I think it's hilarious. LinkedIn is rushing to de-legitimize themselves so hard that they're inventing a new market for someone else to step into. Apparently indeed doesn't want to take it... not sure what's going on there.
I think this is only accurate when no external ideas are used, but I'd like to suggest that nearly all new discovery is built on a combination of old ideas and LLMs are really good at the latter.
If you bring something new to the table, then in my experience, AIs are really good at helping you ground it old ideas. If you want to set it and forget it, then you will get the mean. If you want to do something new, in my experience, they are enablers and not blockers.
Bingo. Value is the operative word here. Money and privacy are valuable too, and I'm assuming that there must be some pitch deck somewhere that is presumably good and selling city councils on this. Where is it? What is the value we're supposed to get from this?
Can anybody find trustworthy stats that these actually reduce crime? All I see are occasional anecdotes about how they were used to find one person one time.
Skeptical me seriously doubts this is an effective solution for crime. But maybe that's because this country has a history of being willing to do a million expensive and privacy violating things, and only if it's a punitive measure.
> Because I love swiping, but all my problems with it come from the fact that the QWERTY layout is far from ideal for it. I am 100% willing to learn a new layout if anyone will develop an optimal one for English so that swiping has a 99.9% accuracy rate instead of what currently feels more like 90% or 95%.
90-95% is a very good estimate! That's about what we measure on our test set. I have good news for you, and we will have a blog post about it soon. Because of how our models are built, we are able to optimize for detection accuracy directly by constructing synthetic swipes on each layout for ~50k words, and then testing them through the model. We tested around 800,000 layouts this way.
The biggest issue with QWERTY is that there are far too many words that swipe colinear or obtuse angle letter trigrams. These are both hard to detect and frustrating for swipe users, because you can't clearly indicate the letters you're gesturing. Neural swipe models (at least ours) look for indicators in the gesture pattern that suggests a user was targeting a specific letter, rather than trying to match a gesture shape like algorithmic detection does.
The shape of the keyboard can significantly improve the way the gestures are formed so that there is better indication of letters. The model can still respond to dwell times because unlike shape matching it uses the temporal information. But dwell interrupts flow, and in my opinion should be minimized in swipe layouts.
I'd been working with language models for several years before LLMs were a solution to this kind of problem. These are some ideas "off the top of my head" about how you can do classification in various ways. There's really a lot of ways to tackle it now, and a lot of trade-offs you can learn by experimenting with them.
There's even more options still, especially if you go further back toward more traditional methods. Static word vectors like GloVe or fasttext (optionally more modern equivalents like WordLlama or Model2Vec). Then there's sklearn-style stuff too. Those can be really small/fast but have more accuracy-level tradeoffs.
> Unions exist to benefit the median and bring up the floor, but it stifles competition among those who really do desire to be at the top. And in doing so while it brings up the floor, it also brings down the ceiling because people who would normally be motivated enough to move up would not have much incentive to do so anymore.
I think people tend to fixate on the worker-to-worker differences inside of unions. Yes, that is the most visible part of a union when in place, and at least in the US has valid arguments about meritocracy.
What is missed when limiting the scope to just that is the population-level abuses of workers that no amount of meritocracy will fix. When corporations engage in collusion against workers (now common and nearly unpunished in the US) the top-level wages are suppressed industry wide.
The whole pay band alignment that comes out of that undermines the meritocracy argument, and doesn't even begin to address the wage-fixing that has gone almost unchecked in tech for decades[1,2]. As a merited employee, you might have more options to where you can go, but it won't protect you from predatory hiring/layoff cycles and it certainly won't guarantee that you'll receive a truly competitive wage.
On paper, meritocracy sounds great. I have worked many places in tech and never once observed it, personally. Best case, if you have warmed a seat for enough years, then you advance that way. Worst, your employer knows they can just take advantage of you because you're willing to work without a dangling carrot.
As before, either the government frees itself from corruption and enacts justice or unions will come back. That is point we are at.
Flock is only one company. Someone in my town smashed one from a different company and was treated like a hero in Facebook comments. It’s not mentioned in the article.
Not that I’d want to work there given what they do, but every time I’ve been contacted by a recruiter there, it seems like it’s within a month of a mass layoff they’ve had… which is maybe just because they seem to have mass layoffs every quarter now.
They also seem to have adopted a no-remote hire policy and are in an extreme high CoL location. It’s a truly awful mix for trying to attract outside talent. I don’t know why they even bother.
Any discussion related to this topic always seems to assume everyone uses code the same way and for the same function, and then forces the rest of the world through that lens.
So here we walk around the circle one more time again, voicing our anxieties, talking past each other, waiting for the next opportunity for commentary to come in half an hour.
> At the time of the publication, Meta admitted subcontracted workers might sometimes review content filmed on its smart glasses when people shared it with Meta AI.
They just got fired for "piercing the veil". They committed the sin of bringing attention to the invasion of privacy.