AI & Machine Learning

Agentic AI in Action - Part 17 - Building a Domain Expert Agent with Memory, Reasoning, and...

28 sec read268 views0 listens

A minimal implementation of an AI agent that retrieves knowledge and learns from its responses has been developed. This system uses OpenAI models for reasoning and NumPy for semantic similarity searches to build a small evolving knowledge base over interactions. Developers can leverage this approach to create domain-specific agents capable of incremental learning without complex orchestration frameworks, enhancing the utility of language models in practical applications.

Read the full article at Towards AI - Medium

Want to create content about this topic? Use Nemati AI tools to generate articles, social posts, and more.

You Might Like

Legal & PolicySep 328 sec read

DOJ Tells Court AI Training Is Fair Use, Forgets To Mention It's Negotiating A Stake In OpenAI

The Justice Department filed a statement declaring that training large language models on copyrighted text is fair use, citing national security concerns. However, it omitted mentioning ongoing negotiations for the government to acquire a 5% equity s...

Ali Nemati

AI & Machine LearningApr 2626 sec read

The Voice Interface Endgame: When Large Language Models Meet Your Living Room

Xiaomi has launched MiClaw, an advanced AI agent that uses large language models to understand user intent and autonomously orchestrate complex tasks across devices, marking a significant shift from traditional voice assistants. This development is c...

Ali Nemati

Education & EdTechMar 2426 sec read

Opinion: When Language Becomes a Barrier to Special Education

The article discusses how Latino families face significant barriers in accessing special education services due to language and structural issues, highlighting disparities in timely evaluations and meaningful parent participation as mandated by feder...

Ali Nemati