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      "body": "If you've always been curious about Dota 2 but never taken that first step, then now is the perfect time. The biggest event in the Dota calendar, the International, is only weeks away. Start today and you'll be in a great position to enjoy the event, and the event itself is one of the best ways to deepen your appreciation for the game. My relationship with Dota 2 began right before TI2 in 2012, and contrasting my nascent understanding of the game with the quality of play at that tournament—including 'The Play', one of the most celebrated moments in pro Dota—sparked a four year commitment spanning thousands of hours of play.\nhttps://youtu.be/tDaJes0T1Ig\nThis is my favourite game—possibly my favourite game ever—and I would never have assumed that it was for me before I tried it. I've written almost a hundred thousand words about the game over the last couple of years, but in this article I'm going to dial it back to the start. If you've not played Dota 2, why should you? What do you need to invest? And where can you look for further help?\nhttp://cdn.mos.cms.futurecdn.net/yGvNrQ8Do4qpKZEsxvWiwU-650-80.jpg\n\nWhat is Dota 2, really?\n\nLet's put aside the word 'MOBA' for a moment, because it doesn't help. Dota 2 is a multiplayer game that draws in elements from strategy games, RPGs, and competitive action. Although your objective is simple—kill AI minions, gain power, destroy your opponent's outer defenses, and siege their base—Dota's appeal lies in its breadth.\n\nThere comes a point with most competitive games where you've seen more or less everything. You've fired every gun, captured every objective. Though the details may differ, eventually matches begin to run into one another.\n\nThis doesn't happen with Dota 2. You play as one of more than a hundred distinct characters, as do all of your teammates and every member of the enemy team. Between the ten heroes present in any given game there are dozens of special abilities, rules, counters, and interactions. This is further complicated by items—hundreds of bolt-on upgrades for your hero that grant new skills and bonuses. Every character and item has, in turn, a differing relationship with the map—the eleventh character in the match, a labyrinth of pathways, neutral monsters, buffs, and so on. And all of the above stems from a deep and internally-consistent stat system.\n\nDota 2 is notably complex even when compared to its closest peers: games like League of Legends, Smite, and Heroes of the Storm. It was the result of years of community development before it was taken under Valve's wing, and as a result it's packed with special rules and exceptions and mechanics that feel like hacks. It's far from elegant, but you'll never stop being surprised by it. A single tree in Dota 2 has more special rules attached to it than some Heroes of the Storm maps.\n\nhttp://cdn.mos.cms.futurecdn.net/uLPjKukjxG4Je3H6JenUKh-650-80.jpg\n\nWhat do I get out of playing?\n\nIf you're fascinated by systems, competition, and teamplay, then Dota 2 has unsurpassed depth and longevity (and honestly, I'm surprised you don't play it already.) This is a game for people who want to commit to something, who are excited rather than repelled by the idea of climbing a mountain. It helps a great deal if the idea of learning excites you.\n\nIt's not all work, however. Dota 2's complexity makes it a brilliant generator of anecdotes, and sharing these moments with friends is a pleasure independent of the game's difficulty. Similarly, fluency with the game gives you access to an esports scene with decades of history and a community that, in its brighest moments, feels like a massive exclusive club for people who 'get' Dota.\n\nAlso: Dota 2 is fun. That helps too.\n\nhttp://cdn.mos.cms.futurecdn.net/FCqYkkQeziDmL7xSKu4eKc-650-80.jpg\n\nWhat do I need ?\n\n\nIn terms of financial outlay, Dota 2 is remarkably generous. The entire game including every character, item and ability is entirely free. You can play at the highest level immediately without spending a penny. The in-game store is limited to cosmetic items and seasonal 'Battle Passes', which amount to minigames and cosmetic collections tied to specific esports events. It's possible to spend a lot of money on Dota 2 in that regard, and if you want the rarest and flashiest cosmetics then you'll certainly need to spend for them—but you don't need to.\n\nMinimum System Requirements\nOS: Windows, OS X, Linux\nProcessor: Intel dual core/AMD at 2.8Ghz+\nMemory: 4Gb RAM\nGPU: nVidia GeForce 8600, ATI/AMD Radeon HD2600\nStorage: 15.5Gb\n\nIn terms of time, Dota 2 is trickier to assess. The road to understanding is extremely long, and you need to be okay with the idea that thousands of hours of experience will make you an intermediate player at best. Mastery takes decades. There's also the matter that there's no way to surrender a match in progress, and leaving early carries a punishment. You can't play Dota 2 unless you're willing to give it 45 minutes to an hour at a time.\n\nOn the other hand, Dota 2 has no grinding and, while it does have account levels, these don't have anything to do with your in-game capabilities. Everything you can do in Dota 2, you can do from the moment you load the game. Your skill is gated by your own ability, not by an unlock system.\n\nYou will certainly need patience. Not just patience for learning, but patience with a community which, at its worst, can be one of the most hostile in gaming. Toxicity is Dota 2's most serious problem, and arguably a bigger barrier to entry than the learning curve. To an extent, this is not the game or Valve's fault. There are in-game reporting tools and punishments for repeat offenders. The issue is simply that the game attracts players with a certain attitude—ready to rage at their teammates, ready to say anything to get a rise out of their opponents, willing to make people uncomfortable for its own sake. Play game for any length of time and it is sadly inevitable that you will encounter racism, sexism, prejudice and hostility.\n\nThis isn't everyone, however. Dota 2 is able to forge positive bonds between people, too, and it's much more fun to learn if you make the journey with people you enjoy making friends with. You'll make friends playing Dota 2, but you'll need to develop a thick skin on the way.\n\n\nhttp://cdn.mos.cms.futurecdn.net/PvGfYFyijiorqcdCpL9FvJ-650-80.jpg\n\nThe best resources for beginners\n\nOver the years, the Dota 2 community has fostered some amazing teachers who make the journey a lot easier. When you begin, you'll want to start by playing against bots with a selection of guides and videos to hand. Only when you feel like you've got a grasp of all of the different factors that go into a victory should you step into the game proper.\n\nKevin 'Purge' Godec has a longstanding reputation as one of the best tutors in the scene and his beginner's guide, 'Welcome To Dota, You Suck' is a great place to start. His YouTube channel, PurgeGamers, is also helpful. When you're ready to start learning each new hero, check to see if there's a relevant 'Purge plays' video and watch at least the first ten minutes to get a sense of the character's role and item progression. Mind the time and date stamp, however: Dota 2 changes a lot from patch to patch, so information in older videos might lead you astray if you're not careful.\n\nOther great beginner's guides include devilesk's 'Comprehensive Dota 2 Guide' and Flipside3Tactics' YouTube series, 'Dota for Dummies'. Finally, the Dota 2 guides section in the Steam Community is a useful resource of hero-specific builds that can be loaded in-game using the book icon in the top left. Again, look for guides designed for the current patch and use the star rating associated with each to guide you.\n\nhttps://youtu.be/3ElXGllTpp0\n\nGet into the pro scene\n\nThis is a very, very good time to start watching professional Dota 2. The International is the biggest event of the year, and boasts the largest prize pool in competitive gaming. The Dota 2 scene is diverse and competitive, with no single dominant team or region.\n\nThe best places to watch professional Dota 2 are on Twitch and via in-game spectator tools. Click the 'Watch' tab to see games in progress, which includes professional play as well as pub games taking place around the world. Once you're in, you can control the camera yourself or give over control to a particular commentary crew using the in-game menus. If you own a Vive, you can also spectate in virtual reality using the incredible Dota VR Hub.\n\nWhile watching pro play is a great way to learn the game, it certainly helps if you already know the basics. Following some of the guides above and getting a couple of bot games under your belt is the way to go if you want to be able to follow the competition.",
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      "body": "![Engineers-Develop-Programmable-Nanophotonic-Processors.jpg](https://steemitimages.com/DQme4TMBCxKwqb2o2aHjTtSycTRmTQGroLATtFXzztxxn4b/Engineers-Develop-Programmable-Nanophotonic-Processors.jpg)\n\nA team of engineers propose a new architecture for a fully optical neural network that, in principle, could offer an enhancement in computational speed and power efficiency over state-of-the-art electronics for conventional inference tasks.\n\n“Deep learning” computer systems, based on artificial neural networks that mimic the way the brain learns from an accumulation of examples, have become a hot topic in computer science. In addition to enabling technologies such as face- and voice-recognition software, these systems could scour vast amounts of medical data to find patterns that could be useful diagnostically, or scan chemical formulas for possible new pharmaceuticals.\n\nBut the computations these systems must carry out are highly complex and demanding, even for the most powerful computers.\n\nNow, a team of researchers at MIT and elsewhere has developed a new approach to such computations, using light instead of electricity, which they say could vastly improve the speed and efficiency of certain deep learning computations. Their results appear today in the journal Nature Photonics in a paper by MIT postdoc Yichen Shen, graduate student Nicholas Harris, professors Marin Soljačić and Dirk Englund, and eight others.\n\nSoljačić says that many researchers over the years have made claims about optics-based computers, but that “people dramatically over-promised, and it backfired.” While many proposed uses of such photonic computers turned out not to be practical, a light-based neural-network system developed by this team “may be applicable for deep-learning for some applications,” he says.\n\nTraditional computer architectures are not very efficient when it comes to the kinds of calculations needed for certain important neural-network tasks. Such tasks typically involve repeated multiplications of matrices, which can be very computationally intensive in conventional CPU or GPU chips.\n\nAfter years of research, the MIT team has come up with a way of performing these operations optically instead. “This chip, once you tune it, can carry out matrix multiplication with, in principle, zero energy, almost instantly,” Soljačić says. “We’ve demonstrated the crucial building blocks but not yet the full system.”\n\nBy way of analogy, Soljačić points out that even an ordinary eyeglass lens carries out a complex calculation (the so-called Fourier transform) on the light waves that pass through it. The way light beams carry out computations in the new photonic chips is far more general but has a similar underlying principle. The new approach uses multiple light beams directed in such a way that their waves interact with each other, producing interference patterns that convey the result of the intended operation. The resulting device is something the researchers call a programmable nanophotonic processor.\n\nThe result, Shen says, is that the optical chips using this architecture could, in principle, carry out calculations performed in typical artificial intelligence algorithms much faster and using less than one-thousandth as much energy per operation as conventional electronic chips. “The natural advantage of using light to do matrix multiplication plays a big part in the speed up and power savings, because dense matrix multiplications are the most power hungry and time consuming part in AI algorithms” he says.\n\nThe new programmable nanophotonic processor, which was developed in the Englund lab by Harris and collaborators, uses an array of waveguides that are interconnected in a way that can be modified as needed, programming that set of beams for a specific computation. “You can program in any matrix operation,” Harris says. The processor guides light through a series of coupled photonic waveguides. The team’s full proposal calls for interleaved layers of devices that apply an operation called a nonlinear activation function, in analogy with the operation of neurons in the brain.\n\nTo demonstrate the concept, the team set the programmable nanophotonic processor to implement a neural network that recognizes four basic vowel sounds. Even with this rudimentary system, they were able to achieve a 77 percent accuracy level, compared to about 90 percent for conventional systems. There are “no substantial obstacles” to scaling up the system for greater accuracy, Soljačić says.\n\nEnglund adds that the programmable nanophotonic processor could have other applications as well, including signal processing for data transmission. “High-speed analog signal processing is something this could manage” faster than other approaches that first convert the signal to digital form, since light is an inherently analog medium. “This approach could do processing directly in the analog domain,” he says.\n\nThe team says it will still take a lot more effort and time to make this system useful; however, once the system is scaled up and fully functioning, it can find many user cases, such as data centers or security systems. The system could also be a boon for self-driving cars or drones, says Harris, or “whenever you need to do a lot of computation but you don’t have a lot of power or time.”\n\nThe research team also included MIT graduate students Scott Skirlo and Mihika Prabhu in the Research Laboratory of Electronics, Xin Sun in mathematics, and Shijie Zhao in biology, Tom Baehr-Jones and Michael Hochberg at Elenion Technologies, in New York, and Hugo Larochelle at Université de Sherbrooke, in Quebec. The work was supported by the U.S. Army Research Office through the Institute for Soldier Nanotechnologies, the National Science Foundation, and the Air Force Office of Scientific Research.\n\nPublication: Yichen Shen, et al., “Deep learning with coherent nanophotonic circuits,” Nature Photonics (2017) doi:10.1038/nphoton.2017.93\n\nSource: David Chandler, MIT News",
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