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කෘතිම බුද්ධි විප්ලවයට අප සූදානම්ද?

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කෘතිම බුද්ධි විප්ලවයට අප සූදානම්ද?

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Raamuwa
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283 095 просмотров · 3 года назад
🎯🎖 වසරක් තුල ඔබගේ මනස, අධ්‍යාපනය, ව්‍යාපාර, මනුෂ්‍ය සබඳතා, හා හැකියාවන් රැසක ධනාත්මක වෙනසක් දැකීමට කැමති අයට👇👇 "අතික්‍රමණ" - මා විසින් සිංහල භාෂාවෙන් පවත්වන විභව වර්ධන වැඩසටහනට ලියාපදිංචිය - https://student.intellectii.global/of... "Metamorphosis" - මා විසින් ඉංග්‍රීසි භාෂාවෙන් පවත්වන විභව වර්ධන වැඩසටහනට ලියාපදිංචිය - https://student.intellectii.global/of... My Corporate Training Programs - https://intellectii.global/training-p... New Website - https://intellectii.global මාගේ විභව වර්ධන වැඩසටහන් පිළිබඳ ජාත්‍යන්තර විද්වතුන්ගේ ඇගයීම් -    • රන්දික විජේසිංහ ගැන බිඳක්...   මගේ අනිත් Youtube නාලිකාව -    / @randikawijesinghe   🚀 -----------------------✴-------------------------- 🚀 Instagram -   / randikawijesingheofficial   Facebook -   / randikawijesingheofficial   Twitter -   / randikatweets   රාමුව ෆේස්බුක් පිටුව -   / raamuwaofficial   __________________ Resources McCulloch, W. S., & Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathematical Biophysics, 5(4), 115-133. Turing, A. M. (1950). Computing machinery and intelligence. Mind, 59(236), 433-460. Dartmouth Conference N/A (Conference Proposal) McCarthy, J. (1960). Recursive functions of symbolic expressions and their computation by machine, Part I. Communications of the ACM, 3(4), 184-195. Weizenbaum, J. (1966). ELIZA - A computer program for the study of natural language communication between man and machine. Communications of the ACM, 9(1), 36-45. Winograd, T. (1972). Understanding natural language. Cognitive Psychology, 3(1), 1-191. A., & Kanoui, H. (1972). Un système de communication homme-machine en français (in French). Technical report, Université Aix-Marseille II, France. J. (1973). Artificial Intelligence: A General Survey. Artificial Intelligence: A Paper Symposium. Science Research Council, United Kingdom. Moravec, H. P. (1980). Obstacle Avoidance and Navigation in the Real World by a Seeing Robot Rover. Technical report, Stanford University. Buchanan, B. G., & Shortliffe, E. H. (Eds.). (1984). Rule-Based Expert Systems: The MYCIN Experiments of the Stanford Heuristic Programming Project. Addison-Wesley. Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323(6088), 533-536. Campbell, M., Hoane, A. J., & Hsu, F. H. (2002). Deep Blue. Artificial Intelligence, 134(1-2), 57-83. Fujita, M., & Kitano, H. (1998). Development of an Autonomous Quadruped Robot for Robot Entertainment. Autonomous Robots, 5(1), 7-18. Thrun, S., Montemerlo, M., Dahlkamp, H., et al. (2006). Stanley: The robot that won the DARPA Grand Challenge. Journal of Field Robotics, 23(9), 661-692. ImageNet dataset Deng, J., Dong, W., Socher, Ferrucci, D., Brown, E., Chu-Carroll, J., et al. (2010). Building Watson: An Overview of the DeepQA Project. AI Magazine, 31(3), 59-79. Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet Classification with Deep Convolutional Neural Networks. NIPS 2012, 1097-1105. Goodfellow, I., Pouget-Abadie, J., Mirza, M., et al. (2014). Generative Adversarial Networks. NIPS 2014, 2672-2680. Silver, D., Huang, A., Maddison, C. J., et al. (2016). Mastering the Game of Go with Deep Neural Networks and Tree Search. Nature, 529(7587), 484-489. He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep Residual Learning for Image Recognition. CVPR 2016, 770-778. Van den Oord, A., Dieleman, S., Zen, H., et al. (2016). WaveNet: A Generative Model for Raw Audio. arXiv preprint arXiv:1609.03499. Wu, Y., Schuster, M., Chen, Z., et al. (2016). Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation. arXiv preprint arXiv:1609.08144. Sabour, S., Frosst, N., & Hinton, G. E. (2017). Dynamic Routing Between Capsules. NIPS 2017, 3856-3866. Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2018). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. arXiv preprint arXiv:1810.04805. Radford, A., Wu, J., Child, R., et al. (2019). Language Models are Unsupervised Multitask Learners. OpenAI https://www.microsoft.com/en-us/resea...   / stanford-u-googles-generative-agents-produ...   https://www.biorxiv.org/content/10.11... https://arxiv.org/pdf/2303.10130.pdf