I work at a company that has AI/ML as a big part of its sales
pitch.
For anything important, where users are going to be judging the
results and care about errors, ML is extremely difficult to get right.
The gap between what our data science/ML group has been asked to do,
what they try to do, and what they actually achieve is enormous. On
several occasions we’ve rolled out ML functionality and later had to
disable it.
There’s a reason that many of the most widely used ML applications
are for things like “recommendation engines”. If Amazon or Netflix
recommends something to you that you don’t care about, or misses
something you do care about, it doesn’t really matter that much - at
least, users aren’t going to be too put off. ML can get away with errors
and inaccuracy in those cases without anyone really noticing or caring.
When it comes to things that people do notice and care about, it’s a
completely different story, and you suddenly go from a routine
application of existing technology to an extremely challenging pushing
of the envelope that may exceed current capabilities.
Plus, a lot of what we do relies heavily on manual labeling of data,
which is farmed out to companies that can throw large numbers of cheap
people at it. This blurs the line between ML and just plain old human
effort. See AI’s
dirty little secret: It’s powered by people.
So yeah, the hype vs. reality gap is huge.
A lot of what we see now is because good, experienced ML staff are
rare and expensive, so a lot of companies are making do with the next
best thing and doing cargo cult, copycat stuff, or just plain
bullshitting. You not only need the technical knowledge to know what to
do and how to achieve it, you need a good understanding of how to
translate into something with business value, given its limitations.
Sturgeon’s Law (“90% of everything is crap”) comes into play here, and
crap ML is often worse than useless.
That said, one shouldn’t underestimate it. The top performers in the
space are doing things that have more value and will extend the state of
the art over time. There’s no question that it has a lot of potential to
eliminate jobs, much as e.g. automated factories have. It’s just that
it’s going to take time to both develop what it’s capable of, and for
understanding of how best to use it to become widespread.
**Labeled data**
Labeled data is a group of samples that have been tagged with one or more labels. Labeling typically takes a set of unlabeled data and augments each piece of that unlabeled data with meaningful tags that are informative. For example, labels might indicate whether a photo contains a horse or a cow, which words were uttered in an audio recording, what type of action is being performed in a video, what the topic of a news article is, what the overall sentiment of a tweet is, whether the dot in an x-ray is a tumor, etc.
Labels can be obtained by asking humans to make judgments about a given piece of unlabeled data (e.g., "Does this photo contain a horse or a cow?"), and are significantly more expensive to obtain than the raw unlabeled data.
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Apparently DLSS is based off an upscaling solution used for productivity; good if it doesn't have to be done on the fly, for all different kinds of settings.... fucking useless for gaming.
> There's a reason that many of the most widely used ML applications are for things like "recommendation engines". If Amazon or Netflix recommends something to you that you don't care about, or misses something you do care about, it doesn't really matter that much - at least, users aren't going to be too put off. ML can get away with errors and inaccuracy in those cases without anyone really noticing or caring. When it comes to things that people do notice and care about, it's a completely different story, and you suddenly go from a routine application of existing technology to an extremely challenging pushing of the envelope that may exceed current capabilities.
playing games, making recommendations, "predicting churn"
that's it
No idea what feature you are talking about, but Turing was pretty important in computer science, so his name popping up all over the place isn't that special.
Deep Learning Super Sampling, the up-scaling solution I mentioned in the title. Edit: it's one of the headline features of the 20-series, the full fat Turing arch.
https://www.techspot.com/article/1873-radeon-image-sharpening-vs-nvidia-dlss/
The differences between them are small, sometimes in RIS's favor, and RIS works on anything on DX9,12 and Vulkan, whereas the neural net based Deep Learning Super Sampling needs specifically trained models for each resolution, settings set and game, making it a huge bother for devs.
‘AI’ is just the new marketing buzzword right after blockchain and …
What was it before, ‘quantum computing’? It’s a hype. It has little to
no meaning in most situations where people use it.
I work at a company that has AI/ML as a big part of its sales pitch.
For anything important, where users are going to be judging the results and care about errors, ML is extremely difficult to get right. The gap between what our data science/ML group has been asked to do, what they try to do, and what they actually achieve is enormous. On several occasions we’ve rolled out ML functionality and later had to disable it.
There’s a reason that many of the most widely used ML applications are for things like “recommendation engines”. If Amazon or Netflix recommends something to you that you don’t care about, or misses something you do care about, it doesn’t really matter that much - at least, users aren’t going to be too put off. ML can get away with errors and inaccuracy in those cases without anyone really noticing or caring. When it comes to things that people do notice and care about, it’s a completely different story, and you suddenly go from a routine application of existing technology to an extremely challenging pushing of the envelope that may exceed current capabilities.
Plus, a lot of what we do relies heavily on manual labeling of data, which is farmed out to companies that can throw large numbers of cheap people at it. This blurs the line between ML and just plain old human effort. See AI’s dirty little secret: It’s powered by people.
So yeah, the hype vs. reality gap is huge.
A lot of what we see now is because good, experienced ML staff are rare and expensive, so a lot of companies are making do with the next best thing and doing cargo cult, copycat stuff, or just plain bullshitting. You not only need the technical knowledge to know what to do and how to achieve it, you need a good understanding of how to translate into something with business value, given its limitations. Sturgeon’s Law (“90% of everything is crap”) comes into play here, and crap ML is often worse than useless.
That said, one shouldn’t underestimate it. The top performers in the space are doing things that have more value and will extend the state of the art over time. There’s no question that it has a lot of potential to eliminate jobs, much as e.g. automated factories have. It’s just that it’s going to take time to both develop what it’s capable of, and for understanding of how best to use it to become widespread.
This overhype goes back to Turing himself.
link on this?
‘AI’ is just the new marketing buzzword right after blockchain and … What was it before, ‘quantum computing’? It’s a hype. It has little to no meaning in most situations where people use it.