Waves of Intelligence Series
As we have lost the article which accompanied the second video in this series, we have included the second video below with the article written for the first.
Quantum computers are the secret KEY to unlocking all encrypted files. Privacy is now an illusion of the past.
DWAVE: Funded by InQTel (a branch of the CIA that started Google and Facebook).
The D-Wave took about a hundredth of a second; with a classical computer it’d take about 100 days. (More on DWave further down)
Google, NASA, CERN and other companies have been using DWave for a few years already.
Quantum Computing is the reality of the minority report: precrime. All of your data they have been gathering from your social media accounts and phone apps and every single thing you do online. All of your data in your file and now finally able to be analyzed and sorted thanks to Quantum Computing. Now it will be no problem to make more detailed lists of citizens. Those who will rise up and those who will go along with the plans. Social Engineering just got a lot more interesting. Computers can now predict humans actions before they happen based on their past and present data. This is happening today in China.
Google has this technology: to psychologically profile and predict the behavior of human consumers so that high-value ads can be delivered to them across Google’s search engine and content networks as the main stream story line goes. We know from the PRISM program that this will enable Google to funnel psych profile meta-data on internet users to the NSA.
Quantum computers can be used to search large databases in a fraction of the time that it would take a conventional computer. Other applications could include using quantum computers to study quantum mechanics, or even to design other quantum computers. Teaching machines how to learn and think for themselves.
Machine Learning (Artificial Intelligence) has given us self-driving cars, practical speech recognition, effective web searches and more. All under the guise of convenience.
Google’s algorithm recognizes that you searched for something a couple of seconds after searching something else, and it logs and saves this for future users who make a similar typing mistake. As a result, Google ‘learns’ to correct it for you.
“We hope it helps researchers construct more efficient, effective models for everything from speech recognition, to web search, to protein folding,” Google said in a statement about D Wave.
In 2013, a D-Wave Two system was installed at the new Quantum Artificial Intelligence Lab, a collaboration among Google, NASA and the Universities Space Research Association (USRA). The lab is housed at the NASA Ames Research Center in California. In September 2015 the system was upgraded to a 1000+ qubit D-Wave 2X quantum computer.
Eric Ladizinksy (Founder of Dwave) has told us Dwave first doubled every couple years, then every year, then every six months and now every 3 months!
Rose’s Law is a relatively new observation that states that the computational power of quantum computers doubles every 12 months, as opposed to the 18 month doubling of Moore’s Law.
As D Wave advanced they made hardware upgrades. Also were able to tune the machine into the frequency of the universe which seems to make it sound like a “heart beat.”
We are all made up of and affected by frequency- people, planets, solar systems; we are now connected to an AI machine that scientifically proven, travels/computes in multiple dimensions at once!
“So the enabler for AI is machine learning,” Intel’s Nidhi Chappell, “AI has become so pervasive in our lives we don’t come to recognize that it’s powering a lot of things. You probably use it dozens of times a day without knowing it.”
3 types of learning:
- Reinforcement learning: This type of learning concentrates on how an AI ‘agent’ should behave in order to get the most out of its work. The machine picks an action or a sequence of actions, and gets a reward. This is used when teaching machines to play and win games but needs a large number of trials to learn even simple tasks.
- Supervised learning: This is when researchers tell the machine what the correct answer is for a particular input. For example, they show it an image of a car and tell it the correct answer is “car.” It is the most common technique for training neural networks and other machine learning architectures.
- Unsupervised learning/predictive learning: Humans and animals learn, typically, in an unsupervised manner by watching how the world works and by observing our parents. Basic concepts such as: objects don’t disappear spontaneously and objects that are not supported fall. Researchers don’t know how to do this with machines at the moment, at least not at the level that humans and animals can.
In 2018: Google turns over its search engine algorithm to a massive network of self-learning machines. Soon thereafter, a voice interface is added to Google, achieving the “Star Trek computer” goal that Google first outlined in the 1990’s.
Ray Kurzweil, director of engineering at Google, also claims that the biological parts of our body will be replaced with mechanical parts and this could happen as early as 2100. Kurweil made the claims during his conference speech at the Global Futures 2045 International Congress in New York at the weekend. His goal is to merge with the machines in order to be immortal.
Kurtzweil also states 2000 and 8000 qubit systems are coming in 2 years and 4 years that will speed up solving nearly all machine learning optimization problems in one second after they are formulated properly for the Dwave system. In a few years, Kurzweil will have the classical computer power of Google’s data centers and quantum computer power that could be beyond the power of all classical computers to drive his solution of greater than human intelligence: “artificial intelligence”.
Singularity University describes itself as “a global community using exponential technologies to tackle the world’s biggest challenges” is the creation of Ray Kurzweil who is Google’s Director of Engineering.
With the rapid growth of AI technology will inevitably take over our lives. AI will do our jobs faster and cheaper. AI will drive our cars safer and more efficiently. AI will control our bodies frequencies and mental processes.
In the UK, a report released by thinktank Reform says roughly 250,000 public servants could lose their jobs to robots over the next 15 years. Meanwhile, other governments such as those in Hong Kong and Singapore are increasingly turning to AI to take over various administrative functions.
News is that AI is even creeping up into freedom of speech and press – Google’s Digital Journalism Initiative is spearheading an AI software called JUICE which claims to “help” (CENSOR) reporters write more “accurate and engaging pieces” meaning what fits the elites agenda of propaganda as we have already seen in internet censorship today.
Microsoft is already cutting its workforce and jobs to “move towards cloud tech.” Keyword meaning Artificial Intelligence.
AI can be used to address child abduction issues and prevent traffic congestion, according to the Chinese version of Li’s non-binding proposal. Instead of being better humans we will make machines to do it for us.
“In the future, robotics may be combined with AI, neurology, mechanical engineering and many other fields to deeply influence our life and work,” Ma said at a press conference in Beijing on Friday evening.
AI is also receiving a big push from Facebook and Google as they invest billions of dollars through OpenAI and DeepMind to democratize the technology – not to mention countless acquisitions of AI talent. But the corporate activity merely proves that big-business has plans for AI, whereas AlphaGo beating Lee Sedol signals the possibility that AI could indeed be more capable than humans.
You can consider corporate activity like Facebook, Google, Snapchat, Baidu as a signal of trust. You can also trust the direction of car companies like GM, Mercedes-Benz, Uber, and Tesla towards autonomous cars. And you can trust academics, the White House and other longitudinal thinkers when they say past academic research, computational power and large accessible data sets will together create a perfect storm for AI impacting our lives Asia will be big in AI. If you look at the data trends, there is a tonne of AI research done in China, Japan, South Korea and Singapore. In fact there are more cited publications in China than in the United States.
AI is naturally receiving large contributions from Facebook and Google as they invest billions of dollars through OpenAI and DeepMind to democratize the technology – not to mention countless acquisitions of AI talent. But the corporate activity merely proves that big-business has plans for AI, whereas AlphaGo beating Lee Sedol signals the possibility that AI could indeed be more capable than humans.
Jordan Novet May 16, 2013 Posted:
“It’s been almost two decades since Peter Shor came up with a a breakthrough algorithm for finding the prime factors of a number with a quantum computer, sparking great interest in quantum computing. But commercial adoption has been pretty much nonexistent. On Thursday, though, Google came forward with news that it’s launching a Quantum Artificial Intelligence Lab that will include a quantum computer, apparently making it the second company to pay for a quantum computer. The development suggests that quantum computing could finally be taking off.”
Earlier this year Lockheed Martin shared details of its implementation of a D-Wave Systems quantum computer, which reportedly cost $10 million: The contractor is using the computer to develop new aircraft, radar and space systems.
“Following a recent upgrade, the USC-Lockheed Martin Quantum Computing Center based at the USC Information Sciences Institutes now the leader in quantum processing capacity.
With the upgrade — to 1,098 qubits from 512 — the D-Wave 2X™ processor is enabling QCC researchers to continue their efforts to close the gap between academic research in quantum computation and real-world critical problems. The new processor will be used to study how and whether quantum effects can speed up the solution of tough optimization, machine learning and sampling problems. Machine-learning algorithms are widely used in artificial intelligence tasks.” Read the entire article here.
Now Google is taking steps at incorporating more quantum computing into its operations with the Quantum Artificial Intelligence Lab, which will be located at the NASA Ames Research Center in Moffett Field, Calif. Researchers from the Universities Space Research Association will be able to use the machine 20 percent of the time, Forbes reports. That could lead to lots of interdisciplinary thinking and collaboration.
For Google, though, the goal of the initiative is to make strides in machine learning, according to a Thursday Google Research blog post. The best results could trickle down to end users, perhaps in search results and speech-recognition applications.
Google has already assembled machine-learning algorithms that involve quantum elements. The applications might have arisen after Google’s earlier partnership with D-Wave, which came to light in a different blog post from Neven in 2009.
Google has already used machine learning to recognize faces and other things in photos and videos. New technology Google executives talked about at the Google I/O developer conference in San Francisco on Wednesday also appears to use machine learning to stitch together photos and clean them up.
What Google has learned so far is the best results come from blending regular binary computing using ones and zeros with quantum style computing. Quantum computing accommodates the space between a one and a zero with quantum bits of information, or qubits. It can express likelihood as well as take shortcuts by approximating when handling certain kinds of workloads. Given what Google has observed thus far, it could decide to build hardware combining quantum and classical computing capabilities.
For now, though, Google is diving deeper into quantum computing with the D-Wave machine. In this way, Google could help push the development of quantum computing much like its invention of MapReduce changed the way firms do distributed data processing.
In any case, quantum computing has a long way to go before reaching commercial viability. But because the organization at the helm of the quantum research is Google and not IBM or Bell Labs, regular people could start seeing much more of the advantages in just a few years’ time, which in turn could drive commercialization.
Lets look at some very small points and you can follow the links for more indepth explanations.
Richard Feynman once famously stated that nobody understands quantum mechanics.
Extensive testing and interviews demonstrate that a significant fraction of advanced undergraduate and beginning graduate students, even after one or two full years of instruction in quantum mechanics, still are not proficient at those functional skills. They often possess deep-rooted misconceptions about such features as the meaning and significance of stationary states, the meaning of an expectation value, properties of wave functions, and quantum dynamics. Even students who excel at solving technically difficult questions are often unable to answer qualitative versions of the same questions.
Quantum Mechanics: where the rules of the world you experience don’t apply.
The property of entanglement: where particles can take on related properties if they end up in an equation together. If you separate these particles, they remain entangled with these properties until you observe one of them—then the other particle assumes its corresponding value. (A 1 or a 0) It’s as if the fact that you observed the first particle immediately transmits information to the other particle about how it should look.
Schrödinger’s equation: Tiny particles, such as electrons or photons, can simultaneously take on states that we would normally deem mutually exclusive. They can be in several places at once, for example, and in the case of photons simultaneously exhibit two kinds of polarization. We never see this superposition of different states in ordinary life because it somehow disappears once a system is observed: when you measure the location of an electron or the polarization of a photon, all but one of the possible alternatives are eliminated and you will see just one. Nobody knows how that happens, but it does
“The prowess of quantum computing comes from the “ghost-angel” state of a quantum system. The uncertainty of the state applied to quantum mechanics is only true for a linear superposition of coherent quantum states, which I term the ghost-angel state. This state has not been found to exist for macroscopic objects.”
In March 2000, scientists at Los Alamos National Laboratory announced the development of a 7-qubit quantum computer within a single drop of liquid. The quantum computer uses nuclear magnetic resonance (NMR) to manipulate particles in the atomic nuclei of molecules of trans-crotonic acid, a simple fluid consisting of molecules made up of six hydrogen and four carbon atoms. The NMR is used to apply electromagnetic pulses, which force the particles to line up. These particles in positions parallel or counter to the magnetic field allow the quantum computer to mimic the information-encoding of bits in digital computers.
Some Quantum Physics:
It is still unclear why the observer of an experiment determines behavior of the system and causes it to favor one state over another. When light is introduced to atoms, it recharges them. You cannot observe the system or measure its properties without interacting with it. And where there is interaction, there will be modification of properties.
Basic logic of physics is incorrect and the entire underlying structure of physics is unable to be used in quantum physics because the core logic is entirely different. Not to mention basic physics is mostly lacking because much is based off the work of Albert Einstein who’s theories were based on incorrect assumptions. By default everything built on this foundation is built on lies. Theories-Assumptions and guesses.
We have to choose between the two evils. But remember, now scientists are increasingly convinced that the basis of our mental processes is created by these notorious quantum effects. So, where the observation ends and reality begins, is up to each of us.
Theoretical physics is a branch of physics which employs mathematical models and abstractions of physical objects and systems to rationalize, explain and predict natural phenomena. This is in contrast to experimental physics, which uses experimental tools to probe these phenomena.
The advancement of science depends in general on the interplay between experimental studies and theory.
D-Wave starts off with a set of noninteracting qubits—a collection of supercomputing loops kept at their lowest energy state, called the ground state—and then slowly, or “adiabatically,” transforms this system into a set of qubits whose interactions at its ground state represent the correct answer for the specific problem
According to Forbes, when the world’s first digital computer was completed in 1946 it opened up new vast new worlds of possibility. Still, early computers were only used for limited applications because they could only be programmed in machine code. It took so long to set up problems that they were only practical for massive calculations.
John Backus created the first programming language, FORTRAN, at IBM in 1957. For the first time, real world problems could be quickly and efficiently transformed into machine language, which made them far more practical and useful. In the 1960’s, the market for computers soared.
Like the first digital computers, quantum computing offers the possibility of technology millions of times more powerful than current systems, but the key to its success will be translating real world problems into quantum language.
Quantum computers aren’t limited to two states. They encode information as quantum bits, or qubits. Qubits represent atoms, ions, photons or electrons and their respective control devices that are working together to act as computer memory and a processor. Because a quantum computer can contain these multiple states at the same time. It has the potential to be millions of times more powerful than today’s most powerful supercomputers.
This superposition of qubits is what gives quantum computers their inherent parallelism. According to physicist David Deutsch, this parallelism allows a quantum computer to work on a million computations at once, while your desktop PC works on one.
One problem with the idea of quantum computers is that if you try to look at the subatomic particles, you could bump them, and thereby change their value. If you look at a qubit in superposition to determine its value, the qubit will assume the value of either 0 or 1, but not both. To make a practical quantum computer, scientists have to devise ways of making measurements indirectly to preserve the system’s integrity. Entanglement provides a potential answer. In quantum physics, if you apply an outside force to two atoms, it can cause them to become entangled, and the second atom can take on the properties of the first atom. So if left alone, an atom will spin in all directions. The instant it is disturbed it chooses one spin, or one value; and at the same time, the second entangled atom will choose an opposite spin, or value. This allows scientists to know the value of the qubits without actually looking at them.
Dario Borghino October 6, 2015
In what is likely a major breakthrough for quantum computing, researchers from the University of New South Wales (UNSW) in Australia have managed for the first time to build the fundamental blocks of a quantum computer in silicon. The device was created using standard manufacturing techniques, by modifying current-generation silicon transistors, and the technology could scale up to include thousands, even millions of entangled quantum bits on a single chip.
The real power of this breakthrough is not in a slightly higher operational temperature, but in the fact that these basic building blocks of quantum computers were built by doing simple modifications to current-generation silicon transistors. The researchers say they have worked out a way to extend this technique to a much larger number of qubits, even numbering in the thousands of millions, all reported to be fully entangled.
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