Post in evidenza

AI Revolution: Robot Operation System

Sophia, the first humanoid robot to receive citizenship of a country.   She was featured on the cover of Ella Brazil magazine and has a...

mercoledì 17 giugno 2015

Man Vs. Machine: Artificial Intelligence & Quantum Computing

Humans are gradually becoming surpassed by machines in a number of fields. Scientists have now developed a deep learning machine smarter than your average college-educated human.

In 2011, developers at IBM built an artificial intelligence system and put it in direct competition against two of America’s smartest minds. Appearing on the popular quiz show Jeopardy, the Watson computer crushed opponents Ken Jennings and Brad Rutter.

As insignificant as a game show competition may seem, Watson’s victory represented a major advancement in artificial intelligence. Receiving quiz questions verbally, just like his human counterparts, Watson was the most sophisticated system capable of processing natural language

Still, he wasn’t perfect. Despite the impressively high success rate, Watson still got a few answers wrong.

"Its largest airport is named for a World War II hero; its second largest for a World War II battle," read one question under the "US Cities" category. Watson’s answer: Toronto.

Not to be outdone, Microsoft, in conjunction with the University of Science and Technology of China, may have now developed an even more impressive artificial intelligence. It doesn’t have a catchy, anthropomorphic name, but the computer system can beat out the average person on an Intelligence Quotient test.

"Our RK model can reach the intelligence level between the people with the bachelor degree and those with the master degrees," reads the report conducted by the developers, "which also implies the great potential of the word embedding to comprehend human knowledge and form up certain intelligence."

IQ tests are typically composed of questions which fit into one of three categories. Logic questions rely on identifying picture patterns and mathematical questions rely on identifying numerical patterns. Computers have always been pretty good at both of these, given the fact that computers are built on numerical patterns.

But the third category, verbal reasoning, has proven more elusive for AI systems. These are questions dealing with analogies, classifications, synonyms, and antonyms. While past systems are certainly familiar with human language, that understanding has always been based on simplistic calculations. In a way, words are assigned numbers, and the AI processes sentence structure from those algorithms.

This method does not, however, take into account the fact that many words can have multiple meanings.

"Where do you find chili beans? At the North Pole," is a joke that would be completely lost on your laptop.

But the new AI system takes these weaknesses into account.

First, the computer analyzes any verbal questions to determine the category – analogy, synonym, antonym, etc. From there the system can weigh the words in the question against each other in a more accurate way, and determine the correct answer.

In the question, "which word is most opposite to MUSICAL? (i) discordant, (ii) loud, (iii) lyrical, (iv) verbal, (v) euphonious," the computer can deduce from the word "opposite" that this is an antonym question. "Musical" can be identified as the sentence’s subject, and from there it’s a simple search of antonyms to discover "discordant" is the best answer.

Developers tested their system at Amazon’s Mechanical Turk crowdsourcing facility against nearly 200 human subjects of varying intelligence levels. The AI did phenomenally well.

"To our surprise, the average performance of human beings is a little lower than that of our proposed method," the researchers said.

Of course, this system is custom built for IQ tests, which follow a fairly rigid formula. A real-life scenario straying from those strict parameters could prove more difficult for the computer. Still, it’s a major step forward in computer science.

Ken Jennings may have said it best after losing the Jeopardy tournament.

"Just as factory jobs were eliminated in the 20th century by new assembly-line robots, Brad and I were the first knowledge-industry workers put out of work by the new generation of 'thinking' machines," he wrote for Slate.

"'Quiz show contestant' may be the first job made redundant by Watson, but I’m sure it won’t be the last."

Hi, Robot July/August 2015 Issue

Influential Bilderberg Points to AI?

At the recent highly secretive Bilderberg summit held in Telfs-Buchen, Austria, one of the key subjects on the agenda was Artificial Intelligence. The key question is why are some of the most powerful global leaders in government, military, banking, media, intelligence, business, academia, and more, focussing on Artificial Intelligence, machine learning and humanoid robots? What are the consequences for humanity, our society and government policy? Will this technology revolutionise the world we live in between 2015 and 2030, and beyond? Or will it be declared as the most serious threat to the survival of the human race and ought to be heavily regulated?

Spawning Quantum Artificial Intelligence?

For the first time, clear blue water is being established between Quantum Computing (QC) and Classical Computing (CC), as QC pole vaults in performance within the domain of machine learning, a vital discipline within Artificial Intelligence. Make no mistake, this may be the beginning of the realisation of parts of the holy grail and open the way to a thousand and one mission critical applications for computers and humanoid robots that have proved illusive for decades. Machines may finally be able to do what we do, in some cases better than us and with higher levels of safety and security!

What is Machine Learning?

Machine learning is a branch of artificial intelligence and it learns from previous experience to optimise performance. Machine learning is already ubiquitous in various fields such as computer sciences, financial analysis, robotics, and bioinformatics. Machine learning begins with a set of data to train the device. For example, the idea may be to convert each received image into a vector by analysing the number of image pixels in each part of the picture.

What's a Key Challenge?

A key challenge is that machine learning with the rapidly growing "big data problem" could become intractable for classical computers to handle leaving the field wide open for Quantum computers that can handle vast numbers of samples with relatively few Quantum bits or Qbits. Recently, quantum machine learning algorithms were proposed which could offer an exponential speedup over classical algorithms. The Chinese have now demonstrated some of these algorithms to be workable and true.

NASA to Google: From USA to China

From NASA's Quantum Artificial Intelligence Laboratory (QuAIL) to Google's Quantum AI Lab, the race is on to utilise Quantum Computing for Artificial Intelligence. Yet, some of the early rays of sunlight in this field have begun arriving not just from the United States but more importantly from China.

China Teams Lead The Way

In what may be one of the first clear set of demonstrations of Artificial Intelligence on a Quantum Computer, one Chinese team of physicists has trained a quantum computer to recognise handwritten characters, the first standalone demonstration of “Quantum Artificial Intelligence” and another Chinese team using a small-scale photonic quantum computer is demonstrating that quantum computers may be able to exponentially speed up the rate at which certain machine learning tasks are performed, and in some cases, reducing the time from hundreds of thousands of years to mere seconds.

Zhaokai Li et al at the University of Science and Technology of China in Hefei have demonstrated machine learning on a Quantum Computer for the first time. Their Quantum Computer can recognise handwritten characters, just as humans can do, in what Li et al are calling the first demonstration of “Quantum artificial Intelligence”.

Chao-Yang Lu et al at the University of Science and Technology of China in Hefei, have also demonstrated a quantum entanglement-based machine learning method called quantum-based vector classification. This quantum-based vector classification method can be used for both supervised and unsupervised machine learning, and so could have a wide variety of applications. It's also ubiquitous in our daily lives, such as in face recognition, email filtering, and recommendation systems for online shopping.

Tiny Chinese Quantum Computer

The first Chinese team's tiny Quantum Computing machine consists of a small vat of the organic liquid carbon-13-iodotrifluroethylene, a molecule consisting of two carbon atoms attached to three fluorine atoms and one iodine atom. Crucially, one of the carbon atoms is a carbon-13 isotope.

This molecule is handy because each of the three fluorine atoms and the carbon-13 atom can store a single Qubit. This works by placing the molecule in a magnetic field to align the spins of the nuclei and then flipping the spins with radio waves. Because each nucleus sits in a slightly different position in the molecule, each can be addressed by slightly different frequencies, a process known as nuclear magnetic resonance.

The spins can also be made to interact with each other so that the molecule acts like a tiny logic gate when zapped by a carefully prepared sequence of radio pulses. In this way the molecule processes data. And because the spins of each nucleus can exist in a superposition of spin up and spin down states, the molecule acts like a tiny Quantum Computer.

Having processed the quantum information, physicists read out the result by measuring the final states of all the atoms. Because the signal from each molecule is tiny, physicists need an entire vat of them to pick up the processed signal. In this case, an upward peak in the spectrum from the carbon-13 atom indicates the character is a 6 while a downward peak indicates a 9.

“The successful classification shows the ability of our quantum machine to learn and work like an intelligent human,” say Li et al.

Physicists Point to Holy Grail

Physicists led by the Nobel Laureate Richard Feynman have long claimed that Quantum Computers have the potential to dramatically outperform the most powerful conventional processors utilised in Classical Computing. The secret sauce at work here is the strange quantum phenomenon of superposition, where a quantum object can exist in two states at the same time.

2^20 = 2x2x2...(20 times) = 1 million plus

The advantage comes when one of those two states represents a 1 and the other a 0, forming a Quantum bit or Qubit. In that case, a single quantum object -- an atomic nucleus for example -- can perform a calculation on two numbers at the same time. Two nuclei can handle 4 numbers, 3 nuclei 8 numbers and 20 nuclei can perform a calculation using more than a million numbers simultaneously! That’s why even a relatively modest Quantum Computer could dramatically outperform the most advanced supercomputers today. The new method takes advantage of quantum entanglement, in which two or more objects are so strongly related that paradoxical effects often arise since a measurement on one object instantaneously affects the other. Here, quantum entanglement provides a very fast way to classify vectors into one of two categories, a task that is at the core of machine learning.

What Happens Next?

"To calculate the distance between two large vectors with a dimension of 1021 -- or, in the language of Big Data, we can call it 1 Zettabyte (ZB) or 10^21 -- a GHz clock-rate classical computer will take about hundreds of thousands of years," Prof Lu at the University of Science and Technology of China in Hefei states. "A GHz clock-rate Quantum Computer, if we can build it in the future, with the exponential speed-up, will take only about a second to estimate the distance between these two vectors after they are entangled with the ancillary qubit."

"Machine learning has been all around, and will likely play a more important role in the age of Big Data with the explosion of electronic data," Prof Lu states. "It is estimated that every year [Big Data] grows exponentially by 40%. On the other hand, we have bad news about Moore's law: If it is to continue, then in about 2020, the chip size will shrink down to the atomic level where quantum mechanics rules. Thus, the speed-up of classical computation power faces a major challenge. Today, we may still be good running machine learning and other computational tasks with our good old classical computers, but we might need to think of other ways in the long run."

"We are working on controlling an increasingly large number of quantum bits for more powerful quantum machine learning," Prof Lu said. "By controlling multiple degrees of freedom of a single photon, we aim to generate 6-photon, 18-qubit entanglement in the near future. Using semiconductor quantum dots, we are trying to build a solid-state platform for approximately 20-photon entanglement in about five years. With the enhanced ability in quantum control, we will perform more complicated quantum artificial intelligence tasks."


1. The secretive Bilderberg group is right to focus on one of the biggest revolutions and disruptive technologies to be unleashed in the 21st century via the fusion of Artificial Intelligence (AI), machine learning and Quantum Computing (QC).

2. We at Quantum Innovation Labs ( believe that human society will be fundamentally altered as Artificial Intelligence and Quantum Technologies 2.0 (QT2) arrive in the 21st century.

3. It is befitting that China, with more than a billion people, is leading the way in the first assured steps of demonstrating a successful marriage between Artificial Intelligence, Machine Learning and Quantum Computing. However, let us not underestimate the billions of dollars in AI, QC and QT2 commitments of:

a. United States and Canada; 

b. European Union including separate commitments by United Kingdom, Netherlands, France, Germany, Austria, Italy and Spain; Switzerland; 

c. Russian Federation; 

d. China, India, Israel, Singapore and Australia; 

e. Brazil and South Africa.

4. Visits to and private sessions confirm that powerful governments, advanced militaries, intelligence agencies, technology giants, global banking and finance institutions, the business and corporate worlds, academia around the world are all carefully examining this new and rising opportunity to garner competitive advantage whilst noting the threat to national security.

5. When manifest, the combined effects of these disruptive AI and QT2 technolgies will be even greater, perhaps by an order of magnitude, than the proliferation of the industrial revolution in the 19th century and the arrival of the nuclear arm's race and the mobile, information and internet tech revolutions in the 20th century.

Our view is that there should be some regulatory oversight, maybe at the national and international level, just to make sure that we don’t do something very foolish by summoning this demon of Artificial Intelligence turbo-charged with Quantum Computing that we thought would become our slave but ended up becoming our master!

[STOPS] 1. Funding Form 2. Involvement Form 3. iQ:Ei Club

MIND THE GAP Nuovo appello contro i robot killer

Posta un commento
Related Posts Plugin for WordPress, Blogger...

Ratings by outbrain