I don’t think this is realtime reconstruction, meaning that changes in the scene will not be represented in the reconstructed map immediately.
909 karma · joined July 29, 2012
Another commenter mentioned Dr.Jit which seems to be designed for this use case. This is a quote from their project page.
> Why did we create Dr.Jit, when dynamic derivative compilation is already possible using Python-based ML frameworks like JAX, Tensorflow, and PyTorch along with backends like XLA and TorchScript?
> The reason is related to the typical workloads: machine learning involves small-ish computation graphs that are, however, made of arithmetically intense operations like convolutions, matrix multiplications, etc. The application motivating Dr.Jit (differentiable rendering) creates giant and messy computation graphs consisting of 100K to millions of "trivial" nodes (elementary arithmetic operations). In our experience, ML compilation backends use internal representations and optimization passes that are too rich for this type of input, causing them to crash or time out during compilation. If you have encountered such issues, you may find Dr.Jit useful.
Edit: The claims in the abstract about P and NP were conjectures that the authors plan to investigate in a later paper.
A random forest is also a universal function approximator so anything a neural net can do, so can a random forest (in theory). In practice, neural nets are easier for modern hardware to optimize while I think trees incur computational overhead due to branchiness.
Familiarity aside, using a monadic style introduces constraints in your code. I concede that sometimes those constraints will protect you. But for most use cases, these constraints will just produce nuisance compiler errors, forcing you to write dummy wrapper functions to coerce certain types into other types.
Consider the example of a loop whose condition and body are dependent on user input and a global state variable.
global max_iterations = 100
def get_messages():
iterations = 0
messages = []
while input() != 'quit' and iterations <
max_iterations:
messages.append(input())
iterations += 1
> Just like "mostly secure," "mostly pure" is wishful thinking. The slightest implicit imperative effect erases all the benefits of purity, just as a single bacterium can infect a sterile wound.
Taking the analogy to its logical conclusion, the only healthy diet is a 100% strict diet allowing no impure substances. Very few people can actually live this way and it's questionable whether anyone should.
The author's audience is those who wish to make safe programs. He advocates modeling all effectful computations using the type system using the framework of monads. He acknowledges that there are even more expressive frameworks like separation logic systems, but that these require too much theory to be useful to the everyday developer. So the author clearly acknowledges the tradeoff between pragmatism and safety, but it's unclear why it's better to draw the line at monads rather than separation logic.
Many software developers draw the line at, "seems like it should work" and most are satisfied with the type of "mostly safe, mostly functional" assurances offered by Rust. Very few go as far as to prove their programs safe in separation logic, and indeed the cost benefit is questionable for most software systems.
In my earlier career, I might have read an article like this and become some sort of a monad zealot and insist that the entire codebase has to be rewritten using monads in pure functional style. In most cases, this would be a highly questionable decision. If you are writing a game, or making a web server for a dating app, an imperative style is probably the better choice.
> The most enjoyable time is when you feel yourself growing, but the real test is whether you can persevere when you hit that wall or if you’ll lose interest and give up.
Why does it matter if you persevere or not as long as you had fun?
A gene is deleted only if its population ratio falls below 0.0001. In this way, the culling decision is softened so that doing poorly on one round of fitness evaluation does not instantly cause the gene to disappear.
On the one hand, this promotes genetic diversity which is a major concern for genetic algorithms. On the other hand, it seems that this would increase computational costs since it becomes much harder for the total population size to decrease.
Absent from the authors' framework is any type of sexual reproduction / crossover. In my experience, sexual reproduction vastly increases the quality of evolved genomes, since high fitness snippets of genetic code rapidly spread throughout the population.
Genetic algorithms are cool, but they take a large population to work well. So it's hard to combine this class of algorithms with deep learning. You might have enough compute for a population of tens of organisms if you can keep your neural networks small.
Maybe you would personally say that neither of these cases is truly or essentially exponential, since we have more recognizable closed forms. But then you should also commit to saying exp(it) = cos(t) + i sin(t) is not truly exponential. I would find that a little strange, but to each his own.
Anyway, I agree that you get the same answer in the end, as you must.
Indeed, the solution is y(t) = exp(A t) (y0 + int_0^t exp(-As) b ds).
In Einstein's theory, we have a generalization which says a stone's path through space-time follows a geodesic of the metric. In differential geometry, the exponential function, by definition, sends tangent vectors to geodesic curves.
So the author is right, under this interpretation.
1. They mention the fry tending robot (Flippy) costs $4,000 which seems quite off to me. It seems to be an off the shelf robot arm (Kuka?) attached to a gantry system. These components would cost at least $20,000 and probably much more. Maybe the quoted cost is the price per month to rent one of these systems as a customer. This would be in the right ballpark compared to the cost of hiring minimum wage human labor for a month.
2. I'm not sure it makes sense to use a robot arm to just tend fries because a custom designed machine could probably do it more economically. But even a custom machine may not be as economical as a human, who can tend the fries but also accomplish other tasks simultaneously. At my local McDonald's, the employees that bag orders also tend the fries.
On the other hand, if the robot arm can replace other machines and humans as well, it could justify the cost of the robot arm many times over. Miso's original idea incorporated a autonomous burger flipping capability, hence the name "Flippy" for their robot. From the video, they have since migrated to a mechanical rotating grill. Then I assume the burger flipping was too difficult to do with Flippy and they may be running up against the "hard" part of hard tech.
3. There is an unofficial subreddit following this company, /r/misorobotics, due to their heavy usage of crowd funding. Through the subreddit, I learned that Flippy has been deployed at about 20 White Castle locations, with plans to deploy to over 100 locations. I'm curious to see how this plan works out, given the previous point. If the total system cost is about $100,000, this would be a $10,000,000 expenditure for what amounts to a quality of life improvement for the current staff.
4. The location for this new restaurant is the previous Miso headquarters. I believe that it is operated by Miso, so this restaurant may function as a marketing vehicle more than an existence proof for the economic viability Miso's business.
> S(0)=0 (reference position) S(1)=v₀-½g (initial velocity) S(t+2)=2S(t+1)-S(t)-g
Thus, the value of the previous screen frame (t+1) is added twice, the value of the screen frame before that one (t) is subtracted, and the constant g is subtracted.
The relatedness of human language cognition and machine language performance is already evident, in that studies of machine language performance can already falsify some theories of human language cognition. The pattern goes like this.
1. Language competency requires component X. 2. Humans have language competency. 3. Therefore, humans have component X.
When we see a machine, without component X, using language as a human does, we know the theory has gone wrong at the first step. The article mentions one such instantiation of this pattern. The new data has narrowed the space of plausible theories of human language cognition. The machine learning engineers play the role of experimentalists. They collect data but may not have a theory ready to explain what results.
> You have to have a reason to believe that it would yield insights. There isn't one.
The reason is that models in general yield insights. They have yielded insights before, and so we may expect them to yield insights in the future. The explanation for this is either philosophical or psychological. Whatever the reason, humans who look into a specific system are very good at generalizing to a whole class of systems. F=ma is a model, and certainly wrong. But by probing it, we can begin to discover general principles like energy conservation. In the same way, a large language model is a model of a system that acquires language. By examining its specific behavior, we may come to understand more general principles of language acquisition.
> You have to actually have a rational process to produce insights, not just mix a bunch of numbers in a vat and "assume" that some insight is going to come from it.
Where there is already an understood rational process, you no longer need the insight. Sometimes the big leaps of scientific understanding come from looking at the data and then coming to Eureka.
In terms of scientific understanding in general, I'm not sure why you would say that building a statistical model of a natural process is be irrelevant. On one level, much of science is about building models of natural processes and then probing those models for insights.
On another level, attempts to build artificial intelligence are acts of science. We are studying the limits of statistical algorithms in their ability to pick out patterns from raw data. This study has produced data that has already surprised those who had assumed that a competent language model would need a universal grammar component, as mentioned in the article. If a solid theory is developed, I assume it would yield insights for studies of human cognition.
A fairer analogy can be made by first supposing that submarines do not exist yet, and people are intensely interested if one could actually be built. Add in the situation that many people are claiming that such a swimming machine is inherently impossible because any machine would lack the swim-enabling "stuff" that fish have.
Then one day submarines are built. It passes all tests of swimming ability that naysayers said were impossible for a machine to pass.
And now the naysayers say that they weren't actually all that interested in the question of swimming machines in the first place. The actually interesting question is how do fish do it.
> I personally remember very heated arguments on the MIT campus between Chomsky followers and computer scientists working on statistical models in the early 2000’s. The arguments came up in computer science faculty hiring meetings. The Chomskians claimed that because of the need for universal grammar there would never be anything useful that came out of statistical approaches to language. ChatGPT has proved them wrong. (And yes, I personally shouted angrily at Chomskians in some of those hiring meetings.)
I used to be sympathetic to this viewpoint. But these days I'm more and more willing to believe that we can actually build a ladder to the moon.
[1] https://www.quantamagazine.org/common-sense-comes-closer-to-...