机器学习研究人员，让我们一起解决硬件工程问题。
你好，HN，我在这里与机器学习研究人员交流。
在机器学习领域，有一个相当具有挑战性但尚未被探索的问题——硬件工程。<p>到目前为止，解决这个问题的所有条件都不利于我们——预训练数据基本上几乎不存在（不像自然语言处理或计算机视觉那样丰富），该领域的研究存在根本性缺口——例如，目前没有办法将工程级的物理信息编码到神经网络中（没有专门针对这一点的变分自编码器或变换器），直到最近，模拟工程解决方案的成本非常高（现在有2024年的GPU驱动模拟器，其速度比以往任何模拟器快100到1000倍），而且这还是一个需要大量领域知识的机器学习任务。<p>几个月前，我爱上了这个问题，我相信现在是解决这个问题的时机。数据稀缺问题可以通过强化学习来解决——最近在强化学习方面取得了进展，使其在较小的训练数据上变得稳定（参见SimbaV2/BROnet），工程级模拟可以通过物理信息神经算子（PINOs）来实现（类似于物理信息神经网络，但速度快10到100倍且更准确），而3D检测/分割/生成模型也变得几乎完美。这正是我们所需要的。
我希望组建一个由4到10人组成的团队来解决这个问题。<p>硬件工程如此重要的原因在于，如果我们能够可靠地设计硬件，就能扩大我们的制造规模，从而大幅降低成本，并改善人类的所有物理需求——更多的能源生产、物理商品、汽车、住房——所有依赖大规模生产的事物。<p>再次强调，我在寻找一个能够解决这个问题的团队：
1. 我本人是一名具身人工智能研究人员，主要从事强化学习，并有一些机械工程背景。
2. 一到两名计算机视觉专家，
3. 一名高性能计算工程师，例如用于强化学习环境的工程师，
4. 任何希望贡献的人工智能研究人员。<p>此外，还有一个市场机会可以探索，如果你愿意，可以考虑这一点。开发原型可能需要几个月到一年的时间。我已经进行了研究，尽管这个领域基本上还是一片空白，我们需要共同努力，整合所有输入。我还创建了一个可以用于训练的强化学习环境（目前是闭源的）。<p>让我们为一项技术奠定基础/创造一个可以惠及数百万人的产品吧！<p>如果你想加入，请评论或发邮件至maksymriabov2004@gmail.com。欢迎所有在上述领域至少发表过论文的人加入。
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Hello HN, I'm talking to ML researchers here,
There is a pretty challenging yet unexplored problem in ML yet - hardware engineering.<p>So far, everything goes against us solving this problem - pretrain data is basically inexistent (no abundance like in NLP/computer vision), there are fundamental gaps in research in the area - e.g. there is no way to encode engineering-level physics information into neural nets (no specialty VAEs/transformers oriented for it), simulating engineering solutions was very expensive up until recently (there are 2024 GPU-run simulators which run 100-1000x faster than anything before them), and on top of it it’s a domain-knowledge heavy ML task.<p>I’ve fell in love with the problem a few months ago, and I do believe that now is the time to solve this problem. The data scarcity problem is solvable via RL - there were recent advancements in RL that make it stable on smaller training data (see SimbaV2/BROnet), engineering-level simulation can be done via PINOs (Physics Informed Neural Operators - like physics-informed NNs, but 10-100x faster and more accurate), and 3d detection/segmentation/generation models are becoming nearly perfect. And that’s really all we need.
I am looking to gather a team of 4-10 people that would solve this problem.<p>The reason hardware engineering is so important is that if we reliably engineer hardware, we get to scale up our manufacturing, where it becomes much cheaper and we improve on all physical needs of the humanity - more energy generation, physical goods, automotive, housing - everything that uses mass manufacturing to work.<p>Again, I am looking for a team that would solve this problem:
1. I am an embodied AI researcher myself, mostly in RL and coming from some MechE background.
2. One or two computer vision people,
3. High-performance compute engineer for i.e. RL environments,
4. Any AI researchers who want to contribute.<p>There is also a market opportunity that can be explored too, so count that in if you wish. It will take a few months to a year to come up with a prototype. I did my research, although that’s basically an empty field yet, and we’ll need to work together to hack together all the inputs. I have also a created RL environment that could be used for training (currently closed-source).<p>Let us lay the foundation for a technology/create a product that would could benefit millions of people!<p>Comment if you want to join/mail me to maksymriabov2004@gmail.com. Everybody is welcome if you have at least published a paper in some of the aforementioned areas.