AI's roots go back to ancient myths, but it became a scientific field in the mid-20th century. Alan Turing's 1950 "Turing Test" and the 1956 Dartmouth Conference, led by John McCarthy, marked AI's official start. Early efforts focused on symbolic processing, with key developments like ELIZA and Shakey the Robot in the 1960s. The 1980s saw expert systems, but significant AI advancement came in the 21st century, driven by the concept of deep learning, wider accessibility and storage of digital data due to internet adoption, and increased computational power. As AI evolves, so does the need for ethical considerations—a topic we'll explore soon. (In the meantime, feel free to join AI Ethics & Safety in Autonomous Systems Forum)
Next week, we will explore neuroscience, the human quest to know ourselves.
For the people who are still hungry after eating the nugget, here are a few further reads related to AI.
- Turing Test (1950): Turing, A. M. (1950). Computing Machinery and Intelligence. Mind, 59(236), 433-460. This paper introduced the concept of the Turing Test, which is a criterion for determining whether a machine can exhibit intelligent behaviour equivalent to, or indistinguishable from, that of a human. (Many argue ChatGPT have surpassed Turing test )
- Dartmouth Conference (1956): McCarthy, J., Minsky, M. L., Rochester, N., & Shannon, C. E. (1956). A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence. This conference is widely considered the founding event of AI as a field.
- Early AI Developments (1960s): 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.
- Early AI Developments (1980s): Nilsson, N. J. (1984). Shakey the Robot. Technical Note 323, SRI International. Shakey was the first general-purpose mobile robot capable of making decisions.
- Rise of Expert Systems (1980s): Buchanan, B. G., & Shortliffe, E. H. (1984). Rule-Based Expert Systems: The MYCIN Experiments of the Stanford Heuristic Programming Project. Addison-Wesley.
- Advances in AI (21st century):LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep Learning. Nature, 521(7553), 436-444.
- Advances in AI (21st century): Silver, D., et al. (2016). Mastering the game of Go with deep neural networks and tree search. Nature, 529(7587), 484-489.
- Advances in AI (21st century): Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press. This book covers a wide range of deep-learning techniques and their applications.
- ChatGPT and other LLM foundational breakthrough: Vaswani (2017). Attention Is All You Need.
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7 个月It seems you're diving into the fascinating intersection of robotics and AI, exploring how knowledge nuggets can be leveraged in this dynamic field. The concept of distilling complex information into digestible "nuggets" is particularly relevant given the rapid evolution of both robotics and AI. Think about it just as ancient civilizations relied on oral traditions to pass down knowledge, today's advancements in these fields necessitate a similar approach to ensure widespread understanding and adoption. Historically, breakthroughs in technology have often been accompanied by periods of intense learning and adaptation. The Industrial Revolution, for example, saw a surge in technical manuals and training programs to equip workers with the skills needed to operate new machinery. How might this historical pattern inform our approach to knowledge sharing in the age of robotics and AI?