mfn

I believe the key realization here is that we're applying a rotation matrix as part of the encoding. Why does this work? I've found that it's helpful to consider just the two dimensional case. Say we have two vectors. When designing a positional encoding scheme, what we're trying to do is somehow modify each vector so that it contains some information about its position.

The idea is that we can just rotate the vector by an amount proportional to its position - this has the property that the dot product of two vectors encoded this way only depends on the difference in positions, not the absolute positions themselves (since the dot product is based on angle between the vectors). And we care about the dot product, since that's what the attention operation ultimately applies to the vectors.

I've written up a _somewhat_ first principles derivation of this here: https://mfaizan.github.io/2023/04/02/sines.html, if interested!

Sinusoidal positional embeddings have always seemed a bit mysterious - even more so since papers don't tend to delve much into the intuition behind them. For example, from Vaswani et al., 2017:

That is, each dimension of the positional encoding corresponds to a sinusoid. The wavelengths form a geometric progression from 2π to 10000 · 2π. We chose this function because we hypothesized it would allow the model to easily learn to attend by relative positions, since for any fixed offset k, PE(pos+k) can be represented as a linear function of PE(pos).

Inspired largely by the RoFormer paper (https://arxiv.org/abs/2104.09864), I thought I'd write a post that dives a bit into how intuitive considerations around linearity and relative positions can lead to the idea of using sinusoidal functions to encode positions.

Would appreciate any thoughts or feedback!

(2/2) > I have no idea what's going on here. Why would you measure different points of the field with different coordinate systems and expect sensical results? I'm imagining a surveyor walking in a line starting from the origin: he takes a measurement at the origin, then at (1,0), then at (2,0), then at (3,0), etc. (Imagine that the underlying field is frozen in time so we aren't dealing with the Lagrangian yet.) Since we know the field equations we can predict what he'll measure at each of those points in the line.

But if the coordinate system changes with every step, he's still moving in a straight line as seen from a bird flying overhead, but at his first step he's at (1,0), then at the next step (2,0) turns into (5,1), then at the next step (6,1) (aka (3,0)) turns into (12,-3), etc, because the coordinate system changes each step. It's still (1,0),(2,0),(3,0) if you measure in the original coordinate system. But the underlying field wouldn't change in that case. Sure, if you put (5,1) into the field equation you'll get a different result than if you put in (2,0), but if you're only changing the coordinate system then that has to be compensated for in the field equation itself and you're not going to get different results for the same physical point. I mean, you should get the same result if you do f((2,0), coordinate system a) as if you did f((5,1), coordinate system b)

So I guess a simpler way to see this is to note that changing coordinates should never affect the results of a physical theory. For example, if I measure things with the origin at x = 0, but you use x = 5 - all our measurements of position will differ by 5 units. But when we apply the equations of motion to some object we're both looking at - F = ma, our predictions of how the object will move will agree. My predictions will be the same as yours, but the positions will differ by 5 units. This is because the equation of motion, F = ma, does not care about translations in the coordinate system, the acceleration is the second derivative. If we had an equation like F = ma + x, then we would no longer be coordinate invariant, and the equation would be unphysical. It wouldn't make sense, you could look at the system in a different way (i.e. using different coordinates) and get completely different results.

Why is it called that? I assume someone chose that name because it made sense to them for good reasons

This I'm not sure about - I haven't actually seen the reason mentioned anywhere, other than just an indication that it's for some historical reason.

Wait, "our field" and "the new field"? What fields are those? We were talking about a field defined by the function ϕ(x,t) and thinking about its Lagrangian. We added a term A to the Lagrangian and that was it. What's the "new field"? Why does ϕ(x,t) have an interaction with it? Is A the new field?

Yup! A is the 'new field' - the terminology could be clearer here. So by requiring that our toy Lagrangian with phi be gauge invariant, we now have a need to introduce another field, A, and way this field appears in the Lagrangian (by multiplying by phi) is something that will end up acting like a force.

What is "the way we're measuring it"? I think it's, basically, the coordinate system of the surveyor changes each step, so that's a different "way" of measuring it? I still don't see why changing the coordinate system makes new stuff pop out of the equation

Yes - by way of measuring it I mean that we are using a different coordinate system at each point, just to see what the effects of doing so are. And this is the magic bit - if we don't have this other field A, then the equation will produce different results depending on which coordinate system you use. So any theory without A will not consistently give the same results regardless of coordinate system.

How do you expand it? Are you giving the definition of W(x, x+δx)? How'd you get that? What's O(δx^2)?

Power series expansion. So we are defining W(x, y) as a function that allows us to compare the field at x and y. We don't know what this function is, we just assume it exists. We then expand it out in powers of delta x. Since we are eventually going to take the limit, any higher order term - that involves delta x squared or higher - will be too small to matter, so we just care about the constant term and the linear term.

Where'd the second set come from? Wait, I see, it's just saying that you can say that "multiplying a number by a 1x1 matrix" is the same thing as saying "you can compose a number with a rotation". It's literally the same thing, just said in a less clear manner. Does the new terminology get us anything useful?

Once we make the connection between a symmetry of our Lagrangian and some abstract group like SU(3), we can immediately bring in group theoretic results about that group. For example, since we know (from group theory) that SU(3) has eight generators, we can now use that result and infer that we need eight gauge bosons to make the theory gauge invariant.

What? How do you know how many generators there are? Why does SU(2) have two generators but three gauge bosons?

Typo - will fix! Should be three generators.

Yeah, I think the part I'm not getting is how changing coordinate systems affects the equation. I think I can see that if you insist on doing something ridiculous like this you'd need some math to correct for it and if the new correction functions are fields then it looks like new particles popping out, but I don't see how that doesn't result in an infinite number of new particles. Like, I can add a function f(x) = x^2 to the Lagrangian, then a g(x) = -x^2 to compensate for it, but those don't represent new particles do they? Why do those cancel out but A doesn't? I just don't see how changing coordinate systems results in different results

So anytime something gets added to the Lagrangian, you effectively have a new theory - it's a proposal. The point here is that you don't really have infinite flexibility in adding things to the Lagrangian - if you add complex scalar fields, then you must also add gauge bosons. So symmetry doesn't constrain everything - you still need to figure out what the Lagrangian should be, but it'll force you to add other fields as well to make things gauge invariant.

Despite my questions, I think I have a better idea of what's going on. You have a function; it should spit out the same numbers when you rotate it; you need a function to correct for the rotation; in physics the new function looks like a particle. I can kinda sorta see how it works now. Thanks for the article!

Appreciate the thorough review! Lots of things that I should have been more thorough about - I will fix :) Thanks again.

(1/2) Hey thanks for the in depth review!

The next couple of examples only minimize the Lagrangian; are there any systems in this article that maximize it?

So it's not really relevant whether we minimize or maximize it - AFAIK, the action principle states that any path that maximizes OR minimizes the Lagrangian would be a path that the system could take. I'm actually not sure why it so happens that there's always a unique path in the theories that physicists use - I'm guessing that the argument could be that a Lagrangian that gives multiple paths would be unphysical? Not sure here.

What in this case are the objects? Just particles? Is mass in the first example (of classical motion) an object? I'm trying to figure out what kind of objects to use to build an equation, or basically, what type (in the programming sense) an object is

I'm using objects as a superclass/base class (in the programming sense) of both fields and particles. In classical mechanics you have two things - particles, where particles are described by their position (along with derivatives). You also have fields (electric, magnetic) that are described by the amplitude of the field at all points in space (along with derivatives).

Why? Are there other theories, maybe from earlier in the development of physics, that used a different approach?

It's surprising at first glance since it's hard to imagine an intuitive reason for why every theory we've developed, classical mechanics, quantum mechanics, quantum field theory - can be reformulated in a way where some function L is being minimized or maximized. I suppose there could be theories that can't be framed in such a way, but AFAIK all theories 'in use' can.

I assume that in this passage "building blocks" is equivalent to "objects" in the last passage? Why are fields more useful? Is there an example of what using particles as objects would look like? In particular, a field looks to me like a function; if you used particles as an object, would you represent a particle as a function using its position and velocity? Would that function have time as a parameter like fields do? Typing that out I can kind of see why you'd use fields

This is a really good point and something I definitely should clarify. So this jump from particles to "everything is a field" happens when we jump from classical mechanics to quantum mechanics (and quantum field theory). In quantum mechanics you never really know the 'position' of a particle anymore, it's not defined - a particle is represented by a probability distribution over all space, which is where its field nature comes in - you're assigning a probability density to each point in space.

This is definitely something that I hand waved away for simplicity, but can definitely see how this is confusing.

What does the output represent? Anything in particular? If not, then it seems like you could define the field function to be anything since the output doesn't represent anything, then when you feed the field function into the Lagrangian eventually you'd get massively different results

So what the output is is completely up to you, as the 'author' of the theory. If the field is a complex number representing the probability density of the particle, then you'll end up with a theory of scalar particles, such as the Higgs boson. If the field represents spinors (vector like objects that transform differently), then you get electrons. If the field spits out vectors, then you get a theory of the electromagnetic field.

You bring up an interesting point - we can have fields with arbitrarily exotic objects, so which ones do we use? I believe this just comes down to experiment.

How is the Lagrangian constructed? This "simplest" Lagrangian is the derivative of the field with respect to time, along with the derivative of the field's complex conjugate with respect to time, but how'd you know to do that? What makes this the simplest possible Lagrangian? Calling this the "simplest Lagrangian" hints that there are other equally valid ways to create a Lagrangian; is that correct? What are the rules for that? Why would you make a more complex Lagrangian?

Another really good point. Schwichtenberg's books flesh out the argument in more detail, but you're right that there are many ways to create a Lagrangian, in principle, any theory you come up with that follows the action principle can be (by definition) expressed by a Lagrangian. L can be whatever you want.

Now, why this particular choice of L? One argument is - let's assume that L can be expanded out in a series. In that series we'll have terms with time derivatives of the field, and terms that involve just the field. This is where the 'simple' part comes in - we're chopping off all time derivatives except the first derivative, multiplied by nothing else. The only other consideration is that the term with the time derivative needs to be of even order, because otherwise you will not have a stable theory. So this leaves the 'simplest' time derivative terms as (d/dt)(phi) squared, or multiplied by the complex conjugate.

The key thing to note is that there aren't really precise rules the conclusively dictate exactly what the Lagrangian must be. The Standard Model uses a set of terms, and there are some heuristic reasons for why those terms are used (Lorentz invariance being an imporant one), but at the end of the day - you can build any Lagrangian you want for your theory. The one used in this piece is just a simple Lagrangian has enough 'interestingness' that it somewhat reproduces the effects of how symmetry influences the actual, full scale Standard Model Lagrangian.

What is V(ϕ)? My initial assumption would be velocity, but how do you take velocity of a field? Actually, I can see what they're doing: velocity of a particle is the derivative of it's position with respect to time, so I guess V(ϕ) aka velocity of the field is the derivative of the field with respect to time. That could've stood to have been spelled out

Apologies for the confusion here - this just represents any function that doesn't depend on the time derivative of phi. It's called potential energy since it depends on the value of the field, so where it 'is', not how it's changing.

Yes, that's an excellent book, along with his book on QFT.

I also can't recommend this course enough, Susskind has done a remarkably good job at making advanced physics concepts accessible: https://theoreticalminimum.com/

Also, "Symmetry and the Standard Model: Mathematics and Particle Physics" by Matthew Robinson does a great job of developing the group theory needed before diving into the physics.

Yeah I wasn't sure how to set things up there - if I kept a single space dimension + a time dimension, then I'd have to explain the negative sign on one of the terms, and probably also talk about the Einstein summation convention to keep things clean. Whereas with a single time dimension, it's not really 'spacetime' as you pointed out.

What motivated this post was that I wanted to give a concrete example of what it really means for some symmetry to 'dictate' the structure of a physical theory, but do so in the simplest way possible - i.e. not deal with spinors, gamma matrices, quantum fields - and the rest of the actual machinery of the standard model. The core idea is so profound that I felt like there has to be a way to get a taste of it across in a way that's accessible.

Turned out to be a lot harder than I thought - I had to skip quite a few steps in the post to keep it from becoming too long, but I'm hoping the model still conveys the essence of how a symmetry + action principle can 'predict' particles.

Regarding how to derive Maxwell’s equations from QED, I’d recommend this lecture: https://theoreticalminimum.com/courses/special-relativity-an...

This derivation is in the context of classical field theory, but QED is only a short hop away through path integrals.

It’s quite remarkable how the complexity of Maxwell’s equations can be reduced to a single term in the Lagrangian - (F_uv)(F^uv), assuming no charges. That’s really it!

Thanks for the link! I'm a huge fan of Peter Woit - his blog especially.

So my understanding is that while the way Lagrangians are written in classical theory doesn't extend directly to quantum mechanics, the concept of a Lagrangian is still useful since the Lagrangian can be fed into the path integral formulation (as opposed to being used as an input to the Euler-Lagrange equations). Also, in quantum field theory the starting point for canonical second quantization is typically a Lagrangian, where the fields are changed to operator fields.

Also, something interesting I came across - the Euler-Lagrange equations do have a quantum analogue as well: https://en.wikipedia.org/wiki/Schwinger%E2%80%93Dyson_equati...

That’s the approach I used as well in the second half of the article - I just mentioned the transformation law in the beginning since that’s what most physics students encounter first.

Most of the article tries to provide some intuition behind why multilinear maps, which sound like a fairly abstract concept, might be relevant in physics. The key link being the importance of coordinate invariance.

I didn’t go into deriving the coordinate transforms from the multilinear map definition as I didn’t feel that it’d provide much better intuition, but I did mention the equivalence near the end.

Thoughts on the switch? I decided to become a PM full-time after a couple of dev internships, thinking that given the pace of change in tech, it would be a better idea to spend time building people skills instead as those seemed to be more 'durable'.

After a couple of years, I've completely changed my mind. Though I've done well in my role, I find that it's hard to point to a concrete skill set (such as deep domain knowledge in some field), and the fact that the PM role varies so much across companies makes it difficult to transition.

Maybe I'm missing something but it seems like medicine close to ideal once you're in your late 30s and have paid off med school debt. You've got insane job security (due to the extremely high barriers to entry), high pay, location independence (in the sense that you can find employment in whichever city you'd like to move to without taking a significant pay cut), and you don't need to worry about ageism unless you're in surgery. I'd also think there's probably less pressure to stay up to date with new developments or risk irrelevance than there is in tech.

Honestly, seems pretty ideal. Anything I'm missing, other than the debt? And that might not be that much of an issue for someone transitioning from having spent some time in tech, as they'd probably have built up enough savings to not have to incur significant med school debt?

Thanks, that's a very insightful way to think about it. Reminded me of PG's "How You Know" (http://www.paulgraham.com/know.html), where he points out that not being able to recall information from a book doesn't mean that the book was "useless", because the process of reading a book weaves it into your subconscious model of the world.

Makes me feel a lot better about all the time I've spent reading books that I can't immediately recall the details of.

If you don't mind me asking, any other resources you found useful for PM interviews?

It might also be that individuals who have these traits self-select into studying math. So we can't easily conclude that studying math leads to the development of these "highly mathematical habits", because the causation could also flow the other way.

I'm currently reading through 'The Machinery of Life' by David Goodsell, and I'm really enjoying the intuitive explanations it has of the basic workings of cells. It's not a textbook by any means, but it provides a gentle intro to thinking about biological systems and seems like a great starting point.

Let me guess: most of your hard work came from doing something physical like ingesting drugs like caffeine, adderall, or LSD

I agree that our biology probably plays a much bigger part in determining how productive we are than any simple behavioral change, especially of the type you typically find in self-help books and articles on 'productivity tips'.

But this doesn't necessarily mean that these biological factors are mostly fixed at birth and can only be affected through the use of stimulants. For example, regular exercise, a healthy non-inflammatory diet, and adequate sleep all have the potential to significantly improve productivity.

And even if ultimately all of this is mostly fixed and genetic, and there's nothing we can do to become more productive, I feel that it's a far healthier attitude to at least keep trying than to resign completely. Simply believing that we have zero control over our ability to work hard can become a type of self fulfilling prophecy - believing it just drives productivity even lower.

Optogenetics is incredibly exciting. Trying to understand the brain without such a technique seems analogous to trying to debug a complex program without being able to vary its inputs and instrument it to dump state information.

Boyden mentions that optimistically, this technique could be perfected within just 5 years and potentially yield enough information to allow us to construct computational models of (small) neural circuits. Would be interesting to see how quickly this progresses now that it seems that we have the tools.

Great point. I think this applies to scientific research in general, which is why the constant emphasis on only funding research with clear and immediate economic payoff seems a bit shortsighted.

In reality, chances are that most research won't lead to anything significant - but the 1% that does will have outsized impact that will more than pay for the rest. And we don't know which 1% this will be in advance.

Out of curiosity, and if you don't mind sharing, do you have a site/blog where I'd be able to find more of your writing?

I'm surprised C# is gaining this much traction in the games community despite the complete abandonment of both XNA and Managed DirectX by Microsoft.

Could this have any implications when it comes to learning new programming languages?

For example, is it better to just dive into existing code in the language and play around with it, as opposed to picking up a book on the language and first understanding the theory and the idioms behind it end-to-end?

Also, compiling down to human-readable and well formatted JS has always been a priority. So it's never difficult to migrate away from typescript, since the typescript can simply be replaced by the compiler's javascript output.

IIRC, this is actually one of the main reasons they're holding off on implementing async/await, since that would require the compiler to convert functions into unreadable state machines.

I don't think that's a fair comparison. Rust doesn't come with a set of functioning, well-tested and cross-platform GUI libraries and database drivers either.

Running C# the language with the BCL on mono is pretty painless, and offers a ton of functionality.

I think that in the long run, our biggest bottleneck is technological innovation, and not any static resource constraints. The return on new scientific breakthroughs is huge, and even if only 1% of the population can come up with and develop such ideas, the return will often be enough to increase the quality of life of the other 99% by orders of magnitude.

How about computational finance? Finance is what drives pretty much all modern economic activity around the world, and the fundamental concepts in finance haven't changed for hundreds of years, which takes care of both the stable/slow-changing and in demand criteria. It is also heavily mathematical if you choose to focus on the quant side, with probability, statistics, stochastic calculus, PDE's, and even measure theory being used in some form. Also, software is becoming an increasingly important part of modern finance, with things like electronic exchanges, high frequency trading, complex derivative/option pricing, prop trading, and actuarial science.

And the skills you'll learn in finance will be transferable to a range of other fields - advertising and cloud distribution, which pretty much drive most tech startups' revenue, heavily rely on concepts and techniques that you'll learn in finance.

Here's an interesting course you can take a look at: http://www.algorithm.cs.sunysb.edu/computationalfinance/ Note that this is by steven skiena, the author of the well-known Alogrithmm Design Manual. Khan Academy also has a pretty extensive series on modern finance.

I'm wondering what the benefit is of opening these apps in full screen, without any browser capabilities? Why not just allow users the option to run any browser tab in full-screen, and separately allow adding web shortcuts in the home screen that open in browser.

These 'home screen web apps' will just complicate things for the average user, since now users will get a different UI depending on whether they are visiting the site from a browser, or using the same site from the home screen.

I'd rather have public misunderstanding of aspects of science than have a deep public mistrust of science, which is the only thing that comments like yours create.

We need more of the public engaged with science and we need a wider appreciation of its value. Misunderstandings can be corrected over time.

Out of curiosity, what don't you like about Scott Aaronson? I find him to be really good at making parts of TCS accessible to laypeople, his new book being a good example.

In your first code sample, I'm pretty sure you don't need the return await GetSession (provider, isLast, options, token);

Unless there's more to the method, just remove the async modifier and directly return the task returned by GetSession.

One benefit is that it's portable: your text will be rendered properly with the correct formatting anywhere markdown is supported (github, forum comments, blog post, etc). It essentially frees you from having to worry about layout and visual styling, which shouldn't interfere with the writing process.