Regression, the task of predicting a continuous scalar target y based on some features x is one of the most fundamental tasks in machine learning and statistics. It has been observed and theoretically analyzed that the classical approach, mean squared error minimization, can lead to suboptimal results when training neural networks. In this work, we propose a new method to improve the training of these models on regression tasks, with continuous scalar targets. Our method is based on casting this task in a different fashion, using a target encoder, and a prediction decoder, inspired by approaches in classification and clustering. We showcase the performance of our method on a wide range of real-world datasets. — Read More
#trainingDaily Archives: February 11, 2025
Deep Research and Knowledge Value
“When did you feel the AGI?”
This is a question that has been floating around AI circles for a while, and it’s a hard one to answer for two reasons. First, what is AGI, and second, “feel” is a bit like obscenity: as Supreme Court Justice Potter Stewart famously said in Jacobellis v. Ohio, “I know it when I see it.”
I gave my definition of AGI in AI’s Uneven Arrival: …My definition of AGI is that it can be ammunition, i.e. it can be given a task and trusted to complete it at a good-enough rate (my definition of Artificial Super Intelligence (ASI) is the ability to come up with the tasks in the first place).
The “feel” part of that question is a more recent discovery: DeepResearch from OpenAI feels like AGI; I just got a new employee for the shockingly low price of $200/month. — Read More
Rick's Cafe AI 