Models seem to be able to do anything these days, but I am curious if you see any cases where it doesn't work and can't seem to match up or hit a wall.
The benchmarks show a pretty picture, but the real-world performance in areas such as healthcare, legal analysis, finance, and scientific research has not been objectively quantified.
So, I am curious about which domain your work is in, and within your workflows, where does the current AI fall short?
I am an AI Engineer, and I find synthetic data to be of great help, not as a replacement for real data, but to augment it.
It has always been useful for training, but for me, it has been especially useful for coming up with various scenarios to test different kinds of inputs. I believe synthetic data can be very helpful for evaluating agent traces and outputs, simulating different scenarios, and testing edge cases.
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