This distinction between ambiguous and incidental friction is brilliant. Was just talking about it with my wife because your specific example of a map has been a running debate between us. I just pick the least cost path via the algorithm to the point where I have poor spatial awareness. My father in law insists on using paper maps because he wants to know where everything is to be helpful. For me, the ease of the algorithm solves incidental friction. For him, it eliminates helpful ambiguous friction by denying him the opportunity to learn.
The wrinkle that needs to be added is that there are no truly universal rules as to what counts for incidental vs ambiguous friction - the definitions are relative to individual/project goals. I am working with some scientific instruments to map out chemical data, and 3d modeling is needed. I don’t particularly care about 3d modeling - it is incidental to me. The chemistry is the focal point. So the STL files are vibe coded so I can keep my focus on chemistry. But if I were working for the latest marvel movie, the reverse would be true. The actual chemistry would need to fit the script and the visual effect intended. To a scientist the visualization just needs to be good enough. To the film director, the world building physics and chemistry instead become the supporting actor.
The challenge introduced by AI is that in ruthlessly eliminating incidental friction, you are being deliberate about what you choose not to learn. This is fine at a task level - but how many of us “found” our current expertise through incidental friction in the first place? I never wanted to do chemistry, I went to school for something else. But incidental friction led to discovery. That is my biggest worry, particularly for students and early career folks.
In the past I found that I had poor spacial awareness - a few months ago I started using navigation apps solely in the “north up” orientation. this is much less intuitive when navigating, but forces me to think spatially about where i am and I’ve found has helped me retain much more spatial context about my environment.
I’m much more able to navigate without the map than I was before I started this experiment - and it’s had the added benefit of giving me the ability to know what part of the 101 I’m on solely by the angle and shape of the nearest exit.
I hope eventually AI gets to a point where I can split the difference between ambiguous and incidental friction like this, although tbh I’m not really sure what that would look like.
I was hunting for a way in to side with your father in law. But you landed the point: friction is relative.
Digital maps clearly solve the end-goal needs for most people. But like your father in law, there’s definitely a loss in that exchange.
Bearings are incredibly useful. I remember navigating myself and my partner out of a small town on vacation by the position of the sun. It was international so we didnt have internet at the time. Im never going to live that one down!
> That is my biggest worry, particularly for students and early career folks
Agree, and I have the same worry, and so did the OP to the point that he considers canceling his AI subscription. Most of have to first get through the incidental friction to be able to arrive at expertise to address ambiguity.
“Chemical analysis was performed using a Thermo Scientific Niton XLt-900 XRF Analyzer equipped with a GOLDD X-ray fluorescence detector and real-time digital signal processing at a speed of 80 MHz. The XRF measurements were taken in the laboratory, using a Thermo Scientific SmartStand, ensuring consistent positioning. Since the Niton pXRF is programmed to utilize predefined matrices provided by the manufacturer, the “General Mining” matrix, which includes the relevant metallic and non-metallic elements, was selected for the current research. Calibration was performed using an internal calibration system defined by the manufacturer and validated using a known standard (SRM 2780).”
Translation: “We used a closed-source proprietary method by a manufacturer and validated it on a sample the manufacturer recommended.”
XRF generates spectra which represent atomic abundance (eg K-, L-, and M-line emissions). However, these are not international standard units. Laboratory units translate from spectra to mass fraction through meticulous preparation of standards. Handheld units often use a secret sauce algorithm to do this to keep analysis non destructive. I’m skeptical of the results - but what’s more important is that these results are not fully reproducible. If the company goes belly up, or less dramatically changes/updates the proprietary algorithms used to create mass fraction estimates, the reported results of this study won’t be replicable even with the same samples.
Not billions, but I was in a similar position. What saved me was the GPL license on the open source code and hiring a lawyer that kept my ownership of any software I wrote (though at a reduced hourly for them) and patenting new ideas connected to the project. When it came time for the company to scale up, I couldn’t have been in a better position.
The fact that so much of Çatalhöyük remains unexcavated is one of the reasons I am skeptical of so much speculation on its social structure. A palace could be under the vast majority that has yet to experience a trowel or brush. It, and sites like it, are so early that it is helpful - though extremely difficult - to forget the 9,000 years of future you know will happen to be able judge it on its own terms.
Thanks! First thank you for all the work to develop a math LLM. Those prompts are helpful. I shrank the weights down to f16 to fit on my system from f32 using the procedure at ggerganov/llama.cpp, then tried some of those prompts. From line 34:
> Problem:
> If $\det \mathbf{A} = 2$ and $\det \mathbf{B} = 12,$ then find $\det (\mathbf{A \mathbf{B}).$
> Solution:
> We know that for a matrix \mathbf{M}, the determinant of its inverse is given by $\frac{1}{\det \mathbf{M}}.$ We also know that the determinant of a product of matrices is equal to the product of their respective determinants. Therefore, we have:
\begin{align} \det (\mathbf{A}^{-1}\mathbf{B}) &= \frac{\det(\mathbf{AB})}{\det\mathbf{A}}\\ &= \frac{\det\mathbf{A}}{\det\mathbf{B}} \end{align}
> Therefore, we have:
> $\det(\mathbf{AB}) = 24
The f32 answer:
> We have that $\det (\mathbf{A} \mathbf{B}) = (\det \mathbf{A})(\det \mathbf{B}) = (2)(12) = \boxed{24}.$
> Final Answer: The final answer is $24$. I hope it is correct.
Final answer is the same, which is encouraging for quantization to expand hardware options.
can you please ask it if algebraic numbers are closed under addition? chatgpt4 just told me no, and then i asked it again and it said yes, and then it apologized profusely for saying no.
I assume that this primarily benefits games and not any deep learning right? The most attractive aspect of Mac M1 is the huge memory boost. Might not be great for training due to the inability to distribute across multiple cards, but it makes for a great inference engine for stable diffusion, llama, and other large models.
There are two modern cross-platform GPGPU standards that Apple Silicon can theoretically use or implement - SYCL and Vulkan Compute.
SYCL is Khronos Group's vendor-neutral, high-level programming framework. Application support is limited, but hopefully with Intel's backing, the situation would gradually improve. Meanwhile, Vulkan Compute sidesteps the entire headache with compute shaders. But I'm not familiar with it in terms of application support.
SYCL can be implemented on top of OpenCL and OpenCL's SPIR-V extension. It soon turned out that this route is unfeasible due to prevalent vendor lock-in that's not going to change anytime soon, so it has largely been abandoned by everyone else but Intel and Mesa. Right now SYCL is usually implemented by backends to GPU vendor's respective APIs, like ROCm, HIP or CUDA. Doing the same for Metal would be very challenging.
Mesa already has experimental support of OpenCL w/ SPIR-V on Intel and AMDGPU, so theoretically it can be extended to Apple Silicon. Difficulty of implementing OpenCL's SPIR-V extension should be comparable with Vulkan compute shader (which also uses SPIR-V). However, currently OpenCL on Apple Silicon is entirely unsupported. The last time I checked, it's on the roadmap.
The only problem with cross-platform standards is they are never performance portable unless they're so high level someone already their primitives have already implemented the algorithm X different ways for you already.
For any low level performance programming you need to code to the specific microarchitecture, so the pros of a single programming language/library are limited (you're not getting any code reuse that isn't available in the top level non-hardware C code anyway) and often outweighed by the ability to take advantage of the vendor's dedicated extensions provided by their preferred programming mechanism.
This issue was well modeled by OpenCL, which never really caught on for programming Nvidia GPUs for this reason.
Correct. You need CUDA, or ROCm, MPS (native to macOS) backends for running deep learning. I found it relatively easy to train some Pytorch model on beefy server with CUDA and running interference on my Macbook Air.
The compute shader portion is a good step but it's still not going to provide the interfaces most of these deep learning tools expect.
That said eiln wrote an ANE (Apple Neural Engine) driver which enables using the dedicated hardware for this instead of the GPU. It is set to be merged into linux-asahi in the future.
> They found that the model relied on the style of language used in papers to judge if a study was replicable.
I think the failure here is to train the model on published results. I’ve worked with scientists who could write and infer a marvelous amount of information from shit data. And I’ve worked with scientists who poorly described ingenious methods with quality data. The current academic system incentivizes sensationalization of incremental advances which confirm previously published work. I’m not in the least surprised at the manuscript level that replication would fail.
The proper way to do this would be to log experimental parameters in a systematic reporting method specific to each field. With standardized presentation of parameters I suspect replicability would improve. But this would present an near impossible degree of coordination between different research groups. But it would be feasible for the NIH or NSF to demand such standardized logging as a condition of grant awards of a certain size.
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