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originally posted by: soficrow
a reply to: neoholographic
...These microchips will be used for many things and this is just the beginning. One cent microchips will be everywhere in 30 years and everything from roads to tennis shoes will have chips in them.
Do you honestly think it will take that long?
TensorFlow with CPU support only. If your system does not have a NVIDIA CUDA® GPU, you should install this version. Note that TensorFlow with CPU support is typically easier to install than TensorFlow with GPU support. Therefore, even if you have an NVIDIA CUDA GPU, we recommend installing this version first as a diagnostic step just in case you run into problems installing TensorFlow with GPU support.
TensorFlow with GPU support. TensorFlow programs typically run significantly faster on a GPU than on a CPU. Therefore, if your system has a NVIDIA CUDA GPU meeting the prerequisites shown below and you need to run performance-critical applications, you should ultimately install this version.
originally posted by: RedDragon
Yep most people don't realize that the actual intelligence difference between Einstein and someone so retarded they can't tie their shoes is very minimal, maybe like 10% max. These super intelligent computers will be doubling their own intelligence every new development cycle..
Very Superior 130 and above
High Average* 110-119
Low Average* 80-89
Extremely Low* ** 69 and below
Mild Mental Retardation IQ 50-55 to approximately 70
Moderate Retardation IQ 35-40 to 50-55
Severe Mental Retardation IQ 20-25 to 35-40
Profound Mental Retardation IQ below 20 or 25
Mild mental retardation
Approximately 85% of the mentally retarded population is in the mildly retarded category. Their IQ score ranges from 50-75, and they can often acquire academic skills up to the 6th grade level. They can become fairly self-sufficient and in some cases live independently, with community and social support.
Moderate mental retardation
About 10% of the mentally retarded population is considered moderately retarded. Moderately retarded individuals have IQ scores ranging from 35-55. They can carry out work and self-care tasks with moderate supervision. They typically acquire communication skills in childhood and are able to live and function successfully within the community in a supervised environment such as a group home.
Severe mental retardation
About 3-4% of the mentally retarded population is severely retarded. Severely retarded individuals have IQ scores of 20-40. They may master very basic self-care skills and some communication skills. Many severely retarded individuals are able to live in a group home.
Profound mental retardation
Only 1-2% of the mentally retarded population is classified as profoundly retarded. Profoundly retarded individuals have IQ scores under 20-25. They may be able to develop basic self-care and communication skills with appropriate support and training. Their retardation is often caused by an accompanying neurological disorder. The profoundly retarded need a high level of structure and supervision.
You have no clue about deep learning. The intelligent system couldn't use brute force it had to LEARN because it had incomplete information.
A couple of key points. First Protector keeps talking about "brute force" which makes no sense. Tuomas Sandholm, a computer scientist at Carnegie Mellon University, says at around 5:10 that it's not about BRUTE FORCE because there's 10^160 situations that the player can face in this game... So it CAN'T use brute force, it has to learn.
It didn't use brute force, it learned how to play poker and the algorithm wasn't poker specific.
Again, these chips are made for a penny and nothing you said refutes that. The technology isn't meant for just 3rd world countries. That's just ignorant. THE MICROCHIP COSTS A PENNY TO MAKE. It doesn't cost a penny because it will be used in third world countries, it costs a penny because that's how much it costs to make the chip and what Dr. Kaku said is right and he knew about Moore's Law in 2003 in fact he talks about it LOL
The inexpensive lab-on-a-chip technology has the potential to enhance diagnostic capabilities around the world, especially in developing countries. Due to inferior access to early diagnostics, the survival rate of breast cancer patients is only 40 percent in low-income nations — half the rate of such patients in developed nations. Other lethal diseases, such as malaria, tuberculosis and HIV, also have high incidence and bad patient outcomes in developing countries. Better access to cheap diagnostics could help turn this around, especially as most such equipment costs thousands of dollars.
“Enabling early detection of diseases is one of the greatest opportunities we have for developing effective treatments,” Esfandyarpour said. “Maybe $1 in the U.S. doesn’t count that much, but somewhere in the developing world, it’s a lot of money.”
THE PRODUCTION COSTS IS JUST ONE PENNY.
Here's Dr. Kaku talking about Moore's Law.
So this makes no sense. These microchips will be used for many things and this is just the beginning. One cent microchips will be everywhere in 30 years and everything from roads to tennis shoes will have chips in them.
Even more important, the victory demonstrates how AI has likely surpassed the best humans at doing strategic reasoning in “imperfect information” games such as poker. The no-limit Texas Hold’em version of poker is a good example of an imperfect information game because players must deal with the uncertainty of two hidden cards and unrestricted bet sizes. An AI that performs well at no-limit Texas Hold’em could also potentially tackle real-world problems with similar levels of uncertainty.
“The algorithms we used are not poker specific,” Sandholm explains. “They take as input the rules of the game and output strategy.”
In other words, the Libratus algorithms can take the “rules” of any imperfect-information game or scenario and then come up with its own strategy. For example, the Carnegie Mellon team hopes its AI could design drugs to counter viruses that evolve resistance to certain treatments, or perform automated business negotiations. It could also power applications in cybersecurity, military robotic systems, or finance.
And just so you aren't confused by anything above, your statements on 10^160 are correct. 10^160 possible outcomes cannot be brute forced. But once you get down to a limited number of cards/chess pieces/go pieces, there are only "a certain number" of possible outcomes remaining. This is "end game" strategy. It is a combination of Discrete Mathematics and Statistics. Chug through them and store the results. You win!
Algorithm 2 - Algorithm for computing hand distributions
Inputs: ... number of possible private hands H, betting history of current hand h, array of index conflicts IC ...
In the course of this loop, we also look up the probability that each player would play according to the observed betting history in the precomputed trunk strategies, which we then normalize in accordance with Bayes’ rule.
. The core algorithm is domain independent, although we present the signals as card-playing hands for concreteness.
Algorithm 1 - Algorithm for endgame solving
Inputs: number of information buckets per agent ki, clustering algorithms Ci, equilibrium-finding algorithm Q, number of private hands H, hand rankings R
Deep Learning has NOTHING TO DO WITH BRUTE FORCE CALCULATIONS!
No, it's not poker specific and no they didn't teach it winning poker strategies...
We also showed that endgame solving guarantees a low exploitability in certain games, and presented a framework that can be used to evaluate its applicability more broadly.
The system LEARNS how to play the game. It's not taught how to play the game. It learns how to play the game. THIS HAS NOTHING TO DO WITH BRUTE FORCE CALCULATIONS. You just don't have a clue as to what you're talking about.
I should take issue with the guy who created the system and has explained it but not you?? You don't know what you're talking about.
This is why there's no brute force and the system learns because there's INCOMPLETE INFORMATION.
Stop with the long diatribes that are meaningless because you're trying to obfuscate the fact that you don't know what you're talking about.
Libratus is an artificial intelligence computer program designed to play Poker, specifically no-limit Texas hold 'em. Libratus isn't Poker-specific, the algorithms and ideas employed by Libratus are of a very general nature and could be applied to a wide range of real-world problems.
Libratus was built with more than 15 million core hours of computation as compared to 2-3 million for Claudico. The computations were carried out on the new 'Bridges' supercomputer at the Pittsburgh Supercomputing Center. According to one of Libratus' creators, Professor Tuomas Sandholm, Libratus does not have a fixed built-in strategy, but an algorithm that computes the strategy.
During the tournament, Libratus was competing against the players during the days. Overnight it was perfecting its strategy on its own by analysing the prior gameplay and results of the day, particularly its losses. Therefore, it was able to continuously straighten out the imperfections that the human team had discovered in their extensive analysis, resulting in a permanent arms race between the humans and Libratus.
SHOW ME WHERE Tuomas Sandholm SAID THEY TAUGHT IT WINNING POKER STRATEGIES AS YOU SAID!
The algorithms that power Libratus aren’t specific to poker, which means the system could have a variety of applications outside of recreational games, from negotiating business deals to setting military or cybersecurity strategy and planning medical treatment – anywhere where humans are required to do strategic reasoning with imperfect information.