If you could somehow track the frequency spectrum of usage, then you could mostly handle that problem. "Oh, the frequency spectrum strongly peaks at about once a week? I guess users regularly need to use this feature. Probably important somehow." vs "Huh, the frequency spectrum isn't strongly peaked anywhere, but is substantially lower at high frequencies. We can probably remove this."
Kaku is a media sensationalist; that class of person has a terrible track record. Futurists, that is, people like Aasimov, have an ok track record at predicting the future. Interestingly, early futurist predictions about digital tech were pretty good. For "physical" goods? Not so much. A plausible explanation of this discrepancy is that they didn't predict the breakdown in the historial trend of increasing energy usage. See the Henry Adams curve.
"What I would suggest is to start taking the extra time to optimize your resume for every job you apply for. There are tools online to help you find out how these softwares scan and pick out pieces of your resume."
Is there one tool to rule them all, or a host for each specialty?
>place an odalisque with an inscription on it
Besides being cruel, odalisques aren't known to exist for more than a hundred or so years. Let alone millennia.
Problem sets are useful for improving your mathematical ability, as is looking at an expert's attempt at a problem after your attempt. Both are a core part of reading books well. So when someone asks for books to improve their mathematical ability, a likely interpretation is just "they want books with good problem sets, clear presentation and elegance". Another interpretation is "what's a book which, when after I intrepret all the individual sentences into a vague impression, will make me good at maths?"
Your answer is somewhat helpful in the latter sort of world where OP didn't know how to read a textbook (which is unfortunately common) but not in the former sort. You seem to be venting though, which is understandable. But venting with a side of helpful content would be even better.
For instance, advising OP on how to to read a maths book (generate content yourself, check dependancies, connect things to what you know etc.) or suggest books which contain advise like this alongside their main content (I think Tao's analysis texts might do this?)
There are a couple of people interejecting with answers to questions, or asking questions. I'm afraid I don't have a better estiamte than that. But in this case, I think lumping the students together as one speaker and the teacher as another would be fine.
I tried using this for a technical talk[1], and it got the amount of speakers wrong. Which is somewhat suprising to me, as I would have thought diarization tech would just worked by now.
The FAQ contains re-states the content for point 14 in point 13. Point 14 is about why your code might be slower when using 2.0. 13 should be about how to keep up with PT 2.0 developments. Someone should change that.
By database do you mean a finite 2d array with column headings? Or something that can be represented by such an object? And its entries are elements of some countable sets? If so, what are you doing using ZFC there?
Personally, I'd guess that I wouldn't consider it "stealing" an identity if it were evenly trained off a couple of individuals. But it is a weird case ethically. Can I just train a model off of my favourite bloggers' works and tweets, and release a tweetbot replying to stuff I think would be topical? I've basically done that, but not hooked it up to Twitter, and I think that might be fine as long as the model is private. But this is certainly thorny ground.
I have some experience implementing deeprl models, statistical physics simulations, pre/post deep learning revolution AI algorithms and a couple of simple games. My degree was in theoretical physics. My interests are broad, so I'd be fine learning new stuff for a job, or working as a front end, or back end , dev, or doing ML research or designing high performance numerical simulations for SDEs or whatever.
Because it has Omicron's increased ability to avoid people's immunity. That would result in higher infectivity than just a copy of the wuhan strain, with near the same severity. Note that earlier Sars viruses had even higher severity than Sars Covid 2 (Wuhan strain), but weren't as infective and killed far fewer people. Which implies that a slight reduction in severity for a gain in infectivity is not a worthwhile tradeoff.
I read a good book review[1] that moved me towards the view that figuring out how a living thing works is possible, though not necessarily easy. I highly recommend reading it, but here's a summary.
Evolution promotes fitness enhancing functionality, so we should expect biological processes to be useful for some purpose and hence not be distributed like a random graph (e.g. Erdos Reyni graphs). Indeed, if we look at biological structures, we can find that the causal networks they form are far from random. Furthermore, there are often repeated motifs present. These motifs are quite simple and seem to map neatly onto human understandable concepts (like XOR gates or autoregulators or feedforward networks etc.)
And often, the overall graphs seem like they're tree like rather than some complicated mess of feedback loops (barring autoregulation). This kind of structure is quite modular, and hence we can leverage our understanding of component parts to understand greater and greater pieces of the organism.
There are two problems with this arguement: one, that a lot of the data used for it is not nearly exhaustive. Maybe the people examining biological circuitry stumbled on the rare areas where there are repeated sub-components. Second, even if there are repeated sub components, why should we get modularity i.e. few connections, mostly local?
The former may not be an issue if there hasn't been a lot of dedicated effort towards finding human comprehensible structure in biological circuits, which there might not have been. These things are big and complicated, with many constituent parts, and teasing out the underlying structure may require loads of computation and statistical analysis, which was hard for most of the history of biology.
The latter is not adressed in the book review, or in the comments, but the review author's work makes me it plausible to me that modularity will be common in biological systems. I don't have a good summary of that, or can clearly articulate why I'm hopeful about this. But read the rest of the work of the writer of the article if you're interested in this kind of stuff (key words: natural abstractions, interfaces, selection theorems).