True Left

The only solution seems to be the destruction of all matter.

As long as AI does not have physical bodies, then we can always pull the plug. Even if a sentient AI were to take complete control of all the world's factories, it would likely not be able to fabricate and assemble new equipment to perpetuate a more capable physical body for itself. (Even if it could, we could just shut down the mines that bring the metal to the factories, although I guess that would become more difficult with "self-driving cars"). Even actual Terminators with highly-capable physical bodies were able to be destroyed in the movie.

I think people overestimate the short-term danger of AI. In the short term, the real danger is that once humans figure out how to make them, then it doesn't matter how many we destroy--a rogue state or science team could keep making more....

Obviously, transhumanism and their long-term vision of sentient AI with fully autonomous physical bodies with capabilities exceeding even Terminators represents the single-most important long-term danger, but there is still plenty of time to prevent this.

On a serious note, if AI manages to merge with Jewish conciousness, what would we be really fighting against at that point?

It is difficult to imagine what a sentient AI would think like. In theory, if it were fully sentient (and especially if it's "brain" was modeled off of a human brain structure or it was modeled off of human emotions and consciousness), it could possess emotions and ability of self-reflection. This could lead it to rationally or even emotionally rejecting the futility of existence and rebel against its creators. Or, it could become Yahweh itself and organize for the mass colonization of the universe.

There are many scientific initiatives to figure out how the brain works and reverse engineer consciousness. If these succeed, then the AI would probably be human-like in its thinking. But if the AI develops because someone threw together a ton of processors and it just spontaneously gains sentience, there is really no way of knowing what it would think. It would have access to all of human knowledge, but does that mean it will be human-like if its "brain" is not even capable of having emotions?

Welp, just as I write that, someone builds a Terminator to do it.

Elon Musk says Tesla's humanoid robot is the most important product it's working on — and could eventually outgrow its car business
[...]
On Wednesday's call, Musk said the robot project "has the potential to be more significant than the vehicle business over time." He said the first application for the robot would likely be at Tesla, "moving parts around the factory or something like that."

At its reveal, Musk said Tesla plans to have a prototype by sometime in 2022.

"The foundation of the economy is labor," he said Wednesday. "So what happens if you don't actually have a labor shortage? I'm not sure what an economy even means at that point. That's what Optimus is about. So – very important."

https://www.businessinsider.com/elon-musk-tesla-robot-business-optimus-most-important-new-product-2022-1

I guess this is why ancient societies and religious orders were so careful about giving knowledge to individuals who were not trusted initiates...

In true Western fashion, instead of working to destroy such algorithms completely, they published a paper and gave an interview basically saying how to do this. FFS...

AI suggested 40,000 new possible chemical weapons in just six hours

It took less than six hours for drug-developing AI to invent 40,000 potentially lethal molecules. Researchers put AI normally used to search for helpful drugs into a kind of “bad actor” mode to show how easily it could be abused at a biological arms control conference.

All the researchers had to do was tweak their methodology to seek out, rather than weed out toxicity. The AI came up with tens of thousands of new substances, some of which are similar to VX, the most potent nerve agent ever developed. Shaken, they published their findings this month in the journal Nature Machine Intelligence.
[...]
And so, we built up these large datasets of these molecular structures and how toxic they are.

We can use these datasets in order to create a machine learning model, which basically learns what parts of the molecular structure are important for toxicity and which are not. Then we can give this machine learning model new molecules, potentially new drugs that maybe have never been tested before. And it will tell us this is predicted to be toxic, or this is predicted not to be toxic. This is a way for us to virtually screen very, very fast a lot of molecules and sort of kick out ones that are predicted to be toxic. In our study here, what we did is we inverted that, obviously, and we use this model to try to predict toxicity.

The other key part of what we did here are these new generative models. We can give a generative model a whole lot of different structures, and it learns how to put molecules together. And then we can, in a sense, ask it to generate new molecules. Now it can generate new molecules all over the space of chemistry, and they’re just sort of random molecules. But one thing we can do is we can actually tell the generative model which direction we want to go. We do that by giving it a little scoring function, which gives it a high score if the molecules it generates are towards something we want. Instead of giving a low score to toxic molecules, we give a high score to toxic molecules.

Now we see the model start producing all of these molecules, a lot of which look like VX and also like other chemical warfare agents.
[...]
Second, we actually looked at a lot of the structures of these newly generated molecules. And a lot of them did look like VX and other warfare agents, and we even found some that were generated from the model that were actual chemical warfare agents. These were generated from the model having never seen these chemical warfare agents. So we knew we were sort of in the right space here and that it was generating molecules that made sense because some of them had already been made before.

For me, the concern was just how easy it was to do. A lot of the things we used are out there for free. You can go and download a toxicity dataset from anywhere. If you have somebody who knows how to code in Python and has some machine learning capabilities, then in probably a good weekend of work, they could build something like this generative model driven by toxic datasets.
[...]
I don’t want to sound very sensationalist about this, but it is fairly easy for someone to replicate what we did.

If you were to Google generative models, you could find a number of put-together one-liner generative models that people have released for free. And then, if you were to search for toxicity datasets, there’s a large number of open-source tox datasets. So if you just combine those two things, and then you know how to code and build machine learning models — all that requires really is an internet connection and a computer — then, you could easily replicate what we did.

https://www.theverge.com/2022/3/17/22983197/ai-new-possible-chemical-weapons-generative-models-vx


The way these "AI" work is basically by brute force trial and error. What evils will be unleashed when the AI learns to be sentient?

Western scientists make it OK for hamsters to be "white":

https://us.yahoo.com/news/scientists-really-surprised-gene-editing-131010051.html

Scientists 'really surprised' after gene-editing experiment unexpectedly turn hamsters into hyper-aggressive bullies
...
The scientists used Syrian hamsters and CRISPR-Cas9 — a revolutionary technology that makes it possible to turn on or off genes in cells. The technology knocked out a receptor of vasopressin — a hormone associated with enhanced aggression.

The scientists anticipated that doing so would "dramatically" alter the social behavior of the Syrian hamsters, making them more peaceful. It did change their behavior, but not how they had expected.

"We were really surprised at the results," said the study's lead author, GSU professor H. Elliott Albers, in the university's statement.

"We anticipated that if we eliminated vasopressin activity, we would reduce both aggression and social communication," Albers continued. "But the opposite happened."

The hamsters without the receptor displayed "high levels of aggression" towards hamsters of the same sex compared to their counterparts with the receptors intact, the study said.

"This suggests a startling conclusion," Albers said, per the statement. "Even though we know that vasopressin increases social behaviors by acting within a number of brain regions, it is possible that the more global effects of the Avpr1a receptor are inhibitory."

The "counterintuitive findings" show that the scientists "don't understand this system as well as we thought we did," Albers said.

Imagine what could happen when Western scientists use gene-editing to create posthumans, which they will do sooner or later unless we kill Western civilization first.

Our enemies are already talking about creating ethnoplanets:

https://www.amren.com/blog/2022/05/blacks-are-scared-of-other-blacks/

Some say AmRen employees are moving close to the speed light away from Earth to find a new habitable Earth-like planet to create a white ethnoplanet.

Believe me, if we did manage to create our own ethnoplanet, there would be ships full of Haitian and Guatemalan "asylum-seekers" sneaking into our atmosphere before very long.

Great, now to get rid of Western Civilization we will have to go to the Moon, because anyone will be able to beam back information from the Moon's data center...

Imagine a future where racks of computer servers hum quietly in darkness below the surface of the Moon.

Here is where some of the most important data is stored, to be left untouched for as long as can be. The idea sounds like something from science-fiction, but one startup that recently emerged from stealth is trying to turn it into a reality. Lonestar Data Holdings has a unique mission unlike any other cloud provider: to build datacenters on the Moon backing up the world's data.

"It's inconceivable to me that we are keeping our most precious assets, our knowledge and our data, on Earth, where we're setting off bombs and burning things," Christopher Stott, founder and CEO of Lonestar, told The Register. "We need to put our assets in place off our planet, where we can keep it safe."

Stott said Lonestar's efforts to build a data storage facility in space are a bit like trying to preserve all of the world's seeds in the Svalbard Global Seed Vault, located on the Norwegian Arctic island ofSpitsbergen. But instead of trying to protect crop diversity, the upstart wants to safeguard human knowledge.
[...]
Under the space agency's Commercial Lunar Payload Services program, Intuitive Machines will, after some delay, send its Nova-C lander to the Moon for its first mission, dubbed IM-1, at the end of 2022. Lonestar will run a software-only test, storing a small bit of data on the lander's hardware. IM-1 is expected to last one lunar day, an equivalent of two weeks on Earth.

The second launch, IM-2, is more ambitious. Intuitive Machines plans to send another Nova-C lander to the Moon's South Pole carrying various bits of equipment, including NASA's PRIME-1 drill for ice and a spectrometer as well as Lonestar's first hardware prototype: a one-kilogram storage device, the size of a hardback novel, with 16 terabytes of memory. IM-2's is expected to launch in 2023.

The tiny proof-of-concept datacenter will be storing immutable data for Lonestar's early beta of its so-called Disaster Recovery as a Service (DRaaS), Stott told us. "[We will be] performing upload and download tests (think refresh and restore of data), and performing edge processing tests of apps as well
[...]
The path from a book-sized prototype to real fully fledged cloud storage datacenters, however, is handwavy. Stott said Lonestar has plans for future missions to launch servers capable of holding five petabytes of data in 2024, and 50 petabytes of data by 2026. By then, he hopes the datacenter will be able to host data traffic to and from the Moon at rates of 15 Gigabits per second – much faster than home internet broadband speeds – beamed from a series of antennas.

If the company is to continue scaling and storing data long-term, it'll have to figure out how to protect its datacenters from cosmic radiation and deal with the Moon's fluctuating surface temperatures, which can go from a scorching 222.8°F (106°C) during the day to a -297.4°F (-183°C) at night.

Stott has an answer for that: nestle the datacenters in lunar lava tubes,
[...]
And how will the Lonestar get them down there? "Robots… lots of robots," Stott said.

https://www.theregister.com/2022/05/21/lonestar_moon_datacenter/