201–210 of 276 posts
Earlier quoted context omitted.
Currently on the job market in the AI space in the Bay Area - 400k to 600k is senior level at FAANG + similar. Low 7 figures at staff wouldn't shock me (although I don't have any actual data on that)
Can we pause and admire a data scientist drawing conclusions while being utterly unconcerned with underlying data? ;)
Earlier quoted context omitted.
On the other hand, there’s a lot of real problems that real people actually deal with that just need a logistic regression to save million bucks here and there. I like that space more.
I've heard about this, in different contexts. What it mainly comes down to is that incremental improvements can have massive impacts when you can apply them at a scale available at FAANG. I first read about this outside the context of machine learning, but it certainly would apply here. For those of us who don't work at such scale, can you (maybe with a little fuzziness to avoid telling too much about an internal pro…
This post feels like author is insecure about his position and wants to establish some validity. Having going through it all, it feels delusional at best. The glorified pattern matching can only take us so far. You know it's working as long as there is a pattern. I wouldn't call it a general intelligence per se. There is no "juice" in these algorithms. If we use these tools, we can immediately see where they fail and…
Earlier quoted context omitted.
This has become increasingly important to me too. I am employed by a small (2-4 engineers at any time) company and I'm often disappointed because we're just so far behind in manpower & technical expertise that we have to dramatically reduce the scope of any problem we want to tackle. On the other hand, I also worry about getting sucked into the bureaucracy of FAANG sized companies & not having any accountability or a…
I'm surprised. Apart from DALLE, I haven't seen any AI approach that's off limits for 4 highly motivated people with 3090 GPUs. At that compute level, you should be able to at least replicate SOTA in optical flow, structure from motion, speech recognition, text to speech, translation, text summary, sentiment analysis, image classifications, image segmentation, and of course playing video games or optimizing processes…
Earlier quoted context omitted.
Yep, at that level, there is a ton of competition. The self-driving industry is almost like the video game industry and actually has a ton of employees who previously worked in video games. They are definitely much better compensated in ADAS but the work culture is similar (anecdotally so YMMV). ML is the sexy wing of the tech industry, so it tends to attract the people who are willing to put in the hours (for interv…
This is because there isn't really much demand for genuinely innovative ML in the tech world. It's a third-circle nice-to-have, not part of the core business. It enables the business to say they invest in R&D (tax writeoffs, marketing) and it also makes it easier to hire the grunts who'll do the scut work, thinking they'll one day be working on something more interesting (which they won't be). Competition for bullshi…
Earlier quoted context omitted.
I've heard about this, in different contexts. What it mainly comes down to is that incremental improvements can have massive impacts when you can apply them at a scale available at FAANG. I first read about this outside the context of machine learning, but it certainly would apply here. For those of us who don't work at such scale, can you (maybe with a little fuzziness to avoid telling too much about an internal pro…
Here's a ~5 minute talk with 5 such examples (where relatively simple ML models made a 1M+ impact at a FAANG) :) https://youtu.be/zyOEOd1HkSY?t=946 Happy to talk about more details if you message me through my profile!
Interesting examples, and yes, they're all the kind of thing that might not justify the effort for an ML model (and might not have enough data to train) for a small website or operation, but can easily justify the cost and effort when you have a huge number of transactions.
On another note, this is why I often like lightening talks. So many people think that what they're doing falls below the threshold for what is an interesting presentation, when in fact it's the most relevant thing a lot of people will see at a conference.
Earlier quoted context omitted.
This is because there isn't really much demand for genuinely innovative ML in the tech world. It's a third-circle nice-to-have, not part of the core business. It enables the business to say they invest in R&D (tax writeoffs, marketing) and it also makes it easier to hire the grunts who'll do the scut work, thinking they'll one day be working on something more interesting (which they won't be). Competition for bullshi…
The OP actually started his career as a ML engineer (or SWE even) and transitioned into research without a PhD. Can you elaborate on your "which they won't be" comment?
Today, the competition for research positions in a "sexy" STEM field (think quantum computing, black holes, DL, RL) is quite high. DL just happens to be a field where it is possible to get into research without investing too much time while also making a lot of money, so this is a dream job for many.
Earlier quoted context omitted.
On the other hand, there’s a lot of real problems that real people actually deal with that just need a logistic regression to save million bucks here and there. I like that space more.
This describes 95% of machine learning at FAANG+, unfortunately nobody likes to talk about. Context: I work at a FAANG.
Writing TFX code at Google is like having your soul-sucked through your rear-end! Imagine TF1 with all the broken APIs, but now it's all distributed! Fun.
Earlier quoted context omitted.
Doesn't the author's sentence mean: hospitals and insurance companies have coopted regulators for the benefits of their own businesses, at the detriment of medical device companies (developing AI)? I think the author's point still stands.
Possibly that was the intended meaning, but the point is still invalid. Hospitals want more cool gadgets. They want to be able to treat more conditions and charge more for it. If anything, the FDA is a constant annoyance to a healthcare provider because it hamstrings them from providing care. This is why so many patients are enrolled in clinical trials, to get care ahead of the FDA approval time frame.
This post reeks of someone who doesnt ‘need’ a job and has made enough money already. Good for op to go after ‘exciting problems’ rather than the mundane will I make rent and fees