input: I just ate lunch.

output: I’m thrilled to share that I’ve just successfully completed my midday refueling session!

Nutrition is the ultimate foundation for high-performance leadership and sustained productivity. Taking this intentional break has allowed me to sharpen my focus and realign my goals for the afternoon sprint.

How are you optimizing your energy levels today to drive impact? Let’s connect and discuss the power of strategic self-care in the workplace.

#Leadership #Productivity #HighPerformance #WellnessAtWork #GrowthMindset

I do some tangent work from this field for applications in robotics, and I would consider (metric) depth estimation (and 3D reconstruction) starting to be solved only by 2025 thanks to a few select labs.

Car vision has some domain specificity (high similarity images from adjacent timestamps, relatively simpler priors, etc) that helps, indeed.

True for some approaches, but numerical analysis does account for machine epsilon and truncation errors.

I am aware that Inria works with Coq as your link shows. However, the link itself does not answer my question. As a concrete example, how would you prove an implementation of a Kalman filter is correct?

My question is the same, albeit more technically refined. How do you prove the correctness of a numerical algorithm (operating on a quantized continuum) using type-theoretic/category-theoretic tools like theorem provers like Coq? There are documented tragedies where numerical rounding error of the control logic of a missile costed lives. I have proved mathematical theorems before (Curry-Howard!) but they were mathematical object driven (e.g. sets, groups) not continuous numbers.

I wonder if AI's compute graph would benefit from a language-level rigor as of Coq.

This is incredible. It's far less than I would imagine. It represents how well optimized the warehouses are. If we roughly estimate a median product price to be $20, then the automation represents less than 2% cost saving. Of course, Amazon is at a scale that this is still net positive despite all the R&D cost. But if automation was to reduce the cost of living, there are probably better areas to focus on.

What's interesting is that action tokens are learned from video. In other words, the training dataset does not include actions like "go left" and "go right"; and these actions are learned from the pixels that moved. This means that learned actions may not map exactly to the game actions available to the user. That means we (humans) cannot necessarily use this world model to play the game.

I suspect the inferred actions probably directly correspond to human-understandable actions; and after playing with the action tokens, a reasonable human can probably guess what, say, the third action token in the dictionary corresponds to ("jump"). This is likely as game actions are sparse (in both time and action spaces) and often independent/orthogonal (in action space).

So, in 2015, 20% of 15-yo could not understand a simple text. Isn't that unbelievably high?

This take of designers being superior being to engineers is something I consistently observed among designers over the decade.

Here is a light-hearted video: https://www.youtube.com/watch?v=uvU5dmu4sl8

I want to add that, for many in the industry, it is well worth the price.

There are countries (including Ukraine) that produce on-board flight controllers, but the controllers themselves often rely on components from China. I actually do think it is feasible to create a passable quadrotor using non-Chinese components only but I do not know of a rigorous study or a manufacturer that does this.

If we don't learn from history, why do we do history? Is it a form of pure entertainment, i.e. of arts? If so, does that give more credence to White's argument?

I have had a PhD colleague who genuinely believed that history ought to be (in the philosophical normative sense) contributing to national propaganda, thus of national interest. By extension, this is why history departments should be funded by tax dollars.

Perhaps so. But not in the (quasi-)academic sense that the author is thinking. It's not the lack of an engineer's academic knowledge in history and philosophy that makes an AI system fail.

> Then there’s the newfound ability of non-technical people in the humanities to write their own code. This is a bigger deal than many in my field seem to recognize. I suspect this will change soon. The emerging generation of historians will simply take it for granted that they can create their own custom research and teaching tools and deploy them at will, more or less for free.

This is the lede buried deep inside the article. When the basic coding skill (or any skill) is commoditized, it's the people with complementary skills that benefit the most.