be a proof of your identity won't work... and I find that quite reasonable.
Companies might bet that it is safer to base their businesses on more fungible explicated domain knowledge rather than knowledge that is siloed in human brains.
In my mind this is it, the colloquial seasons, and with vague boundaries depending on feeling, whereas the calendar "seasons" are there just to quarter the year artificially.
Hmm I think you made the grid lines thicker? I prefer it, but the thickness looks inconsistent (several divisions of the paper do not seem have thicker grid lines on my display)
It's so funny to me that you compare a decapitation strike with the stated aim of regime change to vandalism; I'd compare the actions taken to Iran in 2025 to vandalism over this.
There are areas of mathematics where the standard proofs are very interesting and require insight, often new statements and definitions and theorems for their sake, but the theorems and definitions are banal. For an extreme example, consider Fermat's Last Theorem.
Note on the other hand that proving standard properties of many computer programs are frequently just tedious and should be automated.
I think I have your new build(s) as I can play from the archive, but the contrast is still too low for me. Note that displays are different, so it's likely that things are more indistinct for me than for you. I've just played through the archive, and all my mistakes came from off-by-one errors because the grid lines were indistinct and I misplaced the holes.
I think we may have similar perspectives. Regarding empirical knowledge, consider when the knowledge is in relation to chaotic systems. Characterize chaotic systems at least as systems where inaccurate observations about the system in the past and present while useful for predicting the future, nevertheless see the errors grow very quickly for the task of predicting a future state. Then indeed, prediction is difficult.
One domain of knowledge I think you have yet to mention. We can talk about fundamentally computationally hard problems. What comes to mind regarding such problems that are nevertheless of practical benefit are physics simulations, material simulations, fluid simulations, but there exist problems that are more provably computationally difficult. It seems to me that with these systems, the chaotic nature is one where even if you have one infinitely precise observation of a deterministic system, accessing a future state of the system is difficult as well, even though once accessed, memorization seems comparatively trivial.