Non un paper, ma di interesse. Da Yann LeCun (Meta "Chief AI scientist"):
https://twitter.com/ylecun/status/1776151785624801336
Quote:
Video of the Ding-Shum Lecture I gave at Harvard's Center of Mathematical Sciences and Applications on 2024-03-28.
"Objective-Driven AI: Towards AI systems that can learn, remember, reason, and plan"
Abstract: How could machines learn as efficiently as humans and animals?
How could machines learn how the world works and acquire common sense?
How could machines learn to reason and plan?
Current AI architectures, such as Auto-Regressive Large Language Models fall short. I will propose a modular cognitive architecture that may constitute a path towards answering these questions. The centerpiece of the architecture is a predictive world model that allows the system to predict the consequences of its actions and to plan a sequence of actions that optimize a set of objectives. The objectives include guardrails that guarantee the system's controllability and safety. The world model employs a Hierarchical Joint Embedding Predictive Architecture (H-JEPA) trained with self-supervised learning. The JEPA learns abstract representations of the percepts that are simultaneously maximally informative and maximally predictable.
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Paper:
https://openreview.net/forum?id=BZ5a1r-kVsf
Slides:
https://drive.google.com/file/d/1Ymx...qbpd9k_bo/view
Presentazione (video):
https://www.youtube.com/watch?v=MiqLoAZFRSE
Come noto, l'intelligenza degli LLM autoregressivi (il cui output dipende strettamente dal precedente input) al momento è solo apparente, non sono in grado di realmente pensare o pianificare azioni guardando al futuro. LeCun ha in mente una architettura che dovrebbe risolvere la maggior parte di questi problemi, al momento almeno parti di essa con risultati positivi in limitate applicazioni.