3.AI\u6a21\u578b\u201c\u66b4\u529b\u201d\u5730\u628a\u73b0\u5b9e\u4e16\u754c\u7684\u8868\u89c2<\/strong>\u884c\u4e3a\u7279\u5f81\u5b66\u4e60\u538b\u7f29\u8fdb\u4e86\u6a21\u578b\u3002\u67d0\u79cd\u610f\u4e49\u4e0a\uff0c\u6d77\u91cf\u201c\u8868\u89c2\u201d\u5373\u4e3a\u201c\u5185\u5728\u201d<\/strong>\uff1b<\/p>\n\n\n\n 4.AI\u5e76\u975e\u5168\u80fd\uff0c\u5927\u5343\u4e16\u754c\u4e2d\u5177\u6709\u8ba1\u7b97\u4e0d\u53ef\u7b80\u5316\u7279\u6027\uff08\u6216\u8ba1\u7b97\u4e0d\u53ef\u7ea6\u6027\uff0ccomputational irreducibility<\/a>\uff0c\u6570\u5b66\u5927\u795eStephen Wolfram\u63d0\u51fa\u7684\u6982\u5ff5\uff09\u7684\u73b0\u8c61\uff0c\u7528\u76ee\u524d\u7684AI\u53bb\u5904\u7406\u53ef\u80fd\u5c31\u610f\u4e49\u4e0d\u5927\u3002\u6362\u8a00\u4e4b\uff0c\u6ca1\u6709\u4e00\u4e01\u70b9\u513f\u5185\u5728\u7ed3\u6784\u5316\u7279\u5f81\u7684\u4e8b\u7269\uff0c\u53ef\u80fd\u5e76\u65e0\u5fc5\u8981\u53bb\u7a7f\u4e00\u4ef6AI\u5916\u8863<\/strong>\u3002\u5982\u679c\u975e\u8981\u7528AI\u5904\u7406\u8ba1\u7b97\u4e0d\u53ef\u7ea6\uff0c\u4e5f\u5c06\u662f\u5728\u9ed1\u76d2\u91cc\u62fc\u51d1\u548c\u5806\u780c\u8ba1\u7b97\u4e0d\u53ef\u7ea6\u7684\u7816\u5757\u3002\u4f46\u95ee\u9898\u662f\uff0c\u5927\u90e8\u5206\u60c5\u51b5\u4e0b\uff0c\u4eba\u7c7b\u5e76\u4e0d\u77e5\u9053\u773c\u524d\u7684\u4e8b\u7269\u662f\u5426\u5177\u5907\u7ed3\u6784\u5316\u7279\u5f81\u3002<\/p>\n\n\n\n 5.\u673a\u7406\/\u89c4\u5f8b\u7684\u201c\u534a\u7834\u89e3\u201d\u72b6\u6001\u5e76\u4e0d\u5f71\u54cdAI\u5728\u5de5\u7a0b\u4e0a\u7684\u5e94\u7528\u3002\u5de5\u7a0b\u610f\u4e49\u4e0a\u7684\u63a2\u7d22\u548c\u5e94\u7528\u5148\u884c\uff0c\u7406\u8bba\u6162\u6162\u8ddf\u4e0a\u3002\u5148\u201c\u5f62\u800c\u4e0b\u201d\uff0c\u518d\u201c\u5f62\u800c\u4e0a\u201d<\/strong>\u3002\u7406\u8bba\u5373\u4f7f\u8ddf\u4e0d\u4e0a\u4e5f\u4e0d\u6253\u7d27\uff0c\u5b9e\u7528\u4e3b\u4e49\u7684AI\u5de5\u7a0b\u5e94\u7528\u5df2\u7ecf\u5728\u521b\u9020\u4ef7\u503c\uff1b<\/p>\n\n\n\n 6.\u8ba9\u673a\u7406\/\u89c4\u5f8b\u83b7\u5f97\u5b8c\u5907\u7684\u6570\u5b66\u8bc1\u660e\uff0c\u4ece\u800c\u8ba9\u5176\u53d8\u5f97\u900f\u660e\uff0c\u4f9d\u7136\u662f\u79d1\u5b66\u5bb6\u7684\u8ffd\u6c42\u3002AI\u7684\u5e94\u7528\uff0c\u53cd\u8fc7\u6765\u5e94\u8be5\u4e5f\u80fd\u5bf9\u8fd9\u6837\u7684\u8ffd\u6c42\u63d0\u4f9b\u7ebf\u7d22<\/a>\uff1b<\/p>\n\n\n\n 7.AI\u5f53\u524d\u7684\u6f14\u8fdb\u9053\u8def\u548c\u672a\u6765\u7684\u6f14\u8fdb\u9053\u8def\uff0c\u53ef\u80fd\u5e76\u4e0d\u662f\u540c\u4e00\u6761\uff1b<\/p>\n\n\n\n 8.\u7231\u56e0\u65af\u5766\u5927\u8111\u91cc\u7684\u795e\u7ecf\u5143\u8fde\u63a5<\/a>\u548c\u7535\u5316\u5b66\u4fe1\u53f7<\/a>\u4f5c\u7528\uff0c\u4ea7\u751f\u51fa\u4e86\u8d28\u80fd\u65b9\u7a0b\u3001\u76f8\u5bf9\u8bba\u2026\u3002AI\u662f\u5426\u80fd\u591f\u6f14\u5316\u51fa\u7231\u56e0\u65af\u5766\u7684\u5927\u8111\uff1f\u4e0d\u5f97\u800c\u77e5\u3002\u4e5f\u8bb8\u4e0a\u5e1d\u77e5\u9053\u3002<\/p>\n\n\n\n \u778e\u7422\u78e8\u4e00\u901a\uff0c\u89c1\u7b11\u263a\ufe0f\u3002\u8bf7\u8def\u8fc7\u7684\u65b9\u5bb6\u670b\u53cb\u7559\u8a00\u6307\u6559\u3002<\/p>\n\n\n\n Some Random Thoughts on Artificial Intelligence (AI):<\/p>\n\n\n\n 1. At the current stage, the machine learning process is essentially a data fitting process, a super data fitting in a high-dimensional space.<\/p>\n\n\n\n 2. AI models can decipher the operational mechanisms\/patterns of certain phenomena. Once a model is established, predictions based on it are effective, indicating that the deciphering is successful<\/a>. However, this deciphering is “brute force” \u2014 even if the model lacks complete mathematical proof, it can still be refined using massive data and immense computational power. Thus, this deciphering is not a “full deciphering” but rather a “partial deciphering.”<\/p>\n\n\n\n 3.AI models “brutally” compress the apparent behavioral characteristics of the real world into the model. In a certain sense, the vast amount of “appearance” becomes the “essence.”<\/p>\n\n\n\n 4. AI is not omnipotent. Phenomena in the vast world that possess the characteristic of computational irreducibility<\/a><\/strong> (a concept introduced by the renowned mathematician Stephen Wolfram) may not be meaningfully addressed with current AI. In other words, for things that lack any intrinsic structured features, it might not be necessary to wrap them in an AI “shell.” If we insist on using AI to deal with computational irreducibility, it would be akin to assembling and stacking bricks of irreducibility inside a black box.. The problem is, however, that in most cases, humans do not know whether the things in front of them have structured characteristics.<\/p>\n\n\n\n 5. The “partial deciphering” state of mechanisms\/patterns does not affect the application of AI in engineering. Exploration and application in engineering take the lead, while theory gradually follows. Practical implementation comes first, followed by theoretical understanding. Even if theory cannot keep up, it does not matter, as pragmatic AI engineering applications are already creating value.<\/p>\n\n\n\n 6. Achieving complete mathematical proof of mechanisms\/patterns to make them transparent remains the pursuit of scientists. Conversely, the application of AI should also provide clues to such pursuits<\/a>.<\/p>\n\n\n\n 7. The current and future evolutionary paths of AI may not be the same.<\/p>\n\n\n\n 8. The neural connections<\/a> and electrochemical signals<\/a> in Einstein’s brain produced the mass-energy equation, relativity, etc. Can AI evolve to the level of Einstein’s brain? It is unknown. Maybe only God knows.<\/p>\n\n\n\n These are just some random musings, please feel free to leave your comments and suggestions. \u263a\ufe0f<\/p>\n\n\n\n \u5bf9\u4eba\u5de5\u667a\u80fdAI\u7684\u4e00\u4e9b\u80e1\u601d\u4e71\u60f3\uff1a 1.\u76ee\u524d\u9636\u6bb5\u7684\u673a\u5668\u5b66\u4e60\u8fc7\u7a0b\u662f\u4e00\u4e2a\u6570\u636e\u62df\u5408\u8fc7\u7a0b\uff0c\u662f\u8d85\u9ad8\u7ef4\u7a7a\u95f4\u7684\u8d85\u7ea7\u6570\u636e\u62df\u5408\uff1b 2 […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","ast-disable-related-posts":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"set","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center 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