Top on 5 of 6 healthcare benchmark panels. OpenAI-compatible API, 256K context, text + vision.
While chatting with Sarah Deaton about the formats agents could use to talk to each other, we got to questions that might sound obvious: why should machines (say, LLMs) communicate with each other like we do? Do they need all the rhetoric we carry around? Or could they get by with a terser register, one that carries the same meaning but in far fewer tokens?
Amazon’s recent pivot on the Selling Partner API is an encouraging reminder that developer pushback can breach walled gardens.
I’m training a single-class segmentation model for large rectangular artwork placed on the floor and photographed from above. We have around 3,000 accurately masked original images taken by six different photographers. They are not the same height and do not hold the camera in exactly the same way, so the photos naturally vary in: roll pitch yaw camera distance object coverage in the frame centering and X/Y shift orientation perspective lighting The photos taken with flagship iPhone. I want to use on-the-fly augmentation to simulate realistic human-hand variation and save our designer from adjusting each time to make it flat. is 100 augmentation combinations per original be useful, or excessive? Should the policy be: mostly isolated transforms, mostly crossover combinations such as orientation + roll + pitch + yaw + coverage + shift, or a controlled hybrid of both? The goal is maximum segmentation accuracy, especially around the object boundary, not speed. I plan to train for around 300 epochs and keep validation and test images unaugmented. submitted by /u/Loganbirdy [link] [Kommentare]
Prediction markets are taking off across the country and are sucking in SoCal youth.