Why do people Google questions anyway?
Because it gives better responses.
Google and all the other major search engines have built in functionality to perform natural language processing on the user’s query and the text in its index to perform a search more precisely aligned with the user’s desired results, or to recommend related searches.
If the functionality is there, why wouldn’t we use it?
Search engine algorithms are way better than in the 90s and early 2000s when it was naive keyword search completely unweighted by word order in the search string.
So the tricks we learned of doing the bare minimum for the most precise search behavior no longer apply the same way. Now a search for two words will add weight to results that have the two words as a phrase, and some weight for the two words close together in the same sentence, but still look for each individual word as a result, too.
More importantly, when a single word has multiple meanings, the search engines all use the rest of the search as an indicator of which meaning the searcher means. “Heat” is a really broad word with lots of meanings, and the rest of the search can help inform the algorithm of what the user intends.
I can travel forward in time at a rate of 60 seconds per minute, and I think the US government can, too.
I think back to the late 90’s investment in rolling out a shitload of telecom infrastructure, with a bunch of telecom companies building out lots and lots of fiber. And perhaps more important than the physical fiber, the poles and conduits and other physical infrastructure housing that fiber, so that it could be improved as each generation of tech was released.
Then, in the early 2000’s, that industry crashed. Nobody could make their loan payments on the things they paid billions to build, and it wasn’t profitable to charge people for the use of those assets while paying interest on the money borrowed to build them, especially after the dot com crash where all the internet startups no longer had unlimited budgets to throw at them.
So thousands of telecom companies went into bankruptcy and sold off their assets. Those fiber links and routes still existed, but nobody turned them on. Google quietly acquired a bunch of “dark fiber” in the 2000’s.
When the cloud revolution happened in the late 2000’s and early 2010’s, the telecom infrastructure was ready for it. The companies that built that stuff weren’t still around, but the stuff they built finally became useful. Not at the prices paid for it, but when purchased in a fire sale, those assets could be profitable again.
That might happen with AI. Early movers over invest and fail, leaving what they’ve developed to be used by whoever survives. Maybe the tech never becomes worth what was paid for it, but once it’s made whoever buys it for cheap might be able to profit at that lower price, and it might prove to be useful in the more modest, realistic scope.
For example, as a coding assistant, a lot of people quite like them. But as a replacement for a human coder, they’re a disaster.
New technology is best when it can meaningfully improve the productivity of a group of people so that the group can shrink. The technology doesn’t take any one identifiable job, but now an organization of 10 people, properly organized in a way conscious of that technology’s capabilities and limitations, can do what used to require 12.
A forklift and a bunch of pallets can make a warehouse more efficient, when everyone who works in that warehouse knows how the forklift is best used, even when not everyone is a forklift operator themselves.
Same with a white collar office where there’s less need for people physically scheduling things and taking messages, because everyone knows how to use an electronic calendar and email system for coordinating those things. There might still be need for pooled assistants and secretaries, but maybe not as many in any given office as before.
So when we need an LLM to chip in and reduce the amount of time a group of programmers need in order to put out a product, the manager of that team, and all the members of that team, need to have a good sense of what that LLM is good at and what it isn’t. Obviously autocomplete has always been a productivity enhancer for long before LLMs have been around, and extensions of that general concept may be helpful for the more tedious or repetitive tasks, but any team that uses it will need to use it with full knowledge of its limitations and where it best supplements the human’s own tasks.
I have no doubt that some things will improve and people will find workflows that leverage the strengths while avoiding the weaknesses. But it remains to be seen whether it’ll be worth the sheer amount of cost spent so far.
You cant remove that double negative without making it incorrect
Sure you can: The IP Laws That Reinforce Enshittification.
I’m pretty sure every federal executive agency has been on Active Directory and Exchange for like 20+ years now. The courts migrated off of IBM Domino/Notes about 6 or 7 years ago, onto MS Exchange/Outlook.
What we used when I was there 20 years ago was vastly more secure because we rolled our own encryption
Uh that’s now understood not to be best practice, because it tends to be quite insecure.
Either way, Microsoft’s ecosystem on enterprise is pretty much the default on all large organizations, and they have (for better or for worse) convinced almost everyone that the total cost of ownership is cheaper for MS-administered cloud stuff than for any kind of non-MS system for identity/user management, email, calendar, video chat, and instant messaging. Throwing in Word/Excel/PowerPoint is just icing on the cake.
They were largely unaffected by the tariffs targeting China, because US trade policy distinguishes between mainland China and Taiwan. Problem was that Trump announced huge tariffs on everyone, including a 32% tariff on Taiwan.
I wonder what the use case is for 480W though. Gigantic 80" screens generally draw something like 120W. If you’re going bigger than that, I would think the mounting/installation would require enough hardware and labor that running out a normal outlet/receptacle would be trivial.
this battery can deliver 0.03mA of power
0.03mA of current. That times the 3 volts = 0.1 mW of power.
This is pretty normal, in my opinion. Every time people complain about common core arithmetic there are dozens of us who come out of the woodwork to argue that the concepts being taught are important for deeper understanding of math, beyond just rote memorization of pencil and paper algorithms.
Do you have a source for AMD chips being especially energy efficient?
I remember reviews of the HX 370 commenting on that. Problem is that chip was produced on TSMC’s N4P node, which doesn’t have an Apple comparator (M2 was on N5P and M3 was on N3B). The Ryzen 7 7840U was N4, one year behind that. It just shows that AMD can’t get on a TSMC node even within a year or two of Apple.
Still, I haven’t seen anything really putting these chips through the paces and actually measuring real world energy usage while running a variety of benchmarks. And the fact that benchmarks themselves only correlate to specific ways that computers are used, aren’t necessarily supported on all hardware or OSes, and it’s hard to get a real comparison.
SoCs are inherently more energy efficient
I agree. But that’s a separate issue from instruction set, though. The AMD HX 370 is a SoC (well, technically, SiP as pieces are all packaged together but not actually printed on the same piece of silicon).
And in terms of actual chip architectures, as you allude, the design dictates how specific instructions are processed. That’s why the RISC versus CISC concepts are basically obsolete. These chip designers are making engineering choices on how much silicon area to devote to specific functions, based on their modeling of how that chip might be used: multi threading, different cores optimized for efficiency or power, speculative execution, various specialized tasks related to hardware accelerated video or cryptography or AI or whatever else, etc., and then deciding how that fits into the broader chip design.
Ultimately, I’d think that the main reason why something like x86 would die off is licensing reasons, not anything inherent to the instruction set architecture.
it’s kinda undeniable that this is where the market is going. It is far more energy efficient than an Intel or AMD x86 CPU and holds up just fine.
Is that actually true, when comparing node for node?
In the mobile and tablet space Apple’s A series chips have always been a generation ahead of Qualcomm’s Snapdragon chips in terms of performance per watt. Meanwhile, Samsung’s Exynos has always been behind even more. That’s obviously not an instruction set issue, since all 3 lines are on ARM.
Much of Apple’s advantage has been a willingness to pay for early runs on each new TSMC node, and a willingness to dedicate a lot of square millimeters of silicon to their gigantic chips.
But when comparing node for node, last I checked AMD’s lower power chips designed for laptop TDPs, have similar performance and power compared to the Apple chips on that same TSMC node.
Yeah, Firefox in particular gave me the most issues.
Configuring each app separately is also annoying.
And I definitely never got things to work on an external monitor that was a different DPI from my laptop screen. I wish I had the time or expertise to be able to contribute, but in the meantime I’m left hoping that the Wayland and DE devs find a solution to be at least achieve feature parity with Windows or MacOS.
I mean, that’s basically the author’s problem, then. I suspect the software support just isn’t there for the hardware that ships on this particular laptop, to where it’s easiest to manually put it in some blurry non native resolution, as the least crappy solution.
What’s the current state of Linux support for high dpi screens? As of two years ago I had some issues with getting things to work right in KDE, especially with GTK apps, by manually fiddling with system font sizes and button sizes, before I ended up donating that laptop to someone else.
What’s tricky is figuring out the appropriate human baseline, since human drivers don’t necessarily report every crash.
Also, I think it’s worth discussing whether to include in the baseline certain driver assistance technologies, like automated braking, blind spot warnings, other warnings/visualizations of surrounding objects, cars, bikes, or pedestrians, etc. Throw in other things like traction control, antilock brakes, etc.
There are ways to make human driving safer without fully automating the driving, so it may not be appropriate to compare fully automated driving with fully manual driving. Hybrid approaches might be safer today, but we don’t have the data to actually analyze that, as far as I can tell.
What’s annoying, too, is that a lot of the methods that have traditionally been used for discounts (education, nonprofit, employer-based discounts) are now only applicable to the subscriptions. So if you do want to get a standalone copy and would ordinarily qualify for a discount, you can’t apply that discount to that license.
Is it just me, or do new office features seem kinda pointless or unnecessary?
I feel like almost all the updates of the last two decades have been:
Some of these are important (especially the first two), but the user experience shouldn’t change much for them.
Longer queries give better opportunities for error correction, like searching for synonyms and misspellings, or applying the right context clues.
In this specific example, “is Angelina Jolie in Heat” gives better results than “Angelina Jolie heat,” because the words that make it a complete sentence question are also the words that give confirmation that the searcher is talking about the movie.
Especially with negative results, like when you ask a question where the answer is no, sometimes the semantic links in the kndex can get the search engine to make suggestions of a specific mistaken assumption you’ve made.