Simple addition of these features can then make the classification (i.e. learning is used to create a scoring system classifier, which is transparent). Artificial intelligence is the intelligence of machines or software, versus the intelligence of people or animals. It is also the sphere of study in pc science that develops and research intelligent machines. The algorithm would then study this labeled assortment of images to differentiate the shapes and its traits, such as circles having no corners and squares having four equal sides. After it's educated on the dataset of images, the system will have the flexibility to see a new picture and decide what shape it finds.

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?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?+k3AV4juDikHYsnaLtprjgeSLWGCcsLAQodjiK07NNE96ixJE1qNKlrLcdl1YCnlhJUQj9RCQTbrYXw6rXEPK1IrCcvVGapqUWg8ToJbSCf4lDof8AHEmqYqVknZK9HXDSrJWpi57YcU+bFnxUSIb7TzShsttYUD9RtjmekKaIPcYVKpGbIVOChq8N8BHlvpvZnEqlsgkkqG+BEiKVA2VjtgK3RG35EhNwGThouXIB3YODj8ZSNWtaQBuSTsBhmhpp?+5aktOW2OlQNsWtCuVgsy13/ANH/AHx6JTl/9HVgkYKr2Kxj33Bd9lj741sVbGaJDgIJYO+HrDzij?/mcerZbjNKkSZDbTaBdS1rCUpHmScBJPEXJVLOl2vNOqvbSwhTn7gWwkpryy8YSl0TCHrUpIUg2w3zhWPwHL8icl2Ywo6UJcittrdRc7qCVmygB12Jt0xAp/GVhSdOWoawpJup2W2LK3+FKUkncdyR8sR/MOap2cpTMucyy2wykBiGlWqzljqXfbci3XtYb44eTmTtRZ6XB8SX1cuhjXJ7mYOUahKakpPhEppJSnVf4VoVuhXzxKMkcKq?+H0S41FeYWuy0OvIDaR+lV1Dp32B/vEu4L8Nac5HdzXX4yZciQ+pcZKwUJCO2pv4SUq1WNie98XY02hNglIAAAAA6Y5lDJ3I6+f/I/wvi4UQmk8KMowaXHh1Chwp8hAJdkPMgrWsm5N+tr4fp4e5KiMuoZyzAs8CF6mQo2KSDYm9rg22xKim+2EnkgYokkeV/scku5P/6Zg4leypluVEk1LLcia2pAU57klsOa7keFAFugvYfIXxBpmUc90xhTjuSqkhphHxIYcKUhIPkLAY2Q+2m/UYHPNouNSht0sMZ8EZdaOyP+S5cVCe6MOV0ZmbiCU7lWtJStHgUqI4kHYWIBG9zgDJ4BcR+My6DLgw26bR4TqxIlT3OW4FXRqU2gAqJABsbWvtfG+VNs6LlZOk3GwwlpY6B028vXFeP48Ut7F5vn5O4xSZlDM/sTHNjjLlTzpMnLZaLSFTpBUUIO5SnQhNgTvbEH4sexnxPpvDGTTcszGK6zT0ITFpcQOqfCCtOrlazsQNyEg6rWsb43awhrqFYKRA2bJO++GlGK0kcP95t7Z+RHC/LMfLdLi03McF+l1dite8LgSmi260EFFgpC7KTsgmxF7b4udzMiZNWeeTJGltgA+LoSf?/bG0uM3AqicX4DLa6oujTmNSBMZjJdK0EglCgSkkXSO/n5nGQK/7JnH+gVOVDotAFdiavBObmx2uanon8tboUBYdx1J+eFWkC1PbI9V84xozWl1JdsbkBdtQHa4va/ngBlLifJXUCpUCFFblkxNEfUtXNb2SpZPoCBttscD888KuMOTpYp?+a8kSYrzrReQBJZXqRe2oFCldN/XY7d8QTLkSu0x15TsCXEdmuKPJWBrHkkE/Dexur7XxSEnF6FkkWjNzPHpdS91mSCvnLU6kjcaVbqAPmlVz6hW2BFWzSY0ua1VIrUalNK5v4m68EsBogJSOnUqI72sfPbEWzrlzMNHZRMbVzUrAU4kg6kHzSrqP3+1waoz9mWZPpjGXqo9JDCF88htzSDtYXHcEm9if4TthZ3F7NFWtFoP5Hyrm1x2q0urU2bHT4nVsPIdSkAXNykkDbzxL6HlenUCCmNCCBzAFkgbKH0xlDIdVcy1mmPPpy5E5S7shhYN1Bdkn+IXOkqAvsFEHtjfHsz?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?/LR3t/jjLFRzzmFysImVzMFTnPsLvqlLcdFvQHYYZ90FKzQufONrWf8ANkeiUcutUWnPBa1LGlUhf6iOw8h1++L/AMl5kZfgsOx5AuEi51bjGN6Nl6Jmp5GZsvVVyLKdCFyGXLqbB0ixA6pB227dOmk4tGC1xNpGTJuYoOV58+OyhbKJkBpTrRd07J8Ive5GwwIxoLdqi8qjxHp2Xmp3F2vOCQzR9cWkIIWpbSgrRqQlPdxfUgE6QkDvhnQ+PuROINTeq68xyorylELaqMfkKUomwUo3IT0sAT++KuaoHE7MkiiZBhcOswSfwll2fNhvxlMrRoSEt6i4Ak3KjYarqJ2BscM+EPsrcY6JUszZg4l8N7QprRblU2a8tceap1etbiHYjnMjrRoQA6kKFiQoHoGsy6NUUXMNSpKhOoFUU0FbgtOAoWPUdFD5jEup/tFwotRby?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?+MgnUfRIClH/ZGPz4qcXPPEfOjkTKdcnUxqQhxVTmRXNJ90SAoIU72CnAne4JO/U7X47WxH6HXFOK1FokmQGxqCQhHht4yQE/8AqOMDVyb7/WprsWc?+gKeWUhd9Kki9vMbpKtv7RxrbOPCfjjRIcxNPbq+Z0FpaWgiooW22ogpS4da73F7gC+MoVvKuYMpz3o+ZsoVmiOujTz5ERYZUpXSzttN/QHG5pOVX2U40l0aN/wDhz8M6fxJ9ohmVmymQarSct0mTVtDqA425K1tts6k/Cbc1xe4tdKdsfsEFkJSARYbYwl?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?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?+lVNaWEEOXIuNvl?/jiO5u4uRste7pjUpyat9Shp5oQAAbE9D3wHCTAtFjE23wkogrHzxTC/aFkC4GUwf/wDaf/swFrHtTmjTYUJ/Ji1LnOBDam5Y8JLiEbgp?/wDEGFwaM3ZE/bY4zwuHbNKpqGUS6m+sNU+MoFTapDikpJcAIJSlBJIG51W2vhrLVHgZWVOjwVwXao6UqZPhUW0E6FrFz4inRtfw2sO+M/8AEGsJ49e2KGGkcymUJ9ploOfCAPzHlDyUEDl269DjQnECRrmMwgokMMpvcW8RFz/PHTHURXplfRfxOJLkyXKq84l1YU00QkBkW3AIFzv5/LHlXXErMR2DWoUeay?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?/BbXSIxjxXltqlFhA0qsbkuEDf4h12Ow32FsdcRvaUq9X4zTczx1N1Km0ZwU2mQJKeY08EKICx3SpbilL1JsT4QbgWxsPNmS865jypTnMssU0++oacqqDICJDLOkLU2lKhY+K4V4h0Nr3xmr6M9F18I6/PrWT2XqlVX585iQ/HlOulNytC7C2kAAadJHU79cTVL67nfpinciZrpGTac3BloQy2spLiUAWSrSlNydrmwHn077YsmlZpoFXnOU6n1Ft19CEuFNikqBF7i439bXthml0LsEZ+4fjP7keFWM0VeLRmUgvU6C97uJawdua6BzCkACyUlPmbm1jeV8tUHKNNFMy5TGobF7kJJKlHuVKN1KPqSTgiQFHVbrjsfCMDpUbb7PnUtyGlx5LSHWnE6VoWkKSoeRB2IxUefuFGVKNFezJlehvQpDikx5LVPfU0hTS1AFSUDYKBAIKbdTi3MJzIcedFXEkt62nRpWnzHp5YWWkNCrKEju0x95FPbTzVxUpWLoUoJ7AhRFr7HvfbfCEOo06dmuBR/cz+IqlpZjqcQkkpJHMWnckJCep2v03xT/tKUnMtGzbNy5SMxzYzDEdHu7jKy0rSpGvcpI1G5Iub9MSf2HqI47UarMzBJenVCnNBcN11Z/LDqrOW9TpTvf18rc/HN8jx9F+TixSkn2bCbeWp2wUcOOYoHr0w1b3UMOrDHVSXRC6IpxA4a0rPcVD/OTAqjRARNQyFqKd?/wAtYuCU79iDilK/lHPHDiZHqlU5MylNvDVKjr8I9VJVYp/ceuNLa1JBta2I9nvKyc8ZUm5fW/yHH0HlO2uEL7EjoRbb9+2FltFYcjjopxLkyvUyW9+JNuR5iLRwhOlTKSDcKI/iufptiH0HLNepNa?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?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?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?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?/N8WEdLP6MVTVE3aZLzNaSdK1pPrfDeVKj21BSTiDtVlxdruE4Vdqi1IsFmwGOX+Z6DikS6LKiDYqRc4etmKuxuj74rVNTcDmzp6+eH7dSdCQeYb?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?+ygfLAY1F5ZvqP3x0Ki905n745sCrl6MpcZ+G2WuGmalUXKTq2INQ5lRchFN0Rw64slDRv4QV6iEgDSmwGK5zJXZFEy/NVEJLspPKbZSNZccOyLI1J1EEiwuOtrjqLw9q6Upa8uzm20Ke/rDGtWmx?+A+K5F+p74fcOOEmSq3litGfqqv4sG2G5riklxhHIZcIasNKVJdUo3te6RcnCLjkpFlNYgTJPC+bLoMWtZHq8Gn1mE03IFKK1KJkp+JxD6iEpUSB4AkIttc3KsSPi77R06JklmlUSmyqfmKU2lmqa29C4KioIUlsHdSlXuFC4Sgg98V1mPLOYskzZGUptXXISAHWpcdOgLa20hQN7K2II3G3ytEZ8ZuRWYjL0ZkrkKbcQHkakmygDqA622uL9Dis6fZNESrbknLMGNV8wwpkJiotKkxXnWj?/WW0qsVI6lW56jz9cKZaddzShMilR3kx1LaRz5f9XaTzF8tKipXwgK7m3TG5KPlOlQ0mVWGmKvU32g0/Lkx0H8vrym0EENtDsgbbXNzc4PNN0+PGXEYp8VDC06VNJZSEKHkQBYjBw9CObr6mSsv8SeJHs95mFGr1PUYD6eYYi1lTcnf4mVi4tY3Ch32ONoZRzFCzdlumZmix1ss1OK3JQ25bWhKgCAbbXxlX2iOH1Gjf0aby8l+JGlVUR/dEukx2Cvc8lB/zeo9Upsm4FgDe?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?/haJ1qJHS4A9e2PPY84BUKlUOHxQrlKS5PqTDTkfnp1XAJUhYSfhSLjSO58X6caplVd1katW/zxGPHNu2wykgouDBbskNIGkWGwHT5Y9REbV8NkjEcaqzr916z17Y7dqb6BcLP3x0KLFUkHJUSOhIUVBWG/KiemIlU8xvMDTzOvrgb?/Sh7/W4OMjWgdTJal2GrBgO6k2tiLUaU0QLJ3GD3vH5ZIAGHpl3JX2IKfCJB1KFwemHzMjUL6sRp6QRLJv1OCkN64FzhoxYk560G23LnHPPHM64QZcBwgXfzzvh6ISla0GEO7YU1XQQLjV3HUYYsubAY7lvhqG64OyD9/8Ak4EuhVszPxS4VsrzJFy+xUH34kFKKnpkgrK3Q7qShyxAUm6E7dPsMU5mz2daM9Gn1BdSW3KeUt9bpaClFRJV6G1/XGm8xPS5EoLckur3IVqWTcdsQmvQG3oq2luugLBuAq4t9cTbQSIO8Xcioyw9GpOYI70xiK61oUeUpLiUEfCuxI1WG18Tf2JmlxKFmrXIWbz20pbJBSkAL3Hqf7higMzcOYNKq7NQp5K0ykugpcSCEm+q/wBxiyfZnrdVyrmmotLWFUp4MtywVAEKUV6VgE3Nrb2vscBSSlQ9VHRs7n9icYx9qKbPrmbFUCO+0iHUJRTNuglehgI0hJB28aetu2NjtJCgF6goWvcd8Yk40yKu/mR+bTKFJmvsKCXGDdtwKcWpR2IPQWJ6dU9b4o5XpCYsA8PaRMhVKqQXVOyWihl1UqQ8XHVKsUhBJHQJH740B7GrC3KDmyv2GiZVg2gg3GydZ/ZwYp7h1JByZVa9PpcinyQ48hbL4ssFtO1r7m4sR88a+4eZWyzkrJsCkZTi+7wFtJkJBUVKcUtIJWSbkk7dcSexorRMYy7psbbY9cBBuMN4bvhvhcm6Ce+FjJ2O1oAZsyNl3PlMEGupUCwouMPNqAcaUepSTcfQgjFd5U4d8S+GVZScsV+m1zL8h7VLp0pK47yEn+NtV1IKx/ug9x0xZ8tam1XBIHfCrTl2d1YrjlskrWiiPa6rj+VaNQ8zUqM29NbfW060t1SA6wBcX0+Slfueu2IBwZytmnjRkZefpym6XOjVfTTozatSFIbUjmFSyAbFJWkC3Ub32s59rOuZe4hs0yhUGqRHZNOTKWl2+m0lQDaGwpQI0kglVkqvYWI3voTh5lug5IynS8s5bjtNwIjCQjQvWFKO6lFX8RJuSTuT1wj48p34HypaJwlZShIHkAccOyCAd8JFRAAGEZCtifTFFHYgGr2WoOa5NIdnOLSaPUWqi0BuFLQFAJI7jxfcDEq1km+rbAWEs89WCWo3tfBaSZj2S8QDvj2G8ooJvhnLUQDfHcFd21YAU6H5fN73xUvtHQZdR4byapTHVonZfkMVaOpHxAtLuux7eAqP0xZzi7X3wBr0OLVYUmlzUhUeYyth0f2VJIP7E42FoKl7M7Vnipw+i01Mas5kiNSXo6S5FZVz3hqSCRoQCf4vK2I9l7ilRv6QtUXKtBqrkmWwtbcuoqEdhSEkatIsVEi42tfFa8LsiScuZ/zXJqlLUoxT7kFvMgtakPFCtBIsTqSBtf1xckCHT8xO+5gMOVCEOZDIA/LWAQRsPCFC4P37Y5OWDfReE0maaybRpGXKSliZPRKkSV89xTQIaBKQLIB3tsNzub79rTBty6Rivsi1d2p5WpbkhR94aaDDyVG6gpBKd/sMTptd209N8UilWhH2LuPAAjbAtbx96SCeuHjhsm5wJK9UxO+HoAf1gJ3IAt1vjPHH7jO3TmxQaTzHQ68IyFNJKkuPm9gq2+kHYW6m2LbzZmGntU92MueYyCPzXEi5U2B4gk32J6Xt54pqjRKfX8+wTAorUaIw8l5xKlqWoNtDUVHVexUQBa+18bGhZVROll7LkOIl6MhdTciNMykFSigOqAKkoANkjV5W6Ygebc5Zfj1+oUx+qQ2ZNOszyVOBBIteyNRJVdWrpfEwqtVcXWHZ7pCm4DTkxd+6gAEJ/wCMp/fzxRWZsqUJypuZ3raEvyKcyuQCoAk6QVbeux+qsVk3FURqypc8e0GxlriNX6RUskR6zSg60wXQ4eaVJbTrGld0bKJTsAdt8WZkKt5YzRlb?+lGX6RUIkSoBSeRJBCjpBBCU6iAk3Va1vligp9G/FJDiqiwr3uW6p1zWg6i4tRJPn1N8aCQI2SshMMw4zY9yhhLLSBdK3Sk6Ujzus29b4njirZS70bNpZjppcJMNpLUcR0BlCRYIRpFkgdrC2GlVWpLRN7Y7oIqSaDT01jle/pitCUWhZBdCBr0jsNV7Dyw1rJPIUR88FCsSo7pcQTfvhzKcOk28sDcvqKmleisEJZFiBikexSH5jfKNwe+APvJ/V++CuaVBKR33wB1o8sOYVoU0G1j5YkvvYDe6u2Kyy/VBsCodsSo1O7JIV2wWh83Z87NBmnxdDgzElggWNrYgSqolU0+Lvg9BqIIG+MlYZyfRN4r4I64QL1nyPXAyHPHZRxHMzZ3p2XkyHpM6NH5LJkOvyV6GI7Y6rcWdh1Fh1PYHAtLsRKywVzo8VvnSZCG2x1WtVh?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?/AAGzi9Cee4ZZhZVFqNMWr3dLhF1pPiUnY2PXUCNiDt0xdCnE6d7dO4xUeSsuszuKdbzWzEVHptIdVTIII2ddCQHVIvvoQSpA7X1AWCQMOlYC7FOWPXphN90FB33GEC+LA7DCT73gNjgUazuCv8874JcyxJGAUF/8+1++CJeOo74zjZls6lueE747gKHKJwylveDrjuA/+UoXxmtBseOLFsCZigV79MPVPAjrgTNdssX+mClRm7Kl4n1TLFHzmqPUac3CjPwR7y8tKS06VOaiopA63WST5i+KOzBVM25Y4iw5kfNsljKkZ6PJjU5CEoa93UAFBZAClgHUfET0Gwxe3H2IzBqOXM0qZQ8hKuQ+hVrKG4F79vH19MV9PksZrXT6g3R0N8wmM+3NjpUtLIChsLkJ8Srg33CfXEmt7GUizuHddZYzm0lElt1iqMAJUhYKdVrjpt0A+4xf7K08tO/bFGcNeFKafBpkl?+OmB+HKDqW2P43DuSrvuRew3+WLMqVeh0qOp2dN0csaVEEhOoDoLX3PkLn?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?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?/EDMkgLVl9iQdQN5igr4l9m/knv63xBHnQhNhuR0OEZtdjyK2mnyXz75LQ5JAt8QSRq+viB+V8NffGVuuoU6mzSikqvcAjrv?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?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?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?+2K4ezPJJFkhQUCb8wX+1/TDV6tyHBugX8tYvf74ZRSNvwXPRuPOY6K4Gqq01Vow2IdJQ8B6Oi5PzUFYtbKnFvIObVIixqqIExZA91nkNqUo9kLvoX8gb+mMbPVJSlAFKTfpqWPnv4v3w3VJJB/KFyPEFOJtY9gL+e3bEp/HhPa0xo8ko/p+g6ottim1/MYg/GTJ8/N3DyrUqlNqVU2kJlwAFBJL7R1pSCdgVWKQT01YzHk7jdn3JXKbp9U96htJGmHMVzWgkC1k3VqSLXFkkDbp2xe+TParyRWFNR8zRnKJKCgkuFYXHJvbZwdNx?/EEj1OOPk+NyQ62WjyRl2UJL4lqz7ldtiE6qDXG2VQXEuWCmpFrDUkjqCCLEbkYF/j1fy/DhvZhkMu1aS28ytxpGhvXfmNJt2Guw+uPvbly3lbJDcDjNwnlOR3a264msoYaC4RNgQ+o2s2pZNtrhShceL4s7f/AKh8vZrymujcQ3TSalHUFxqgUlbCnEjwqXpuUdbE2tvfEk2tso1l0bdq+bKfmegU6rxJTb7cpttwC+42Nxb5qGI3DrNPTMaksSEFLUkMatYuHL20/O5xmXLnFep0WltlAS9Rg0iVIltuF1plTi1WVcdEqt0FwLE4mdDpNEzJU15/pdTU8FnmBpDh0oeCSnWR0vY7G17HBck2Lias4W5xlKpb7SpAbdZeWlwI6E7Hob7b4d5xzE9KakMr5JUqNYK5SQq3MF9wB6YzZl?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?/IKgFEnSo3KSR0B64eGmBhfhzDkVWq1OLHkIacHLeC1J1CyVi?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?/wgpF/wCDskW9L+XX5dXQuOpwocBUi1/d39uYoJSPh8kjfvvbyxrRgymTKK7OLSFNJBUS4o/CEXPl/wDg+WFedNF1BzSAbpJdIuLkXHyIGA34yhSHFpU6RoeWLR3zbxW/QL7J/wAdJwvJqay8W0cxatSxdMZ/VqIDg2079PnvfYWOFc/Jh975IJWG13H8NnTttcfW19vNI89/VPSUqIKt276gHTYd9/QX+1vI2HOVdpai40t4BegauS8keI3Qdk72XrG3ZVh3OPjVkobS8gSEt6UqF2Hja2xPwW8BNiegCrd8MnZgipx0EkWSUkC6nDuegFh?/Lyt17pc+VrQ2hxJCtgpThN9tr9L7Dv267nAtFTRqQgIeBU5YhEd3UBboPBud7joNNwbm2PF1UNJVzi4bN6iksPeIXA28HRRsB6JIPY41owQflyUo5pLelAJ/zmwIANibWtum9+6jhm6+8hWglKbKG3OuSApIvb?/dJ+ZOGy6g6FpUHHVcpekn3Z0agm6lkeHbxWHT/HCCZrYQ2oKeBWWdREd2xJJVcbdxvbf69cazBOkZ3zNlhJTRKi8w0pP5rBPMYdugjxNquk3tbpf5Y+i0b2ec2VxNQ4h8NI8GZpWHZNMLqYTt7eJyKkgbA31AHztfpHHqkkITu8SlDalAtueIJURfYeROGDs5TfhLb6SjUm?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?+OPe2v9YcU/LqbwL60PrABWpNlG2y2VD9lK+mJjdv8As/c4waB8meEsLsT8JxFPe1kOEJXqUHQT8w7/APdhpnDPdEydDalVhTqveFltpppGtSz322AAuOpHXD/LOYKJmSmM1akhKmHbgakWUCDYgjsRiDZQIMS1mQUpSuxWs?/s9b/6k4jFcjVF3MjMz8XmqhKVrMYyF8oKv+m9sTdiW02dWlJPyGDmTKFm7OdTcp2XsrSKlICwENQ45dUB+pZAsi/qbDzwVaYG6M4ZpybU6xmGUiPTCVSEJ5L90ttILYJsSogdP54lNG4XyqtW6NEqzkIRacw2mUYwQsrDbaElC3R8V7ADrYKNvTX0rhVlvh5HTU+O/Emi5RSE600mIoVCquC1yOS1cNnoASSPPENrXtQZEyYRH4HcI4DzjZuK5m5QmSzb4VtsJHKYN9+qr26Ytxx3dCSkyu8zeyzxQ4iMsnI+WRS8u+7BUqp1F9NOpqQSohSnF2Cth/AlR9MWI/T/Z14fNJj1bMFU4gVVlNlQqESxTkqHUGUsanBtsW0jv0xTueeLnEribUDUs85kl1lQF0IfmKSy3e2yGkpDbYt?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?/iFWbbcBampBkhNm5LmxGxSn8snUb7kX6nywQBEOBuKOdMiBtRWyFpYWFatylIG51WBPSwtbDj8wEFL0Al3QsIDLgGrZLoG26QfEokAk?/S4xEyrxitYj1FRbfSkWluG2oj8seD4ioova99+m1l0zasy0pKostAiuBKwZjo1II8LafARe5TsCb7nw9MYw8KmgwtLlRhJCgtZPJcvy1i/NG36gQDa1rHzsqvmtFxbkqCEkhS0lLibOK0hCe9go/uQNwrDeTNrcdTutirOGMtXOtNdOtJKtLd9Fjp1JPc3sdu3zlRrsZ13mtVdxMdJkPn3t48wm9rHlkBQCem52FyQN8YUcCUL5QqEEqJS0FBt4K5ihdXb4gncdCRsMObR7pQahBAcfIFo7hGhItdNht4hunte9/McahXY8orkM1crhsF19JlPICwbjXs3a/gXsNzqNjY7qOVKuRlBpyPWkLjRFLcWZbyeoPjsG9j4T4d7lR3wLC1QqbKiIcXOgJS9GeXdLTvklQOrT03+LoNvPDqWWEOKAqVN1oW26UlhwDSfDc?+Gw728z0thomXmBCVxX49bGmGekty1rHxj8oiw38IFt/LHi5lcdUtCI1cK3ogKbzXVWAHxgls/rTZKvvY4D2a9C3uyVI5bs6Egaiz4GXStvVblmwRcqvYC3QK79ccpZKrMiTTVOrFwyEOW5wB1Ng6b2+I9977YRNSzC/ZtiFWguXHDrCve3SUEf9IfyzqPiT4euw+ePlTK3M5Jix6ulclsyGAJrt0hOx6tDqFdDuN7E9inoB2qM2hIbNRh6dBspLKyVoOkqcvp6psR8tjvji8NTay9Op40gyCeSoJQncIV8AulVj02B6Xscctz6s6rXHj1sF9XMQoy3NKUjTqQAW9lEXNjZW5JvawbJqFTUti0arASNmwqW6bISRqaUC0L9FbWFrd7YOQaFJbTJCmPeYaF6EXStlQOtZB0m6Otha/e/fHDrMb3kJ98hAKkFABZVc6RayRo337bdLp7jCH4lU3w2r3KeCX1eH3x0gpF7tqBbG4sry6Dqd8INT6o4W9TVSds8pKryVhSldNJ/K+IbWO3wjrgWaqOFIZkoS2h+IousOJCghfxBR2+AbD7+Y62bOojEKcD8QoQlEi?/LXa1tKxcJt06m/Xyx8JlSAZd93qKgXFNEOSli5I2Crtix67kp6dDcYbCdUbMqEaoKSFLaUpUhzvcgH8sAG9je1jfCmo95EZY0IeiaWwELVoUSEk3Sq2j1/n1wk6mKm6XlxyRYkcskhR/hPg7+XX0wgZ046dbFRukLZWeerw2Ph/wCjsL2SLnY+WElTZI1qdZnHYJcu+rwqF7E3b2389vIYxqY6ad93cbkQZbbL7Kgth1oaVoWn9JCbhSNrEb?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?/vGDv46r9K/+HByNQKzhk6m50ZYjTS6h1tVmVtC6rq2t63NtsW/wn9k3iCzlpt1+IxlWgMkuv1bMchMNAB3UrSfGe1tgPXDWX7VGRuHUdUP2buE1Jy9IAKTX6yyJlQWOl0JuQg?/7yk79MUXnjiPxI4mzkVLPvECrVh0KJSiSpPLavf4GwAhseiQMN/OMe3YrlZpqpZr9lPhEvlh2tcX66wbKbiKFPpDTgOwU4ohbgv3TrSQNwOmIPnf2zeMeYIC8tZWZg5Ey/uBTMsNohlSTsNTwVzDt6gHrbpjO+l9FyKi8LnY8pG3yFsfJbctYz3BsCRy0de5Vt+2+NrwELuP890vuUptbrp5i3CUEqUd?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?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?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?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?/TkP/AIZMLr50toLjg+JVk3N9tjc7YdKogXLDKqRKIdC3XVKcXa?+pIA+Lf+I7eQxGmaXS3Zb0P8ObSW2bu2JABUpVgN/7Ix4xEpEqLIKaYjSy494VG9gkAKI36+H+X1bINB9NCUliVITRn1ojqWEDnKF7HxW38wRv2w6k5cWBHUijOKdfVpfAcULXQpRJ36XFsRVbFLRCjyzBCnHFNBKv4klRSBv3AwsqnUZh9qMinI/rCHFE9ilKk9fkd8ByoxIlZVSqS9FNKdU21pWn8xdiVEg238kg/XCSKApUNM9dGd1lzlrBeVqUkrCTvfyxH26fRH3nY/4e1oaSgpNjpGoHok?/7I++ORHp4gO1L8PbDrHM+Z5ZI6/Qj5Y2fkFP2SVeWUiYmOuhuCOttxQ8RNiCLfUg9f7Jwi3lxbiJqk0ch5u7baS6QSAkFI3PcKA9cR92FSkNslNMYCXnW2ikDY3v1+qR9zj0wKSiYiAuntFCm3Fo8PTSd9vseuNnqzb9kidy5D5ERz8GUppawVAObjUm?+rr52T6XwmctpExxldJCghtBS4VEHSU2I69R0+R9b4jpYpLjjzfuDSH0qDRUUXslSRbe/9q2EQ1R0UpU5dLb0tnUtIF72JJ26XG9sZyDvyHDQ2kxG310O/LWS83zBsE2SSBffoT8hfr07dobDUoJFK1JcGlLqj4rgW0gavIgjcbJUO+A7kOmj3fm02OWnQlDelGySoEjr0Fyf+ThN6HTkVD3UwWS4tovhYG6kklKr+twevY4Ry/BvF2ElUNLiFoTRk3aNy2T4V+RBv3Fx89scmksAokKo6CFpsVg?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?/D/wA/PD//ACht/wDZUf8AmK/+3C4sx//Z" width="305px" alt="decision"> Our level of intelligence units us other than other dwelling beings and is crucial to the human experience. Some experts outline intelligence as the flexibility to adapt, clear up problems, plan, improvise in new situations, and learn new things. Knowledge engineering is a field of artificial intelligence that enables a system or machine to imitate the thought strategy of a human skilled. AI is used extensively across a variety of purposes at present, with varying levels of sophistication. Recommendation algorithms that counsel what you may like next are well-liked AI implementations, as are chatbots that seem on websites or in the type of good audio system (e.g., Alexa or Siri). Researchers have discovered it's not always possible to define "fairness" in a method that satisfies all stakeholders. However, this tends to offer naïve users an unrealistic conception of how intelligent present computer brokers actually are. Moderate successes related to affective computing embrace textual sentiment evaluation and, extra recently, multimodal sentiment analysis, whereby AI classifies the impacts displayed by a videotaped subject.

h2>Existentiell Danger Och Farhågor[redigera </h2> At its simplest kind, synthetic intelligence is a subject, which combines laptop science and robust datasets, to enable problem-solving. It additionally encompasses sub-fields of machine studying and deep studying, that are incessantly talked about along side artificial intelligence. These disciplines are comprised of AI algorithms which search to create professional systems which make predictions or classifications based on enter data. No established unifying principle or paradigm has guided AI research for most of its historical past. The unprecedented success of statistical machine studying within the 2010s eclipsed all other approaches (so much in order that some sources, especially within the enterprise world, use the term "synthetic intelligence" to mean "machine studying with neural networks"). Critics argue that these questions could need to be revisited by future generations of AI researchers.

div style="text-align:center"> <iframe width="569" height="317" src="https://www.youtube.com/embed/nnboHTfYsfk" frameborder="0" alt="AI" allowfullscreen></iframe></div> During a demo with TechCrunch?, a company spokesperson outlined how the retail big is experimenting with generative AI to assist consumers in all stages of the shopping experience, from the search and discovery part to creating a buy order. Three options coming down the pipeline embrace a purchasing assistant, generative AI-powered search and an inside design characteristic. Our mission is to make sure that synthetic general intelligence?AI techniques which are typically smarter than humans?benefits all of humanity. "Rational agent" is common time period utilized in economics, philosophy and theoretical artificial intelligence. It can discuss with anything that directs its habits to perform goals, similar to an individual, an animal, a company, a nation, or, within the case of AI, a computer program.

h3>How To Use Bing Picture Creator: Comprehensive Guides (</h3> The full scope of that influence, though, remains to be unknown?as are the dangers. But there are some questions we are able to answer?like how generative-AI models are built, what kinds of problems they are finest suited to resolve, and the way they fit into the broader category of AI and machine studying. Machine learning fashions are designed to make "predictions" that are only legitimate if we assume that the future will resemble the previous. If they're trained on knowledge that features the outcomes of racist choices prior to now, machine learning models must predict that racist decisions will be made in the future. Unfortunately, if an purposes then uses these predictions as suggestions, a few of these "recommendations" will probably be racist. Thus, machine studying just isn't nicely suited to help make selections in areas the place there could be hope that the lengthy run will be better than the past.

ul> <li>AdSense? makes use of a Bayesian community with over 300 million edges to be taught which adverts to serve.</li> <li>The new tools come on the heels of Walmart rolling out its AI app, “My Assistant,” to 50,000 corporate staff in the U.S. to streamline duties like summarizing paperwork, helping prep for meetings and rushing up projects.</li> <li>Philosopher Nick Bostrom argued that if one gives nearly any goal to a sufficiently powerful AI, it may select to destroy humanity to attain it .</li> <li>If a biased algorithm is used to make decisions that can critically hurt people then the algorithm could trigger discrimination.Fairness in machine studying is the research of the method to forestall the harm attributable to algorithmic bias.</li> <li>In 2016, issues of equity and the misuse of expertise had been catapulted into heart stage at machine learning conferences, publications vastly increased, funding became available, and heaps of researchers re-focussed their careers on these issues.</li></ul> The decision tree is the simplest and most generally used symbolic machine learning algorithm. It should select an action by making a probabilistic guess and then reassess the state of affairs to see if the action worked.In some problems, the agent's preferences could also be uncertain, particularly if there are other brokers or people concerned. An ontology represents data as a set of ideas inside a domain and the relationships between those ideas.Knowledge illustration and knowledge engineering allow AI applications to reply questions intelligently and make deductions about real-world details. https://bethelnet.io/ are utilized in content-based indexing and retrieval, scene interpretation, medical decision help, knowledge discovery (mining "fascinating" and actionable inferences from massive databases), and other areas.

h2>Machine Consciousness, Sentience And Mind</h2> The subject went by way of multiple cycles of optimism adopted by disappointment and lack of funding, but after 2012, when deep learning surpassed all earlier AI methods, there was a vast increase in funding and curiosity. These are just a few examples of firms leading the AI race, however there are many others worldwide which might be additionally making strides into synthetic intelligence, includingBaidu, Alibaba, Cruise, Lenovo, Tesla, and more. Each one is programmed to recognize a special form or colour within the puzzle pieces. In the coaching course of, LLMs process billions of words and phrases to learn patterns and relationships between them, making the models able to generate human-like solutions to prompts. In reinforcement studying, the system attempts to maximize a reward based on its enter data, mainly going by way of a process of trial and error until it arrives at the best possible consequence. In April 2023, it was reported that 70% of the jobs for Chinese online game illlustrators had been eliminated by generative synthetic intelligence. Neural networks and statistical classifiers , additionally use a type of local search, the place the "landscape" to be searched is shaped by learning. Distributed search processes can coordinate by way of swarm intelligence algorithms. Two popular swarm algorithms used in search are particle swarm optimization and ant colony optimization . State house search searches via a tree of possible states to try to discover a objective state.For example, Planning algorithms search via trees of goals and subgoals, searching for a path to a target aim, a process called means-ends evaluation. Computational learning concept can assess learners by computational complexity, by sample complexity , or by other notions of optimization.


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Last-modified: 2023-10-12 (木) 17:57:32 (209d)