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No other segment of the gaming industry has benefited more from the technology revolution than the slot machine. Once considered the ugly stepdaughter placed on the gaming floor to appease the spouses of table players, the slot machine has been transformed into the fairy princess of the gaming world. With her, she has brought a dowry of riches no one would have imagined for the casino and a few lucky players as well. Over twenty years ago the slot machine accounted for 30 percent of the casinos' profits. Today it accounts for about 70 percent. Computer technology and the ability to play with little to no gambling knowledge makes it possible to offer life-changing jackpots big enough to turn a pauper into a king.
However, the fact that it doesn't take much gambling knowledge to play means that most people don't understand the inner working of the slots -- which makes it easy to explain a loss or a win with some false logic. Like any other 'wives tales' these are passed from person to person until they become gospel. Most of these myths and misconceptions are harmless but they can add to your frustration and take away some of the enjoyment of your casino visit. Let’s take a look at a few of the most popular myths and the truth behind them.
Myth #1
Someone hit a jackpot on the machine you just left -- so you would have won that jackpot if you kept playing.
This is probably one of the most common notions about slot machine gambling -- but it's patently false. The slot machines have a computer chip inside that runs the Random Number Generator (RNG). The RNG is continuously cycling through numbers even when the machine is not being played. These numbers correspond to the stops on the wheel that display the winning or losing symbols that you see when the reels stop. When you hit the spin button or pull the handle, the RNG picks the combination at that given microsecond. If you had stayed at the machine, it is highly unlikely that you would have stopped the RNG at the exact nano-second to display that same combination of numbers. In the time it takes to talk with a friend or sip your drink the RNG has cycled through thousands of combinations.
Myth #2
You can tell the odds of winning by counting the symbols on each wheel.
Actually, you can't. The RNG generates a number for each spin. There can be hundreds of virtual stops on each wheel even though you only see a few symbols. For example, you may see 20 symbols on each wheel of a three-reel machine. You figure 20 x 20 x 20 = 8,000 combinations and your chance of hitting the jackpot is 1 in 8000. In reality, the computer chip may program 256 stops for each wheel which makes the odds 256 x 256 x 256 =16,777,216 combinations. Being able to generate millions of combinations is the reason that slots can offer large paybacks.
Myth #3
Casinos can loosen or tighten the slot machines with the flip of a switch.
In actuality, the slot machines have a computer chip in them that determines the payback percentage. These are preset at the factory. In order for a casino to change the payback, they would have to change the chip. In most jurisdictions, there is paperwork that has to be filled and submitted to the Casino Control Commission for each machine if the chip is changed. It's time-consuming and the chips are very expensive. For this reason, it is more economical to decide on the payback percentages before purchasing the machines and having the factory ship them with the proper chip.
Myth #4
A machine that has not been paying out is due to hit.
There is no way to determine if a machine is due to hit. Each spin is a random occurrence and has no bearing on what has happened previously. Don't ever play more than you should because of this misconception -- it will be devastating to your bankroll if you do.
Myth #5
The temperature of the coins played will affect the way a machine pays.
Unfortunately, the machine is not affected by temperature. It doesn't matter if you play hot, cold, old or new coins. The coin slot is a mechanical device and has no feeling.
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Myth #6
If you use your slot club card the machine will pay back less.
This may well be the most damaging myth of them all. There is no link between the card reader and the RNG, but by not using your player's card you are denying yourself valuable comps and sometimes cash back from the casino.
Machine learning (ML) is quickly coming of age. Today, we’re able to feed very large amounts of data to machine learning applications capable of learning to predict possible outcomes with a high degree of accuracy. The accuracy of these deep learning (DL) models increases proportionally with the size of the training dataset. As trillions of connected devices send data to the system, datasets can reach into the hundreds of terabytes.
We’re seeing the results of this machine learning evolution in the form of self-driving cars, real-time fraud detection, social networks that recognize who’s in your vacation photos … The list is long and promises to include every industry.
Let’s lift the hood and see what makes this new Intel Xeon Phi product family so well suited to handling ML workloads. I’ll also share some of our early performance test results when running ML workloads on a single-node Intel Xeon Phi processor-based system and on a 128-node Intel Xeon Phi processor-based cluster. Finally, I’ll talk about the work we’ve been doing to optimize our software libraries and several popular open-source ML frameworks for x86 architectures.
Intel® Xeon Phi™ Processor Features
In designing the second-generation Intel Xeon Phi chip, we created a massively multicore processor that is available in a self-boot socket. This eliminates the need to run an OS on a separate host and pass data across a PCIe* slot. (However, for those who prefer using the latest Intel Xeon Phi chip as a co-processor, a PCIe-card-version will be available shortly.)
The Intel Xeon Phi Processor x200 has 72 processor cores, each with two Intel® Advanced Vector Extensions 512 (Intel® AVX-512) SIMD processing units for improved per-core floating-point performance. This benefits popular ML algorithms such as floating-point multiply and Fused Multiply-Add (FMA). The Intel Xeon Phi Processor x200 delivers up to 6 teraflops of compute power. That multicore, multithreaded power is coupled with an on-package, high-bandwidth, memory subsystem (Multi-Channel DRAM) and integrated fabric technology called Intel® Omni-Path Architecture (Intel® OPA).
The high-bandwidth integrated memory (up to 16 GB of MCDRAM) helps feed data to the cores very quickly and supplements platform memory of up to 384 GB of commodity DDR4. This lets programmers manage memory by specifying how much data they want and when they want it. MCDRAM also provides flexibility to those who prefer to cache their data so they don’t have to think about memory management. (MCDRAM can be configured as a third-level cache, as non-uniform memory access—allocatable—memory, and in a hybrid combination that’s part cache, part memory.)
When processing large ML/DL workloads, the ability to scale from one node to hundreds or thousands of nodes is crucial. With integrated Intel OPA fabric, the Intel Xeon Phi Processor x200 can scale in near-linear fashion across cores and threads. On a coding level, the fabric makes it possible to efficiently fetch data from remote storage to a local cache with minimal programming and at high speed.
Taken together, these innovations deliver very good machine learning and deep learning time-to-train. For example, on AlexNet, with respect to single-node, we achieve about 50x reduction in training time on 128 nodes of the Intel Xeon Phi Processor x200. For GoogLeNet training, we achieve a scaling efficiency of 87% at 32 nodes of the Intel Xeon Phi Processor x200, 38% better than the best such published data to-date.
The tradeoff is that your apps must be parallelized to take advantage of this massively parallel multicore, multithreaded architecture. Otherwise, you must be willing to accept single-core, single-thread performance.
One factor that offsets single-core, single-thread performance is the fact that each core in an Intel Xeon Phi Processor x200 has multiple vector processing units, so overall you have more compute density. As a result, if your workload can benefit from high levels of parallelism and thread parallelism, the Intel Xeon Phi processor packs more compute into a smaller area and, therefore, draws less power than alternative solutions.
Binary Compatibility
From a software perspective, second-generation Intel Xeon Phi processors are binary compatible with x86 architecture processors including the Intel Xeon® E5 family of processors. That means you only have to modernize your code once to boost your time to train on second-generation Intel Xeon Phi processors and your existing Intel Xeon processor-based servers. By “modernization” I don’t mean that you have to write ninja parallel code. We’re making it easier to parallelize ML/DL code for general-purpose x86 architecture-based CPUs with tools like the popular Intel® Math Kernel Library, which includes new extensions for optimizing deep neural networks in the Intel® MKL 2017 Beta release that’s available now. In addition, we’re hard at work optimizing for x86 architectures on popular open-source ML frameworks such as Caffe* and Theano*. These and other efforts, even without hardware upgrades, are producing performance boosts on the order of 30 times for DL applications.
Summary
With the second-generation Intel Xeon Phi product family, we’re one step closer to reaching our goal of democratizing ML and the multi-layer neural networks required for the computationally intensive training phase of DL applications. In doing so, we’re enabling an emerging class of workloads that enhance the decision-making capabilities of machines.
Come see the Intel Xeon Phi Processor x200 at work at both the International Supercomputing Conference in Frankfurt and the International Conference on Machine Learning in New York this week. If you can’t make it to Germany or New York, visit XeonPhiDeveloper.com for more technical details.
Resources:
Knights Landing — An Overview for Developers (video~250 MB)
https://software.intel.com/content/www/us/en/develop/videos/knights-landing-an-overview-for-developers.html
Migrating Applications from Knights Corner to Knights Landing Self-Boot Platforms
https://software.intel.com/content/www/us/en/develop/articles/migrating-applications-from-knights-corner-to-knights-landing-self-boot-platforms.html
Myth Busted blog posts: General Purpose CPUs can’t tackle DL Neural Network Training
http://itpeernetwork.intel.com/myth-busted-general-purpose-cpus-cant-tackle-deep-neural-network-training/
Intel Processors Slot Machines
Intel® Math Kernel Library (Intel® MKL) 2017 Beta Release
https://software.intel.com/en-us/forums/intel-math-kernel-library/topic/623305
Deep Neural Network Technical Preview for Intel® Math Kernel Library (Intel® MKL)
https://software.intel.com/en-us/articles/deep-neural-network-technical-preview-for-intel-math-kernel-library-intel-mkl