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A humanoid robot is fundamentally an embodied AI system. While motors, actuators, batteries, and sensors provide the body, neural intelligence provides the mind. Without neural computation, a humanoid is simply an advanced machine. With it, the robot becomes capable of adapting to unpredictable environments, understanding language, recognizing objects, planning tasks, and continuously improving through experience. This shift positions "Humanoid Neural" as a foundational concept within the robotics ecosystem. Neural Intelligence is the Core of Future Humanoids. Modern humanoids rely on neural-network-based systems to perform nearly every cognitive function. These include: Visual perception Speech recognition Language understanding Object identification Human pose estimation Motion planning Reinforcement learning Dexterous manipulation Long-term memory Decision making Navigation Emotional recognition Social interaction As robots become more capable, nearly every subsystem transitions from traditional programming toward learned neural models. This makes "neural" less of a niche AI term and more of an umbrella for robot intelligence. A Broad and Scalable Brand One of the strongest characteristics of is that it is not confined to a single product category. #Languageunderstanding# #neuralnetwork# #socialinteraction# #neuralntelligence# #smarthumanoids# #iq#

The future of humanoids isn't just mechanical—it's neural. A humanoid robot is an embodied AI system. Motors, sensors, and actuators provide the body, but neural intelligence provides the mind. Neural models power vision, speech, language, navigation, manipulation, planning, memory, decision-making, and continuous learning. As robotics evolves, nearly every cognitive subsystem is becoming neural-first. That's why represents more than a niche—it's a foundational concept for the next generation of intelligent robotics. #HumanoidRobots# #EmbodiedAI# #PhysicalAI# #NeuralNetworks# #ArtificialIntelligence# #Robotics# #MachineLearning# #DeepLearning# #RobotLearning# #ReinforcementLearning# #ComputerVision# #GenerativeAI# #AIResearch# #Automation# #FutureOfAI#

Cognitive reward shapes in sports and career Sports are amazing environments to learn. When you play a sport for thousands of hours, you start to see the world through that sport. It is a simple fact—your biological neural network is being conditioned to respond to the behavior incentivized by the rules of the sport. The funny thing is that most people choose their sports for accidental reasons such as parents, geography, or school programs. People rarely think about how the particular sport you play influences how your brain thinks more generally. Going a step further, playing the right sport may even benefit your career. My two favorite sports are tennis and soccer. Tennis is one of the best sports for teaching consistency. In tennis, there are hundreds of points in a match, and each point is worth exactly one unit, regardless of whether your opponent made an unforced error or if you constructed the most beautiful point ending with a winner. Tennis is low-variance optimization—you win by reducing unforced errors, playing percentages, and grinding out small advantages. Tennis is also an individual sport, which teaches you to rely on yourself consistently. Tennis has a similar cognitive reward shape to professions like being a surgeon or a pilot. Surgery and aviation require consistency, self-accountability, and deep focus. And similar to how you can only win one point at a time in tennis no matter how spectacular it was, there is no extra credit for the best appendectomy or the smoothest SFO-JFK flight. Your craft is to provide consistency with very low tolerance for error. On the other hand, the tennis mindset transfers relatively little to entrepreneurship. Entrepreneurship is a high-variance, team game where failure is tolerated and occasional creativity gets rewarded exponentially. Minimizing unforced errors in tennis is a totally different mindset from deciding whether to make a moonshot business move that will likely fail but could potentially net a billion dollars. Obviously I am not saying that tennis players cannot be great entrepreneurs, but I do think it is a totally different cognitive reward shape. Being a forward in soccer has a much closer reward shape for entrepreneurship. What a forward in soccer learns is to create many small chances. It is a fact that most of the game, you are not scoring—even if you look at all the times that Mbappe got on the ball in one of his best games, most of those led to nothing! But all that matters is creating enough chances to score once (or a few times) and win the game. If you break down a 90-minute game for a forward, almost all the time is failure or noise, a few minutes will be leverage, and a few seconds will determine the fate of the game. I have not played soccer for thousands of hours, but I can imagine that being a lifetime forward in soccer would teach you to be comfortable with failure and asymmetric returns. In summary, I am claiming that there can be substantial value when the cognitive reward shape of your sport mirrors that of your career. I’ll admit that I’ve done some cherry-picking for illustration purposes—entrepreneurship also requires consistency and error avoidance; and goalies in soccer have reward shapes that are very different from strikers. But I think the point stands. If sports shape how we perceive risk, effort, and reward, then we should choose them wisely.

The new Huberman Lab episode is out: Using AI to Increase Your Intelligence & Enrich Humanity | Dr. Fei-Fei Li (@drfeifei) 0:00 Fei-Fei Li 3:46 Vision & Intelligence; Human Vision & Contribution to AI 12:11 Computer Vision & the AI Revolution 18:34 Sponsors: Lingo & Wealthfront 21:19 Speech, Sound & AI Development 23:36 AI & Contextual Learning, Human Intelligence 33:43 Current AI Gaps, Emotion & Creativity 45:48 Computers Enhancing Humanity; Tool: Personal Agency & Learning about AI 53:04 Sponsors: AG1 & LMNT 55:37 Public Discourse about AI 57:34 AI to Enhance Scientific Discovery & Healthcare; Human Collaboration 1:07:38 Intuition, Motivation & Human States Beyond AI 1:19:18 Sponsor: David 1:20:37 Social & Ethical Considerations for AI 1:27:38 Kids, Development & AI Tools; Tool: Prompt AI Effectively 1:35:04 Next Frontier for Robotics & AI; Human Agency 1:43:52 Human-Centered AI Future 1:50:10 World Labs, Spatial Intelligence 1:54:12 Concerns about AI & Creativity; Movies, Art, Storytelling 1:59:51 Younger Generation & AI, Teachers 2:05:38 Zero-Cost Support, YouTube, Spotify & Apple Follow, Reviews & Feedback, Sponsors, Protocols Book, Social Media, Neural Network Newsletter Includes paid partnerships.

Backpropagation by hand ✍️ ~ 11 steps walkthrough below Backpropagation is the algorithm that actually trains a neural network, and it is where most people stop following along. It is not calculus you cannot do. It is matrix multiplication, working backward, one layer at a time. So I drew and calculated one entirely by hand. Goal: push the loss gradient back through a 3-layer network and land on a new value for every weight and bias. = 1. Given = A 3-layer perceptron, an input X, predictions Ypred = [0.5, 0.5, 0], and the truth Ytarget = [0, 1, 0]. = 2. Backprop gradient cells = Let us draw empty cells for every gradient we are about to compute. The shape of the answer comes first. = 3. Layer 3 softmax = We get dL/dz3 straight from Ypred minus Ytarget = [0.5, -0.5, 0]. No chain rule needed, and that shortcut is the whole reason softmax and cross-entropy are paired. = 4. Layer 3 weights and biases = Let us multiply dL/dz3 by [a2 | 1]. One multiplication gives the gradient for W3 and b3 together. = 5. Layer 2 activations = We multiply dL/dz3 by W3 to get dL/da2. The gradient moves back across a layer the same way the signal moved forward. = 6. Layer 2 ReLU = Let us pass it through the gate: keep the gradient where the activation was positive, zero it everywhere else. = 7. Layer 2 weights and biases = We multiply dL/dz2 by [a1 | 1]. The same figure as step 4, one layer up. = 8. Layer 1 activations = Let us multiply dL/dz2 by W2. = 9. Layer 1 ReLU = We apply the same gate again, now on a1. = 10. Layer 1 weights and biases = Let us multiply dL/dz1 by [x | 1], and every weight in the network now has a gradient. = 11. Update = We subtract, and the network has learned. In practice a learning rate scales this step. The gradients: dL/dz3 = [0.5, -0.5, 0] dL/da1 = [1, -2, 2, -1] dL/dz1 = [0, -2, 2, -1] The takeaway: matrix multiplication is all you need. Just like the forward pass, backpropagation is matrix multiplications end to end. You can do every one by hand, slowly and imperfectly, which is exactly why a GPU's ability to do them fast mattered so much to deep learning. 💾 Save this post!

Using Fable 5 to add fur and a new voice to my RL creature. The voice is based on the neural network activation (pictured top left). Made with @threejs @webgl_webgpu @runpod trained with JAX and MuJoCo from @GoogleDeepMind

一个中国 crypto trader，在 TikTok 上发了一段 neural network visualization 结果疑似不小心把系统正在 Polymarket 实时交易的画面露出来了 画面里全是蓝色连接线 hidden layers 纵向堆叠 neurons 在屏幕上不断触发 大多数人第一次看时，都忽略了中间一个很小的标签： “Bitcoin XVIII” 他把这条视频包装成一个普通 AI experiment 虚拟水族馆模拟 reinforcement learning “教神经网络学习生存行为。” 这是视频标题 但暂停在 0:16，细节就不对了 Profile： 模型似乎并不是在学习鱼的行为 hidden layer 里的标签，几乎和实时 Bitcoin prediction markets 对上了： price windows directional probabilities volatility ranges 这些信息被直接映射到 neural network 的 nodes 上，而所谓“模拟”还在后台继续运行 然后大家找到了这个 wallet 30 天 profit：$367,385 1,988 predictions 最大单笔 win：$183,000 几乎所有 active positions，都和 Bitcoin range markets 有关 entry price 集中在 94-98¢ 这正是自动化系统最喜欢 farm 的那类低波动 spreads： 赔率很高 空间很小 但可以持续重复 而且不需要人工一直盯着 1 小时内，评论区直接变成 detective board 有人把 TikTok 调到 0.25x 逐帧拼接 neural network 画面 然后把 hidden layer labels 和这个 Polymarket wallet 的 active positions 一一对比 时间点匹配得太精准 观众以为自己在看 AI visualization 但后台看起来更像是一个模型正在实时分类 market conditions，并根据 BTC 短线波动，把交易自动分配到不同 probability buckets 原 TikTok 只有 11,000 views。 但那条曝光 wallet 的 repost，一夜之间超过 600,000 views。 第二天早上，已经有人开始 clone 这个 interface，重建 network layout，并试图弄清楚： 为什么这个账户几乎所有 positions 都集中在 96-99¢，而且投入金额异常高。 最有意思的是： 原作者没有删除任何内容。 Wallet 也仍然 active。 问题是： 这类 Polymarket bot 的 edge，来自预测 BTC，还是来自把实时市场状态映射成可自动执行的概率分组？

GitHub 上极具知名度且极具价值的开源学习库，拥有超过 20 万颗星，它就是Build- your -own- X。 这个项目本质上是一个庞大的教程索引，汇集了互联网上优质的教程资源，指导小白如何不依赖现成的高级框架，亲手写出各种底层技术和流行工具的核心原型。研究好这个教程，你也可以当科学家啦，就是过程会比较痛苦。 1. 核心价值：打破封闭的技术栈，知其然，知其所以然 在日常开发中，我们习惯了调用现成的 API、使用高度封装的框架或直接集成第三方服务。这种方式效率极高，但也容易让技术栈变成一个个封闭的黑盒。 这个项目的价值在于，它强迫你深入底层。通过亲自用代码实现一个基础版本，你能真正搞懂底层的运行逻辑、数据结构和系统设计。这让个人开发者能跨越技术瓶颈、从API 调用者进阶为系统工程师。 2. 怎么用这个开源学习库？ 项目涵盖了数十个领域，从 3D 渲染器到操作系统，其中有几个模块尤其具有实战启发意义： Build your own Blockchain / Cryptocurrency这里汇集了使用 Go、Python、Rust 等语言从零实现简易区块链、P2P 节点通信、工作量证明或基础智能合约引擎的教程。 对于身处加密货币行业或需要深度理解链上逻辑、共识机制的开发者来说，亲手敲出一个最小可用模型，比阅读无数篇白皮书都要深刻。 Build your own Bot, Neural Network涵盖了各种自动化机器人以及基础 AI 模型、大语言模型架构的实现原理。 在当下利用 AI Agent 自动化处理内容工作流、或者编写高并发的数据监控及交互脚本时，理解这些底层的逻辑是如何运转的，能显著提升脚本的健壮性和执行效率。 Build your own Database, Network Stack教你如何手写一个简易版的 Redis 或 HTTP 服务器。在处理对延迟要求极高的场景时，深刻理解内存数据库的 I/O 模型和网络协议栈的底层逻辑，往往是优化性能的关键突破口。 对于初级开发者，带着目的去应用这个教程效率会比较高，比如，你想优化某个自动化脚本或研究某种代币的交互，就可以去看看这个开源项目相关的实现教程。