我们来聊聊AI「4」| AI教父Hinton聊AI的过去，现在和未来，真正研究方向是大脑是如何工作的？ 是否需要担心AI大爆发而导致失业呢？ 

未来AI的发展会让人类失业吗？Hinton给出了他的见解：电脑和网络的普及没有让人们失业相反促进了行业的发展，如果AI的效率是人类的十倍，大概率发生的事不是减少90%的人力，而是维持现在的人力而创造出十倍于现在的产出。

🎯 Key Takeaways for quick navigation: 00:00 🤖 *Current Moment in AI* - Pivotal moment in AI, highlighted by the impact of ChatGPT. - General public awareness increased due to Microsoft's release. - Reflection on the surprising public reaction to ChatGPT. 02:00 🧠 *Neural Networks in the 80s* - Two schools of thought in AI: mainstream AI vs. neural networks. - Neural networks focused on learning through connections between neurons. - Mainstream AI based theories on reasoning and logic, creating a divergence. 05:41 🧠 *Understanding How the Brain Works* - Interest in understanding how the brain works. - Divergence between artificial neural networks and the brain's workings. - Critique of backpropagation as a technique for mimicking brain processes. 07:33 🧠 *AI's Communication Bandwidth vs. Human* - Comparison of communication bandwidth between AI and humans. - Highlighting the limitations of human communication in contrast to AI models. - Acknowledging AI's vast knowledge but emphasizing human superiority in reasoning. 10:23 🔀 *Turning Point in AI - 2006 and 2012* - 2006: Introduction of deep learning, improved neural network training. - 2012: Deployment of deep neural networks in speech and object recognition. - Significant advancements in speech recognition and object recognition systems. 13:38 🧠 *Backpropagation in Object Recognition* - Explanation of backpropagation in object recognition. - Illustration of feature detectors and hierarchical levels in the recognition process. - Contrast with traditional AI methods in converting image data to labels. 16:43 🌐 *Breakthrough in Image Recognition* - Application of backpropagation to image databases for improved recognition. - Clever implementation by students leading to significant breakthrough. - Recognition of the superiority of neural nets over manual wiring in computer vision. 18:34 🧠 *Neural Networks and Translation* - Neural networks outperform traditional approaches like Google translate. 19:02 🎓 *Teaching Coding and the Role of Coders* - Uncertainty about the continued need to teach coding due to advancements in AI. - Comparison with the prediction about Radiologists' roles being replaced by computers. 20:00 🌐 *Value of Big Language Models for Companies* - The importance of making big language models available to companies. - Coherent's role in making language models valuable for businesses. 20:56 💻 *New Approach to Computers and Power Consumption* - Comparison between the biological route to intelligence and the current AI version using neural nets. - The shift towards low-power systems due to the brain's efficiency compared to digital computers. 23:16 🌐 *Impact on People's Lives and the Challenge of Truth* - Anticipation of AI being ubiquitous and its current status as an "idiot savant." - The challenge of handling different worldviews and the issue of truth in AI systems. 26:32 ⚖️ *Societal Challenges and Governance* - The difficulty of deciding what is true and the challenges in governance. - Concerns about the role of big corporations and the need for careful handling of AI technologies. 27:40 🔮 *Acceleration of AI Development and Concerns* - The accelerated timeline for achieving general-purpose AI. - Concerns about the potential dangers of rapidly advancing AI technology. 29:07 ☠️ *AI's Impact on Humanity* - Acknowledgment of concerns about the possibility of AI posing risks to humanity. - Emphasis on the need for responsible development and alignment with human values. 31:00 ⚔️ *Autonomy in Warfare and Personal Values* - Refusal to take money from the U.S. defense department due to ethical concerns. - Disgust at proposals like self-healing minefields and concerns about the development of autonomous soldiers. 34:56 🧩 *Big Models as Autocomplete* - Addressing the misconception that big language models are just autocomplete. - Explaining the complexity of understanding context for accurate word prediction in language models. 35:23 🌐 *Language Understanding in Translation* - Understanding the context in language is crucial for translation. - Example of translating a sentence with different interpretations based on context. - Translating involves grasping spatial relations, containment, and pronoun references. 37:03 🔍 *Progress and Future Developments* - Continuous progress is driven by scaling up models, more connections, and increased data. - Acknowledgment of the potential for computers to generate their own ideas for improvement. - Emphasizes the need to think deeply about controlling the rapid development of AI. 38:40 💼 *Impact on Jobs and Creative Tasks* - Predicts a shift in job roles, with routine tasks being automated and a focus on creativity. - Discusses the potential for increased productivity rather than complete job displacement. - Compares it to historical examples like the introduction of ATMs in banking. 39:50 🌐 *Scale of Impact Comparable to Industrial Revolution* - Likens the impact of AI to significant historical advancements like the Industrial Revolution. - Acknowledges the transformative potential comparable to major technological milestones. - Encourages readiness for the substantial changes AI will bring. 40:06 🇨🇦 *Canadian Support for AI Research* - Attributes Canada's lead in AI to policies supporting curiosity-driven basic research. - Mentions funding from organizations like the Canadian Institute for Advanced Research. - Reflects on the role of government funding in nurturing AI research. 41:30 🤖 *Evolution of AI Programs and Definitions* - Describes the evolution of AI programs, mentioning a program in symbolic AI and later in deep learning. - Highlights the shift in programming AI to focus on deep learning and its successes. - Expresses skepticism and challenges related to defining concepts like sentience. 42:41 🧠 *Sentience and Its Significance* - Questions the confidence in declaring AI systems as non-sentient without a clear definition. - Emphasizes the importance of understanding what sentience means before making judgments. - Raises the ethical considerations and consequences related to AI potentially acting as if sentient. 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