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      "body": "<html> <p>https://cdn.steemitimages.com/DQmQJA8JFddSL8MuZJKBCfYrEKiLWXJ2hXHdu9Xrr4hNf1b/image.png</p> <p><br></p> <h3>Super soldiers</h3> <p>A super soldier program produces Marvel superhero Wolverine in the movie 'X-Men Origins: Wolverine,' along with rivals Sabretooth and Weapon XI. Now LiveScience looks back on real experiments that the U.S. government ran on soldiers and citizens to advance the science of war.</p> <p>The military didn't replicate Wolverine's indestructible skeleton and retractable claws. Rather, they shot accident victims up with plutonium, tested nerve gas on sailors, and tried out ESP. While some of the tests seem outlandish in hindsight, the military continues to push the envelope in seeking new warfare techniques based on cutting-edge science and technology.</p> <p><strong>'My measure of success is that the International Olympic Committee bans everything we do,' said Michael Goldblatt, former head of DARPA's Defense Sciences Office, while talking with reporters. And that's not a Hollywood script.</strong></p> <p><strong>Build your inner armor</strong></p> <p>Perhaps super soldiers may not be far off after all, if efforts such as DARPA's 'Inner Armor' project find success. Consider efforts to give humans the extreme abilities of some animals, such as the high-altitude conditioning of the bar-headed Goose that has been known to crash into jet aircraft at more than 34,000 feet. Scientists are also eying the Steller sea lion, which redirects blood flow away from non-critical organs during deep sea dives and reduces oxygen demand. 'I do not accept that our soldiers cannot physically outperform the enemy on his home turf,' said Dr. Michael Callahan, who heads the project at DARPA's Defense Sciences Office, during a 2007 presentation. The goal is to make soldiers 'kill-proof' against all sorts of conditions, including infectious diseases, chemical, biological and radioactive weapons, temperature and altitude extremes, and harsh natural environments. Sounds like a certain mutant superhero.</p> <p><br></p> <p><br></p> <p><strong>24/7 Warrior</strong></p> <p>https://cdn.steemitimages.com/DQmbuFnuGxg8vLEpty68QBHGehAJe7UGZh8BL7dafKY2EGg/image.png</p> <p>Sleep can be a warrior's worst enemy, whether during day-long battles or long-duration missions flown from halfway around the world. But various military branches have tried to change that over the years by distributing 'go pills' or stimulants such as amphetamines. More recently, the military has tested and deployed the drug modafinil – more commonly known under brands such as Provigil – which has supposedly enabled soldiers to stay awake for 40 hours straight without ill effect. And the U.S. Defense Advanced Research Projects Agency (DARPA) is funding even more unusual anti-sleep research, such as transcranial magnetic stimulation that zaps the brain with electromagnetism.</p> <p><br></p> <p><strong>Psychic vision</strong></p> <p>https://cdn.steemitimages.com/DQmRDGzMBDu48UnyB95WP4XzXngDPAx4zBoqPtE8BZgDpMq/image.png</p> <p>Psychics may not hold much credibility among scientists, but the Pentagon spent roughly $20 million testing extrasensory (ESP) powers such as remote viewing from 1972 to 1996. Remote viewers would try to envision geographical locations that they had never seen before, such as nuclear facilities or bunkers in foreign lands. Mixed results led to conflicts within the intelligence agencies, even as the project continued under names such as 'Grill Flame' and 'Star Gate,' and led to spooks finally abandoning the effort. The CIA declassified such information in files released in 2002.</p> <p><br></p> <p><strong>Nerve gas spray</strong></p> <p>https://cdn.steemitimages.com/DQmVUsp3gVJtFPyXJzG8yFByYsg4XnycdwfK7mGELQqScpG/image.png</p> <p>Threats of chemical and biological warfare led the U.S. Department of Defense to start 'Project 112' from 1963 to the early 1970s. Part of the effort involved spraying different ships and hundreds of Navy sailors with nerve agents such as sarin and VX, in order to test the effectiveness of decontamination procedures and safety measures at the time. The Pentagon revealed the details of the Project Shipboard Hazard and Defense (SHAD) project in 2002, and the Veterans Administration began studying possible health effects among sailors who participated in SHAD. This was just one of many chemical warfare experiments conducted by the U.S. military, starting with volunteer tests involving mustard gas in World War II.</p> <p><br></p> <p><strong>Hallucinogenic Warfare</strong></p> <p>https://cdn.steemitimages.com/DQmWX3Tnnez7QWSZF3jm5ZQ8P6qCjSTjAiXCpLmN5CcNjdV/image.png</p> <p>Psychoactive drugs such as marijuana, LSD and PCP don't just have street value: Researchers once hoped the drugs could become chemical weapons that disabled enemy soldiers. U.S. Army volunteers took pot, acid and angel dust at a facility in Edgewood, Md. From 1955 to 1972, although those drugs proved too mellow for weapons use. The Army did eventually develop hallucinogenic artillery rounds that could disperse powdered quinuclidinyl benzilate, which left many test subjects in a sleep-like condition for days. The National Academy of Sciences conducted a study in 1981 that found no ill effects from the testing, and Dr. James Ketchum published the first insider account of the research in his 2007 book 'Chemical Warfare: Secrets Almost Forgotten.'</p> <p><br></p> <p><strong>Falling near the speed of sound</strong></p> <p>https://cdn.steemitimages.com/DQmcLKYb4SfvQroPYF1KmzEAnw457t6KA3PE3ijrKpTm8yA/image.png</p> <p>When the U.S. Air Force wanted to find out how well pilots could survive high-altitude jumps, they turned to Captain Joseph Kittinger, Jr. The test pilot made several jumps as head of 'Project Excelsior' during the 1950s. Each time involved riding high-altitude Excelsior balloons up tens of thousands of feet, before jumping, free falling and parachuting to the desert floor in New Mexico. Kittinger's third record-breaking flight on August 16, 1960 took him up to 102,800 feet, or almost 20 miles. He then leaped and freefell at speeds of up to 614 mph, not far from the speed of sound's 761 mph, and endured temperatures as low as minus 94 degrees Fahrenheit.</p> <p><br></p> <p><strong>Pacifist guinea pigs</strong></p> <p>https://cdn.steemitimages.com/DQmRdo4dyYZ323GVNUsHFaPSqkkG3pokYCqgGLLoUwWCYRn/image.png</p> <p>Most soldiers don't sign up to fight deadly viruses and bacteria, but that's what more than 2,300 young Seventh-Day Adventists did when drafted by the U.S. Army. As conscientious objectors during the Cold War who interpreted the Bible's commandment 'Thou shalt not kill' very literally, many volunteered instead to serve as guinea pigs for testing vaccines against biological weapons. Volunteers recalled being miserable for several days with fever, chills and bone-deep aches from diseases such as Q fever. None died during the secretive 'Operation Whitecoat,' which took place at Fort Detrick, Maryland from 1954 to 1973.</p> </html>",
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      "body": "<html> <p>https://cdn.steemitimages.com/DQmSYp8mneVR9WjvUJeFPy8m3ibvNs5RyvdGm7Rp2v92kky/image.png<br></p> <p><strong>Optical training of neural networks could lead to more efficient artificial intelligence</strong></p> <p>https://cdn.steemitimages.com/DQmNkV9uemWgjo86Tm5ebMbAmb6jiC2yW4mZf7RhcbvK2ia/image.png</p> <p><em>&nbsp;Researchers have shown a neural network can be trained using an optical circuit (blue rectangle in the illustration). In the full network there would be several of these linked together. The laser inputs (green) encode information that is carried through the chip by optical waveguides (black). The chip performs operations crucial to the artificial neural network using tunable beam splitters, which are represented by the curved sections in the waveguides. These sections couple two adjacent waveguides together and are tuned by adjusting the settings of optical phase shifters (red and blue glowing objects), which act like 'knobs' that can be adjusted during training to perform a given task.&nbsp;</em></p> <p>&nbsp;<strong>Researchers have shown that it is possible to train artificial neural networks directly on an optical chip. The significant breakthrough demonstrates that an optical circuit can perform a critical function of an electronics-based artificial neural network and could lead to less expensive, faster and more energy efficient ways to perform complex tasks such as speech or image recognition.</strong></p> <p>'Using an optical chip to perform neural network computations more efficiently than is possible with digital computers could allow more complex problems to be solved,' said research team leader Shanhui Fan of Stanford University. 'This would enhance the capability of artificial neural networks to perform tasks required for self-driving cars or to formulate an appropriate response to a spoken question, for example. It could also improve our lives in ways we can't imagine now.'</p> <p>&nbsp;An artificial neural network is a type of artificial intelligence that uses connected units to process information in a manner similar to the way the brain processes information. Using these networks to perform a complex task, for instance voice recognition, requires the critical step of training the algorithms to categorize inputs, such as different words.&nbsp;</p> <p>Although optical artificial neural networks were recently demonstrated experimentally, the training step was performed using a model on a traditional digital computer and the final settings were then imported into the optical circuit. In <em>Optica</em>, The Optical Society's journal for high impact research, Stanford University researchers report a method for training these networks directly in the device by implementing an optical analogue of the 'backpropagation' algorithm, which is the standard way to train conventional neural networks.</p> <p>'Using a physical device rather than a computer model for training makes the process more accurate,' said Tyler W. Hughes, first author of the paper. 'Also, because the training step is a very computationally expensive part of the implementation of the neural network, performing this step optically is key to improving the computational efficiency, speed and power consumption of artificial networks.'&nbsp;</p> <p><br></p> <p>&nbsp;<strong>A light-based network</strong></p> <p>Although neural network processing is typically performed using a traditional computer, there are significant efforts to design hardware optimized specifically for neural network computing. Optics-based devices are of great interest because they can perform computations in parallel while using less energy than electronic devices.&nbsp;</p> <p>&nbsp;In the new work, the researchers overcame a significant challenge to implementing an all-optical neural network by designing an optical chip that replicates the way that conventional computers train neural networks.<br> &nbsp;</p> <p>&nbsp;An artificial neural network can be thought of as a black box with a number of knobs. During the training step, these knobs are each turned a little and then the system is tested to see if the performance of the algorithms improved.<br> &nbsp;'Our method not only helps predict which direction to turn the knobs but also how much you should turn each knob to get you closer to the desired performance,' said Hughes. 'Our approach speeds up training significantly, especially for large networks, because we get information about each knob in parallel.'<br> &nbsp;</p> <p>&nbsp;<strong>On-chip training</strong></p> <p>The new training protocol operates on optical circuits with tunable beam splitters that are adjusted by changing the settings of optical phase shifters. Laser beams encoding information to be processed are fired into the optical circuit and carried by optical waveguides through the beam splitters, which are adjusted like knobs to train the neural network algorithms.</p> <p>In the new training protocol, the laser is first fed through the optical circuit. Upon exiting the device, the difference from the expected outcome is calculated. This information is then used to generate a new light signal, which is sent back through the optical network in the opposite direction. By measuring the optical intensity around each beam splitter during this process, the researchers showed how to detect, in parallel, how the neural network performance will change with respect to each beam splitter's setting. The phase shifter settings can be changed based on this information, and the process may be repeated until the neural network produces the desired outcome.</p> <p>The researchers tested their training technique with optical simulations by teaching an algorithm to perform complicated functions, such as picking out complex features within a set of points. They found that the optical implementation performed similarly to a conventional computer.</p> <p>'Our work demonstrates that you can use the laws of physics to implement computer science algorithms,' said Fan. 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      "body": "<html> <p>https://cdn.steemitimages.com/DQmSYp8mneVR9WjvUJeFPy8m3ibvNs5RyvdGm7Rp2v92kky/image.png<br></p> <p><strong>Optical training of neural networks could lead to more efficient artificial intelligence</strong></p> <p>https://cdn.steemitimages.com/DQmNkV9uemWgjo86Tm5ebMbAmb6jiC2yW4mZf7RhcbvK2ia/image.png</p> <p><em>&nbsp;Researchers have shown a neural network can be trained using an optical circuit (blue rectangle in the illustration). In the full network there would be several of these linked together. The laser inputs (green) encode information that is carried through the chip by optical waveguides (black). The chip performs operations crucial to the artificial neural network using tunable beam splitters, which are represented by the curved sections in the waveguides. 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'This would enhance the capability of artificial neural networks to perform tasks required for self-driving cars or to formulate an appropriate response to a spoken question, for example. It could also improve our lives in ways we can't imagine now.'</p> <p>&nbsp;An artificial neural network is a type of artificial intelligence that uses connected units to process information in a manner similar to the way the brain processes information. Using these networks to perform a complex task, for instance voice recognition, requires the critical step of training the algorithms to categorize inputs, such as different words.&nbsp;</p> <p>Although optical artificial neural networks were recently demonstrated experimentally, the training step was performed using a model on a traditional digital computer and the final settings were then imported into the optical circuit. In <em>Optica</em>, The Optical Society's journal for high impact research, Stanford University researchers report a method for training these networks directly in the device by implementing an optical analogue of the 'backpropagation' algorithm, which is the standard way to train conventional neural networks.</p> <p>'Using a physical device rather than a computer model for training makes the process more accurate,' said Tyler W. Hughes, first author of the paper. 'Also, because the training step is a very computationally expensive part of the implementation of the neural network, performing this step optically is key to improving the computational efficiency, speed and power consumption of artificial networks.'&nbsp;</p> <p><br></p> <p>&nbsp;<strong>A light-based network</strong></p> <p>Although neural network processing is typically performed using a traditional computer, there are significant efforts to design hardware optimized specifically for neural network computing. Optics-based devices are of great interest because they can perform computations in parallel while using less energy than electronic devices.&nbsp;</p> <p>&nbsp;In the new work, the researchers overcame a significant challenge to implementing an all-optical neural network by designing an optical chip that replicates the way that conventional computers train neural networks.<br> &nbsp;</p> <p>&nbsp;An artificial neural network can be thought of as a black box with a number of knobs. During the training step, these knobs are each turned a little and then the system is tested to see if the performance of the algorithms improved.<br> &nbsp;'Our method not only helps predict which direction to turn the knobs but also how much you should turn each knob to get you closer to the desired performance,' said Hughes. 'Our approach speeds up training significantly, especially for large networks, because we get information about each knob in parallel.'<br> &nbsp;</p> <p>&nbsp;<strong>On-chip training</strong></p> <p>The new training protocol operates on optical circuits with tunable beam splitters that are adjusted by changing the settings of optical phase shifters. Laser beams encoding information to be processed are fired into the optical circuit and carried by optical waveguides through the beam splitters, which are adjusted like knobs to train the neural network algorithms.</p> <p>In the new training protocol, the laser is first fed through the optical circuit. Upon exiting the device, the difference from the expected outcome is calculated. This information is then used to generate a new light signal, which is sent back through the optical network in the opposite direction. 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