That actually the important part though. If people don't know why they are doing something, they don't do it.
553 karma · joined September 23, 2019
That actually the important part though. If people don't know why they are doing something, they don't do it.
> # tl;dr > > Here’s how getting code reviewed and submitted with Gerrit is different from doing the same with GitHub: > > - You need to add a commit-msg hook script when you clone a repo for the first time using a snippet you can find e.g. here (link)
Well, that was enough to make me think twice.
Hardware is likely fine, but the drivers ... Well... I have no idea who's writing them.
I think part of the problem is something you're touching on here. The ML software space is horrifically put together. It's a bunch of research projects cobbled together. Hence the dependency hell, and very few people who actually know how to port it. Cuda siits at the bottom of the stack with its design assumptions baked into everything above it (see PyTorch for a good example of that). Nobody wrote a hardware agnostic layer over the top, and now the thought of pulling out a Jenga block at the bottom scares everyone.
Cuda is an intentionally leaky abstraction layer over the hardware, and its worked.
It is ported (I think by AMD). I use it daily. Biggest problem is that the people maintaining PyTorch aren't very interested as they don't use it themselves. Hence there are silly bugs in it. Users contribute fixes and they get closed down as "unsupported" or get plain misunderstood.
Unfortunately AMD only claim parity with Nvidia for training right now, and uplifts on inference. That's the wrong way round to get people to spend time porting as a priority.
Drive in a city and you may be merging 5-6 times as you move from road to road.
I'm just saying that assuming a linear relationship to distance is probably incorrect as well.
For an app like that I just need the UX to be "good enough".
https://fedoraproject.org/wiki/Changes/fno-omit-frame-pointe...
1. People observe that there is a disparity in the number of pages on Wikipedia about men and women in science. 2. This leads to a number of people searching out people to write about that would redress the balance. 3. Unfortunately some are really only notable for their gender and/or ethnicity, and Wikipedia does not view that as a factor. 4. Hence, a lot of these pages get flagged for deletion.
The discrepancy is largely a reflection of our history. Until recently women and non-white ethnicities weren't in positions of scientific research. With fewer people in the field, there will be fewer notable people even if the chance of one person being notable is the same. It sounds like the "deletion of female scientists" is the system working correctly in response to people submitting articles for reasons other than refection the historical truth.
I'm sure there are cases of someone having their contribution ignored due to racism/sexism, but artifically inflating the achievements of people just to redress some perceived imbalance doesn't help. Now another person gets credit they don't deserve whilst the true talent gets buried deeper.
https://bletchleypark.org.uk/wp-content/uploads/record_attac...
Sounds like they did the manual work of running the Bombe machines.
Hence somebody who was one of one thousand doesn't get described "one of the army of people who helped defeat evil", but have their role inflated to an individual responsible for some key aspect. That way you don't have to acknowledge the contribution of others who, in truth, did the same as they did.
As long as they were hired in good faith, and not to just see which people worked out, I don't have a problem.
I'm sure there's all kinds of nuance to your tale, but it still sounds like Oracle's bug to me. They just found what it was in your system that introduced the behaviour they hadn't anticipated, and then got you to remove it.
For example: it's fairly easy to imagine somebody asking an AI for medical advice, and gives their child the wrong medicine, poisoning them.
Now imagine that person is a busy political figure with no time to do proper research on a topic.
Sigh
GPT produces output which obeys the patterns it has been trained on for definitions of true and false. It does not understand anything. It is a token manipulation machine. It does it well enough that it convinces you, a walking ape, that it understands. It does not.
Nothing in the ISA precludes this. It's all about the software stack, and what the compilers do. All you need to do is assign another register the role of data stack pointer and define the ABI to use it.
Even the stack pointer is just a convention in RISC V
I just use my local firm.
Yes they will stop being fuel stations, but they're the last places I want to charge.
I could very easily believe that interaction by itself would be enough to have measurable effects over interstellar distances.
Difference on what scale? ... because (hint hint) it's not number of photons that hit the sensor. Nor is it photons emitted from the display.
The truth is, it's a linear measure of the voltage which drives the electron beam of a CRT. Not a very useful measure anymore, but we've encoded this response curve into all of our images and, now, this is proving to be a mistake.
Working with images would be so much easier if we stored values that represent linear light (i.e. proportional to photons entering/leaving a device) with no device curves baked in. Log formats do this, but because the order of magnitude of light is more important than the absolute value, it takes the log of the value. It's a more efficient use of bits in the storage / transmission.
The chromatic aberration is an important but subtle effect. Remember that lenses are multiple pieces of glass, and every interface diffracts the wavelengths of light like a prism. One of the considerations in lens design is converging all those different wavelengths of light in the same place. Not just at one point, but at every point across the image plane.
Poor lenses might do the well in an area. Good lenses do it everywhere.
The adoption of CUDA has been such a coop for Nvidia, it's going to take some time to dismantle it.