LeNet, a pioneering deep neural network, designed to do image classification. Over the years, deep learning has required an ever-growing number of these multiply-and-accumulate operations. Inverting one photodetector signal and adding it to the other then results in a signal proportional to the product of the two inputs. The beams leaving the beam splitter shine on photodetectors (ovals), which provide electrical signals proportional to these electric fields squared. Two beams whose electric fields are proportional to the numbers to be multiplied, x and y, impinge on a beam splitter (blue square). The relevant matrix calculations for deep learning boil down to a large number of multiply-and-accumulate operations, whereby pairs of numbers are multiplied together and their products are added up. This is great news because modern computer hardware has been very well optimized for matrix operations, which were the bread and butter of high-performance computing long before deep learning became popular. Matrices, which are just rectangular arrays of numbers-spreadsheets if you will, minus the descriptive column headers you might find in a typical Excel file. What are these mysterious linear-algebra calculations? They aren't so complicated really. This is true for both training (the process of determining what weights to apply to the inputs for each neuron) and for inference (when the neural network is providing the desired results). While they are not the whole story, these linear-algebra calculations are the most computationally demanding part of deep learning, particularly as the size of the network grows. The benefit of arranging things that way, as opposed to allowing connections between any two neurons, is that it allows certain mathematical tricks of linear algebra to be used to speed the calculations. Reducing the energy needs of neural networks might require computing with lightįor computational efficiency, these neurons are grouped into layers, with neurons connected only to neurons in adjacent layers. So bear with me as I outline what goes on under the hood. To understand how optics can serve here, you need to know a little bit about how computers currently carry out neural-network calculations. Here, I will describe a very different approach to this problem-using optical processors to carry out neural-network calculations with photons instead of electrons. This dynamic has spurred engineers to develop electronic hardware accelerators specifically targeted to deep learning, Google's Tensor Processing Unit (TPU) being a prime example. But the computing demands of deep learning have been rising even faster. Harnessed for nongraphical calculations, a trend that has become increasingly pervasive over the past decade. The amount of computing power at people's fingertips started growing in leaps and bounds at the turn of the millennium, when graphical processing units (GPUs) began to be The reason for that has mostly to do with the increasing amounts of computing power that have become widely available-along with the burgeoning quantities of data that can be easily harvested and used to train neural networks. While machine learning has been around a long time, deep learning has taken on a life of its own lately. Learn more → Join the world’s largest professional organization devoted to engineering and applied sciences and get access to all of Spectrum’s articles, archives, PDF downloads, and other benefits. Join the world’s largest professional organization devoted to engineering and applied sciences and get access to all of Spectrum’s articles, podcasts, and special reports. For more exclusive content and features, consider Joining IEEE. Enjoy more free content and benefits by creating an account Saving articles to read later requires an IEEE Spectrum account The Institute content is only available for members Downloading full PDF issues is exclusive for IEEE Members Access to Spectrum's Digital Edition is exclusive for IEEE Members Following topics is a feature exclusive for IEEE Members Adding your response to an article requires an IEEE Spectrum account Create an account to access more content and features on IEEE Spectrum, including the ability to save articles to read later, download Spectrum Collections, and participate in conversations with readers and editors. Stay ahead of the latest technology trends.
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You're tasked with bringing down the organizations in power, specifically the ones that framed you for the bombings, but there's no plan after that though. That's why the existence of DedSec in this world is both heroic and confusing. Watch Dogs: Legion is immensely political and consistent in its messaging. Bodies are expendable because in a fascist world, all that matters is power and those who can hold onto it. The head of Albion believes that humans have limited potential and are just "bugs in the software." Another tech CEO believes that bodies are limiting. The unemployed are protesting on multiple corners, and potential recruits want you to find out what happened to their jobs. Albion and other tech companies are replacing workers with drones in multiple industries, putting people out of work but also blanketing the skies with moving machinery. in Watch Dogs: Legion is dealing with issues concerning autonomous work. That's just the start of what it does to undocumented people it considers a danger to the country.īeyond immigrant discrimination, the U.K. has been giving hysterectomies to people at the border and separating families, as just one example. However, the government's potential for bodily harm is expansive, considering the U.S. Your brain might initially deem this as the plot of a mad scientist, (the literal dehumanization of the most marginalized communities and turning them into profit is the perfect combination of Brexit-era anxieties and late stage capitalism). For example, you learn that one of the organizations, a criminal gang with deep connections, has been trafficking refugees and harvesting their organs. The fact this sort of oppression is happening in many countries as fascism rises across the world makes it feel less far-fetched. They'll either be tackled and handcuffed or worse. Even if they claim to be documented citizens, it doesn't work out well. Albion officers are stopping people on the street on just about every corner and checking their "legal" status. Fears about immigrants taking jobs are on full display, covering ad space on bus stops and spewing from the mouths of pundits. into political and societal chaos (and out of the EU). Watch Dogs: Legion is a post-Brexit game and heavily draws upon the xenophobia and classism that threw the U.K. The organizations involved, all powerful in their own way, have their own motives, but all involve reducing humanhood and agency, especially of marginalized immigrant communities. Most importantly, they're all thematically consistent. They're all separate, but intertwined in small but meaningful ways. The main storyline of the game involves taking down multiple organizations that had a hand in the bombings. We've seen similar procedurally-generated systems before, but none are quite as robust or work as well as the one in Watch Dogs: Legion. Creative Director Clint Hocking, from Far Cry 2, was brought on to helm the project and has said in interviews how much work the team put in to making this system work. Recruit them to your team and use their skills to counteract fascism. Everybody you walk by on the street, from businesspeople to artists to old women and beekeepers, are playable. The open-world game has marketed itself on a neat gimmick: it allows you to play anybody you encounter. It's both Ubisoft's most ambitious title and the one least like other studio mainstays, and that's all good news for players. However, Watch Dogs: Legion manages to stand out among the holiday clutter. That isn't to say Watch Dogs is an unpopular franchise - previous entries have sold quite well - but it doesn't have the same clout as a new Assassin's Creed game or even the new Far Cry game due out next year. 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Missing codepage or helper program, or other error Mount: wrong fs type, bad option, bad superblock on /dev/loop0, Mount -o loop -t hfsplus file.dmg /mountpoint Unfortunately this tool didn't actually successfully convert the dmg image: Also if you get carat M at the end of each line, remove the carat M on the first line or it will always complain perl does not exist. pl file to point to the correct path to the perl binary. Instead I just downloaded the perl version. For some reason I'm getting dmg2iso compile errors on my Ubuntu 8.04 install:ĭmg2iso.h:45: error: â_int32â does not name a typeĭ: In function âint main(int, char**)â:ĭ:117: error: â_int32â does not name a typeĭ:122: error: â_int32â does not name a typeĭ:123: error: â_int32â does not name a typeĭ:124: error: âblock_typeâ was not declared in this scopeĭ:126: error: âout_offsâ was not declared in this scopeĭ:127: error: âout_sizeâ was not declared in this scopeĭ:128: error: âin_offsâ was not declared in this scopeĭ:129: error: âin_sizeâ was not declared in this scopeĭ:130: error: âconvert_endianâ was not declared in this scopeĭ:140: error: âlast_offsâ was not declared in this scopeĭ:158: error: âlast_in_offsâ was not declared in this scopeĭ:161: error: âlast_offsâ was not declared in this scopeĭ:172: error: âlast_offsâ was not declared in this scopeĭ:172: error: âlast_in_offsâ was not declared in this scopeĭ:84: warning: ignoring return value of âsize_t fread(void*, size_t, size_t, FILE*)â, declared with attribute warn_unused_result It's just silly and doesn't make sense that Ubuntu doesn't have this in the repository at all, it's such a useful and almost essential tool. 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