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Google Cofounder Sergey Brin Warns of AI's Dark Side (wired.com)
Google co-founder Sergey Brin has warned that the current boom in artificial intelligence has created a "technology renaissance" that contains many potential threats. In the company's annual Founders' Letter, the Alphabet president struck a note of caution. "The new spring in artificial intelligence is the most significant development in computing in my lifetime," writes Brin. "Every month, there are stunning new applications and transformative new techniques." But, he adds, "such powerful tools also bring with them new questions and responsibilities." From a report: When Google was founded in 1998, Brin writes, the machine learning technique known as artificial neural networks, invented in the 1940s and loosely inspired by studies of the brain, was "a forgotten footnote in computer science." Today the method is the engine of the recent surge in excitement and investment around artificial intelligence. The letter unspools a partial list of where Alphabet uses neural networks, for tasks such as enabling self-driving cars to recognize objects, translating languages, adding captions to YouTube videos, diagnosing eye disease, and even creating better neural networks.
Brin nods to the gains in computing power that have made this possible. He says the custom AI chip running inside some Google servers is more than a million times more powerful than the Pentium II chips in Google's first servers. In a flash of math humor, he says that Google's quantum computing chips might one day offer jumps in speed over existing computers that can be only be described with the number that gave Google its name, a googol, or a 1 followed by 100 zeroes.
As you might expect, Brin expects Alphabet and others to find more uses for AI. But he also acknowledges that the technology brings possible downsides. "Such powerful tools also bring with them new questions and responsibilities," he writes. AI tools might change the nature and number of jobs, or be used to manipulate people, Brin says -- a line that may prompt readers to think of concerns around political manipulation on Facebook. Safety worries range from "fears of sci-fi style sentience to the more near-term questions such as validating the performance of self-driving cars," Brin writes.
Brin nods to the gains in computing power that have made this possible. He says the custom AI chip running inside some Google servers is more than a million times more powerful than the Pentium II chips in Google's first servers. In a flash of math humor, he says that Google's quantum computing chips might one day offer jumps in speed over existing computers that can be only be described with the number that gave Google its name, a googol, or a 1 followed by 100 zeroes.
As you might expect, Brin expects Alphabet and others to find more uses for AI. But he also acknowledges that the technology brings possible downsides. "Such powerful tools also bring with them new questions and responsibilities," he writes. AI tools might change the nature and number of jobs, or be used to manipulate people, Brin says -- a line that may prompt readers to think of concerns around political manipulation on Facebook. Safety worries range from "fears of sci-fi style sentience to the more near-term questions such as validating the performance of self-driving cars," Brin writes.
Al Yankovic sure does have a dark side...
More like a virtue signal. A warning would come with specifics of things to look out for, details about what needs to be done to prevent bad stuff within his own company, etc.
Shifty that one.
his company is one of them building great, big, huge "AI"... one specifically to abuse the fuck out of its users (err, i mean 'product').and users' data.
" loosely inspired by studies of the brain"
Understatement of the year. There are many programmers today (and a lot more non-programmers) who think that neural nets are "smart" or have "thought processes" or are even conscious. Most neural nets are just a cascading if-then directed acyclic-graph and a weight assigned to each. It's a deterministic feature finder. We can't figured out "thought" processes for computers, and our neurons operate in a much more complicated way than backprop neural nets.
I'm a lot more concerned about Google than I am FrankenAI. So full of shit, all of those companies.
It's not really artificial intelligence yet. Sure 95% of the time it can identify objects in a picture, or listen to an audio recording and transcribe the text 90% correctly, or translate from one language to another 60% of the time, or drive a car in 98% of the situations. That makes it a bit smarter than a chimp perhaps, but "intelligent"?
As always, it's the 80% of the features that take 20% of the work. The remaining 20% is the hard part.
Brin warns of AI yet his own employees have spoken against Google's involvement in a Pentagon - Google partnership involving AI and military drones. https://gizmodo.com/thousands-...
everything online lasts forever.
[($)]
Artificial Intelligence is trying to match Natural Intelligence.
Artificial neural networks are created through the training of Deep Machine Learning.
AI and ML seem not dangerous, and it is as controlling the dangerous dogs, or worse, wild lions.
The most important characteristic of them is to be autonomous machines.
Remote controlled self-destruction might be used when there are dangers derivated from malfunctioning machines. Electro-Magnetic Pulse (EMP) might be used at last instance.
Pffft. Everybody knows that Google's name really comes from Barney Google with his goo-goo-googley eyes.
==========
Who's the most important man this country ever knew?
Do you know what politician I have reference to?
Well, it isn't Mr. Bryan, and it isn't Mr. Hughes.
I've got a hunch that to that bunch I'm going to introduce:
(Again you're wrong and to this throng I'm going to Introduce:)
Barney Google, with the goo-goo-googley eyes.
Barney Google bet his horse would win the prize.
When the horses ran that day, Spark Plug ran the other way.
Barney Google, with the goo-goo-googley eyes.
Barney Google, with the goo-goo-googley eyes.
Barney Google had a wife three times his size.
She stood Barney for divorce,
Now he's living with his horse.
Who's the greatest lover that this country ever knew?
And who's the man that Valentino takes his hat off to?
No, it isn't Douglas Fairbanks that the ladies rave about.
When he arrives, who makes the wives chase all their husbands
out?
Why, it's Barney Google, with the goo-goo-googley eyes.
Barney Google is the guy who never buys.
Women take him out to dine, then he steals the waiter's dime.
Barney Google, with the goo-goo-googley eyes.
Barney Google, with the goo-goo-goo-ga-ly eyes.
Barney Google is the luckiest of guys.
If he fell in to the mud, he'd come up with a diamond stud.
Barney Google with the goo-goo-googley eyes.
Who's the greatest fire chief this country ever saw?
Who's the man who loves to hear the blazing buildings roar?
Anytime the house is burning, and the flames leap all about,
Say, tell me do, who goes, "kerchoo!" and puts the fire out?
Barney Google, with the goo-goo-googley eyes.
Barney Google, thought his horse could win the prize.
He got odds of ten to eight; Spark Plug came in three days late.
Barney Google, with the goo-goo-googley eyes.
Barney Google, with the goo-goo-googley eyes.
Barney Google tried to enter paradise.
When Saint Peter saw his face, he said, "Go to the other place".
Barney Google, with the goo-goo-googley eyes.
Knowing how to optimise society for people might be just the job for a computer, as it sure isn't done very well by humans... of course, if the computer is owned by a mega-corporation then we have more reason to be sceptical. Hopefully someone is implementing the Three Laws.
https://www.youtube.com/watch?v=tQd_5as_cMY
Their mobs fire off attacks *before* they appear to the players.
Their mobs are able to shoot "through" walls, which the players cannot.
Their mobs fire off attacks upwards of 10 seconds after being killed.
The programmers that did this at Blizzard need to be fired. Blizzard needs to yank that code out.
Consider the first law (before the fourth law was added as number zero):
1. A robot must not harm a human being, or, through inaction, allow a human being to come to harm.
Ok, so, what the hell is a robot supposed to do when it can save one person by hurting another?
Before you answer, realize that anything you say will be an addition to the law. This law by itself is woefully inadequate to handle the kinds of moral evaluations that any kind of autonomous robot will have to face.
They are, in fact, a terrible formulation of morality, and they aren't anywhere near up to the task of serving as a guide for an AI. Not even close.
It would be just great if everyone else were denied creating one also.
AIs seem to be good at learning to classify things from the input data set. One difficulty is that the AI programmers don't really know what patterns the neural network is actually looking at in the data. This leads to a potential danger--discrimination. For example, what if credit scores converted from the current complex formulas to using an AI to generate a credit score from the same input data. Once trained, how could one prove that the AI-generated score was NOT engaging in illegal discrimination based on sex, race, etc.? At least with a complex formula or algorithm, one might examine the logic for such problems. How does one determine if an AI is wrongly discriminating? How does one account for an unusual outlier AI-generated score when input data would seem to indicate the generated score should be vastly different? Did the AI see something important and the score is legit? Or is this just a case of the AI's neural network being "fooled" much like a biological optical processing neural network can be "fooled" into seeing something not really present (i.e. optical illusions)?
A ton of research led up to the creation of AlphaGo by Demis Hasabis and his company, DeepMind. AlphaGo, and later AlphaGo Zero, and Alpha Zero (for chess) were landmark programs taking advantage of Google's custom tensor processing unit (TPU) chip.
The problem that the rest of us have with Alphabet, is that the company is not dedicating AlphaGo Zero and Alpha Zero as learning tools for the analysis of games played by humans.
By abandoning the AlphaGo project, Alphabet is demonstrating their abuse and misuse of artificial intelligence for more lucrative AI applications. Alphabet would gain a lot more good will by keeping the AlphaGo project intact, rather than abandoning it.
When Google was founded in 1998, Brin writes, the machine learning technique known as artificial neural networks, invented in the 1940s and loosely inspired by studies of the brain, was "a forgotten footnote in computer science."
Yeah, right. Maybe you might want to emerge out of your man cave into the real world, Serge? There is some of that also outside of the US computer science departments and industry, you know.
The brain is a lot more complex than people are giving it credit. The whole body and brain is even more complex and it is a system that works together.
Neural networks of today are nothing like how the brain works. (They are similar to how a neuron works)
There are a few architectures that are used to simulate how the brain works on a computer (Cognitive Architectures), but they don't cover all aspects of them.(i.e. speech, cognition, emotion, creativity, reactive planning, complex situations, being above to apply knowledge in one field to another.)
Just by advancing the massive data gathering that is going on, is not a theory on how the brain processes it. I like to view all this deep learning today in vision as one part of the eye and brain, the part that can see something and recognize it, under certain constraints.
But it is no where near how a human can look out and see and track and classify and think about all the 1000's of objects that you see and the tens of thousands of objects, sounds, and things you see through out your day.
Hard AI will come, but it will take quite a few years.
Just having new patents on deep learning algorithms is not a measure of understanding the complexity of the brain.
Neuroscience is advancing our understanding of the brain quite a bit these days, but there is no general architecture on how it all works.
excellent technology and Google become an updated search engine
Seriously.
We're already informed about the possible problems with supremely advanced AI.
Does this mean we should just throw our wooden shoes into the gears and kick of the Butlerian Jihad now?
Of course not.
We still need to do as much research as possible on AI. So we can actually understand the delineation point between "Assistive software" and "Crazy, kill everything AI."
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NI has obviously substantially greater capabilities, in major qualitative areas, than AI today.
We haven't entirely figured out "thought" processes for natural intelligence, and biological neurons are themselves much more complicated than a 'unit' in a backprop-trained multi-layer perceptron. But in the end, do we know what there is in natural intelligence that can't ever be re-implemented? Is there evidence this is so?
To the contrary, there is increasing evidence that certain behaviors and perceptual capabilities previously thought to be exclusively the domain of natural intelligence, can now be achieved with human-level performance in restrictive domains. Statistical/neural machine translation has improved enormously, and empirical evidence shows that it creates internal semantic representations which can perform well across languages, and which, often, agree with human categorization. Unsurprisingly, since the machine is translating between human languages, but the point is that humans have ideas which can be discerned with data and algorithms which train neural networks, and no other algorithm has been able to do this with this performance.
Next, neural networks can today induce internal semantic representations of combined text and video. In what way is this not a step to comprehension of the full sensory apparatus of an 'idea'?
Is there reason to believe that humans don't do statistical neural linguistics? I don't see it.
Of course, compared to computers, humans have a very strong neural prior and seem to be able to learn a given level of performance with much less input and training than large scale translation systems.
Yes, that's the point.
Not that AI-driven cyborgs will rebel against humans, but that powerful billionaire *humans*, who have known intelligence, and unlike AI, drive and will, creating cybernetic slaves who will *never* rebel. These slaves further increase their master's wealth and power to the harm of the billions of other plebeians on Earth.
The Roman Republic and Empire had this problem---the slaveowners were so vastly richer than the average person. (Gaius Julius Caesar supported restricting slavery to help the wages of the free citizens, one reason for his murder)
Now imagine none of the human constraints on the slaves.
Fear uncertainty and doom, commercial ulteriorization.
"We don't know why the model works."
That's basically what caused the financial market crash in 2008. People made "inscrutable" financial models that appears to work, then management no longer question the assumptions it made since that was convenient. So, many threw caution to the wind and tried to maximize profits based on the "no longer fits reality" models.
Seems like many firms attempting to make expert systems ("AI") are doing something similar, but for all other industries. All the papers stating "We tried stuff, and the model seems to be working now!" suggests people might do the same with these things.
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