Smartphone Usage Linked to Male Infertility

By: Alexandria Addesso

The use of cellular phones, have become so common in our day-to-day lives that the inanimate objects almost become another appendage. Smart phones, the most commonly used type of cellular phones today rely on electromagnetic frequency (EMF) radiation to receive real time messaging. But could this form of frequencies be harmful when they are being transmitted all day?

Being that cell phones are portable, people tend to have them on their person all day. Men, more often than women, usually keep their cellular smart phones in their front pockets. Multiple recent studies have been conducted on whether keeping these cellular devices in such a close proximity to a man’s genitalia while transmitting EMF radiation could be harmful.



“Collectively, the research indicates that exposure to cell phone radiation may lead to decreases in sperm count, sperm motility and vitality, as well as increases in indicators of sperm damage such as higher levels of reactive oxygen species (chemically reactive molecules containing oxygen), oxidative stress, DNA damage and changes in sperm morphology ,” said The Environmental Working Group (EWG) after publishing a scientific literature review of 10 studies linking smartphone usage and male infertility.

Other studies even indicate specifically an 8 percent decrease in sperm motility and an approximate 9 percent decrease in sperm viability.
“Overall, these findings raise a number of related health policy and patient management issues that deserve our immediate attention. Specifically, we recommend that men of reproductive age who engage in high levels of mobile phone use do not keep their phones in receiving mode below waist level,” wrote researcher GN De Iuliis in the study Mobile phone radiation induces reactive oxygen species production and DNA damage in human spermatozoa in vitro published in 2009.



Even though keeping your cell phone on a belt clip has long been seen as more safe, much data has shown that it is only slightly better than carrying it in your front pocket. If a man is trying to conceive a child it is best that he reduces his cell phone usage. Data on smartphone usage and female infertility is still widely unknown.

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Parallel Computation Provides Deeper Insight into Brain Function

Unlike experimental neuroscientists who deal with real-life neurons, computational neuroscientists use model simulations to investigate how the brain functions. While many computational neuroscientists use simplified mathematical models of neurons, researchers in the Computational Neuroscience Unit at the Okinawa Institute of Science and Technology Graduate University (OIST) develop software that models neurons to the detail of molecular interactions with the goal of eliciting new insights into neuronal function. Applications of the software were limited in scope up until now because of the intense computational power required for such detailed neuronal models, but recently Dr. Weiliang Chen, Dr. Iain Hepburn, and Professor Erik De Schutter published two related papers in which they outline the accuracy and scalability of their new high-speed computational software, "Parallel STEPS". The combined findings suggest that Parallel STEPS could be used to reveal new insights into how individual neurons function and communicate with each other.

The first paper, published in The Journal of Chemical Physics in August 2016, focusses on ensuring that the accuracy of Parallel STEPS is comparable with conventional methods. In conventional approaches, computations associate with neuronal chemical reactions and molecule diffusion are all calculated on one computational processing unit or 'core' sequentially. However, Dr. Iain Hepburn and colleagues introduced a new approach to perform computations of reaction and diffusion in parallel which can then be distributed over multiple computer cores, whilst maintaining simulation accuracy to a high degree. The key was to develop an original algorithm separated into two parts - one that computed chemical reaction events and the other diffusion events.

"We tested a range of model simulations from simple diffusion models to realistic biological models and found that we could achieve improved performance using a parallel approach with minimal loss of accuracy. This demonstrated the potential suitability of the method on a larger scale," says Dr. Hepburn.



In a related paper published in Frontiers in Neuroinformatics this February, Dr. Weiliang Chen presented the implementation details of Parallel STEPS and investigated its performance and potential applications. By breaking a partial model of a Purkinje cell - one of the largest neurons in the brain - into 50 to 1000 sections and simulating reaction and diffusion events for each section in parallel on the Sango supercomputer at OIST, Dr. Chen and colleagues saw dramatically increased computation speeds. They tested this approach on both simple models and more complicated models of calcium bursts in Purkinje cells and demonstrated that parallel simulation could speed up computations by more than several hundred times that of conventional methods.

"Together, our findings show that Parallel STEPS implementation achieves significant improvements in performance, and good scalability," says Dr. Chen. "Similar models that previously required months of simulation can now be completed within hours or minutes, meaning that we can develop and simulate more complex models, and learn more about the brain in a shorter amount of time."

Dr. Hepburn and Dr. Chen from OIST's Computational Neuroscience Unit, led by Professor Erik De Schutter, are actively collaborating with the Human Brain Project, a world-wide initiative based at École Polytechnique Fédérale de Lausanne (EPFL) in Switzerland, to develop a more robust version of Parallel STEPS that incorporates electric field simulation of cell membranes.

So far STEPS is only realistically capable of modeling parts of neurons but with the support of Parallel STEPS, the Computational Neuroscience Unit hopes to develop a full-scale model of a whole neuron and subsequently the interactions between neurons in a network. By collaborating with the EPFL team and by making use of the IBM 'Blue Gene/Q' supercomputer located there, they aim to achieve these goals in the near future.



"Thanks to modern supercomputers we can study molecular events within neurons in a much more transparent way than before," says Prof. De Schutter. "Our research opens up interesting avenues in computational neuroscience that links biochemistry with electrophysiology for the first time."
Source: Journal of Chemical Physics. Provided by: Okinawa Institute of Science and Technology

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New Computer Operating System unlock DNA's Molecules nearly full storage potential

In a study in Science, researchers Yaniv Erlich and Dina Zielinski describe a new coding technique for maximizing the data-storage capacity of DNA molecules.Credit: New York Genome Center

An algorithm designed for streaming video on a cellphone can unlock DNA's nearly full storage potential by squeezing more information into its four base nucleotides, say researchers. They demonstrate that this technology is also extremely reliable.

Humanity may soon generate more data than hard drives or magnetic tape can handle, a problem that has scientists turning to nature's age-old solution for information-storage -- DNA.

In a new study in Science, a pair of researchers at Columbia University and the New York Genome Center (NYGC) show that an algorithm designed for streaming video on a cellphone can unlock DNA's nearly full storage potential by squeezing more information into its four base nucleotides. They demonstrate that this technology is also extremely reliable.



DNA is an ideal storage medium because it's ultra-compact and can last hundreds of thousands of years if kept in a cool, dry place, as demonstrated by the recent recovery of DNA from the bones of a 430,000-year-old human ancestor found in a cave in Spain.

"DNA won't degrade over time like cassette tapes and CDs, and it won't become obsolete -- if it does, we have bigger problems," said study coauthor Yaniv Erlich, a computer science professor at Columbia Engineering, a member of Columbia's Data Science Institute, and a core member of the NYGC.

Erlich and his colleague Dina Zielinski, an associate scientist at NYGC, chose six files to encode, or write, into DNA: a full computer operating system, an 1895 French film, "Arrival of a train at La Ciotat," a $50 Amazon gift card, a computer virus, a Pioneer plaque and a 1948 study by information theorist Claude Shannon.

They compressed the files into a master file, and then split the data into short strings of binary code made up of ones and zeros. Using an erasure-correcting algorithm called fountain codes, they randomly packaged the strings into so-called droplets, and mapped the ones and zeros in each droplet to the four nucleotide bases in DNA: A, G, C and T. The algorithm deleted letter combinations known to create errors, and added a barcode to each droplet to help reassemble the files later.



In all, they generated a digital list of 72,000 DNA strands, each 200 bases long, and sent it in a text file to a San Francisco DNA-synthesis startup, Twist Bioscience, that specializes in turning digital data into biological data. Two weeks later, they received a vial holding a speck of DNA molecules.

To retrieve their files, they used modern sequencing technology to read the DNA strands, followed by software to translate the genetic code back into binary. They recovered their files with zero errors, the study reports. (In this short demo, Erlich opens his archived operating system on a virtual machine and plays a game of Minesweeper to celebrate.)

They also demonstrated that a virtually unlimited number of copies of the files could be created with their coding technique by multiplying their DNA sample through polymerase chain reaction (PCR), and that those copies, and even copies of their copies, and so on, could be recovered error-free.



Finally, the researchers show that their coding strategy packs 215 petabytes of data on a single gram of DNA -- 100 times more than methods published by pioneering researchers George Church at Harvard, and Nick Goldman and Ewan Birney at the European Bioinformatics Institute. "We believe this is the highest-density data-storage device ever created," said Erlich.

The capacity of DNA data-storage is theoretically limited to two binary digits for each nucleotide, but the biological constraints of DNA itself and the need to include redundant information to reassemble and read the fragments later reduces
its capacity to 1.8 binary digits per nucleotide base.

The team's insight was to apply fountain codes, a technique Erlich remembered from graduate school, to make the reading and writing process more efficient. With their DNA Fountain technique, Erlich and Zielinski pack an average of 1.6 bits into each base nucleotide. That's at least 60 percent more data than previously published methods, and close to the 1.8-bit limit.

Cost still remains a barrier. The researchers spent $7,000 to synthesize the DNA they used to archive their 2 megabytes of data, and another $2,000 to read it. Though the price of DNA sequencing has fallen exponentially, there may not be the same demand for DNA synthesis, says Sri Kosuri, a biochemistry professor at UCLA who was not involved in the study. "Investors may not be willing to risk tons of money to bring costs down," he said.



But the price of DNA synthesis can be vastly reduced if lower-quality molecules are produced, and coding strategies like DNA Fountain are used to fix molecular errors, says Erlich. "We can do more of the heavy lifting on the computer to take the burden off time-intensive molecular coding," he said.
Source: Materials provided by Columbia University School of Engineering and Applied Science.

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Artificial ß for Neural Networks

Alberto Salleo, associate professor of materials science and engineering, with graduate student Scott Keene characterizing the electrochemical properties of an artificial synapse for neural network computing. They are part of a team that has created the new device. Credit: L.A. Cicero

A new organic artificial synapse could support computers that better recreate the way the human brain processes information. It could also lead to improvements in brain-machine technologies.

For all the improvements in computer technology over the years, we still struggle to recreate the low-energy, elegant processing of the human brain. Now, researchers at Stanford University and Sandia National Laboratories have made an advance that could help computers mimic one piece of the brain's efficient design -- an artificial version of the space over which neurons communicate, called a synapse.

"It works like a real synapse but it's an organic electronic device that can be engineered," said Alberto Salleo, associate professor of materials science and engineering at Stanford and senior author of the paper. "It's an entirely new family of devices because this type of architecture has not been shown before. For many key metrics, it also performs better than anything that's been done before with inorganics."



The new artificial synapse, reported in the Feb. 20 issue of Nature Materials, mimics the way synapses in the brain learn through the signals that cross them. This is a significant energy savings over traditional computing, which involves separately processing information and then storing it into memory. Here, the processing creates the memory.

This synapse may one day be part of a more brain-like computer, which could be especially beneficial for computing that works with visual and auditory signals. Examples of this are seen in voice-controlled interfaces and driverless cars. Past efforts in this field have produced high-performance neural networks supported by artificially intelligent algorithms but these are still distant imitators of the brain that depend on energy-consuming traditional computer hardware.

Building a brain
When we learn, electrical signals are sent between neurons in our brain. The most energy is needed the first time a synapse is traversed. Every time afterward, the connection requires less energy. This is how synapses efficiently facilitate both learning something new and remembering what we've learned. The artificial synapse, unlike most other versions of brain-like computing, also fulfills these two tasks simultaneously, and does so with substantial energy savings.

"Deep learning algorithms are very powerful but they rely on processors to calculate and simulate the electrical states and store them somewhere else, which is inefficient in terms of energy and time," said Yoeri van de Burgt, former postdoctoral scholar in the Salleo lab and lead author of the paper. "Instead of simulating a neural network, our work is trying to make a neural network."



The artificial synapse is based off a battery design. It consists of two thin, flexible
films with three terminals, connected by an electrolyte of salty water. The device works as a transistor, with one of the terminals controlling the flow of electricity between the other two.

Like a neural path in a brain being reinforced through learning, the researchers program the artificial synapse by discharging and recharging it repeatedly. Through this training, they have been able to predict within 1 percent of uncertainly what voltage will be required to get the synapse to a specific electrical state and, once there, it remains at that state. In other words, unlike a common computer, where you save your work to the hard drive before you turn it off, the artificial synapse can recall its programming without any additional actions or parts.

Testing a network of artificial synapses
Only one artificial synapse has been produced but researchers at Sandia used 15,000 measurements from experiments on that synapse to simulate how an array of them would work in a neural network. They tested the simulated network's ability to recognize handwriting of digits 0 through 9. Tested on three datasets, the simulated array was able to identify the handwritten digits with an accuracy between 93 to 97 percent.

Although this task would be relatively simple for a person, traditional computers have a difficult time interpreting visual and auditory signals.

"More and more, the kinds of tasks that we expect our computing devices to do require computing that mimics the brain because using traditional computing to perform these tasks is becoming really power hungry," said A. Alec Talin, distinguished member of technical staff at Sandia National Laboratories in Livermore, California, and senior author of the paper. "We've demonstrated a device that's ideal for running these type of algorithms and that consumes a lot less power."



This device is extremely well suited for the kind of signal identification and classification that traditional computers struggle to perform. Whereas digital transistors can be in only two states, such as 0 and 1, the researchers successfully programmed 500 states in the artificial synapse, which is useful for neuron-type computation models. In switching from one state to another they used about one-tenth as much energy as a state-of-the-art computing system needs in order to move data from the processing unit to the memory.
This, however, means they are still using about 10,000 times as much energy as the minimum a biological synapse needs in order to fire. The researchers are hopeful that they can attain neuron-level energy efficiency once they test the artificial synapse in smaller devices.

Organic potential
Every part of the device is made of inexpensive organic materials. These aren't found in nature but they are largely composed of hydrogen and carbon and are compatible with the brain's chemistry. Cells have been grown on these materials and they have even been used to make artificial pumps for neural transmitters. The voltages applied to train the artificial synapse are also the same as those that move through human neurons.

All this means it's possible that the artificial synapse could communicate with live neurons, leading to improved brain-machine interfaces. The softness and flexibility of the device also lends itself to being used in biological environments. Before any applications to biology, however, the team plans to build an actual array of artificial synapses for further research and testing.

Additional Stanford co-authors of this work include co-lead author Ewout Lubberman, also of the University of Groningen in the Netherlands, Scott T. Keene and Grégorio C. Faria, also of Universidade de São Paulo, in Brazil. Sandia National Laboratories co-authors include Elliot J. Fuller and Sapan Agarwal in Livermore and Matthew J. Marinella in Albuquerque, New Mexico. Salleo is an affiliate of the Stanford Precourt Institute for Energy and the Stanford Neurosciences Institute. Van de Burgt is now an assistant professor in microsystems and an affiliate of the Institute for Complex Molecular Studies (ICMS) at Eindhoven
University of Technology in the Netherlands.



This research was funded by the National Science Foundation, the Keck Faculty Scholar Funds, the Neurofab at Stanford, the Stanford Graduate Fellowship, Sandia's Laboratory-Directed Research and Development Program, the U.S. Department of Energy, the Holland Scholarship, the University of Groningen Scholarship for Excellent Students, the Hendrik Muller National Fund, the Schuurman Schimmel-van Outeren Foundation, the Foundation of Renswoude (The Hague and Delft), the Marco Polo Fund, the Instituto Nacional de Ciência e Tecnologia/Instituto Nacional de Eletrônica Orgânica in Brazil, the Fundação de Amparo à Pesquisa do Estado de São Paulo and the Brazilian National Council.
Story Source:
Materials provided by Stanford University. Original written by Taylor Kubota

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The Internet and your brain are more alike than you think

Salk scientist finds similar rule governing traffic flow in engineered and biological systems. Credit: Salk Institute

A similar rule governs traffic flow in engineered and biological systems, reports a researcher. An algorithm used for the Internet is also at work in the human brain, says the report, an insight that improves our understanding of engineered and neural networks and potentially even learning disabilities.

Although we spend a lot of our time online nowadays -- streaming music and video, checking email and social media, or obsessively reading the news -- few of us know about the mathematical algorithms that manage how our content is delivered. But deciding how to route information fairly and efficiently through a distributed system with no central authority was a priority for the Internet's founders. Now, a Salk Institute discovery shows that an algorithm used for the Internet is also at work in the human brain, an insight that improves our understanding of engineered and neural networks and potentially even learning disabilities.



"The founders of the Internet spent a lot of time considering how to make information flow efficiently," says Salk Assistant Professor Saket Navlakha, coauthor of the new study that appears online in Neural Computation on February 9, 2017. "Finding that an engineered system and an evolved biological one arise at a similar solution to a problem is really interesting."
In the engineered system, the solution involves controlling information flow such that routes are neither clogged nor underutilized by checking how congested the Internet is. To accomplish this, the Internet employs an algorithm called "additive increase, multiplicative decrease" (AIMD) in which your computer sends a packet of data and then listens for an acknowledgement from the receiver: If the packet is promptly acknowledged, the network is not overloaded and your data can be transmitted through the network at a higher rate. With each successive successful packet, your computer knows it's safe to increase its speed by one unit, which is the additive increase part. But if an acknowledgement is delayed or lost your computer knows that there is congestion and slows down by a large amount, such as by half, which is the multiplicative decrease part. In this way, users gradually find their "sweet spot," and congestion is avoided because users take their foot off the gas, so to speak, as soon as they notice a slowdown. As computers throughout the network utilize this strategy, the whole system can continuously adjust to changing conditions, maximizing overall efficiency.

Navlakha, who develops algorithms to understand complex biological networks, wondered if the brain, with its billions of distributed neurons, was managing information similarly. So, he and coauthor Jonathan Suen, a postdoctoral scholar at Duke University, set out to mathematically model neural activity.



Because AIMD is one of a number of flow-control algorithms, the duo decided to model six others as well. In addition, they analyzed which model best matched physiological data on neural activity from 20 experimental studies. In their models, AIMD turned out to be the most efficient at keeping the flow of information moving smoothly, adjusting traffic rates whenever paths got too congested. More interestingly, AIMD also turned out to best explain what was happening to neurons experimentally.

It turns out the neuronal equivalent of additive increase is called long-term potentiation. It occurs when one neuron fires closely after another, which strengthens their synaptic connection and makes it slightly more likely the first will trigger the second in the future. The neuronal equivalent of multiplicative decrease occurs when the firing of two neurons is reversed (second before first), which weakens their connection, making the first much less likely to trigger the second in the future. This is called long-term depression. As synapses throughout the network weaken or strengthen according to this rule, the whole system adapts and learns.

"While the brain and the Internet clearly operate using very different mechanisms, both use simple local rules that give rise to global stability," says Suen. "I was initially surprised that biological neural networks utilized the same algorithms as their engineered counterparts, but, as we learned, the requirements for efficiency, robustness, and simplicity are common to both living organisms and the networks we have built."



Understanding how the system works under normal conditions, could help neuroscientists better understand what happens, when these results are disrupted, for example, in learning disabilities. "Variations of the AIMD algorithm are used in basically every large-scale distributed communication network," says Navlakha. "Discovering that the brain uses a similar algorithm may not be just a coincidence."
Story Source:
Materials provided by Salk Institute.

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The incredible Artificial Intelligence Systems: They may See the world as Humans Do

A Northwestern University team developed a new computational model that performs at human levels on a standard intelligence test. This work is an important step toward making artificial intelligence systems that see and understand the world as humans do.

"The model performs in the 75th percentile for American adults, making it better than average," said Northwestern Engineering's Ken Forbus. "The problems that are hard for people are also hard for the model, providing additional evidence that its operation is capturing some important properties of human cognition."



The new computational model is built on CogSketch, an artificial intelligence platform previously developed in Forbus' laboratory. The platform has the ability to solve visual problems and understand sketches in order to give immediate, interactive feedback. CogSketch also incorporates a computational model of analogy, based on Northwestern psychology professor Dedre Gentner's structure-mapping theory. (Gentner received the 2016 David E. Rumelhart Prize for her work on this theory.)

Forbus, Walter P. Murphy Professor of Electrical Engineering and Computer Science at Northwestern's McCormick School of Engineering, developed the model with Andrew Lovett, a former Northwestern postdoctoral researcher in psychology. Their research was published online this month in the journal Psychological Review.

The ability to solve complex visual problems is one of the hallmarks of human intelligence. Developing artificial intelligence systems that have this ability not only provides new evidence for the importance of symbolic representations and analogy in visual reasoning, but it could potentially shrink the gap between computer and human cognition.

While Forbus and Lovett's system can be used to model general visual problem-solving phenomena, they specifically tested it on Raven's Progressive Matrices, a nonverbal standardized test that measures abstract reasoning. All of the test's problems consist of a matrix with one image missing. The test taker is given six to eight choices with which to best complete the matrix. Forbus and Lovett's computational model performed better than the average American.



"The Raven's test is the best existing predictor of what psychologists call 'fluid intelligence, or the general ability to think abstractly, reason, identify patterns, solve problems, and discern relationships,'" said Lovett, now a researcher at the US Naval Research Laboratory. "Our results suggest that the ability to flexibly use relational representations, comparing and reinterpreting them, is important for fluid intelligence."

The ability to use and understand sophisticated relational representations is a key to higher-order cognition. Relational representations connect entities and ideas such as "the clock is above the door" or "pressure differences cause water to flow." These types of comparisons are crucial for making and understanding analogies, which humans use to solve problems, weigh moral dilemmas, and describe the world around them.

"Most artificial intelligence research today concerning vision focuses on recognition, or labeling what is in a scene rather than reasoning about it," Forbus said. "But recognition is only useful if it supports subsequent reasoning. Our research provides an important step toward understanding visual reasoning more broadly."

Source: Amanda Morris - Journal reference: Psychological Review

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New Laser based on unusual physics phenomenon could improve telecommunications, and computing applications

This is a schematic of the BIC laser: a high frequency laser beam (blue) powers the membrane to emit a laser beam at telecommunication frequency (red). Credit: Kanté group, UC San Diego

Researchers at the University of California San Diego have demonstrated the world's first laser based on an unconventional wave physics phenomenon called bound states in the continuum. The technology could revolutionize the development of surface lasers, making them more compact and energy-efficient for communications and computing applications. The new BIC lasers could also be developed as high-power lasers for industrial and defense applications.

"Lasers are ubiquitous in the present-day world, from simple everyday laser pointers to complex laser interferometers used to detect gravitational waves. Our current research will impact many areas of laser applications," said Ashok Kodigala, an electrical engineering Ph.D. student at UC San Diego and first author of the study.



"Because they are unconventional, BIC lasers offer unique and unprecedented properties that haven't yet been realized with existing laser technologies," said Boubacar Kanté, electrical engineering professor at the UC San Diego Jacobs School of Engineering who led the research.

For example, BIC lasers can be readily tuned to emit beams of different wavelengths, a useful feature for medical lasers made to precisely target cancer cells without damaging normal tissue. BIC lasers can also be made to emit beams with specially engineered shapes (spiral, donut or bell curve) -- called vector beams -- which could enable increasingly powerful computers and optical communication systems that can carry up to 10 times more information than existing ones.

"Light sources are key components of optical data communications technology in cell phones, computers and astronomy, for example. In this work, we present a new kind of light source that is more efficient than what's available today in terms of power consumption and speed," said Babak Bahari, an electrical engineering Ph.D. student in Kanté's lab and a co-author of the study.

Bound states in the continuum (BICs) are phenomena that have been predicted to exist since 1929. BICs are waves that remain perfectly confined, or bound, in an open system. Conventional waves in an open system escape, but BICs defy this norm -- they stay localized and do not escape despite having open pathways to do so.

In a previous study, Kanté and his team demonstrated, at microwave frequencies, that BICs could be used to efficiently trap and store light to enable strong light-matter interaction. Now, they're harnessing BICs to demonstrate new types of lasers. The team published the work Jan. 12 in Nature.



Making the BIC laser
The BIC laser in this work is constructed from a thin semiconductor membrane made of indium, gallium, arsenic and phosphorus. The membrane is structured as an array of Nano-sized cylinders suspended in air. The cylinders are interconnected by a network of supporting bridges, which provide mechanical stability to the device.

By powering the membrane with a high frequency laser beam, researchers induced the BIC system to emit its own lower frequency laser beam (at telecommunication frequency).
"Right now, this is a proof of concept demonstration that we can indeed achieve lasing action with BICs," Kanté said.

"And what's remarkable is that we can get surface lasing to occur with arrays as small as 8 × 8 particles," he said. In comparison, the surface lasers that are widely used in data communications and high-precision sensing, called VCSELs (vertical-cavity surface-emitting lasers), need much larger (100 times) arrays -- and thus more power -- to achieve lasing.

"The popular VCSEL may one day be replaced by what we're calling the 'BICSEL' -- bound state in the continuum surface-emitting laser, which could lead to smaller devices that consume less power," Kanté said. The team has filed a patent for the new type of light source.

The array can also be scaled up in size to create high power lasers for industrial and defense applications, he noted. "A fundamental challenge in high power lasers is heating and with the predicted efficiencies of our BIC lasers, a new era of laser technologies may become possible," Kanté said.

The team's next step is to make BIC lasers that are electrically powered, rather than optically powered by another laser. "An electrically pumped laser is easily portable outside the lab and can run off a conventional battery source," Kanté said.
Story Source:
Materials provided by University of California - San Diego. Original written by Liezel Labios.

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Artificial Intelligence and Machine Learning: What's the Next Step?

It's difficult to describe in a concise list with less than 1,000 words what the definitive direction of artificial intelligence is going to be in a 12-month span. The year 2016 surprised several people in terms of the speed of certain technologies' development and the revised ETA of new AI-driven products hitting the public market.
Here are the four trends that will dominate artificial intelligence in 2017.

1. Language processing will continue
We could call this "natural language processing" or NLP, but let's think more broadly about language for a moment. The key to cognition, for you mavens of Psychology 101, is sophisticated communication, even internal abstract thinking. That will continue to prove critical in driving machine learning 'deeper.'

One place to keep track of progress in the space is in machine translation, which will give you an idea of how sophisticated and accurate our software currently is in translating some of the nuance and implications of our spoken and written language.



That will be the next step in getting personal assistant technology like Alexa, Siri, Google Assistant, or Cortana to interpret our commands and questions just a little bit better.

2. Efforts to square machine learning and big data with different health sectors will accelerate
"I envision a system that still has those predictive data pools. It looks at the different data you obtain that different labs are giving all the time," eBay Director of Data Science Kira Radinsky told an audience at Geektime TechFest 2016 last month, pioneering "automated processes that can lead to those types of discoveries."

Biotech researchers and companies are trying to get programs to automate drug discoveries, among other things. Finding correlations in data and extrapolating causation is not the same in all industries, nor in any one sector of medicine. Researchers in heart disease, neurological disorders, and various types of cancer are all organizing different metrics of data. Retrieving that information and programming the proper relationship between all those variables is an endeavor.



One of the areas where this is evident is in computer vision, exemplified by Zebra Medical Vision, which can detect anomalies in CT scans for a variety of organs including the heart and liver. But compiling patient medical records and hunting for diagnostic clues there, as well as constructing better treatment plans, are also markets machine learning is opening in 2017. Other startups like Israel's ‘HealthWatch’ are producing smart clothes that constantly feed medical data to doctors to monitor patients.

This developing ecosystem of health trackers should produce enough information about individual patients or groups of people for algorithms to extract new realizations.
3. They will probably have to come up with another buzzword to go deeper than 'deep learning'

Machines building machines? Algorithms writing algorithms? Machine learning programs will continue adding more layers of processing units, as well as more sophistication to abstract pattern analysis. Deep neural networks will be expected to draw even more observations from unsorted data, just as was mentioned above in regards to health care.



That future buzz term might be “generative” or “adversarial,” as in generative adversarial networks (GANs). Described by MIT Technology Review as the invention of Open AI scientist Ian Goodfellow, GANs will set up two networks like two people with different approaches to a problem. One network will try to create new data (read “ideas”) from a given set of data while the other “tries to discriminate between real and fake data” (let’s assume this is the robotic equivalent to a devil’s advocate).

4. Self-driving cars will force an expensive race among automotive companies
I saved this for last because many readers probably consider this patently obvious. However, the surprise many laypeople and people who might fancy themselves tech insiders had by seeing the speed of the industry’s development might be duplicated in 2017 for the opposite reason. While numbers of companies are testing the technology, it will run into some pun-intended roadblocks this year.



While talking about an “autonomous” vehicle is all the rage, several companies in the testing stage not only are cautious to keep someone behind the wheel if needed, but are also creating entire human-administered command centers to guide the cars.
There are some companies that will likely be able to avoid burning capital because of competition. Consider how NIVDIA is developing cars in conjunction with Audi and Mercedes-Benz, but separately. Still, BMW, Mercedes-Benz, Nissan-Renault, Ford, and General Motors are all making very big bets while trying to speed up their timeline and hit autonomous vehicle research milestones more quickly.

Even if the entire industry were to be wrong in a cataclysmic way about the unstoppable future of the self-driving car (which it won't be, but bear with me), there will still be more automated features installed in new vehicle models relatively soon. Companies will be forced to spend big, and fast to match features offered by their competitors.

By Gedalyah Reback

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Quantum Computing: Information can be carry using a Photon

A Princeton University-led team has built a device that advances silicon-based quantum computers, which when built will be able to solve problems beyond the capabilities of everyday computers. The device isolates an electron so that can pass its quantum information to a photon, which can then act as a messenger to carry the information to other electrons to form the circuits of the computer.

In a step that brings silicon-based quantum computers closer to reality, researchers have built a device in which a single electron can pass its quantum information to a particle of light.

In a step that brings silicon-based quantum computers closer to reality, researchers at Princeton University have built a device in which a single electron can pass its quantum information to a particle of light. The particle of light, or photon, can then act as a messenger to carry the information to other electrons, creating connections that form the circuits of a quantum computer.

The research published in the journal Science and conducted at Princeton and HRL Laboratories in Malibu, California, represents a more than five-year effort to build a robust capability for an electron to talk to a photon, said Jason Petta, a Princeton professor of physics.



"Just like in human interactions, to have good communication a number of things need to work out -- it helps to speak the same language and so forth,"Petta said. "We are able to bring the energy of the electronic state into resonance with the light particle, so that the two can talk to each other."

The discovery will help the researchers use light to link individual electrons, which act as the bits, or smallest units of data, in a quantum computer. Quantum computers are advanced devices that, when realized, will be able to perform advanced calculations using tiny particles such as electrons, which follow quantum rules rather than the physical laws of the everyday world.

Each bit in an everyday computer can have a value of a 0 or a 1. Quantum bits -- known as qubits -- can be in a state of 0, 1, or both a 0 and a 1 simultaneously. This superposition, as it is known, enables quantum computers to tackle complex questions that today's computers cannot solve.

Simple quantum computers have already been made using trapped ions and superconductors, but technical challenges have slowed the development of silicon-based quantum devices. Silicon is a highly attractive material because it is inexpensive and is already widely used in today's smartphones and computers.

The researchers trapped both an electron and a photon in the device, then adjusted the energy of the electron in such a way that the quantum information could transfer to the photon. This coupling enables the photon to carry the information from one qubit to another located up to a centimeter away.

Quantum information is extremely fragile -- it can be lost entirely due to the slightest disturbance from the environment. Photons are more robust against disruption and can potentially carry quantum information not just from qubit to qubit in a quantum computer circuit but also between quantum chips via cables.



For these two very different types of particles to talk to each other, however, researchers had to build a device that provided the right environment. First, Peter Deelman at HRL Laboratories, a corporate research-and-development laboratory owned by the Boeing Company and General Motors, fabricated the semiconductor chip from layers of silicon and silicon-germanium. This structure trapped a single layer of electrons below the surface of the chip. Next, researchers at Princeton laid tiny wires, each just a fraction of the width of a human hair, across the top of the device. These nanometer-sized wires allowed the researchers to deliver voltages that created an energy landscape capable of trapping a single electron, confining it in a region of the silicon called a double quantum dot.

The researchers used those same wires to adjust the energy level of the trapped electron to match that of the photon, which is trapped in a superconducting cavity that is fabricated on top of the silicon wafer.

Prior to this discovery, semiconductor qubits could only be coupled to neighboring qubits. By using light to couple qubits, it may be feasible to pass information between qubits at opposite ends of a chip.

The electron's quantum information consists of nothing more than the location of the electron in one of two energy pockets in the double quantum dot. The electron can occupy one or the other pocket, or both simultaneously. By controlling the voltages applied to the device, the researchers can control which pocket the electron occupies.

"We now have the ability to actually transmit the quantum state to a photon confined in the cavity," said Xiao Mi, a graduate student in Princeton's Department of Physics and first author on the paper. "This has never been done before in a semiconductor device because the quantum state was lost before it could transfer its information."



The success of the device is due to a new circuit design that brings the wires closer to the qubit and reduces interference from other sources of electromagnetic radiation. To reduce this noise, the researchers put in filters that remove extraneous signals from the wires that lead to the device. The metal wires also shield the qubit. As a result, the qubits are 100 to 1000 times less noisy than the ones used in previous experiments.

Eventually the researchers plan to extend the device to work with an intrinsic property of the electron known as its spin. "In the long run we want systems where spin and charge are coupled together to make a spin qubit that can be electrically controlled," Petta said. "We've shown we can coherently couple an electron to light, and that is an important step toward coupling spin to light."
Story Source: Princeton University

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Why Artificial Intelligence has not yet revolutionized healthcare

Artificial intelligence and machine learning are predicted to be part of the next industrial revolution and could help business and industry save billions of dollars by the next decade.

The tech giants Google, Facebook, Apple, IBM and others are applying artificial intelligence to all sorts of data.
Machine learning methods are being used in areas such as translating language almost in real time, and even to identify images of cats on the internet.

So why haven't we seen artificial intelligence used to the same extent in healthcare?
Radiologists still rely on visual inspection of magnetic resonance imaging (MRI) or X-ray scans – although IBM and others are working on this issue – and doctors have no access to AI for guiding and supporting their diagnoses.



The challenges for machine learning

Machine learning technologies have been around for decades, and a relatively recent technique called deep learning keeps pushing the limit of what machines can do. Deep learning networks comprise neuron-like units into hierarchical layers, which can recognize patterns in data.
This is done by iteratively presenting data along with the correct answer to the network until its internal parameters, the weights linking the artificial neurons, are optimized. If the training data capture the variability of the real-world, the network is able to generalize well and provide the correct answer when presented with unseen data.

So, the learning stage requires very large data sets of cases along with the corresponding answers. Millions of records, and billions of computations are needed to update the network parameters, often done on a supercomputer for days or weeks.
Here lie the problems with healthcare: data sets are not yet big enough and the correct answers to be learned are often ambiguous or even unknown.

We're going to need better and bigger data sets

The functions of the human body, its anatomy and variability, are very complex. The complexity is even greater because diseases are often triggered or modulated by genetic background, which is unique to each individual and so hard to be trained on.
Adding to this, specific challenges to medical data exist. These include the difficulty to measure precisely and accurately any biological processes introducing unwanted variations.

Other challenges include the presence of multiple diseases (co-morbidity) in a patient, which can often confound predictions. Lifestyle and environmental factors also play important roles but are seldom available.
The result is that medical data sets need to be extremely large.



This is being addressed across the world with increasingly large research initiatives. Examples include Biobank in the United Kingdom, which aims to scan 100,000 participants.

Others include the Alzheimer's Disease Neuroimaging Initiative (ADNI) in the United States and the Australian Imaging, Biomarkers and Lifestyle Study of Ageing (AIBL), tracking more than a thousand subjects over a decade.
Government initiatives are also emerging such as the American Cancer Moonshot program. The aim is to "build a national cancer data ecosystem" so researchers, clinicians and patients can contribute data with the aim to "facilitate efficient data analysis". Similarly, the Australian Genomics Health Alliance aims at pooling and sharing genomic information.

Eventually the electronic medical record systems that are being deployed across the world should provide extensive high quality data sets. Beyond the expected gain in efficiency, the potential to mine population wide clinical data using machine learning is tremendous. Some companies such as Google are eagerly trying to access those data.

What a machine needs to learn is not obvious

Complex medical decisions are often made by a team of specialists reaching consensus rather than certainty.
Radiologists might disagree slightly when interpreting a scan where blurring and only very subtle features can be observed. Inferring a diagnosis from measurement with errors and when the disease is modulated by unknown genes often relies on implicit know-how and experience rather than explicit facts.

Sometimes the true answer cannot be obtained at all. For example, measuring the size of a structure from a brain MRI cannot be validated, even at autopsy, since post-mortem tissues change in their composition and size after death.



So a machine can learn that a photo contains a cat because users have labelled with certainty thousands of pictures through social media platforms, or told Google how to recognizes doodles.
It is a much more difficult task to measure the size of a brain structure from an MRI because no one knows the answer and only consensus from several experts can be assembled at best, and at a great cost.

Several technologies are emerging to address this issue. Complex mathematical models including probabilities such as Bayesian approaches can learn under uncertainty.

Unsupervised methods can recognize patterns in data without the need for what the actual answers are, albeit with challenging interpretation of the results.

Another approach is transfer learning, whereby a machine can learn from large, different, but relevant, data sets for which the training answers are known.

Medical applications of deep learning have already been very successful. They often come first at competitions during scientific meetings where data sets are made available and the evaluation of submitted results revealed during the conference.
At CSIRO, we have been developing CapAIBL (Computational Analysis of PET from AIBL) to analyze 3-D images of brain positron emission tomography (PET).



Using a database with many scans from healthy individuals and patients with Alzheimer's disease, the method is able to learn pattern characteristics of the disease. It can then identify that signature from unseen individual's scan. The clinical report generated allows doctors to diagnose the disease faster and with more confidence.
In the case (above), CapAIBL technology was applied to amyloid plaque imaging in a patient with Alzheimer's disease. Red indicates higher amyloid deposition in the brain, a sign of Alzheimer's.

The problem with causation

Probably the most challenging issue is about understanding causation. Analyzing retrospective data is prone to learning spurious correlation and missing the underlying cause for diseases or effect of treatments.
Traditionally, randomized clinical trials provide evidence on the superiority of different options, but they don't benefit yet from the potential of artificial intelligence.

New designs such as platform clinical trials might address this in the future, and could pave the way of how machine learning technologies could learn evidence rather than just association. So, large medical data sets are being assembled. New technologies to overcome the lack of certainty are being developed. Novel ways to establish causation are emerging.

This area is moving fast and tremendous potential exists for improving efficiency and health. Indeed, many ventures are trying to capitalize on this. Startups such as Enlitic, large firms such as IBM, or even small businesses such as Resonance Health, are promising to revolutionize health.

Impressive progress is being made but many challenges still exist.

by Olivier Salvado, The Conversation

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Two prestigious Universities set new mark for 'Deep Learning'

Neuroscience and artificial intelligence experts from Rice University and Baylor College of Medicine have taken inspiration from the human brain in creating a new "deep learning" method that enables computers to learn about the visual world largely on their own, much as human babies do.

In tests, the group's "deep rendering mixture model" largely taught itself how to distinguish handwritten digits using a standard dataset of 10,000 digits written by federal employees and high school. In results presented this month at the Neural Information Processing Systems (NIPS) conference in Barcelona, Spain, the researchers described how they trained their algorithm by giving it just 10 correct examples of each handwritten digit between zero and nine and then presenting it with several thousand more examples that it used to further teach itself. In tests, the algorithm was more accurate at correctly distinguishing handwritten digits than almost all previous algorithms that were trained with thousands of correct examples of each digit.

"In deep-learning parlance, our system uses a method known as semi supervised learning," said lead researcher Ankit Patel, an assistant professor with joint appointments in neuroscience at Baylor and electrical and computer engineering at Rice. "The most successful efforts in this area have used a different technique called supervised learning, where the machine is trained with thousands of examples: This is a one. This is a two.



"Humans don't learn that way," Patel said. "When babies learn to see during their first year, they get very little input about what things are. Parents may label a few things: 'Bottle, chair, momma.' But the baby can't even understand spoken words at that point. It's learning mostly unsupervised via some interaction with the world."

Patel said he and graduate student Tan Nguyen, a co-author on the new study, set out to design a semi supervised learning system for visual data that didn't require much "hand-holding" in the form of training examples. For instance, neural networks that use supervised learning would typically be given hundreds or even thousands of training examples of handwritten digits before they would be tested on the database of 10,000 handwritten digits in the Mixed National Institute of Standards and Technology (MNIST) database.

The semi supervised Rice-Baylor algorithm is a "convolutional neural network," a piece of software made up of layers of artificial neurons whose design was inspired by biological neurons. These artificial neurons, or processing units, are organized in layers, and the first layer scans an image and does simple tasks like searching for edges and color changes. The second layer examines the output from the first layer and searches for more complex patterns. Mathematically, this nested method of looking for patterns within patterns within patterns is referred to as a nonlinear process.

"It's essentially a very simple visual cortex," Patel said of the convolutional neural net. "You give it an image, and each layer processes the image a little bit more and understands it in a deeper way and by the last layer, you've got a really deep and abstract understanding of the image. Every self-driving car right now has convolutional neural nets in it because they are currently the best for vision."

Like human brains, neural networks start out as blank slates and become fully formed as they interact with the world. For example, each processing unit in a convolutional net starts the same and becomes specialized over time as they are exposed to visual stimuli.



"Edges are very important," Nguyen said. "Many of the lower layer neurons tend to become edge detectors. They're looking for patterns that are both very common and very important for visual interpretation, and each one trains itself to look for a specific pattern, like a 45-degree edge or a 30-degree red-to-blue transition.

"When they detect their particular pattern, they become excited and pass that on to the next layer up, which looks for patterns in their patterns, and so on," he said. "The number of times you do a nonlinear transformation is essentially the depth of the network, and depth governs power. The deeper a network is, the more stuff it's able to disentangle. At the deeper layers, units are looking for very abstract things like eyeballs or vertical grating patterns or a school bus."

Nguyen began working with Patel in January as the latter began his tenure-track academic career at Rice and Baylor. Patel had already spent more than a decade studying and applying machine learning in jobs ranging from high-volume commodities training to strategic missile defense, and he'd just wrapped up a four-year postdoctoral stint in the lab of Rice's Richard Baraniuk, another co-author on the new study. In late 2015, Baraniuk, Patel and Nguyen published the first theoretical framework that could both derive the exact structure of convolutional neural networks and provide principled solutions to alleviate some of their limitations.

Baraniuk said a solid theoretical understanding is vital for designing convolutional nets that go beyond today's state-of-the-art.

"Understanding video images is a great example," Baraniuk said. "If I am looking at a video, frame by frame by frame, and I want to understand all the objects and how they're moving and so on, that is a huge challenge. Imagine how long it would take to label every object in every frame of a video. No one has time for that. And in order for a machine to understand what it's seeing in a video, it has to understand what objects are, the concept of three-dimensional space and a whole bunch of other really complicated stuff. We humans learn those things on our own and take them for granted, but they are totally missing in today's artificial neural networks."

Patel said the theory of artificial neural networks, which was refined in the NIPS paper, could ultimately help neuroscientists better understand the workings of the human brains.



"There seem to be some similarities about how the visual cortex represents the world and how convolutional nets represent the world, but they also differ greatly," Patel said. "What the brain is doing may be related, but it's still very different. And the key thing we know about the brain is that it mostly learns unsupervised.

"What I and my neuroscientist colleagues are trying to figure out is, what is the semi supervised learning algorithm that's being implemented by the neural circuits in the visual cortex? and How is that related to our theory of deep learning?" he said. "Can we use our theory to help elucidate what the brain is doing? Because the way the brain is doing it is far superior to any neural network that we've designed."

Explore further: New AI algorithm taught by humans learns beyond its training
Provided by: Rice University*

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Living Robot with 'Human Brain'

Close to the creation of Super-Computer with AI

COMPUTER scientists attempting to electronically replicate the human brain are close to creating a 'living PC'.

Engineers at the University of Massachusetts are developing microprocessors which mimic biological synapses - the nerve cells which pass messages across the human body.

The science fiction-style project is being undertaken by Joshua Yang and Qiangfei Xia, professors of electrical and computer engineering at the US college.

Their work focuses heavily on ‘memristors’ - a computer component which could change science forever, switching the focus from electronics to ‘ionics’.

Ionics, unlike electronics, is not dependent on a power source. It essentially has a memory, so even if it loses power it can remember what it was doing before and continue the action.



“The computers will send messages in the same manner of the human brain”
This means computers of the near-future will be able to shut on and off like a lightbulb, not losing any data or files in the process.

Different researchers and developers, including Mr. Yang and Mr. Xia, are now racing to be the first to harness this technology and use it to create a new generation of computers.

Professor Jennifer Rupp said: “I think there is a race going on. There is a strong driving force, but at the same time it's very important that there are players like HP, because they want to get to the market, show everyone that this is real.”

Mr. Yang and Mr. Xia explained the process in more detail in their report, explaining the process behind neuromorphic computing - computers which mimic humans.

Computers will soon have memories and be able to operate without power
They said: “Memristors have become a leading candidate to enable neuromorphic computing by reproducing the functions in biological synapses and neurons in a neural network system, while providing advantages in energy and size”.

“This work opens a new avenue of neuromorphic computing hardware based on ‘memristors’”.



“Specifically, we developed a diffusive-type ‘memristor’ where diffusion of atoms offers a similar dynamics and the needed time-scales as its bio-counterpart, leading to a more faithful emulation of actual synapses i.e. a true synaptic emulator”.

“The results here provide an encouraging pathway toward synaptic emulation using diffusive ‘memristors’ for neuromorphic computing."
Source: Joey Millar

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Detecting Our Emotions with Wireless Signals

Measuring your heartbeat and breath, CSAIL device can tell if you’re excited, happy, angry, or sad.

As many a relationship book can tell you, understanding someone else’s emotions can be a difficult task. Facial expressions aren’t always reliable: A smile can conceal frustration, while a poker face might mask a winning hand.

But what if technology could tell us how someone is really feeling?

Researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) have developed “EQ-Radio,” a device that can detect a person’s emotions using wireless signals.

By measuring subtle changes in breathing and heart rhythms, EQ-Radio is 87 percent accurate at detecting if a person is excited, happy, angry or sad — and can do so without on-body sensors.

MIT professor and project lead Dina Katabi envisions the system being used in entertainment, consumer behavior, and health care. Film studios and ad agencies could test viewers’ reactions in real-time, while smart homes could use information about your mood to adjust the heating or suggest that you get some fresh air.



“Our work shows that wireless signals can capture information about human behavior that is not always visible to the naked eye,” says Katabi, who co-wrote a paper on the topic with PhD students Mingmin Zhao and Fadel Adib. “We believe that our results could pave the way for future technologies that could help monitor and diagnose conditions like depression and anxiety.”

EQ-Radio builds on Katabi’s continued efforts to use wireless technology for measuring human behaviors such as breathing and falling. She says that she will incorporate emotion-detection into her spinoff company Emerald, which makes a device that is aimed at detecting and predicting falls among the elderly.

Using wireless signals reflected off people’s bodies, the device measures heartbeats, as accurately as an ECG monitor, with a margin of error of approximately 0.3 percent. It then studies the waveforms within each heartbeat to match a person’s behavior to how they previously acted in one of the four emotion-states.



The team will present the work next month at the Association of Computing Machinery’s International Conference on Mobile Computing and Networking (MobiCom).
How it works:

Existing emotion-detection methods rely on audiovisual cues or on-body sensors, but there are downsides to both techniques. Facial expressions are famously unreliable, while on-body sensors such as chest bands and ECG monitors are inconvenient to wear and become inaccurate if they change position over time.

EQ-Radio instead sends wireless signals that reflect off of a person’s body and back to the device. Its beat-extraction algorithms break the reflections into individual heartbeats and analyze the small variations in heartbeat intervals to determine their levels of arousal and positive affect.

These measurements are what allow EQ-Radio to detect emotion. For example, a person whose signals correlate to low arousal and negative affect is more likely to tagged as sad, while someone whose signals correlate to high arousal and positive affect would likely be tagged as excited.

The exact correlations vary from person to person, but are consistent enough that EQ-Radio could detect emotions with 70 percent accuracy even when it hadn’t previously measured the target person’s heartbeat.

“Just by knowing how people breathe and how their hearts beat in different emotional states we can look at a random person’s heartbeat and reliably detect their emotions,” says Zhao.

For the experiments, subjects used videos or music to recall a series of memories that each evoked one the four emotions, as well as a no-emotion baseline. Trained just on those five sets of two-minute videos, EQ-Radio could then accurately classify the person’s behavior among the four emotions 87 percent of the time.



Compared with Microsoft’s vision-based “Emotion API”, which focuses on facial expressions, EQ-Radio was found to be significantly more accurate in detecting joy, sadness, and anger. The two systems performed similarly with neutral emotions, since a face’s absence of emotion is generally easier to detect than its presence.

One of the CSAIL team’s toughest challenges was to tune out irrelevant data. In order to get individual heartbeats, for example, the team had to dampen the breathing, since the distance that a person’s chest moves from breathing is much greater than the distance that their heart moves to beat.

To do so, the team focused on wireless signals that are based on acceleration rather than distance traveled, since the rise and fall of the chest with each breath tends to be much more consistent — and, therefore, have a lower acceleration — than the motion of the heartbeat.

Although the focus on emotion-detection meant analyzing the time between heartbeats, the team says that the algorithm’s ability to capture the heartbeat’s entire waveform means that in the future it could be used for non-invasive health monitoring and diagnostic settings.



“By recovering measurements of the heart valves actually opening and closing at a millisecond time-scale, this system can literally detect if someone’s heart skips a beat,” says Adib. “This opens up the possibility of learning more about conditions like arrhythmia, and potentially exploring other medical applications that we haven’t even thought of yet.”
Source: Adam Conner-Simons | Rachel Gordon | CSAIL

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Would a Machine be as Smart as God Want?

The field of “scientific theology” ponders the ultimate purpose of mind

At a recent AI conference I was listening to smart people ponder what super-smart machines will want, I kept thinking of things I’d heard, watched and read before.


As some speakers acknowledged, countless science fictions have already imagined what artificial minds will desire. Common cinematic answers are power (2001: A Space Odyssey, The Terminator, The Matrix), freedom (I Robot, Ex Machina) and love (Steven Spielberg’s Artificial Intelligence, Spike Jones’s Her).



But what if the machines have all the power and freedom (which are arguably equivalent) and love they need? Or what if all the machines merge into one gigantic mind? At that point, freedom, power and love, which are social goals, become irrelevant. What will that cosmic computer want? What will it do to pass the time?

In The End of Science, I called this sort of speculation “scientific theology.” Physicist Freeman Dyson is my favorite practitioner. In 1979 he published “Time Without End: Physics and Biology in an Open Universe” in Reviews of Modern Physics. Dyson wrote the paper to counter physicist Steven Weinberg's infamous remark that "the more the universe seems comprehensible, the more it also seems pointless."

No universe with intelligence is pointless, Dyson retorted. He sought to show that even in an eternally expanding universe, intelligence could persist virtually forever and ward off heat death through shrewd conservation of energy.

In his 1988 essay collection Infinite in All Directions, Dyson envisioned intelligence spreading through the entire universe, transforming it into a vast cosmic mind. "What will mind choose to do when it informs and controls the universe?" Dyson asked. We “cannot hope to answer" this question definitively, he suggested, because it is theological rather than scientific:
"I do not make any clear distinction between mind and God. God is what mind becomes when it has passed beyond the scale of our comprehension. God may be considered to be either a world-soul or a collection of world souls. We are the chief inlets of God on this planet at the present stage in his development. We may later grow with him as he grows, or we may be left behind."

Dyson’s musings were inspired by the science-fiction writer (and philosopher) Olaf Stapledon, who died in 1950. In his books Last and First Men and Starmaker, Stapledon imagined what mind would become after millions or billions of years. He postulated that a cosmic mind will want to create. It will become an artist, whose works are entire universes.



That’s a cool idea (and it implies that we live in one of those works of art), but I prefer Dyson’s hypothesis. He guessed that a cosmic mind would be not an artist but a scientist, a knowledge-seeker. When I interviewed Dyson in 1993, he expressed confidence that the quest for knowledge would never end, because knowledge is infinite.

His optimism derived in part from Godel's theorem, which demonstrates that every system of axioms poses questions that cannot be answered with those axioms. The theorem implies that mathematics is open-ended and hence can continue forever.

"Since we know the laws of physics are mathematical,” Dyson told me, “and we know that mathematics is an inconsistent system, it's sort of plausible that physics will also be inconsistent" and therefore open-ended.

I have a hard time imagining the cosmic computer at the end of time—a.k.a. “God”—fussing over math or physics puzzles. My idea (admittedly drug-inspired) is that It will ponder the riddle of Its own origin. Here’s the meta-question: Will It solve that mystery of mysteries, or will It be forever stumped?

Postscript: Two other scientific theologians are worth mentioning. Physicist Frank Tipler, in The Physics of Immortality (1994), argues that the God-like machine at the end of time would resurrect every creature that ever lived in a blissful cyber-paradise. The sex will be fantastic. In his 1961 novel Solaris, about the encounter between humans and a sentient planet, Stanislaw Lem suggests that superintelligence will be inscrutable. His perspective evokes negative theology, which holds that God will always be beyond our ken. Lem’s brilliant twist is that plain old human minds are pretty fringing’ inscrutable too.

Source: John Horgan

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Can a brain-computer interface convert your thoughts to text?

Recent research shows brain-to-text device capable of decoding speech from brain signals, creating a breakthrough on the Artificial Intelligence field.

Ever wonder what it would be like if a device could decode your thoughts into actual speech or written words? While this might enhance the capabilities of already existing speech interfaces with devices, it could be a potential game-changer for those with speech pathologies, and even more so for "locked-in" patients who lack any speech or motor function.

"So instead of saying: 'Siri, what is the weather like today' or 'Ok Google, where can I go for lunch?' I just imagine saying these things," explains Christian Herff, author of a review recently published in the journal Frontiers in Human Neuroscience.



While reading one's thoughts might still belong to the realms of science fiction, scientists are already decoding speech from signals generated in our brains when we speak or listen to speech.

In their review, Herff and co-author, Dr. Tanja Schultz, compare the pros and cons of using various brain imaging techniques to capture neural signals from the brain and then decode them to text.
The technologies include functional MRI and near infrared imaging that can detect neural signals based on metabolic activity of neurons, to methods such as EEG and magnetoencephalography (MEG) that can detect electromagnetic activity of neurons responding to speech. One method in particular, called electro-corticography or ECoG, showed promise in Herff's study.

This study presents the Brain-to-text system in which epilepsy patients who already had electrode grids implanted for treatment of their condition participated. They read out texts presented on a screen in front of them while their brain activity was recorded. This formed the basis of a database of patterns of neural signals that could now be matched to speech elements or "phones."



When the researchers also included language and dictionary models in their algorithms, they were able to decode neural signals to text with a high degree of accuracy. "For the first time, we could show that brain activity can be decoded specifically enough to use ASR technology on brain signals," says Herff. "However, the current need for implanted electrodes renders it far from usable in day-to-day life."

So, where does the field go from here to a functioning thought detection device? "A first milestone would be to actually decode imagined phrases from brain activity, but a lot of technical issues need to be solved for that," concedes Herff.

Their study results, while exciting, are still only a preliminary step towards this type of brain-computer interface.
Source: Physics Today

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Mind over matter: Our Thoughts may control Robots

We can use the power of thought to control a robot that helps to move a paralyzed hand: a project from the ETH Rehabilitation Engineering Laboratory could fundamentally change the therapy and daily lives of stroke patients.

One in six people will suffer a stroke in their lifetime. In Switzerland alone, stroke affects 16,000 people every year. Two thirds of those affected suffer from paralysis of the arm. Intensive training can -- depending on the extent of damage to the brain -- help patients regain a certain degree of control over their arms and hands. This may take the form of classic physio- and occupational therapy, or it may also involve robots.

Roger Gassert, Professor of Rehabilitation Engineering at ETH Zurich, has developed a number of robotic devices that train hand functions and sees this as a good way to support patient therapy. However, both physio- and robot-assisted therapy are usually limited to one or two training sessions a day; and for patients, travelling to and from therapy can also be time consuming.



Exoskeletons as exercise robots
"My vision is that instead of performing exercises in an abstract situation at the clinic, patients will be able to integrate them into their daily life at home, supported -- depending on the severity of their impairments -- by a robot," Gassert says, presenting an exoskeleton for the hand. He developed the idea for this robotic device together with Professor Jumpei Arata from Kyushu University (Japan) while the latter was working in Gassert's laboratory during a sabbatical in 2010.

"Existing exoskeletons are heavy, and this is a problem for our patients because it renders them unable to lift their hands," Gassert says, explaining the concept. The patients also have difficulty feeling objects and exerting the right amount of force. "That's why we wanted to develop a model that leaves the palm of the hand more or less free, allowing patients to perform daily activities that support not only motor functions but somatosensory functions as well," he says. Arata developed a mechanism for the finger featuring three overlapping leaf springs. A motor moves the middle spring, which transmits the force to the different segments of the finger through the other two springs. The fingers thus automatically adapt to the shape of the object the patient wants to grasp.



However, the integrated motors brought the weight of the exoskeleton to 250 grams, which in clinical tests proved too heavy for patients. The solution was to remove the motors from the hand and fix them to the patient's back. The force is transmitted to the exoskeleton using a bicycle brake cable. The hand module now weighs slightly less than 120 grams and is strong enough to lift a liter bottle of mineral water.

Researching brain processes
Gassert is currently driven by the question of what happens in the brain and how commands pass from the brain to reach the extremities after a stroke. "Especially with seriously affected patients, the connection between the brain and the hand is often severely or completely disrupted," Gassert explains, "so we are looking for a solution that will help patients pass on commands to the robotic device intuitively." The idea is to detect in the brain a patient's intention to move his or her hand and directly pass this information on to the exoskeleton. This may also produce a therapeutic benefit. According to Gassert, a number of studies show that it is possible to strengthen existing neural connections between the brain and the hand with regular exercise. An important component for this is that the brain receives somatosensory feedback from the hand when it produces a command to move.

In order to understand what goes on in the brain, Gassert is carrying out fundamental research with clinicians, neuroscientists and therapists. For their research, the scientists can draw on a number of imaging techniques, such as functional magnetic resonance imaging (fMRI), which allows them to map the activities of the whole brain. While this technology allows them to gain fundamental new insights, fMRI is both very expensive and highly complex and consequently not suitable for therapy. "And of course, it's not portable," Gassert adds with a mind to his project. He therefore focuses on simpler techniques such as electroencephalography (EEG) -- and in particular functional near-infrared spectroscopy (fNIRS), the least expensive of these technologies. Gassert is currently engaged in the challenging task of figuring out whether and how fNIRS can be robustly employed. He is working on this together with a group from the University Hospital, who are contributing their experience in clinical application of the technology.



Fundamental insights
Another question that is still not fully understood is how the brain controls limbs that interact with the environment. "Here, robotics is making a valuable contribution to basic research because it is ideally suited for capturing a movement, perturbing it and measuring the reaction," Gassert explains. For example, the robotics experts have developed an exoskeleton that makes it possible to block the knee for 200 milliseconds while walking and extend it by 5 degrees. With the help of sensors, the scientists measure the forces that are involved and use this data to infer how the brain modulates the stiffness of the knee. These findings then flow into applications such as the control of new, active prostheses.

If the researchers succeed in establishing an interaction between the brain and the exoskeleton, the result will be a device that is ideally suited for therapy. If, on the other hand, the deficits are permanent, a robotic device could offer long-term support -- as an alternative to invasive methods, which are also being researched. These for instance envisage implanting electrodes in the brain and triggering stimulators in the muscles. However, as long as stroke patients can expect to experience a reasonable degree of recovery, the robot-assisted therapy will be the obvious choice.
Source: ETH Zurich

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Our Cognitive process is in danger?

The Internet is increasingly taking over human memory

Our increasing reliance on the Internet and the ease of access to the vast resource available online is affecting our thought processes for problem solving, recall and learning. In a new article published in the Journal Memory, researchers at the University of California, Santa Cruz and University of Illinois, Urbana Champaign have found that 'cognitive offloading', or the tendency to rely on things like the Internet as an aide-mémoire, increases after each use.

We might think that memory is something that happens in the head but increasingly it is becoming something that happens with the help of agents outside the head. Benjamin Storm, Sean Stone & Aaron Benjamin conducted experiments to determine our likelihood to reach for a computer or smartphone to answer questions. Participants were first divided into two groups to answer some challenging trivia questions -- one group used just their memory, the other used Google. Participants were then given the option of answering subsequent easier questions by the method of their choice.



The results revealed that participants who previously used the Internet to gain information were significantly more likely to revert to Google for subsequent questions than those who relied on memory. Participants also spent less time consulting their own memory before reaching for the Internet; they were not only more likely to do it again, they were likely to do it much more quickly. Remarkably 30% of participants who previously consulted the Internet failed to even attempt to answer a single simple question from memory.

Lead author Doctor Benjamin Storm commented, "Memory is changing. Our research shows that as we use the Internet to support and extend our memory we become more reliant on it. Whereas before we might have tried to recall something on our own, now we don't bother. As more information becomes available via smartphones and other devices, we become progressively more reliant on it in our daily lives."



This research suggests that using a certain method for fact finding has a marked influence on the probability of future repeat behaviour. Time will tell if this pattern will have any further reaching impacts on human memory than has our reliance on other information sources.

Certainly the Internet is more comprehensive, dependable and on the whole faster than the imperfections of human memory, borne out by the more accurate answers from participants in the internet condition during this research. With a world of information a Google search away on a smartphone, the need to remember trivial facts, figures, and numbers is inevitably becoming less necessary to function in everyday life.

Source: Taylor & Francis

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DDoS Attacks: Bringing Websites to a Halt

By: Alexandria Addesso

Imagine your personal, business, or corporate computer servers completely flooded to the point that they cannot be accessed, halting any attempts at transactions on a given website. This could be crippling to a small business, devastating to a large corporation, and a threat to security for a government.

A distributed denial of service (DDoS) attack is usually carried out by a group of hackers that create a swarm of false traffic to a website by commonly done by requesting information. This form of hacking is not done to steal user information or money but to cease business for a matter of time which would result in a lost of capital gains for the amount of time that the website is inaccessible.



“Well it's not that hard, there are places on the web you can acquire software that can do it with relative ease. Some of the more sophisticated software can be found on the dark web,” said IT specialist Eric Torres.

Just recently a DDoS attack was carried out against Australian Census Bureau's server. The site had to be immediately shut down to secure the integrity of the data. In the past two years there have also been DDoS attacks done against Irish and Thai government servers.
“Any server on the public internet is at risk. When a DDoS attack takes place basically you are overwhelming a particular system or systems with requests that it basically comes to a crawl or stop responding all together,” said Torres.

Even highly secure United States government servers have fell victim to DDoS attacks. In March 2015 a group of hackers connected to the twitter handle “ @Vikingdom2015” took down several government sites including Maine.gov and In.gov.

“We will knock all American government’s websites offline. We do not care if we get caught. We all like doing this. So you better be prepared for the battle,” said an audio clip on a SoundCloud account connected to @Vikingdom2015.

Although there is now a market out there of software to supposedly stop DDoS attacks, in reality every server online is at risk.



“Don’t connect to the internet,” said Torres. “Not connecting to server to the Internet may not be feasible and 99.9 percent of the cases but it's the only way almost guarantee you will not be attacked.”

Or at the very least, don’t piss off any hackers.

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