Time’s almost up! There’s only one week left to request an invite to The AI Impact Tour on June 5th. Don’t miss out on this incredible opportunity to explore various methods for auditing AI models. Find out how you can attend here.
Foundation models have revolutionized the fields of computer vision and natural language processing. Now, a group of researchers believe the same principles can be applied to create foundation agents, AI systems that can perform open-ended decision-making tasks in the physical world.
In a new position paper, researchers at the University of Chinese Academy of Sciences describe foundation agents as “generally capable agents across physical and virtual worlds” that will be “the paradigm shift for decision making, akin to[large language models] LLMs as general-purpose language models to solve linguistic and knowledge-based tasks.”
Foundation agents will make it easier to create versatile AI systems for the real world and can have a great impact on fields that rely on brittle and task-specific AI systems.
The challenges of AI decision-making
Traditional approaches to AI decision-making have several shortcomings. Expert systems heavily rely on formalized human knowledge and manually crafted rules. Reinforcement learning systems (RL), which have become more popular in recent years, must be trained from scratch for every new task, which makes them sample-inefficient and limits their ability to generalize to new environments. Imitation learning (IL), where the AI learns decision-making from human demonstrations also requires extensive human efforts to craft training examples and action sequences.
June 5th: The AI Audit in NYC
Join us next week in NYC to engage with top executive leaders, delving into strategies for auditing AI models to ensure optimal performance and accuracy across your organization. Secure your attendance for this exclusive invite-only event.
In contrast, LLMs and vision language models (VLMs) can rapidly adapt to various tasks with minimal fine-tuning o …
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Time’s almost up! There’s only one week left to request an invite to The AI Impact Tour on June 5th. Don’t miss out on this incredible opportunity to explore various methods for auditing AI models. Find out how you can attend here.
Foundation models have revolutionized the fields of computer vision and natural language processing. Now, a group of researchers believe the same principles can be applied to create foundation agents, AI systems that can perform open-ended decision-making tasks in the physical world.
In a new position paper, researchers at the University of Chinese Academy of Sciences describe foundation agents as “generally capable agents across physical and virtual worlds” that will be “the paradigm shift for decision making, akin to[large language models] LLMs as general-purpose language models to solve linguistic and knowledge-based tasks.”
Foundation agents will make it easier to create versatile AI systems for the real world and can have a great impact on fields that rely on brittle and task-specific AI systems.
The challenges of AI decision-making
Traditional approaches to AI decision-making have several shortcomings. Expert systems heavily rely on formalized human knowledge and manually crafted rules. Reinforcement learning systems (RL), which have become more popular in recent years, must be trained from scratch for every new task, which makes them sample-inefficient and limits their ability to generalize to new environments. Imitation learning (IL), where the AI learns decision-making from human demonstrations also requires extensive human efforts to craft training examples and action sequences.
June 5th: The AI Audit in NYC
Join us next week in NYC to engage with top executive leaders, delving into strategies for auditing AI models to ensure optimal performance and accuracy across your organization. Secure your attendance for this exclusive invite-only event.
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