Quantum AI and quantum computing are transformational technologies enabling a revolutionary future.

Quantum AI refers to the use of quantum computing for the computation of machine learning algorithms. With the computational advantages of quantum computing, quantum AI can now achieve results that were not possible with classical computers.

Alan Turing published a paper on Computing Machinery and Intelligence in 1950, and since then computers have come a long way. In the current modern age, computer limitations are gradually fading away, and machine learning has the ability to learn from its experiences. Traditionally, this type of intelligence was only achievable by using multiple computers and complicated machine learning algorithms. However, Nature Nanotechnology journal had a paper published recently where scientists proposed a new method – designing a computer with embedded intelligence and using the atom’s quantum spins to revolutionize computing as we know.

Next-Gen Computing

To understand this concept, let cover the basics of neuromorphic computing. In layman’s language, neuromorphic computing attempts to imitate the way a human brain works. From a technical perspective, neuromorphic computing is concerned with computer engineering where the elements of a computer, both hardware, and software, are wired according to the human nervous system and cerebral system.

Engineers study several disciplines like computer science, biology, mathematics, electronic engineering, and physics to create accurate neural structures. Neuromorphic computing aims to create devices that can learn, retain information, and make logical deductions the way a human brain does, a cognition machine. Alongside, it also attempts to prove how the human brain works by scavenging new information.

As a step forward in artificial intelligence technology, neuromorphic computing allows robots embedded with small computing hardware to make their own decisions in the future.

The Quantum Brain

The Quantum brain is a prime example of neuromorphic computing, the future of computing. Our human brains use signals sent by our neurons to make all kinds of computations. Similarly, the quantum brain uses cobalt atoms on a superconducting black phosphorus surface to imitate the process of human brain signals.

Cobalt atoms have quantum properties like unique spin states which carry information to stimulate ‘neuron firing’ with applied voltages. This helped the atoms to achieve a self-adaptive behavior based on the external stimuli.

Can AI Work With A Quantum Brain?

Artificial intelligence is an evolving technology, but it still has not overcome technological limitations. But with quantum computing, obstacles to achieving artificial general intelligence, AGI, can be discarded. Quantum computing can rapidly train machine learning models to generate optimized algorithms. Quantum computing can power an optimized and steady AI to complete analysis in a short time, as opposed to years of work that would delay any and all technological advancements.

According to researchers, a realistic aim for quantum AI is to replace traditional algorithms with quantum algorithms. These quantum algorithms can have several use cases to further advancements.

• Developing quantum algorithms for traditional learning models can provide possible boosts to the deep learning training process. Quantum computing can help machine learning by presenting the optimal solution set of the weights of artificial neural networks, quickly.

• When traditional decision-making problems are formulated with decision trees, the next course of action to reach the solution sets is by creating branches for a particular point. However, this method becomes complicated when the problem is too complex. Quantum algorithms can solve the problem faster.

Can neuroscience-inspired quantum computing and AI mesh? Yes, says several similarities between the brain and machine learning techniques like deep learning. Is that future near? Yes and no. Right now, the quantum AI industry needs to work to eliminate immaturities in the technology and achieve crucial milestones such as less error-prone and more powerful computing, developing the right AI applications where quantum computing can outperform traditional computing, and creating a widely adopted open-source modeling and training frameworks. These milestones will push quantum AI towards future developments.

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The discovery by a bunch of researchers reveal how AI can now read and interpret our personal choices

Artificial Intelligence has been disrupting many industries, business processes, and our lifestyle. With artificial intelligence technology, it is now possible to augment human intelligence and use it in decision-making and customer interactions. The ongoing digital transformation has brought many cutting-edge technologies to the mainstream and stressed the significance of AI and Big Data in revolutionizing industries. The role of artificial intelligence in business has been proved to be positively redefining operations and encouraging cost-efficiency.

But there are still areas connected to AI that researchers are studying to enhance the simulation of human intelligence to an extent, which enables sentiment analysis. Although researchers at the University of Helsinki and the University of Copenhagen have come up with an interesting discovery, wherein AI can read the brainwaves to understand and define subjective notions. In a paper published by these universities, AI can interpret the data generated from a brain-computer interface to build facial images that appeal to or attract different individuals.

A brain-computer interface (BCI), also known as brain-machine interface technology, is a communication system that connects the brain with an external machine or device. A brain-Computer interface is capable of measuring the activity in the Central Nervous System (CNS). This measured brain activity is converted into electronic and software signals that can be interpreted by AI.

Electroencephalography (EEG) and electromyography (EMG) are already in use by doctors to understand the neural activities of our brain and muscles, respectively.

BCI is extensively used in the healthcare and medical fields to treat broken neural connections between our brain and other body parts.

How interesting is it that this technique literally explains the old proverb, ‘beauty is in the brain’? Beauty is in fact inside our brains, which can now be interpreted by some machines and the wide range of AI applications can enable this.

But jokes apart, this study opens up new avenues for artificial intelligence, machine learning, and data analytics and also. According to a Daily Mail report, “The team strapped 30 volunteers to an electroencephalography (EEG) monitor that tracks brain waves, then showed them images of ‘fake’ faces generated from 200,000 real images of celebrities stitched together in different ways.”

The machine learning model called Generative Adversarial Neural Networks was trained to familiarise with individual preferences of faces so that it could easily generate new facial dimensions according to the brainwaves.

A report by Technology Networks revealed that the researchers developed new portraits for each participant, to test the validity of their modeling, and predicted that they will personally find these models attractive. Further, the researchers tested them in a double-blind procedure against matched controls to find that the new images match the preferences of the subjects with an accuracy of over 80%.

Connecting artificial neural networks to our brain can now produce results based on our personal preferences through a non-verbal communication process. This development is new since the neural networks or BCIs couldn’t peek into our personal choices and only establish the pattern of activities.

If it is possible to understand something this unique and personal, AI is not very far from augmenting and understanding the human brain to a more satisfying extent. However, such an invasion of artificial intelligence and technology into the internal structures of our brain will raise concerns about privacy and ethics. This new development will enable the understanding of individual and subjective biases that are internalized deep in our brains. Well, these innovations and developments in the field of AI will aid AI companies in expanding their business avenues and services.

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When chasing a bouncing ball, a human will head where they anticipate the ball is going. If things change — for example a cat swats the ball and it bounces off in a new direction — the human will correct to an appropriate new route in real time.

Robots can have a hard time making such changes, as they tend to simply observe states, then calculate and execute actions, rather than thinking while moving.

Google Brain, UC Berkeley, and X Lab have proposed a concurrent Deep Reinforcement Learning (DRL) algorithm that enables robots to take a broader and more long-term view of tasks and behaviours, and decide on their next action before the current one is completed. The paper has been accepted by ICLR 2020.

Deep Reinforcement Learning (DRL) has achieved tremendous success in scenarios such as zero-sum games and robotic grasping. These achievements however were seen largely in blocking environments — where the model assumes there will be no change of state in the time between a state being observed and any action(s) being executed.

In the real world “concurrent environments,” however, the environmental states can evolve substantially in real time, and actions executed in a sequential blocking fashion can fail because the environment has changed since the agent initially computed the action.

The main idea of the proposed model is to enable a robot to act with concurrent control, “where sampling an action from the policy must be done concurrently with the time evolution.”

The researchers first used standard RL methods in both discrete-time and continuous-time settings. They then applied Markov Decision Processes (MDPs) with concurrent actions, where concurrent action environments capture the current state while a previous action is still being executed. The team concluded that MDP modifications are sufficient to represent concurrent actions.

The research team introduced value-based DRL algorithms that can cope with concurrent environments, and evaluated their methods on both a large-scale robotic grasping task simulation and a real-world robotic grasping task.

In the concurrent large-scale simulated robotic grasping task the proposed concurrent model acted 31.3 percent faster than the blocking execution baseline model. In the real-world robotic grasping task, the concurrent model was able to learn smoother trajectories that were 49 percent faster.

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When someone compares his company’s product to the 10 Commandments you know he’s a true believer. “The same way that Moses came down from Mount Sinai with no more than 10 Commandments, we give customers the 10 most relevant services or solutions they are searching for,” was how Yoav Tzuker, Chief Innovation and Ecosystem Officer at Natural Intelligence Ltd., described his company’s business in an interview with CTech earlier this week.

Employing over 400 workers and pulling in annual revenues of approximately $300 million, and all that without ever receiving an external investment, Tzuker and Natural Intelligence have good reason to be confident. Natural Intelligence describes itself as “a global leader in intent marketing” and operates comparison websites, such as Top10.com, that “drive high-value customer acquisition for leading brands.” The company essentially aims to offer impartial and concise information to consumers who are looking to compare services provided online. Anything from mortgages to dating apps.

“Our technology helps customers make wise purchasing decisions because we refine all the available information online and present them with 10 personalized options,” explained Tzuker. “So we are helping the customer on the one hand and also helping the brands grow by sending them potential clients.”Tzuker spoke about ‘The paradox of choice’, a term coined by Barry Schwartz in his 2004 book, ‘The Paradox of Choice: Why More is Less’. Schwartz explained in the book that having many options to choose from can actually have negative consequences rather than making people happy.”A lot of research we have done in the past has shown that customers don’t want someone to make the decision for them. They want to make the decision themselves only based on reliable information,” said Tzuker. “We are living in an age where there is a paradox of choice. There is too much information and there are too many options and paradoxically the more options we have the more miserable we are. We want to limit our options. This was already done back in the Bible with Moses and the 10 Commandments. So we provide the customer with the 10 most relevant services or solutions according to filters and widgets that help us make the right recommendations for them. They can then take this information and compare it and make a quick and efficient decision while saving a lot of time.”Tzuker described Natural Intelligence, which generates income by receiving referral fees from its partners, as the extension of Google. “When you have a certain need, you will usually go to Google to search for it. We are the continuation of that. You already have a certain intent if you have reached our website, and according to your behavior on our website and our algorithm we know which recommendations to make,” he said.Tzuker said that the economic crisis brought on by the coronavirus (Covid-19) pandemic has seen traffic to comparison sites almost double, but that the trend doesn’t mean much in the long run. “We are supposedly in a good place as our business is based online. Services and online sales have been on the rise recently and it is clear that in the long run more services will be moving online,” he said. “Traffic to comparison sites has nearly doubled, but we still can’t quite say what this will result in as we don’t know if the U.S. is facing a recession. What we do know is that people want to compare and select the best product for them and that the price of services is more important to them.”By analyzing so much traffic and comparisons conducted in the company’s marketplace in recent weeks, Natural Intelligence is able to garner some interesting phenomena to point out. According to data collected in the U.S. from March until the second week of April, lowering interest rates in the U.S. almost doubled comparison searches for various financial services in areas such as mortgages, student loans, business loans, and student loan turnover. There has also been a 10-15% increase in searches in the field of wellbeing and leisure. New subscribers for online therapy are growing by tens of percent. There are also new trends for cultural consumption, with one example being virtual tours of museums, which doubled during March. As of mid-April, there has been a 50% increase in visits as more and more museums opened their doors to virtual tours, with the supply leading people to comparison sites to find the best museum for them.”Marketing people from brands treat us like a marketing channel and want to be on our lists, obviously as high as possible. But not every company is right for our lists and what interests us is how many deals are actually ultimately completed,” said Tzuker. “It doesn’t help anyone if we have a company high on the list but they don’t close any deals. We are always trying to find the right balance so that people make the right decisions for them and make a purchase. That is what our system is about, constantly learning how to best personalize the lists.”

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Back in 2008, theoretical physicist Stephen Hawking used a speech synthesizer program on an Apple II computer to “talk.” He had to use hand controls to work the system, which became problematic as his case of Lou Gehrig’s disease progressed. When he upgraded to a new device, called a “cheek switch,” it detected when Hawking tensed the muscle in his cheek, helping him speak, write emails, or surf the Web.

Now, neuroscientists at the University of California, San Francisco have come up with a far more advanced technology—an artificial intelligence program that can turn thoughts into text. In time, it has the potential to help millions of people with speech disabilities communicate with ease.

“We exploit the conceptual similarity of the task of decoding speech from neural activity to the task of machine translation; that is, the algorithmic translation of text from one language to another,” the scientists wrote in a new paper published in the scientific journal Nature Neuroscience.

They’ve taken an AI approach that is akin to translating text in different languages. The underlying theory is the same in both cases—the goal is to convert one sequence of some arbitrary length into another—but the inputs are different, neural signals in the brain versus text.

To test out their hypothesis, the researchers used human trials. The scientists implanted electrodes into the brains of four participants with epilepsy to monitor their speech. Each person then read sentences aloud from one of two datasets: a set of picture descriptions, composed of 30 sentences and 125 unique words, which contained 460 sentences and about 1,800 unique words.

Each participant read 50 sentences aloud multiple times, including lines like “Tina Turner is a pop singer” and “there is chaos in the kitchen.” As each person spoke, the researchers monitored their brain activity. Then, they input the data into a machine learning algorithm that could switch the brain waves into a string of numbers that encoded the sentences. In another portion of the system, the numbers were converted back into a sequence of words.

At the outset, the system came up with some nonsensical phrases, like “the spinach was a famous singer;” lines with improper grammar, like “several adults the kids was eaten by;” and some ultimately philosophical-sounding sentences, such as “the oasis was a mirage.” Over time, the system improved as the researchers fed the system the initial sentences that the participants read aloud, to compare against.

In one case, the system got 97 percent of the sentences correct, representing less errors than the average human transcriber. Still, the algorithm is only processing a small number of sentences and words compared to what a user would ultimately desire.

Still, the system currently only works on verbal speech—meaning those who suffer from speech disorders caused by muscle paralysis won’t benefit just yet.

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Machine learning is helping Penn Medicine researchers identify the size and shape of brain networks in individual children, which may be useful for understanding psychiatric disorders. In a new study published today in the journal Neuron, a multidisciplinary team showed how brain networks unique to each child can predict cognition. The study—which used machine learning techniques to analyze the functional magnetic resonance imaging (fMRI) scans of nearly 700 children, adolescents, and young adults—is the first to show that functional neuroanatomy can vary greatly among kids, and is refined during development.

The human brain has a pattern of folds and ridges on its surface that provide physical landmarks for finding brain areas. The functional networks that govern cognition have long been studied in humans by lining up activation patterns—the software of the brain—to the hardware of these physical landmarks. However, this process assumes that the functions of the brain are located on the same landmarks in each person. This works well for many simple brain systems, for example, the motor system controlling movement is usually right next to the same specific fold in each person. However, multiple recent studies in adults have shown this is not the case for more complex brain systems responsible for executive function—a set of mental processes which includes self-control and attention. In these systems, the functional networks do not always line up with the brain’s physical landmarks of folds and ridges. Instead, each adult has their own specific layout. Until now, it was unknown how such person-specific networks might change as kids grow up, or relate to executive function.

“The exciting part of this work is that we are now able to identify the spatial layout of these functional networks in individual kids, rather than looking at everyone using the same ‘one size fits all’ approach,” said senior author Theodore D. Satterthwaite, MD, an assistant professor of Psychiatry in the Perelman School of Medicine at the University of Pennsylvania. “Like adults, we found that functional neuroanatomy varies quite a lot among different kids—each child has a unique pattern. Also like adults, the networks that vary the most between kids are the same executive networks responsible for regulating the sorts of behaviors that can often land adolescents in hot water, like risk taking and impulsivity.”

To study how functional networks develop in children and supports executive function, the team analyzed a large sample of adolescents and young adults (693 participants, ages 8 to 23). These participants completed 27 minutes of fMRI scanning as part of the Philadelphia Neurodevelopmental Cohort (PNC) a large study that was funded by the National Institute of Mental Health. Machine learning techniques developed by the laboratory of Yong Fan, PhD, an assistant professor of Radiology at Penn and co-author on the paper, allowed the team to map 17 functional networks in individual children, rather than relying on the average location of these networks.

The researchers then examined how these functional networks evolved over adolescence, and were related to performance on a battery of cognitive tests. The team found that the functional neuroanatomy of these networks was refined with age, and allowed the researchers to predict how old a child with a high degree of accuracy.

“The spatial layout of these networks predicted how good kids were at executive tasks,” said Zaixu Cui, PhD, a post-doctoral fellow in Satterthwaite’s lab and the paper’s first author. “Kids who have more ‘real estate’ on their cortex devoted to networks responsible for executive function in fact performed better on these complex tasks.” In contrast, youth with lower executive function had less of their cortex devoted to these executive networks.

Taken together, these results offer a new account of developmental plasticity and diversity and highlight the potential for progress in personalized diagnostics and therapeutics, the authors said.

“The findings lead us to interesting questions regarding the developmental biology of how these networks are formed, and also offer potential for personalizing neuromodulatory treatments, such as brain stimulation for depression or attention problems,” said Satterthwaite. “How are these systems laid down in the first place? Can we get a better response for our patients if we use neuromodulation that is targeted using their own personal networks? Focusing on the unique features of each person’s brain may provide an imporant way forward.”

This article has been republished from the following materials. Note: material may have been edited for length and content. For further information, please contact the cited source.

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The terminology humans have conceived to explain and study our own brain may be mis-aligned with how these constructs are actually represented in nature. For example, in many human societies, when a baby is born either a “male” or a “female” box is checked on the birth certificate. Reality, however, may be less black and white. In fact, the assumption of dichotomic differences between only two sex/gender categories may be at odds with our endeavors that try to carve nature at its joints. Such is the case with a new paper, published recently in the journal Cerebral Cortex, where researchers argue that there are at least nine directions of brain-gender variation.

Many classical statistical approaches pre-assume which groups they expect to see in the data; such as old vs. young participants, or introverted vs. extroverted participants. Everything else that follows after that critically depends on the initial decision of assigning individuals into strict groups. In this new study, the researchers did not pre-assume what the brain gender groups, transcending male, female, and individuals in-between, should be. Instead, they derived the brain-gender groups directly from brain-imaging and psychological assessment items in an agnostic data-driven fashion.

“Our goal was to demonstrate that widely available brain-imaging methods are capable of providing evidence against a strict binary view of how sex/gender is manifested in the brain,” explains Dr. Danilo Bzdok, Associate Professor in the Department of Biomedical Engineering at McGill University’s Faculty of Medicine and a senior author on the paper. “These findings have important consequences for the movement towards improved equity, diversity, and inclusion in Canada and other countries. By raising awareness from the biological perspective we may contribute to building a society where individuals identifying themselves in between the labels of male and female do feel included rather than discriminated against.”

Pulling the data together

In order to conduct their study, the researchers acquired a unique dataset comprised of individuals of wide sex/gender diversity. Rather than only studying gender behavior in a male and a female group, as is commonly done, they acquired a rich sample that also included individuals that underwent sex transformation from male to female as well as individuals that have undergone sex transformation from female to male. The measured brain connectivity fingerprints of these four groups were then related to a comprehensive profile of gender-stereotypical behavioral traits, working closely with Professor Ute Habel and Dr. Benjamin Clemens at the Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University.

The researchers used machine learning algorithms that could provide evidence that sex/gender may not be a dichotomic entity in the human brain. In an unbiased pattern-learning approach they could show that at least nine dimensions of brain-gender variation can be robustly identified. That is, the particular individuals can be assigned to nine “expressions” or coordinate system axes of how much they fall along a particular distribution of brain-gender variation.

“My lab works at the interface between systems neuroscience and tailoring machine-learning algorithms to answer questions in large neuroscience datasets,” says Dr. Bzdok, who recently moved to Montreal to join the McGill community. “Montreal has the advantage of combining world-class neuroscience institutions, such as The Neuro, with top-notch Artificial Intelligence institutions, such as the Mila Quebec AI Institute, in the same city. In both of these research areas, there is a lot of legacy and, now, momentum to build a critical mass to promote forward progress. As such, Montreal is a particularly promising place that is likely to make important contributions to bridging neuroscience and AI.”

Moving the research forward

Dr. Bzdok is optimistic that budding clinical consortium initiatives will allow them to pool even richer and multi-modal datasets to acknowledge even more facets of sex/gender variation existing in the wider population. From a data analytics standpoint, he explains that the more data we can gather, the more likely it is that we will discover a greater number of sex/gender dimensions.

“I am currently reaching out to various investigators across the McGill community to try to take these and other projects to the next level,” shares Dr. Bzdok. “Such questions of mapping societally-relevant behavioral variation to brain variation can now be addressed from cross-cutting perspectives including genetics, genomics, interventional responses such as from temporary brain lesions, immunological markers, and so many more. McGill provides fertile ground to work towards such ambitious questions.”

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In just one-year job listings for “data scientist” increased by an incredible 15,000% between 2011 and 2012. The need for data scientists is expected to continue to grow as more and more data is created each day. In 2013 IBM reported that between 2011-2013 90% of the world’s data had been created just in the two years prior to that. And now it is estimated that in the next 5 years 175 billion terabytes of data will be created every day. By 2020 there will be over 2.7 million data scientist job openings to take on this massive growth.

Data science is a continually growing field and anyone can become a data scientist. According to Dr. Jenn Gamble, Director at Noodle.ai:

“You don’t necessarily need a Ph.D. to do data science – you need an aptitude for math and a creative, problem-solving mentality.”

Data scientists use heterogeneous data to solve complex problems and their position just requires learned skills in mathematics, computer science and more.

Data scientists also use advanced tools that help them understand and analyze the data they collect. Among the most popular tools of the trade is Python, a software development language based on C; PyTorch, an open-source machine learning framework and R, a programming language and free software used for statistical analysis. Another growing tech need is data storage methods that are scalable and optimized with AI. One method is “In the Cloud” which gives teams the ability to share and collaborate easily and, “At the Edge” which allows for more complex secure sharing at the source.

Data scientists and powerful tech are essential to today’s growing data. The most popular jobs in this field today are data engineers, software engineers, and AI hardware specialists. Someone in these roles will be able to not only build AI software, create new tech but also virtually change the world. To learn more about the history, people, and tech behind data science read on to the infographic below.

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