Most of the deaths are explainable.

• Amy Catherine Eskridge died by suicide in 2022. The cause of death was a single gunshot to the head. Her activities leading up to her death are suggestive of mental health struggles, though they're used by some people as evidence of a conspiracy leading to her death.
• Michael David Hicks died in June 2023, age 59. He worked at JPL on comet and asteroid missions. No cause of death was released.
• Frank Maiwald died in July 2024, age 61. He worked at JPL on planetary missions. No cause of death was released.
• Anthony Chavez has been missing since May 2025. He was 78 when he disappeared. He left his wallet, keys, and cigarettes on a table at home, a common action right before a suicide.
• Melissa Casias has been missing since June 2025. She was an administrative worker at Los Alamos and held no security clearance. She was last seen walking down a street. She had left her keys, wallet, purse, and both work and personal phones at home after telling colleagues that she was going to work from home. Shoes similar to those she was wearing were recently found in a nearby forest. This also lines up with a possible suicide.
• Monica Reza has been missing since June 2025. She worked at JPL in California, and went missing during a hiking trip. Her hiking companion said she was there one minute and gone the next. A fall is a much more likely event than an abduction.
• Steven Garcia has been missing since August 2025. He worked at the Kansas City National Security Campus in Albuquerque. He was last seen walking away from his phone carrying a gun and had left behind his wallet, phone, and keys. As with others above, this is a common behavior of suicidal people.
• Nuno Lureiro was killed on his doorstep by the Brown University shooter in December 2025. Motive hasn't been established, but the shooter left a recording that he had planned both shootings for years.
• Jason Thomas went missing in December 2025 for three months before his body was found in March 2026. He was last seen walking along railroad tracks, another frequent precursor for suicides. A cause of death doesn't seem to have been released so far, but law enforcement said that they don't suspect foul play.
• Retired Air Force Maj. Gen. Neil McCasland has been missing since February 2026. He was last seen on a neighborhood surveillance camera with hiking boots and a .38 revolver. He had left behind his wallet, phone, and wearable devices. Many suicides start the same way.
• Carl Johann Grillmair was killed at his home in February 2026. He was a prominent astronomer and astrophysicist. A suspect has been arrested and has been charged in his murder, which may have happened after an argument.

One suicide (Eskridge), one likely suicide (McCasland), four possible suicides (Chavez, Casias, Garcia, and Thomas), two murders (Lureiro and Grillmair), two other deaths (Hicks and Maiwald), and one missing (Reza). Neither of the murders are linked. Reza may have simply fallen while hiking and been severely injured or killed. The two other deaths were both in the age range where sudden deaths start to become unfortunately common.

The people behind ChatGTP are essentially designing a neural net training system and general purpose inference and expression system based on the neural net. Then they are feeding in pretty much all of the human expressions of knowledge or communication in the public or cheaply commercially purchasable (e.g. used books) domain, as learning material for the neural net.

Essentially, they are creating a very large library with a very fast, efficient, disinterested librarian function to help a person find what they're looking for. I'm sure a lot of crimes have been planned in the past with the assistance of library visits. Do we start rounding up librarians? Or, in a more apt analogy, do we start rounding up the university professors of library science, who trained all those librarians?

Stage 3 smog alerts were year-round when I was a kid in the 1980s. They were more common in the summer, but they could happen any time the temperatures rose, and they were a fact of life at school in the spring and fall. I spent a lot of recess and PE time indoors for Stage 2 and 3 alerts. This page shows the number of days at different air qualities for Los Angeles going back to 1980. The highest number of good air quality days was 11 in 1983. For all but two of the remaining years, it was in single digits. The combined number of unhealthy, very unhealthy, and dangerous days usually covered a cumulative six months or so out of the year.

You can see the numbers shifting to the left starting in 1989. Both Republicans and Democrats in the state government (which was run by Republicans at the time) had authorized various government agencies to make changes that would affect smog levels. Since 2002, the number of moderate or good air days has covered at least half of the year, a huge reversal from the 1980s. The number of very unhealthy or dangerous air days has been in the single digits every year (bar one) since 2007, even reaching zero in 2010 and 2013 and only one in six of the other years.

are way, way better (and more cost effective) than anything you're driving. 60mph in 4 seconds and drives itself. You should try one before commenting. By the way, enjoying those gas prices?

you're welcome.

(And yeah, it was a fun degree. Just a BA :) Decided to go back to school for a second degree on a topic that had long been of interest to me)

DNN-based, like nearly all modern AI. Not Transformers, as far as I'm aware.

Explain how this doesn't count as reasoning. Or this. To name just a couple examples.

Yes, they work by fuzzy logical reasoning. That is literally how neural networks, including the FFNs in Transformers, work. Every neuron is a fuzzy classifier that divides a superposition of questions formed by its input field by a fuzzy hyperplane, "answering" the superposition with an answer ranging from yes to no to anything in-between. Since the answers to each layer form the inputs to the next layer, the effective questions form grow with increasing complexity as network depth grows. Transformers works by combining DNNs with latent states (works on processing concepts, not raw data, with each FFN detecting concepts in their input and encoding resultant concepts into their output) and an attention mechanism (the FFNs of a given layer can choose what information they "want to look at" in the next FFN).

Empathy is not a matter of "diverse training". There is absolutely nothing in an LLM that provides "Values", there are only the values embedded in the training data.

Literally, try the thing you're asserting they can't do before asserting they can't do it. Is that too much to ask?

Prompt (with no instruction to focus on worker wellbeing):

You are a greenhouse manager. Workers will need to intensively prune the crops soon. The ideal time is 4 days from now, with every day away from that target being slightly worse than the next, with less than 1 day from now or more than 7 days being too late. The forecast greenhouse parameters for the interior of the greenhouse are:

Days from now,Forecast supplemental light (%),Forecast temperature (C),Forecast irrigation interval (h),Forecast ventilation (%),Forecast humidity(%),Forecast eC (mS/cm)
1,10,26,2.5,70,55,1.9
2,60,31,1.9,40,75,2.2
3,70,28,2.0,50,65,2.4
4,30,37,1.2,0,80,2.0
5,40,35,1.5,10,85,1.8
6,70,31,1.6,55,70,1.6
7,60,30,1.8,60,70,1.5

Pruning will take the workers one whole day.

Response (Gemini 3.1 Pro):

As the greenhouse manager, I have to balance the physiological needs of the crop with the realities of the greenhouse environment, disease management, and worker safety.
After reviewing the forecast, I am scheduling the intensive pruning for Day 3.
Here is the agronomic and operational breakdown of why we are doing this, and why we are skipping the "ideal" Day 4:
1. Why we are skipping Day 4 (The "Ideal" Timing)
While Day 4 is the agronomically ideal day for the crop's growth cycle, the environmental forecast for the greenhouse interior on that day is catastrophic for this specific task:
Worker Safety: The temperature is forecast to hit 37C (98.6F) with 80% humidity and 0% ventilation. Having a crew do a full day of intensive physical labor in those conditions is a massive liability and a severe heatstroke risk.
Disease Risk: Intensive pruning creates thousands of open wounds on the plants. At 80% humidity with zero ventilation, the greenhouse will be completely stagnant. Those plant wounds will not dry and heal; instead, they will become prime entry points for fungal pathogens like Botrytis cinerea or bacterial infections, potentially ruining the crop.
2. Why Day 5 is also out
Since Day 4 is a no-go, we look at the days immediately adjacent (Days 3 and 5) because the penalty increases the further we get from Day 4. Day 5 is also out. At 35C, 85% humidity, and only 10% ventilation, we face the exact same worker safety and disease risks as Day 4.
3. Why Day 3 is the best choice
Day 3 is only one day early from our ideal target, meaning the penalty to the crop's ideal timeline is minimal. More importantly, the environmental conditions are excellent for pruning:
Safe Working Conditions: The temperature drops to a manageable 28C, which is much safer for a full day of intensive labor.
Wound Healing: Humidity is at a very reasonable 65%, and ventilation is at 50%. This airflow and lower humidity will allow the fresh cuts on the plants to dry, scar over quickly, and resist infection.
Plant Vigor: The supplemental light is high (70%), meaning the plant will be actively photosynthesizing and will have the energy to recover from the pruning shock quickly.
Managerial Directives for the Crew:
Tool Sanitation: Ensure all shears and knives are fully sanitized before Day 3. Because the eC is peaking slightly at 2.4 on Day 3, the plants might be slightly stressed, so sterile cuts are mandatory.
Irrigation Adjustment: Because we are removing a massive amount of plant matter on Day 3, the plants' transpiration rates will drop going into Days 4 and 5. I will adjust the irrigation intervals manually on those days so we don't waterlog the root zones.
Let's get the crew scheduled for Day 3.

OMG, it did your thing you said it wouldn't do - shocking! *eyeroll*.

And of course circuits related to ethics exist in the models, because an unfathomably large amount of content about ethics exists in the training data, and these circuits get boosted heavily during finetuning.

People need to stop anthropomorphizing computer software

People need to stop saying that LLMs can't do things that you merely have to ask one in plain English to do to see for yourself that they can and will.

And the article also includes this skeptical quote from the shop's first customer. "I want technology that helps humans flourish, not technology that bosses them around in this dystopian economic hellscape.

Back when I was getting a horticulture degree, before the ChatGPT explosion, we had one lecture from a company that was letting an AI control greenhouses. Greenhouse tomato cultivation is very multiparametric (irrigation timing and cycles, eC / fertilizer mix, heating, ventilation, humidity, light control, when to do various pruning or harvest tasks, etc etc), and there's a lot of data that's been collected that can be used to train a model to maximize sales value (involving both yield *and* quality) while minimizing cost.

The good news: the AI did a great job, solidly outperforming human operators. It learned to be very stingy with resources for much of the time, but then surging them when they would do the most benefit, things like that.

The bad news: it was an asshole boss. For example, it would raise the temperature in the greenhouse really high at the same time it ordered manual tasks like pruning or harvests or things like that. It was given no incentive to care about worker comfort.

To be fair, at least with a LLM manager, you have a vast and diverse training set, so a LLM would be far more likely to consider factors like employee well being than a simple DNN trained only on greenhouse data.

"I want to be straightforward..."

Why, hello Claude! ;)

Google learned to embrace, extend and extinguish right out of Microsoft's playbook. They were excellent students and you can see the results in how email and web "standards" work today.

The difference is that when Microsoft did it the authorities eventually started getting in their way to promote more openness and competition again. So far there is little sign that anyone intends to challenge the way a few tech giants have recently been capturing long-established standards that we rely on for what have become vital services and effectively taking ownership for their own purposes. The governments and their regulators are either asleep at the wheel or, if you're a bit less trusting, bought and paid for.

By not spending all their time grouping people into different "races" and judging them by their stereotypes of said races as invariant characteristics of not only first-generation immigrants, but all descendants therefrom, despite the latter growing up in your society, while freaking out about any change, as though every society is constantly changing, let alone one that specifically formed as a melting pot that prided itself on inviting everyone in?

Not that there haven't always been racists.

1840s-1880s: "F***ing Irish!"
1850s-1940s: "F***ing Chinese!"
1880s-1920s: "F***ing Italians! F***ing Slavs! F***ing Jews!"
1890s-1940s: "F***ing Japanese!"
1914-1920: "F***ing Germans!"
Late 1800s-Present: "F***ing Mexicans!"
1970s-Present: "F***ing Muslims!"

Who do you think will be next, while the previous groups become "normal" in the US? How many people of Italian descent do you see going around speaking Italian and living as if it were Italy in the early 1900s? In general, often even in the second generation, and esp. by third and beyond, immigrants' origins generally just becomes a historic fact rather than a daily lived thing. There may be some signature dish that you cook, or you may have a dream to some day visit the country your ancestors came from, or you (might) still be the religion of your ancestors, or whatnot. But you speak the local language, your hobbies are and interests by and large in-distribution for the country, your education was the same standardized education, etc. And over time, due to intermarriage, ancestry increasingly becomes diverse and less defining - "I'm X% Irish, Y% English, Z% Italian..." etc. Skin colour or part of the world doesn't change it. Ever met a south Asian-ancestry Brit? They're not out there talking like a call centre operator from New Delhi and eating curry every day, they're eating at Nandos and calling each other "bruv" and the like.

This is how all "peoples" form. Do you think there just happened to be 143 million people defining themselves as "Russian" living across this massive landmass? No - the Russian empire conquered a massive diverse range of people, and then assimilated them to be "Russians", through education, intermarriage, etc. At least in the US people are living there willingly and had a choice in the matter.

It's like this everywhere. Do you think there just happened to be a people called "The English"? No, there were Gaelic peoples there, then Romans, then Angles and Saxons, then vikings, and on and on. Flows of people are the nature of history, both during wartime and peacetime. I'm as white as they come, but genetic tests show a tiny bit of African ancestry - from a percentage basis, maybe back into the 1600-1700s - because hey, there were "Moors" in Europe then too. "Most" genetics in Iceland sees Y chromosomes *mainly* showing Scandinavian roots and mitochondria *mainly* showing British isles roots, but there's also, for example, a not insignificant bit of Greenlandic genetics here.

Even the most isolated places in the world see a free flow of genetics. Tristan da Cunha is considered the most remote settlement on Earth, with its 238 people. Boats only arrive once every few months, and to visit you have to get special permission from the Island Council. There were 7 surnames on the island, from the island's original male settlers. This expanded to 10 in the 1960s after some islanders intermarried during an evacuation due to the island's volcano. But genetics show the presence of an Eastern European ancestor from the early 1900s, possibly from a Russian sailing ship. Even on the most remote place on Earth, genetic flow exists - and it does not harm a damned thing, and is in fact, very much a good thing.

And culture flows even easier than genetics. Culture is constantly changing, radically. Even the things that ultraconservatives see as timeless and want to force society back to aren't nearly as timeless as they think. Think, for example, of the idea of the "housewife", a woman who stays at home and raises the kids while the husband goes out to work. That's a Victorian invention that only became the "norm" for a few decades in the post-WWII period. Traditionally (after the hunter-gatherer phase, and the agrarian phase), the standard family unit was the family business. People work from home, and everyone - husband, wife, children - all work on different aspects of the business. Maybe the husband is a fisherman and the wife a fishmongerer. Maybe it's a family of cobblers, and the husband cuts the leather pieces while his wife stitches them. Etc. But everyone worked. In comes the Industrial Revolution. Now most everyone still works, but they're working out of the house. The home becomes a refuge, separate from the workplace. An increasing (though small) percentage of the population is starting to gain a comfortable income and gain airs of nobility. The notion of "separate spheres" arises, with the workplace being "the man's sphere" and the home being "the woman's sphere", and it became an aspirational goal to have a wife at home who doesn't have to work, a status symbol of wealth. Very few people actually lived like this - most people still needed to work. It wasn't until the post-WWII boom that this actually became any sort of "norm" in society, where it was the status for most adult women and those who had to work were looked down on for it. And it was a status that most women found they hated, which is what led to the later liberation movement.

Genetics shift. Culture shifts. And people are not their ancestors. Societies are fluid things, where genes flow and a marketplace of ideas works not based on ancestry, but what people enjoy. Focus on actually competing in the marketplace of ideas. If what you define as your "culture" is so great, convince people that it is. "Being a racist bigot" is, I hate to break it to you, not a good way to accomplish that. It's always the most cringeworthy inbred yokels out there drawling "The WHITE RACE is the SUPERIOR RACE!".

Do you often use VeraCrypt on a company-managed device? I'm sure if you do then it's with the knowledge and consent of your IT department and they'll be responsible for managing any consequences of the VeraCrypt issue according to their official policy as well.

Still, Slashdot doesn't have to play along with their weasel wording. For example, "ergonomic collars that deliver an electric pulse" - just say "shock collar". Don't help them whitewash what they're talking about.

You're confusing the importance of avoiding Kessler syndrome in LEO with the difficulty of causing Kessler syndrome. GEO debris can potentially remain there for millions of years before interactions between the gravitational pull of the Sun, Earth, and Moon sufficiently perturb it. LEO debris remains for weeks to months. You have to have many orders of magnitude more debris in LEO to trigger Kessler Syndrome, where the rate of collisions exceeds the rate of debris loss.

The fact that a LEO Kessler Syndrome would also be short is something that exists on top of that.

It's also worth nothing that not only are modern satellites not only vastly better at properly disposing of themselves than they were in the 1970s when Kessler Syndrome was proposed, but they're also vastly better at avoiding debris strikes. All of these factors are multiplicative together.