Wikipedia itself states "the adage fails to make sense with questions that are more open-ended than strict yes-no questions". I guess this means there's no black or white answer but shades of gray. How shocking: reality is more complex than it seems! ;)
That's assuming human minds and computers won't exponentially increase their processing capabilities. Moore's law disproves the later and I hope Neuralink or some other crazy tech company will disprove the former. That's pretty much the hope of all transhumanists.
Really? I think if we were to bring back our close ancestors (4-5 generations away) they'd look at our world like we see Harry Potter's: pure magic. I mean flying metal birds, fire that instantly turns on and off, machine that move around like ghosts, small boxes that can talk back, musicians on demand in a box? I think you get my point, you're selling humanity short. There's no limit to human ingenuity and there's a reason Elon Musk still doubts we're living in a simulation.
Sounds like a pretty bad experience indeed - surprised that was their recommendation vs completely anonymizing the data. You didn't share if you went ahead with it and saw any results? If so, it sounds like they were not good. Either way, I don't think you can let one bad experience cast doubt on a whole field. There are plenty of example of medical research institutes using synthetic data in combination with real patient data to improve their neural nets. I'm no medical expert, but data augmentation or full simulation works when it's used in the right context. Having said that creating biased algorithms that generate biased data is certainly a reality as well.
All good points. In this case, the original dataset is created from real world body scans. You collect enough scans in this "base collection of scans" to have a "real" distribution of the world. You can then span a latent space on top of this initial distribution and use GANs to further scale it. This isn't as good as real yet, but it generates results that are better than limited quantities of real data alone. Agree with your point around the Monte Carlo simulation. Synthetic data is not the be-all end-all to train neural networks.
I read about the guy who used GTA to train a neural net. I think he was trying to make the point that although obviously imperfect, using simulated data could actually work. I'm not saying using simulated data (SD) should the be-all end-all for training neural nets, but we're seeing algorithms performing better when they're trained using a combination of real labelled data and SD rather than real data alone. I hear your point though about hype cycles and the tunnel vision SV can often fall into.
Respectfully disagree. Again, I'm not suggesting SD will solve all problems. Big data is critical and will remain so. However, using a combination of SD and real data will make AI algorithms more robust than using big data alone. I do agree that the world is messy and it's hard to recreate the chaos and weirdness of the world. However, to think that at some point we won't be able to completely mimic the real world and all the variations out there is strange. Re: your example, its actually pretty easy to span millions of humans based on ethnicity, age, body mass, etc. It's just a matter of time until this problem gets solved.
Disagree, otherwise I obviously wouldn't have written this piece. ;) Synthetic data (SD) is not a silver bullet that will solve all problems, but it opens up a lot of opportunities. I'm seeing cool startups using SD to accelerate their R&D efforts and launch products in production in ways I didn't see 2 years ago. Imho, the quality of SD is reaching a tipping point and the sim2real gap is starting to disappear.
1. Totally agree with the point about data science talent being one of the bottlenecks. And in that area, the big tech companies already have the best & brightest workforce. Most data is crap is a bit of a stretch, but agree that a lot of pre-processing is required. But more and more platforms and/or services company are filling this gap.
2. 100%. This is a short term problem.
3. Agree to some extent. You can span latent spaces on top of an initial batch to overcome scarcity and use GANs to scale up. Data augmentation is not perfect but solves a lot of problems in production. Also specifically as it relates to computer vision, it's "easier" to know what the real world looks like and try to replicate it. It's hard in practice but the assumptions are mostly agreed upon.