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North American car plants close for 6 weeks example GM and FORD.

Why ?

There is a shortage of microprocessor chips in the world because production was cut during the covid.

The average car uses 50 microprocessors.

When A.I. goes bad, not too much you can do.
 
North American car plants close for 6 weeks example GM and FORD.

Why ?

There is a shortage of microprocessor chips in the world because production was cut during the covid.

The average car uses 50 microprocessors.

When A.I. goes bad, not too much you can do.
It's a bit more complicated than that. The shortage is also due to demand for graphics cards for mining.
PC gamers has problems getting their hand on both CPUs and graphics cards these days. Because production of graphics cards take up space and time in production facilities, production of CPUs have to wait. Prices of graphics cards are increasing, both used and new.
 
It's a bit more complicated than that. The shortage is also due to demand for graphics cards for mining.
PC gamers has problems getting their hand on both CPUs and graphics cards these days. Because production of graphics cards take up space and time in production facilities, production of CPUs have to wait. Prices of graphics cards are increasing, both used and new.
It's spilled over into FPGA chips. There's a shortage of those now, presumably because chip manufacturers have switched their production capacity to CPU and graphics chips.
 
When the Russian hackers shut down the gas pipeline about a week ago and paralyzed the eastern third of the U.S., these hackers were paid about 5 million dollars to release the computers.

This is a bad precedent.

Hackers are looking for more money now.

A I that is so easily corrupted is dangerous.
 
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When the Russian hackers shut down the gas pipeline about a week ago and paralyzed the eastern third of the U.S., these hackers were paid about 5 million dollars to release the computers.
This is a bad precedent.
Hackers are looking for more money now.
A I that is so easily corrupted is dangerous.
The pipeline hack had nothing to do with AI. No artificial intelligence was - or is known to be - involved in hacking or ransomware.
 
EnolaGaia,

I am not a computer guy, so how was the pipeline shut down if no computers or software was involved ?
 
EnolaGaia,
I am not a computer guy, so how was the pipeline shut down if no computers or software was involved ?
???? ... I didn't say there was no computer or software involvement - I said there was no AI involvement. AI is a particular category or class of techniques employed to emulate "intelligent" decision / action selections or control.
 
Crudely stated ...

Ordinary algorithmic software processes simply react by triggering outputs determined by the given inputs. If X, then Y - no ifs, no ands, no buts ...

AI involves inference to determine what the X may be and / or what Y is the preferred response. There's an intermediate process akin to "thinking about it" to bridge the gap between input and output.

For example ... It only requires a mere algorithmic response to sweep chess pieces off the game board and end or prevent a chess game. It takes inference to play the game of chess, identifying the state of the game / board and deciding the next move on a repeating basis.
 
An ordinary computer program use IF Then algorithms to determine between different states already set by the programmer.

Artificial Intelligence systems like Artificial neural networks, Deep learning a.n.n. is just fed data which it self is trying to learn from.
Like trying to find patterns and learning to recognize stuff from photos.
 
MINORITY CRIME REPORTS
Cops using artificial intelligence to stop crimes BEFORE they happen, researchers warn
Academics say the technology is letting policemen detect crime that hasn't taken place yet

by JASPER HAMILL
12th September 2016, 12:36 pm
1
Comments
Cops are already using computers to stop crimes before they happen, academics have warned.

In a major piece of research called “Artificial Intelligence and life in 2030”, researchers from Stanford University said “predictive policing” techniques would become commonplace in the next 15 years.


Samantha Morton starred in Minority Report, playing a woman who had pre-cognitive abilities and could predict crimes before they happened

The academics discussed the crime fighting implications of “machine learning”, which allows computers to learn for themselves and then solve problems just like a human.

This technique will have a major effect on transport, healthcare and education, potentially bringing massive benefits as well as putting millions of jobs at risk.

But in the hands of cops, AI has the potential to have a massive impact on society by allowing law enforcement to have an “overbearing or pervasive” presence.

“Cities already have begun to deploy AI technologies for public safety and security,” a team of academics wrote.

“By 2030, the typical North American city will rely heavily upon them.

“These include cameras for surveillance that can detect anomalies pointing to a possible crime, drones, and predictive policing applications.”

Machine learning and AI is already used to combat white collar crime such as fraud. It is also used to automatically scan social media to highlight people of risk of being radicalised by ISIS.
Yet the range of crimes which could be stopped by AI is likely to grow as the technology becomes more advanced.

More text at link...

https://www.thesun.co.uk/news/17684...op-crimes-before-they-happen-researchers-warn

Another disturbing Precrime case.

ROBERT MCDANIEL’S TROUBLES began with a knock on the door. It was a weekday in mid-2013, as he made lunch in the crowded three-bedroom house where he lives with his grandmother and several of his adult siblings.

When he went to answer the door, McDaniel discovered not one person, but a cohort of visitors: two police officers in uniform, a neighbor working with the police, and a muscular guy in shorts and a T-shirt sporting short, graying hair.

Police officers weren’t a new sight for McDaniel. They often drove down his tree-lined street in the Austin neighborhood of Chicago making stops and arrests. Out of the 775 homicides tracked by the Chicago Sun-Times in 2020, 72 of them happened in Austin. That’s almost 10 percent of the city’s murder rate, in a region that takes up just 3 percent of its total area. The City of Chicago puts out a “heat map” of where gun crimes occur, with areas of moderate shooting numbers shaded in blue or green. Red splotches represent large numbers — and hottest concentrations — of shootings. On the map, Austin is the color of a fire engine.

Still, this visit from authorities caught McDaniel off guard: at that point in time, he had nothing remotely violent on his criminal record — just arrests for marijuana-related offenses and street gambling. And despite two officers showing up at his front door with the cohort, neither of them, nor anyone else in the cohort, accused McDaniel of breaking the law. They were not there to arrest him. No one was there to investigate a crime. They just wanted to talk.

“I had no idea why these cops were here,” McDaniel says, recounting it to me years later. “I didn’t do shit to bring them here.”

HE COULD BE THE SHOOTER, HE MIGHT GET SHOT. THEY DIDN’T KNOW. BUT THE DATA SAID HE WAS AT RISK EITHER WAY
He invited them into this home. And when he did, they told McDaniel something he could hardly believe: an algorithm built by the Chicago Police Department predicted — based on his proximity to and relationships with known shooters and shooting casualties — that McDaniel would be involved in a shooting. That he would be a “party to violence,” but it wasn’t clear what side of the barrel he might be on. He could be the shooter, he might get shot. They didn’t know. But the data said he was at risk either way. ...

https://www.theverge.com/22444020/chicago-pd-predictive-policing-heat-list
 
A.I. is now able to predict chaos and randomnesss.
-----------

CHAOS THEORY

Machine Learning’s ‘Amazing’ Ability to Predict Chaos​

In new computer experiments, artificial-intelligence algorithms can tell the future of chaotic systems.

Half a century ago, the pioneers of chaos theory discovered that the “butterfly effect” makes long-term prediction impossible. Even the smallest perturbation to a complex system (like the weather, the economy or just about anything else) can touch off a concatenation of events that leads to a dramatically divergent future. Unable to pin down the state of these systems precisely enough to predict how they’ll play out, we live under a veil of uncertainty.

But now the robots are here to help.

In a series of results reported in the journals Physical Review Letters and Chaos, scientists have used machine learning — the same computational technique behind recent successes in artificial intelligence — to predict the future evolution of chaotic systems out to stunningly distant horizons. The approach is being lauded by outside experts as groundbreaking and likely to find wide application.
https://www.quantamagazine.org/feed/podcast/
“I find it really amazing how far into the future they predict” a system’s chaotic evolution, said Herbert Jaeger, a professor of computational science at Jacobs University in Bremen, Germany.

The findings come from veteran chaos theorist Edward Ott and four collaborators at the University of Maryland. They employed a machine-learning algorithm called reservoir computing to “learn” the dynamics of an archetypal chaotic system called the Kuramoto-Sivashinsky equation. The evolving solution to this equation behaves like a flame front, flickering as it advances through a combustible medium. The equation also describes drift waves in plasmas and other phenomena, and serves as “a test bed for studying turbulence and spatiotemporal chaos,” said Jaideep Pathak, Ott’s graduate student and the lead author of the new papers.

Fire_2880x1220.gif


https://www.quantamagazine.org/machine-learnings-amazing-ability-to-predict-chaos-20180418/
 
I had an odd conversation today. I work for a company, where we sell different products. Sometimes people ask to be contacted by us, via phone.
On this number I called, I got what sounded like a robotic voice. It appears to be saying specific phrases, likely triggered by keywords, to mimic real conversation.
My colleagues called this number several times during the week, where it was the same. I just called it out of curiosity.
Since we are not doing cold-calling, but was asked to call this number, I don't quite understand the purpose. Perhaps someone is trying to test their AI on unsuspecting people.
 
I had an odd conversation today. I work for a company, where we sell different products. Sometimes people ask to be contacted by us, via phone.
On this number I called, I got what sounded like a robotic voice. It appears to be saying specific phrases, likely triggered by keywords, to mimic real conversation.
My colleagues called this number several times during the week, where it was the same. I just called it out of curiosity.
Since we are not doing cold-calling, but was asked to call this number, I don't quite understand the purpose. Perhaps someone is trying to test their AI on unsuspecting people.
Fascinating. If that scam about getting people to say particular words (then to become "evidence" that they'd agreed to financial transfers) didn't appear to be an urban myth, I'd say I'd be worried it was something like that.
 
The missing edges of Rembrants masterpiece 'The Night Watch' has been restored for the first time in 300 years using A.I.

"The missing edges of Rembrandt’s painting The Night Watch have been restored using artificial intelligence.

The canvas, created in 1642, was trimmed in 1715 to fit between two doors at Amsterdam’s city hall.

Since then, 60cm (2ft) from the left, 22cm from the top, 12cm from the bottom and 7cm from the right have been missing.

But computer software has now restored the full painting for the first time in 300 years."

https://www.bbc.com/news/technology-57588270
 
Elon Musk muses on felines forcing AI resarch.

Elon Musk tweeted a fascinating — and frankly unsettling — theory last night about how a brain parasite might be forcing all humans to create advanced AI.

The Tesla CEO was responding to a story from National Geographic about how toxoplasmosis, a common parasite often found in cats, seems to be causing hyenas to be reckless around predators such as lions. In a staggering and perhaps facetious leap of logic, Musk suggested that the parasite is actually what’s causing humans to create advanced artificial intelligence.

“Toxoplasmosis infects rats, then cats, then humans who make cat videos,” Musk tweeted on Friday. “AI trains achieves superhuman intelligence training on Internet cat videos, thus making toxoplasmosis the true arbiter of our destiny.” ...

https://futurism.com/the-byte/elon-musk-brain-parasite-superhuman-ai
 
Elon Musk muses on felines forcing AI resarch.

Elon Musk tweeted a fascinating — and frankly unsettling — theory last night about how a brain parasite might be forcing all humans to create advanced AI.

The Tesla CEO was responding to a story from National Geographic about how toxoplasmosis, a common parasite often found in cats, seems to be causing hyenas to be reckless around predators such as lions. In a staggering and perhaps facetious leap of logic, Musk suggested that the parasite is actually what’s causing humans to create advanced artificial intelligence.

“Toxoplasmosis infects rats, then cats, then humans who make cat videos,” Musk tweeted on Friday. “AI trains achieves superhuman intelligence training on Internet cat videos, thus making toxoplasmosis the true arbiter of our destiny.” ...

https://futurism.com/the-byte/elon-musk-brain-parasite-superhuman-ai
He does get a bit science-fictional occasionally.
 
A. I can predict the structures of nearly all of the proteins made in the human body.

"Artificial intelligence has been used to predict the structures of almost every protein made by the human body.

Proteins are essential building blocks of living organisms; every cell we have in us is packed with them.

Understanding protein structures is critical for advancing medicine, but until now, only a fraction of these have been worked out.

Researchers used a program to predict 350,000 protein structures belonging to humans and other organisms.

The development could help supercharge the discovery of new drugs to treat disease, alongside other applications."

https://www.bbc.com/news/science-environment-57929095
 
A.I. lawyers on the way?

Could your next lawyer be a robot? It sounds far fetched, but artificial intelligence (AI) software systems - computer programs that can update and "think" by themselves - are increasingly being used by the legal community.

Joshua Browder describes his app DoNotPay as "the world's first robot lawyer". It helps users draft legal letters. You tell its chatbot what your problem is, such as appealing against a parking fine, and it will suggest what it thinks is the best legal language to use.

"People can type in their side of an argument using their own words, and software with a machine learning model matches that with a legally correct way of saying it," he says.

The 24-year-old and his company are based in Silicon Valley in California, but the firm's origins go back to London in 2015, when Mr Browder was 18.

https://www.bbc.com/news/business-58158820
 
Maybe the A.I. will decide to take drastic action against humankind.

How AI can help forecast how much Arctic sea ice will shrink

IceNet can predict the future of Arctic sea ice months in advance with 95 percent accuracy

In the next week or so, the sea ice floating atop the Arctic Ocean will shrink to its smallest size this year, as summer-warmed waters eat away at the ice’s submerged edges.

Record lows for sea ice levels will probably not be broken this year, scientists say. In 2020, the ice covered 3.74 million square kilometers of the Arctic at its lowest point, coming nail-bitingly close to an all-time record low. Currently, sea ice is present in just under 5 million square kilometers of Arctic waters, putting it on track to become the 10th-lowest extent of sea ice in the area since satellite record keeping began in 1979. It’s an unexpected finish considering that in early summer, sea ice hit a record low for that time of year.

The surprise comes in part because the best current statistical- and physics-based forecasting tools can closely predict sea ice extent only a few weeks in advance, but the accuracy of long-range forecasts falters. Now, a new tool that uses artificial intelligence to create sea ice forecasts promises to boost their accuracy — and can do the analysis relatively quickly, researchers report August 26 in Nature Communications.

IceNet, a sea ice forecasting system developed by the British Antarctic Survey, or BAS, is “95 percent accurate in forecasting sea ice two months ahead — higher than the leading physics-based model SEAS5 — while running 2,000 times faster,” says Tom Andersson, a data scientist with BAS’s Artificial Intelligence lab. Whereas SEAS5 takes about six hours on a supercomputer to produce a forecast, IceNet can do the same in less than 10 seconds on a laptop. The system also shows a surprising ability to predict anomalous ice events — unusual highs or lows — up to four months in advance, Andersson and his colleagues found.

https://www.sciencenews.org/article/artificial-intelligence-sea-ice-forecast
 
"The missing edges of Rembrandt’s painting The Night Watch have been restored using artificial intelligence.
"Artificial intelligence has been used to predict the structures of almost every protein made by the human body.
Joshua Browder describes his app DoNotPay as "the world's first robot lawyer". It helps users draft legal letters. You tell its chatbot what your problem is, such as appealing against a parking fine, and it will suggest what it thinks is the best legal language to use.

IceNet can predict the future of Arctic sea ice months in advance with 95 percent accuracy

Note that each of these purported AI applications involve inference performed on complex but relatively fixed data sets or models. These applications don't represent any "intelligence" of a general / creative / innovative nature.

They determine or project possibilities strictly defined within a particular domain of reference and subject to a particular set of rules (e.g., for allowable combinations).

That's it; that's all ...
 
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New neural network processor simulating neurons launched by Intel. 86,000 of them and you got something comparable to a human brain.
--------------

Intel launches its next-generation neuromorphic processor—so, what’s that again?​

Intel's Loihi processors have electronics that behave a lot like neurons.​

JOHN TIMMER - 9/30/2021, 8:00 PM


Despite their name, neural networks are only distantly related to the sorts of things you'd find in a brain. While their organization and the way they transfer data through layers of processing may share some rough similarities to networks of actual neurons, the data and the computations performed on it would look very familiar to a standard CPU.

But neural networks aren't the only way that people have tried to take lessons from the nervous system. There's a separate discipline called neuromorphic computing that's based on approximating the behavior of individual neurons in hardware. In neuromorphic hardware, calculations are performed by lots of small units that communicate with each other through bursts of activity called spikes and adjust their behavior based on the spikes they receive from others.

On Thursday, Intel released the newest iteration of its neuromorphic hardware, called Loihi. The new release comes with the sorts of things you'd expect from Intel: a better processor and some basic computational enhancements. But it also comes with some fundamental hardware changes that will allow it to run entirely new classes of algorithms. And while Loihi remains a research-focused product for now, Intel is also releasing a compiler that it hopes will drive wider adoption.

To make sense out of Loihi and what's new in this version, let's back up and start by looking at a bit of neurobiology, then build up from there.

From neurons to computation​

The foundation of the nervous system is the cell type called a neuron. All neurons share a few common functional features. At one end of the cell are structures called a dendrites, which you can think of as receivers. This is where the neuron receives inputs from other cells. Nerve cells also have an axon, which act as a transmitter, connecting with other cells to pass along signals.

The signals take the form of what are called "spikes," which are brief changes in the voltage across the neuron's cell membrane. Spikes travel down axons until they reach the junctions with other cells (called synapses), at which point they're converted to a chemical signal that travels to the nearby dendrite. This chemical signal opens up channels that allow ions to flow into the cell, starting a new spike on the receiving cell.

The receiving cell integrates a variety of information—how many spikes it has seen, whether any neurons are signaling that it should be quiet, how active it was in the past, etc.—and uses that to determine its own activity state. Once a threshold is crossed, it'll trigger a spike down its own axons and potentially trigger activity in other cells.

Typically, this results in sporadic, randomly spaced spikes of activity when the neuron isn't receiving much input. Once it starts receiving signals, however, it'll switch to an active state and fire off a bunch of spikes in rapid succession.

A neuron, with the dendrites (spiky protrusions at top) and part of the axon (long extension at bottom right) visible.
Enlarge / A neuron, with the dendrites (spiky protrusions at top) and part of the axon (long extension at bottom right) visible.
NIH
How does this process encode and manipulate information? That's an interesting and important question, and one we're only just starting to answer.

One of the ways we've gone about answering it was via what has been called theoretical neurobiology (or computational neurobiology). This has involved attempts to build mathematical models that reflected the behavior of nervous systems and neurons in the hope that this would allow us to identify some underlying principles. Neural networks, which focused on the organizational principles of the nervous system, were one of the efforts that came out of this field. Spiking neural networks, which attempt to build up from the behavior of individual neurons, is another.

Spiking neural networks can be implemented in software on traditional processors. But it's also possible to implement them through hardware, as Intel is doing with Loihi. The result is a processor very much unlike anything you're likely to be familiar with.

1634056516442.png


https://arstechnica.com/science/202...ic-computing-and-why-intels-excited-about-it/
 
MacDonalds in Chicago, Illinois has let the company IBM develop a completely automated drive thru window talking to a robot to take your order.

The result, people were happy manly because people seem to understand the robot well instead of a lot of static.

Coming to a MacDonalds’ drive thru window near you, Ronald MacDonald Robot.
 
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