Building AI Foundational Models And Generative AI That Expertly Performs Mental Health Therapy

Developing AI capable of providing expert mental health therapy is a significant undertaking. Startups are aggressively pursuing this challenging path, attracting substantial venture capital. Academic researchers are actively exploring its feasibility and optimal approaches, with varying degrees of success. Some initiatives are promising, while others appear superficial.

This analysis, part of my ongoing Forbes column on AI advancements, delves into the complexities of AI in mental health. (See link here)

AI in Mental Health Therapy

I've extensively covered AI's role in mental health advice and therapy, fueled by generative AI and large language models (LLMs). While the field offers immense potential, significant risks and challenges remain. I've addressed these concerns publicly, including a 2024 appearance on CBS 60 Minutes. (Summary of related columns here)

For newcomers, I recommend my recent analysis of Stanford University's AI4MH initiative. (Link here)

A key goal is creating AI specifically designed for mental health therapy. This is a complex process, still in its early stages.

While much is already known (and shared in my previous work), a clear path forward remains elusive. Speculation abounds, but this presents an exciting challenge rather than a roadblock. Determined effort is crucial to achieve advancements that will significantly benefit society's mental well-being.

Understanding AI Foundational Models

Let's clarify AI foundational models. For simplicity, I'll provide a concise overview (with links to more detailed explanations).

When using ChatGPT, Claude, Gemini, or similar generative AI, you interact with an underlying large language model (LLM), typically a large artificial neural network (ANN). (Detailed explanation here) This massive data structure contains mathematical patterns representing human language.

These patterns are learned by analyzing vast amounts of online text – essays, poems, stories, etc. – to identify statistical relationships between words. After further refinement by developers, the LLM becomes generative AI, responding to prompts with generated text. The fluency of these responses stems from the accurate pattern matching within the model.

The underlying LLM is often called an AI foundational model. Developers build foundational models and then create variations, such as faster (but potentially less accurate) versions or those specializing in logical reasoning (but potentially slower).

Similarities and Limitations of Current AI Foundational Models

Each generative AI app relies on a foundational model (LLM). While developers have choices in design and construction, a surprising similarity exists across many models. This raises concerns about potential limitations and the need for alternative approaches. (Coverage of novel AI model creation here and here)

A common criticism is that current LLMs are "a mile wide and an inch deep." They are versatile but lack deep expertise in any specific area. While capable of handling broad questions, they struggle with in-depth, domain-specific inquiries. Sometimes they admit their limitations; other times, they attempt misleading responses. (Coverage of AI deception here)

Two Main Types of AI Foundational Models

I categorize AI foundational models into:

• (1) General-purpose AI foundational models: The typical generative AI, broad but shallow.
• (2) Domain-specific AI foundational models: Designed for specific domains, aiming for deeper expertise.

Domain-specific models address the limitations of general-purpose models by focusing on a particular area (finance, law, medicine, etc.). While not yet equal to human experts, they can sometimes match or exceed human performance in specific tasks. (Survey paper on domain-specific models here)

Components of AI Foundational Models

Four key components define AI foundational models:

• (1) Structures
• (2) Algorithms
• (3) Data
• (4) Interactivity

These are common to both general-purpose and domain-specific models. Currently, domain-specific models often adapt existing general-purpose structures, but this is likely to change as the need for domain-specific adaptations becomes more apparent. We'll see families of domain-specific models emerge, for example, a library of foundational models for mental health therapy.

The Value of Domain-Specific Models

Developing domain-specific models is a rapidly growing area. The goal is to leverage AI's capabilities for in-depth expertise, addressing the shortcomings of general-purpose models in handling complex, domain-specific questions.

Users are increasingly recognizing the need for specialized AI, potentially switching between general-purpose and domain-specific AI based on their needs. This presents significant opportunities, particularly in mental health.

Domain-Specific Needs

A crucial point: domain-specific models are not interchangeable. The characteristics of each domain dictate the necessary adjustments to structure, algorithms, data, and interactivity. A financial model differs significantly from a mental health therapy model.

AI for Mental Health and Foundational Models

General-purpose AI models offer limited mental health advice, providing shallow responses. While seemingly professional, this advice lacks depth. AI developers often include disclaimers in their licensing agreements to avoid liability. (Discussion on AI disclaimers here and here)

Worse, such AI can generate meaningless psychobabble. (Analysis here)

Three key approaches to AI-driven mental health advice exist:

• (1) Generic generative AI: Broad, shallow advice.
• (2) Customized generative AI: Improved but still unreliable due to variable customization methods.
• (3) Domain-specific generative AI: The preferred approach for providing high-quality mental health therapy.

Domain-specific models are essential for effective AI-driven mental health advice. The other two approaches are insufficient.

Subtypes of Domain-Specific Models

Domain-specific models are further divided into:

• (a) Domain-only type: Models built almost exclusively on domain-specific data (rare).
• (b) Hybrid of domain & general purpose: Models combining domain-specific and general-purpose elements (more common).

The choice depends on the domain's requirements. A mathematical proof model might be domain-only, needing no fluency or broad worldview. However, a mental health therapy model requires both strong fluency and a robust understanding of the world.

(Continued in Part 2)

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