The Localization Problem (LP) is a glaring dark cloud hanging over the state of affairs in applied deep learning. And acknowledging this problem, I believe, will enable us make better use of applied AI and expand our knowledge in how the business market will form. Defining LP: There is a limit to how much large centralized language models can generalize at scale given: 1) that different users inherently have varying definitions of ground-truths due to inter-dependencies to their unique real-world environment and 2) depending whether or not model performance is mission-critical. In other words, in certain conditions, in order for a model to be optimized for accuracy for a given user, the model needs to be "localized" to its user's ground truth in their data assuming that a model can't afford to be wrong too many times. Example: Imagine there is a kazillion parameter encoder transformer called Hal9000.

As I vaguely recall, 2001: A Space Odyssey might have been trying to tell us something about the dangers of artificial intelligence. But I haven't seen the movie in years, and instead of morals, all I can remember is how cool HAL9000 seemed, so I'm already fantasizing about making room on my wall for this replica of the lip-reading computer that includes a real AI of its own. Master Replicas Group has yet to reveal pricing information for its new 2001 Interactive HAL Computer prop replica, which almost certainly means it's going to cost a small fortune. But this is a rare time when a replica does more than just look pretty in a display case. We don't have all the details on what MRG's HAL9000 will be capable of--the group has only posted a couple of teasers so far--but in addition to the computer's glowing, unblinking eye, the replica will also have working displays, and functional voice recognition, as this video demonstrates.