Human Life Model (HLM)
The Human Life Model (HLM) defines the minimum requirements needed for AI agents whose responses and behavior are indistinguishable from an actual human. The HLM focuses on the integration of artificial intelligence (AI) with human-like conversation, memory, learning, and behavior. The concept and model requirements were initially created by Andy LoCascio in late 2023 for Eternos.
Andy LoCascio is the co-founder and sole architect and builder of Eternos which was built by Sound Strategies.
Key Components of the Human Life Model
Though many components comprise our existence, six major components are the focus of ALL aspects of conversation and the associated behaviors.
- Memory – Long-term memory (life experiences), short-term memory (current conversation), and long-term conversation memory (we talked about this in the past).
- Knowledge – Basic principles, speech skills, and comprehension skills.
- Awareness – Self-awareness (I know who am I), audience recognition (I know who you are), role awareness (this is my overall purpose), and conversation awareness (I know what you expect from me).
- Emotions – Mostly determined by the tone of the conversation, unique to each specific response, and expressed through text and voice.
- Learning – Permanent knowledge acquisition through conversation (I will remember that), teaching, and media consumption.
- Escalation – Passing of information to another party (human or AI agent) based on the human visitor’s intent (I need someone to contact me) and the AI agent’s need (I do not know how to answer this).
Each of the major components have multiple elements that further define the requirements.
Frequently Asked Questions
The Human Life Model (HLM) is a framework that defines the minimum requirements for AI agents to behave and respond in ways that are indistinguishable from real humans. It focuses on integrating conversational ability, memory, awareness, learning, emotions, and decision-making into AI systems. The goal is to create AI agents capable of natural, human-like interactions that evolve over time through experience and conversation.
The Human Life Model was created by Andy LoCascio in late 2023 as part of the development of Eternos. LoCascio is the co-founder and sole architect of the Eternos platform, which was built by Sound Strategies. The model was designed to guide the development of AI agents capable of human-like conversation and behavior.
The Human Life Model focuses on six primary components: memory, knowledge, awareness, emotions, learning, and escalation. These elements work together to simulate how humans think, communicate, and adapt during conversations. Each component includes additional elements that help define how an AI agent should behave in realistic interactions.
Memory allows an AI agent to retain information from past interactions and apply it in future conversations. The HLM includes long-term memory for life experiences, short-term memory for the current conversation, and long-term conversation memory for remembering past discussions with the same person. This helps create continuity and more authentic human-like interactions.
Awareness in the Human Life Model refers to an AI agent’s understanding of itself, the person it is interacting with, and the context of the conversation. This includes self-awareness, audience recognition, role awareness, and conversation awareness. Together, these abilities help the AI respond appropriately and maintain a coherent conversational role.
In the Human Life Model, emotions are expressed through tone, wording, and conversational style. Emotional responses are influenced by the context and tone of the conversation and are unique to each interaction. This helps AI agents produce responses that feel more natural, empathetic, and human-like.
Learning enables AI agents to permanently acquire new knowledge through conversations, teaching, and media consumption. This allows the AI to improve over time and adapt its responses based on new information. Continuous learning is essential for creating AI that evolves similarly to human knowledge development.
Escalation refers to the ability of an AI agent to transfer a conversation or request to another party when necessary. This could involve routing the interaction to a human representative or another AI agent with more relevant expertise. Escalation ensures that user needs are met even when the AI cannot fully resolve the request on its own.
