2026年1月7日，a16z（Andreessen Horowitz）联合创始人马克·安德森（Marc Andreessen）在自家播客The a16z Show上做了一场长达81分钟的深度对谈。 作为Netscape浏览器的发明者、硅谷最具影响力的投资人之一，安德森在这场AMA（有问必答）式访谈中罕见地系统阐述了他对AI产业的完整判断。 这不是一场常规的展望演讲。安德森从1943年的神经网络学术论文讲起，把AI的崛起放在80年技术演进史中审视；他详细评价了中国AI公司的突破，承 认DeepSeek和Kimi让硅谷"感到惊讶"；他预测GPU短缺将在十年内转变为产能过剩，AI的单位成本会"像石头一样直线下跌"。 最关键的判断是：这是他一生中见过最大的技术革命，比互联网还要大，可以与电力、蒸汽机相提并论。但我们才刚刚开始。 以下是这场访谈的核心内容编译。 原文链接：https://www.youtube.com/watch?v=xRh2sVcNXQ8 一、这是最大的技术革命，而我们只在第一局 主持人：Marc，我们现在处于AI革命的第几局（Inning）？你最兴奋的是什么？ 安德森：首先，我要说这是我有生以来最大 ...

忆往昔，符号AI曾以规则逻辑统领江湖；今朝卷土重来，它携手神经网络，直指AGI！ 但AI领域的权威们已经开始泼下一盆冷水： 真正的突破，恐怕要靠老牌选手「符号派AI」与神经网络联手登场。 这几年，大模型多次让人惊艳：聊天像真人、写作像专家、画画像大师，仿佛「万能AI」真的要来了。 只靠「神经网络」，远远不够通往人类级智能。 美国人工智能促进协会（AAAI）向会员发出提问： 绝大多数研究者给出的答案是——不行。 符号AI：起死回生 在历史上，符号派AI曾是主角——它相信，世界可以被规则、逻辑和清晰的概念关系穷尽刻画： 像数学那样精确，像流程图那样可追溯，像生物分类法那样层次分明。 后来，神经网络崛起，用「从数据中学习」的范式席卷整个领域。 大模型与ChatGPT成为这个时代的技术图腾，而符号系统被边缘化，几乎只剩下教科书上的一段历史。 然而，自2021年前后开始，「神经–符号融合」急速升温，被视为打破单一神经网络话语权的一次反扑： 未来，计算机能否达到、甚至超越人类智力？ 如果可以，单靠当下火爆的神经网络行不行？ 它试图把统计学习与显式推理拼接在一起，不仅为了追逐通用智能这一远目标，更为了在军事、医疗等高风险场 ...

Technology Enhancement - Tesla FSD (Full Self-Driving) v14.2 features an upgraded neural network vision encoder, utilizing higher resolution features [1] - The upgrade aims to improve handling of emergency vehicles, obstacles, and human gestures [1] Team Acknowledgment - The rapid pace of upgrades by the Tesla AI team is acknowledged [1]

Core Viewpoint - The current programming languages used in the AI field are fundamentally flawed, and a new unified language called Tensor Logic is proposed to bridge the gap between logic reasoning and neural computation [1][10][18]. Group 1: Critique of Current AI Programming Languages - Pedro Domingos criticizes existing AI programming languages, particularly Python, stating it was "never designed for AI" and lacks support for automated reasoning and knowledge acquisition [11][12]. - Other languages like LISP and Prolog, while enabling symbolic AI, suffer from scalability issues and lack learning support [15]. - The attempt to combine deep learning with symbolic AI in neural-symbolic AI is deemed a poor integration of both approaches [16][17]. Group 2: Introduction of Tensor Logic - Tensor Logic aims to provide a unified framework for expressing neural networks and symbolic reasoning, allowing learning, reasoning, and knowledge representation to unfold within the same mathematical framework [18][19]. - The equivalence between logical rules and tensor operations suggests that traditional symbolic reasoning can be transformed into tensor computations, eliminating the need for specialized logic engines [21]. Group 3: Implementation of Tensor Logic - Tensor Logic utilizes tensor equations to represent various AI methods, including neural networks, symbolic AI, kernel methods, and probabilistic graphical models [33][40]. - Each statement in Tensor Logic is a tensor equation, facilitating automatic differentiation and eliminating the distinction between program structure and model structure [28][25]. - The language allows for a continuous transition from precise reasoning to fuzzy analogy by adjusting the temperature parameter of activation functions, balancing logical reliability and neural network generalization [31].

Dropout Mechanism - During training, the average neuron input is significantly lower compared to inference, potentially causing numerical instability due to activation scale misalignment [1] - Dropout addresses this by multiplying inputs during training by a factor of 1/(1-p), where 'p' is the dropout rate [2] - For example, with a dropout rate of 50%, an input of 50 is scaled to 100 (50 / (1 - 0.5) = 100) [2] - This scaling ensures coherence between training and inference stages for the neural network [2] Training vs Inference - Consider a layer with 100 neurons, each with an activation value of 1, and a weight of 1 from each neuron to neuron 'A' in the next layer [2] - With a 50% dropout rate, approximately 50 neurons are active during training [2] - During inference, all 100 neurons are active since Dropout is not used [2]

Here's a hidden detail about Dropout that many people don't know.Assume that:- There are 100 neurons in a layer, and all activation values are 1.- The weight from 100 neurons to a neuron ‘A’ in the next layer is 1.- Dropout rate = 50%Computing the input of neuron ‘A’:- During training → Approx. 50 (since ~50% of values will be dropped).- During inference → 100 (since we don't use Dropout during inference).So essentially, during training, the average neuron input is significantly lower than that during infer ...

[Music] We used to think that the space is empty and silent. But the first American satellite discovered that our earth have two radiation bells full of high energy particles close to the speed of light and making the space very active and not only that later satellites discovered many other particles protons oxygens and today I'm going to take you on a journey to space and I'm going to show you what's out there how the space environment impact ours on earth and most of all how We use AI to make predictions ...

Neural Network Performance - Removing 74% of neurons from a neural network only decreased accuracy by 0.50% [1]

AI Model Architecture Discovery - The AI field is approaching an era where AI can discover new knowledge and apply it to itself, potentially leading to exponential innovation [1][3] - The current bottleneck in AI discovery is human innovation, limiting the scaling of AI advancements [2][3] - The "AlphaGo moment" for model architecture discovery involves AI self-play to hypothesize, code, test, and analyze new model architectures [3][12] - The key to this approach is AI's ability to learn without human input, discovering novel solutions unconstrained by human biases [8] ASI Arch System - The ASI Arch system uses a researcher, engineer, and analyst to autonomously propose, implement, test, and analyze new neural network architectures [13][14][15][16] - The system learns from past experiments and human literature to propose new architectures, selecting top performers as references [14] - The engineer component self-heals code to ensure new approaches are properly tested [15] - The analyst reviews results, learns insights, and maintains a memory of lessons learned for future generations of models [16] Experimental Results and Implications - The system ran 1,700 autonomous experiments over 20,000 GPU hours, resulting in 106 models that outperformed previous public models [17][18] - The potential for exponential improvement exists by increasing compute resources, such as scaling from 20,000 to 20 million GPU hours [19] - The self-improving AI system can be applied to other scientific fields like biology and medicine by increasing compute resources [20] - The open-sourced paper and code have significant implications, with multiple companies publishing similar self-improving AI papers [21]

Model Performance - GPT 4.5% is considered much smarter than previous versions, specifically 40 and 4.1% [1] - Despite its intelligence, GPT 4.5% is deemed not very useful due to being too slow and expensive [1] - Overparameterization caused GPT 4.5% to memorize data excessively during initial training, hindering generalization [2] Development Challenges - OpenAI encountered a bug within PyTorch during GPT 4.5%'s development, which they identified and fixed [2] - The bug fix on GitHub received positive reactions from approximately 20 OpenAI employees [3]