AI Technology & Industry Review
Researchers from Google DeepMind introduce the concept of “Socratic learning.” This refers to a form of recursive self-improvement in artificial intelligence that significantly enhances performance beyond the initial data or knowledge available to the system, as well as a practical framework to implement it.
In a new paper Artificial Generational Intelligence: Cultural Accumulation in Reinforcement Learning, a research team from the University of Oxford and Google DeepMind introduces methods to achieve cultural accumulation in Reinforcement Learning (RL) agents. This research opens new pathways for modeling human culture through artificial systems.
In the new paper TaskMatrix.AI: Completing Tasks by Connecting Foundation Models with Millions of APIs, a Microsoft research team proposes TaskMatrix.AI, a novel ecosystem that connects foundation models with millions of existing models and system APIs to build a “super-AI” capable of addressing a wide range of digital and physical tasks.
In the new paper Sparks of Artificial General Intelligence: Early Experiments with GPT-4, a Microsoft Research team investigates GPT-4, demonstrating its ability to achieve human-level performance on novel and difficult tasks in domains ranging from mathematics and coding to vision, medicine, law and psychology; and proposing it as an early version of an AGI system.
In the new paper The Alignment Problem From a Deep Learning Perspective, a research team from OpenAI, UC Berkeley and the University of Oxford examines the alignment problem with regard to deep learning, identifying potential issues and how we might mitigate them.
A DeepMind research team proposes Gato, a single general-purpose transformer sequence model that can engage in dialogue, caption images, stack blocks with a real robot arm, navigate in simulated 3D environments and even beat human players at Atari games.