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      "body": "<html> <p>https://cdn.steemitimages.com/DQmdtG5NNzRLtyofHtu2ZvzPy9tQVsJZM6gBw28Sc6oveJx/image.png</p> <p>Barclays has been ordered by the competition watchdog to step up its customer communications as the lender was criticised for failings linked to payment protection insurance (PPI).</p> <p>The Competition and Markets Authority (CMA) said on Monday that the lender breached an order requiring banks to provide customers with an annual reminder setting out how much they have paid in to a PPI scheme, and their right to cancel the policy.</p> <p>Between October 2016 and October 2017, Barclays failed to provide a reminder to 2,265 Littlewoods credit card PPI customers, the CMA said.</p> <p><br></p> <p>https://cdn.steemitimages.com/DQmXTJx33quEmmkpststYfA7ZtgyWyjbhhEGkE3rSYWNz83/barcl.PNG</p> <p><br></p> <p>The bank attributed the breach to a technical problem in transferring the customers to its computer system.</p> <p>As a result, the CMA has issued Barclays with “legal directions”, requiring it to put appropriate systems and procedures in place to prevent a similar incident happening again in the future.</p> <p><br></p> <p>Adam Land, the CMA’s senior director of remedies, said: “The annual reminder is an important measure so customers know they still have a PPI policy and how much it is costing them each year, as well as their right to cancel or switch.</p> <p><br></p> <p><em><strong>We now require assurances from Barclays they have now put adequate systems in place to prevent a similar breach from occurring again.</strong></em></p> <p><br></p> <blockquote>This is Barclays’ second breach of the PPI order. As a result, we are issuing legal directions which can be enforced by a court, to ensure they comply with the order</blockquote> <p><br></p> <p>This is the second time Barclays has failed to comply with the order, having reported several breaches to the CMA in 2015 for not providing annual reminders to almost 10,000 PPI customers.</p> <p><br></p> <p>“This is Barclays’ second breach of the PPI order. As a result, we are issuing legal directions which can be enforced by a court, to ensure they comply with the order,” Mr Land added.</p> <p><br></p> <p>Following the latest breach, Barclays wrote to all affected customers, providing a reminder of their right to cancel the policy and the offer of a refund.</p> <p><br></p> <p><em><strong>It has so far paid out almost £336,000 in refunds to customers.</strong></em></p> <p><br></p> <p>https://cdn.steemitimages.com/DQmXTJx33quEmmkpststYfA7ZtgyWyjbhhEGkE3rSYWNz83/barcl.PNG</p> <p><br></p> <p>A Barclays spokeswoman said: <strong>This issue has now been resolved and all customers have received their missing statements. We have written to all affected customers to apologise unreservedly and to outline how we will recompense them where they would have otherwise cancelled their policy.</strong></p> <p><br></p> <p>“We take this matter extremely seriously and have conducted an internal investigation to ensure all stringent controls and policies continue to be upheld.”</p> <p><br></p> <p>Last week Barclays saw half-year profits knocked by a third following a major US settlement and PPI provisions.</p> <p><br></p> <p>The high street lender reported a 29% fall in pre-tax profit to £1.7 billion for the six months to June 30, while total income for the period was flat at £10.9 billion.</p> <p><br></p> <p>Profits were knocked by a £400 million PPI charge and a £1.4 billion settlement with US authorities over its sale of mortgage-backed securities in the lead-up to the financial crisis – both of which were logged in the first quarter.</p> </html>",
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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. 'By training these networks in the optical domain, it shows that optical neural network systems could be built to carry out certain functionalities using optics alone.</p> <p>'The researchers plan to further optimize the system and want to use it to implement a practical application of a neural network task. The general approach they designed could be used with various neural network architectures and for other applications such as reconfigurable optics.&nbsp;</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. 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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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      "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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'By training these networks in the optical domain, it shows that optical neural network systems could be built to carry out certain functionalities using optics alone.</p> <p>'The researchers plan to further optimize the system and want to use it to implement a practical application of a neural network task. The general approach they designed could be used with various neural network architectures and for other applications such as reconfigurable optics.&nbsp;</p> </html>",
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