一段39秒的黑白视频片段，这两天在X上爆火。 画面中，马斯克与印度金融科技巨头Zerodha的创始人尼基尔·卡马斯（Nikhil Kamath）相对而坐。 两人手中握着SpaceX的马克杯，眼神交汇，然后爆发出一阵几乎同步的笑声。 如果是五年前，这只是一次普通的商业会晤预告。 马斯克的一段真实访谈视频在全网引发AI伪造的质疑，这荒诞一幕恰恰印证了他关于「人类逐渐NPC化」的激进预言。在《GTA6》等游戏角 色即将拥有「意志」的同时，现实中的人类却困在算法与思维定势的「对话树」中。这场真假难辨的闹剧迫使我们重新审视那个细思极恐的命 题：在这个「死互联网」时代，我们是否真的活在一个巨大的模拟游戏中？ 但在今天，当这段视频在卡马斯的播客频道「WTF is?」放出时，互联网的第一反应却极其整齐划一且令人背脊发凉：「这是AI生成的吗？」 | Starcommander + . 2 @Starcommander10 . 11月28日 | | | | | | | --- | --- | --- | --- | --- | --- | | ls this Al? 这是AI吗? | | | | | | | 07 | 17 | ...

Core Viewpoint - The article discusses the evolution and challenges of artificial intelligence (AI) in gaming and virtual environments, emphasizing the importance of interactive learning experiences over traditional pre-training methods. It critiques the limitations of large language models (LLMs) and highlights the need for more effective learning frameworks in AI development [16][18][19]. Group 1: Background and Development - Id Software, founded in the 1990s, played a significant role in the development of iconic games that contributed to GPU advancements and the modern AI landscape [3]. - The author has extensive experience in various tech companies, including Armadillo Aerospace and Oculus, focusing on the development of virtual reality technologies [6][8]. Group 2: Learning and AI Models - The article critiques the effectiveness of LLMs, arguing that many people do not fully understand their limitations, particularly in learning from new environments [16]. - It emphasizes the importance of interactive learning, suggesting that AI should learn through experiences similar to how humans and animals do, rather than relying solely on pre-trained models [16][18]. Group 3: Gaming and AI Interaction - The author notes that traditional gaming AI often relies on internal game structures, which can lead to cheating, while cloud gaming could mitigate this issue [18]. - The article discusses the limitations of current AI models in learning from games, highlighting that significant amounts of experience (e.g., 200 million frames) are required to reach human-level performance [20][34]. Group 4: Challenges in AI Learning - The article identifies ongoing challenges in continuous, efficient, and lifelong learning within AI, which are tasks that even simple animals can accomplish easily [20]. - It points out that many AI systems struggle with learning in complex environments, and traditional reinforcement learning frameworks may not be suitable for all scenarios [30][32]. Group 5: Future Directions - The author proposes a mixed approach to learning environments, combining passive and interactive content to enhance AI learning capabilities [22]. - The article suggests that new benchmarks should be established to evaluate AI performance across various games, focusing on long-term learning and retention of skills [95][97].