I have sympathy for any jobs that might be affected (much as my own job has become more tenuous in software engineering). And if the field is disrupted by chaos that makes the research process unproductive, that's bad too and should of course be handled by applying better organization within the institutions that tend to perform mathematical research.

But to a large degree, the notion that "sloppy AI proofs are bad for mathematics research" seems like a total failure of the imagination to me. Attempting to find shorter proofs or more elegant proofs can be turned back in on itself via proof theory. There are proofs in Presburger arithmetic that are doubly exponential in the length of the sentence. Yet a more powerful theory like PA makes quick work of such theorems. The explainability or "subjective beauty" of a proof can be quantified and optimized against. Optimization itself can be optimized against. I really don't understand how this magical ability to know the truth of more theorems much more quickly—even via an "ugly" route—is anything but a net positive.

The Catholic church could have said exactly the same thing at one point. "Why should we even devote time to an argument as absurd as the earth not being the center of the universe?" There are darker examples along the lines of those you give, with beliefs quite opposite to those we have nowadays.

Would you categorize this particular style of rhetoric to be persuasive or annoying? And before you say "persuasive" because you're thinking about this specific issue regarding AI consciousness, consider many things in the past that have been written as though they were absolutely definitive, and yet today we believe exactly the opposite, and for many such issues we find the prevailing viewpoint at the time reprehensible.

That's not to say that Ted is wrong at all here; I'm not commenting on that. But I find the entire style of the article grating because it seems to violate common assumptions regarding "good faith" debate, and I would find the article equally frustrating if he had titled it "Artificial intelligence is conscious" and argued the opposite side, albeit in the same tone and using the same persuasion devices.

Case in point: where I live, the interstate is often congested, and a driver "camping" in the left lane frequently leads to traffic jams that back up for miles. The cars that get backed up become frustrated and start zooming and weaving through traffic in the right lanes to get past the blockage. And while there are plenty of police, they only go after the speeders (presumably because speeding tickets are more lucrative). I don't think I've ever seen someone pulled over for squatting in the left lane, despite the fact that it's illegal where I live and despite the presence of numerous signs that say "Keep Right Except to Past".

This is what I would call an example of a dysfunctional law, as I highly suspect that if one had the capability and interest to analyze aerial footage of traffic patterns, it would be found that left lane campers are a much more significant factor in the root cause of interstate traffic accidents than speeders. But the incentives are too perverse to fix the problem, so the situation persists.

Yeah, I think he summed it up better than I could have there.

As someone well past "peak" fluid intelligence at this point, I always hate reading research like this. "Crystallized intelligence" and "emotional intelligence" are the consolation prizes no one really wants.

I'd rather we instead perform research to identify how one might reverse the decline of fluid intelligence...

Corollary for managers: Do not say "it's your call", then once the decision has been made (and you skipped all the meetings pertaining to that decision), comment about how you would have done it differently and then retroactively request your report to go back and make changes. This is a great way to lose employees.

Well I thought so too. I match those behaviors, and I don't even watch television. But then I worked at a tech company where I could see the actual data on consumer preferences and behaviors, and it's fairly undeniable: most people aren't like you, me, or the average commenter on Hacker News.

I'm certainly not saying "blame the consumer", but if people really don't like ads so much (to the extent that they stop clicking on them), really dislike the subpar streaming services so much (to the extent that they unsubscribe) — then why haven't they abandoned these products?

There are other countries where valuing quality seems to be more deeply embedded in the culture, and most people in these countries will reject subpar offerings altogether. I think the U.S. has had a uniquely precipitous fall in this regard — the average person just doesn't seem to care that much. Why this is the case, I'm not sure, but it's not surprising that since Silicon Valley is located in the U.S., the region simply optimizes on whatever (revealed) consumer preferences return the most. Tech companies are certainly not unique in this regard.

Zuckerberg was in the office and walked up to the guy and said something along the lines of “Just so you are aware, it would probably take a lifetime or more to recoup the revenue lost during that outage. But we don’t assign blame during these sorts of events, so let’s just consider it an expensive learning opportunity to redesign the system so it can’t happen again.”

For example, consider two hypothetical but identical individuals: one born into a low-income neighborhood and one born into a high-income neighborhood. If you develop a model to predict what we currently categorize as "crime" (the definition of which is its own separate issue), you will find that the income of a neighborhood is inversely correlated with the density of crime. If this is the only factor in your predictive model, then you will more effectively reduce crime by directing attention toward the low-income neighborhood. But now there is an inherent unfairness, because the additional scrutiny toward the low-income neighborhood means that individual 1 is more likely to be caught for a crime than individual 2, despite both individuals having an equal likelihood of committing a crime. This also creates a self-reinforcing situation where having more statistics on the low-income subset of the population now allows you to improve your predictive model even further by using additional variables that are only relevant to that subset of the population, meanwhile neglecting other variables that would be relevant to predicting crime in high-income neighborhoods. Repeat this process a few times and soon you have a massive amount of unfairness in society.

It's probably impossible to eliminate all unfairness while still maintaining any sort of ability to control crime, but what is the appropriate threshold for this tradeoff?

With a team of five consultants and their manager, months of useless hour long meetings to answer simple questions that can be looked up in the documentation, and plenty of slideshows and design docs that are mostly filled with fluff, they’ve produced... a dashboard that I could have put together in about 30 minutes.

Whatever social purpose this contract fulfills is a total mystery to me, but all I can think of is what a waste of human time this is.