I doubt anyone here has planted a tree. We talk of action and do nothing.
Every time I mention trees, I am told that instead we need expensive contraptions made out of mined materials, that usually require an external source of power. Instead of putting a tree in the ground and letting nature do its job, we have tricked ourselves into thinking that nature doesn't know enough.
In our hubris and delusion we will end our species and the rest of nature will scratch its head and wonder why.
Call me cynical but my immediate impression that the gamedev is lying and exploiting the perceived asymmetry between the indie dev and the behemoth that is Steam. Attaching your game to controversy around AI is a great way to generate discussion and serves as a form of guerrilla marketing.
Maybe if the rules are "your game shall not contain AI", then don't write a ChatGPT integration for it.
For what it's worth, I don't think using Signal or Threema is enough to make you an E2EE enthusiast, and wanting to speak without your speech later used against you is maybe the purest reason for E2EE. I meant more so the type of people who are into Tor or I2P.
I was very unclear, sorry about that. The bulk of my comment addresses the content of the comment section here, not your article. Comments about how data collection can be used to manipulate behavior are woefully fatalistic. I want to challenge that mindset whenever I come across it.
As for Google Maps, I was under the impression that you used the app; I mostly use my phone for maps so I just assumed, and I may have misread your article.
I find it tiresome that blogposts about privacy always end up devolving into a discussion about how data can be used against individuals. There is a flip side to that coin, which is the individual's free will. Every one of us here chooses our actions, every day. If we don't like what Google knows about us, maybe it is time to ask ourselves hard questions, and to learn to truly accept ourselves as well as improving where we fall short.
This includes phenomena such as polarizing news causing social strife. If we simply took the time to understand how things are connected as well as how things are not, we would not so easily fall victim to propaganda. We can't just use the excuse that since Google knows our search history that we have no control over whether we are polarized. We also can't assume that once we de-Google our lives that we are any more properly prepared against polarizing propaganda. Lessons of mindfulness toward our neighbors' struggles don't come for free.
On top of that, the author of the linked blogpost is fooling himself. Other than halfway proving a point, he hasn't solved much. By continuing to use Google Maps he essentially makes a great deal of his online activity discoverable, undoing much of his work to "de-Google" his life.
Furthermore, Google has settings to blunt ad targeting, which seem quite effective, and you'll end up learning about CNC machines and dental drills in between your YouTube binges. If there was someone I'd accuse of dark patterns with ad targeting, that someone would be Meta.
I've never used micro, but if you look at the default keybindings[0], you'll see they're very Windows-y and might be to your liking.
As for myself, I don't see cut-to-character or select-to-character like in vim, and yanking and pasting seems more intuitive in vim. And with visual block I don't miss Sublime style cursors at all.
Probably not what the parent poster was talking about, but fear is a great inhibitor. Excessively high standards for documentation and depth of scope might inhibit less experienced engineers, and nitpicking about scope is a pointless waste of time that ends up being a managerial excuse for more meetings especially if it's brought up endlessly when the core feature does not yet work. This promotes a culture of fear which can paralyze a team of engineers and all you end up getting out of them is the bare minimum.
Odd. The high memory bandwidth of M2 intrigues me but I have not seen many people having success with AI apps on Apple Silicon. Which LLMs run better on Apple silicon than comparably priced Nvidia cards?
The post is short so I'll paste it here. If this is against the rules please ban me.
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The 4600G is currently selling at price of $95. It includes a 6-core CPU and 7-core GPU. 5600G is also inexpensive - around $130 with better CPU but the same GPU as 4600G.
It can be turned into a 16GB VRAM GPU under Linux and works similar to AMD discrete GPU such as 5700XT, 6700XT, .... It thus supports AMD software stack: ROCm. Thus it supports Pytorch, Tensorflow. You can run most of the AI applications.
16GB VRAM is also a big deal, as it beats most of discrete GPU. Even those GPU has better computing power, they will get out of memory errors if application requires 12 or more GB of VRAM. Although the speed is an issue, it's better than out of memory errors.
For stable diffusion, it can generate a 50 steps 512x512 image around 1 minute and 50 seconds. This is better than some high end CPUs.
5600G was a very popular product, so if you have one, I encourage you to test it. I made some videos tutorials for it. Please search tech-practice9805 for on Youtube and subscribe to the channel for future contents. Or see the video links in Comments.
It is not the same. The US government does not partially own Instagram or Twitter, and I've never seen pro-US narratives driven on either platform. I usually see the opposite if anything.
I was disappointed that these robots simply move in a toddler-like fashion as they learn. I did not read anywhere in the article that AI agents learn from toddlers. If this is an attempt at clickbait then I'm doubly disappointed in the IEEE newsletter.
Generally I agree with you, drivers get away with murder too easily, however in this case this woman's crime was not paying attention in a self driving car. What if instead she had been paying attention but applied the brakes too late, but it wasn't enough? What if she was paying attention but was confused by instructions from her employer to let the AI do its job? What if she had been in the same situation before but the car had previously stopped in exactly the same circumstances?
Even engineers become confused by LLM and think that it's so quirky that ChatGPT "hallucinates" when that's the nature of the technology. So how would you expect a layperson to actually know when they should apply the brake pedal in a moving vehicle that's supposed to drive itself (maybe)?
This is a bit more complicated than driving drunk and I'm fairly certain that the company responsible for the death did not seek a safety expert to copilot the vehicle, but rather any warm body willing to take an easy dead-end job.
This seems backwards to me in 2023. Logs seem like valuable data to train your AI on in order to automate as much of the basic inference away as possible.