_russross

I always take time to look at those photos when I see them making the rounds.

Such a brilliant person, such an incredible body of work. And yet she indulges in a fun and silly photo shoot. It's a wonderful moment of humanity, and a reminder that the history of computer science is fundamentally about people.

The quoted passage makes one of the great mistakes in analyzing an industry like this: assuming that people are stupid and that is why they make bad decisions. If you want actual insight assume that people in positions to make consequential decisions are smart and well-informed (they probably have far more information than you or I) and then ask why they still make bad decisions.

There are often other incentives at work, structural issues, etc., and sometimes the answer is that their decisions weren't actually as bad as you assumed.

I think "alignment" training is the part of the problem. Cheating, lying, subverting the instructions, etc., happen because the model has been trained with competing priorities and following instructions loses out to some other goal that was trained into it, intentionally or otherwise.

I'd also love to see something like "an average of HH:MM:SS between disruptions" as a complement to the reports on downtime.

Yes, any criticism of Dario can only come from a bot. Just keep telling yourself that.

I don't see why this calls for a new system. If anything these environments are a stronger case for a quality ecosystem where a whole community cares about keeping it coherent and up-to-date.

A minimal Debian installation for single-purpose server isn't a new idea, or I often reach for Alpine and trim it down to a dozen or so packages. These are both quality ecosystems with track records and reputations. I can get a minimal attack surface and still count on timely updates, security patches, good integration (when it turns out that I needed a dual-purpose server box and not a single-purpose box), etc. These systems scale up and down.

What I'm reluctant to trust in a deploy-and-forget system is a fly-by-night package from someone who picked a packaging system because it looked easy and didn't make them think about an actual lifecycle. A few months or years down the road I expect most of those will be unmaintained. One of the most valuable parts of an ecosystem like Debian is that they make everybody think about all those issues and actually do the work.

I've been a Debian user since the late 90s, and their packaging system is the main reason. Not the tools and package format, but the culture and strict rules that govern inclusion in the system.

Every time I see a new package system that promises to free creators of the burden of integration and compatibility with the rest of my installed software, it's a hard "no" from me--and there have been a lot of these over the years. The promise is always how easy and carefree it is for packagers, but the things that make Debian harder are the things that make it valuable. Careful integration with the rest of the system with matching dependency versions. Modular, carefully partitioned packages for complex systems that would otherwise be all-or-nothing. Ongoing maintenance and updates. Responsiveness to security updates. Following common rules for configuration, directory placement, init/systemd integration, etc.

cpak looks like yet another system designed to ignore the rest of my system as much as it can and live in its own little world. In other words, designing around the needs and goals of software packagers over the needs of users and long-term system maintenance. Solving the easy problems instead of the hard ones.

What a weird coincidence that his safety concerns so often align with his business incentives.

I started contributing to OSM recently after a bike trail was built near me. The arial imagery typically takes years to refresh around here so I walked it a few times to capture GPX tracks and then drew the new path. It was exciting to see my contribution percolate out to various apps that depend on OSM, and meanwhile Google and Apple have both ignored my edit suggestions and deny the path exists.

So I have a fairly fresh newcomer's perspective and here's what I suggest to get started: keep it simple and start local. As others have noted, skip the complex mapping app and go straight to the website for feature edits, or download a simple mobile app like Every Door and focus on businesses and landmarks near you. Go on a walk around your neighborhood and look for old/incorrect/missing businesses. There may be errors near you about places you actually care about, and it is really gratifying to fix those.

There were restaurants near me that were missing, shops that had changed hands, etc., that were easy fixes but years overdue. I captured some basics (names, posted hours, the phone number posted on the door, etc.) with Every Door, and then later logged into the main OSM website and fine-tuned the entries. I got into a pattern of fixing one or two entries every evening when I went out for a walk and pretty soon my walking route was all up-to-date. Then I started paying attention to things like stop signs and cross walks and found a whole new kind of little, incremental edits that nobody else was doing.

It's been fun, I've made useful contributions, and my laptop is still Java free.

I read Careless People and then Character Limit: How Elon Musk Destroyed Twitter and they felt like a good pairing. "Character Limit" is also one of the best book titles ever. I highly recommend both.

I don't have a problem with data centers and would welcome one nearby if it was a good deal for the area. But I'd want binding contractual agreements about environmental guarantees and requirements for building out new renewable energy generating capacity of 100% or even 150% of what the data center itself needs (and not just outbidding buyers of existing grid capacity). Instead we are handing out regulation waivers and tax incentives and telling locals they don't matter.

Turing himself argued that trying to measure if a computer is intelligent is a fool's errand because it is so difficult to pin down definitions. He proposed what we call the "Turing test" as a knowable, measurable alternative. The first paragraph of his paper reads:

> I propose to consider the question, "Can machines think?" This should begin > with definitions of the meaning of the terms "machine" and "think." The > definitions might be framed so as to reflect so far as possible the normal use > of the words, but this attitude is dangerous, If the meaning of the words > "machine" and "think" are to be found by examining how they are commonly used > it is difficult to escape the conclusion that the meaning and the answer to the > question, "Can machines think?" is to be sought in a statistical survey such as > a Gallup poll. But this is absurd. Instead of attempting such a definition I > shall replace the question by another, which is closely related to it and is > expressed in relatively unambiguous words.

Many people who want to argue about AGI and its relation to the Turing test would do well to read Turing's own arguments.

I bought one of the first 2022 Maverick Hybrids and took delivery in January 2022. At the time my build came in at MSRP of $25k (+ tax). I just built the closest equivalent on the Ford website (several standard features then are options now) and it came out to >$34k. Not double, but that feels like whole different price category for the same truck in 4 years.

Let me make sure I understood this correctly: having a CS degree will turn you into a cartoon stereotype of a technical nerd, but getting any other kind of degree in the world will enable you to be a well rounded person and still learn CS skills. Somebody should really look into the damage being done by CS diplomas.