Doesn't this entirely depend on the latent embeddings of strategies and game space in the AI model, which may not be so concrete and explicit as you've described? That's kind of the magic of LLMs with coding, they can generalize because the abstract patterns are encoded in latent space, not the specifics.
I am not familiar with the standards of publishing in machine learning, but as someone trained in a mathematics background, this paper seems relatively light on details and heavy on exposition. Is that typical? Is this a really novel idea? Not trying to be snarky, just trying to understand how meaningful this is.
The entire thing is so generic and full of wise sounding things with no deep concrete content. It screams "an AI wrote this and a human thought it looked ok and pushed it".
I think we can agree to disagree here. I don't see how a company needs to have an $8000 increase in revenue to justify a $240 software purchase. You are assuming that the current operating cost for every dollar of revenue is also applied to every incremental dollar in revenue gained from software efficiency, and that is just not true.
I understand that. The AI software is meant to be a productivity enhancer for the employees using it. Other than the licenses, some training etc, there are no operating costs associated with it. Just by using the software, I don't suddenly have to pay more for salaries, retirement plans, etc, which are things that in aggregate produce the 3% margin. Maybe I have to pay more in logistics because I'm moving more product now, but I think the point stands.
I'm not an MBA over here, but this math seems wrong. If they are spending $240 in increased costs, then they only have to make about $247 in additional revenue from that spend to preserve a 3% margin. That seems much more reasonable if it increases the probability that customers find the product they are looking for and have a good experience.
I interviewed someone recently who worked at Meta a couple years ago. He was a software engineer, was paid a bunch of money to mostly up dashboards all day, and eventually quit because it was neither interesting nor challenging.
It is not determined by the derivative, it's the antiderivative, as someone else mentioned. The derivative is the rate of change of a function. The "area under a curve" of the graph of a function measures how much the function is "accumulating", which is intuitively a sum of rates of change (taken to an infinitesimal limit).
This is some high quality content. Love the visual animations to go along with the mathematical ideas. Did a great job helping to tie the algebra to geometric intuition, but I think the importance of commutators could have gotten a little bit more exposition.
The disk model of hyberolic geometry is made to map hyperbolic 2 space (which is infinite in area) into the finite interior of the disk. In order to capture this, the normal euclidean notion of distance is distorted by a function which allows "distances" to go to infinity as a curve approaches the boundary of the disk.
Binary search minimizes the number of expected moves until you find the target. If you are already ahead, this is a natural thing to want to do. The reason why this doesn't work when you're behind is that your opponent can also do that and probabilistically maintain their lead.
In my mind that is a problem with your lazy developer colleague, not AI as a whole. You can't expect it to be right on the first try (just like human code), you have to iterate with it and have the experience to know when it's off track and you have to take over.
I don't know about other use cases, but AI is definitively a game changer for software development. You still need to know what you're doing and test/think critically about what it's giving you, but the body of software problems that you can conceptually treat as "boilerplate" becomes massively larger with the help of a good AI coding tool.
You and the article are both correct. The disease does present itself differently as a function of these other characteristics, so since the training dataset doesn't contain enough samples of these different presentations, it is unable to effectively diagnose.
The "richness" definition also seemed hand wavey to me so I looked at the referenced paper. The actual definition of "richness" of an algorithm is that for any arbitrary partition P of your original data (singletons, one cluster, etc), there is a distance function on the data, which when used in the clustering algorithm, produces P.
I am a mathematician by education but programmer by profession for many years now, so the significance of this result is over my head. Could someone elaborate on what makes this is an interesting problem?
Rather than just making a conspiratorial post, could you explain these alternative theories and why you see this article as strong evidence against existing mainstream theories?
You can have unrealized losses since it's possible that the value of your items is less than the value of the goods you paid to receive them. To repeat this strategy the next day, you need to convert your items back into gold and realize the loss (or just introduce more gold via other means).
Do you have any recommended references on this subject? Seems like this sort of system would be able to obfuscate a lot of metadata that can be used to deanonymize activity. Very interesting.
You'd be surprised at how many people I've interviewed over the years have been unable to hide the fact that they would be terrible to work with. In programming circles at least, I think the ability to socially manipulate and mask true character in an interview setting is way less common than the ability to practice questions and memorize answers.
By reading through the code, understanding how they've laid out their abstractions and user interfaces. I am in the field, and in my experience, the most impactful design decisions for libraries like this are related to how researchers will actually interact with the tool.