llm-classifier VS Deep_Object_Pose

We do this for the null hypothesis - is uses an LLM to bootstrap a binary classifier - which handles null easily

https://github.com/lamini-ai/llm-classifier

LLMs introduced zero-shot learning, or “prompt engineering” which is drastically easier to use and more effective than labeling data.

You can also retrofit “prompt engineering” onto good old fashion ML like text classifiers. I wrote a library to do just that here: https://github.com/lamini-ai/llm-classifier

IMO, it’s a short matter of time before this takes over all of what used to be called “deep learning”.

I've been doing some research in this area and there are a few deep learning solutions to this problem. For example, NVIDIA's Deep Object Pose Estimation will estimate the 6DOF pose of a known object. But you'll have to train the network if you want to detect a new object. PoseCNN, which someone else mentioned, does a similar thing. CenterPose is more interesting, as it can estimate then pose of an object from a known category; e.g. sneakers, or laptops, rather that one specific object (as DOPE and PoseCNN do).

For our last event of ArchE Week, the Ohio State Underwater Robotics Team (Website, Instagram) is hosting a workshop tonight on machine learning! The workshop is an interactive walkthrough of using machine learning solutions to make predictions. Some example problems we could be trying to solve are predicting a grade, predicting the weather, and the classic recognize a digit problem. Our team personally uses machine learning to do real-time object detection with YOLO and NVidia DOPE, so we may touch on that as well!