The Future of AI: Small Language Models, Small Agent Models, and Agent AI

I was reading Jeremiah Owyang's latest post—I always read Jeremiah because, well, he is Jeremiah (follow him—you will be smarter as a result)—and I thought about where AI is going and how we can apply it at the enterprise level or for innovation and really for the Good of All.

Here is the link to Jeremiah’s latest post: https://jowyang.beehiiv.com/p/future-relationships-ai-agents-and-companies

I've shared my thoughts below.

As we head towards artificial intelligence’s impact on industries and audiences across the globe, the trend is also shifting towards more efficient and niche AI models. For example, GPT-3 or GPT-4 are examples of large language models that caught the public’s mind, but concerns are growing around compact, task-oriented models that can operate efficiently. In this segment, we shall discuss how three interrelated terms are set to transform the landscape of AI: small language models, small agent models, and agent AI. We will describe what these abstractions are, how each is unique, and how they will likely be used in tandem to improve the design of AI systems.

Small Language Models: The Confluence of Efficiency and Capacity

What are Small Language Models?

Small language models (SLMs) are like their larger variants with a reduced size since they aim to complete particular language tasks rather efficiently. Contrary to the case with large language models (LLMs) built using lots of data to achieve multi-tasking, SLMs are primarily developed in more specific databases with specific purposes.

TLDR: Small Language Models:

• Smaller Size: Small LMs impose fewer parameters, usually a few million to a few billion, unlike LLMs, which are hundreds of billions.
• Domain-Specific Training: They are trained only for well-defined domains, such as opinion mining, named entity recognition, or, in some cases, industry-specific terminology.
• Less Hardware Resources Needed: Because of compactness, SLMs could operate on less configuration and hence can be deployed on edge devices or in scenarios with limited computational space.
• Quick Response: SLMs contain fewer parameters than the former models, so they can generate a response in less time.
• Better Security: One advantage of small models is that they can be deployed on user devices, reducing the need to upload information to the cloud and thus enhancing security.

Possible Usage of Small Language Models:

• Chatbots and Customer Service: SLMs could easily be created for such industries and comprehensively address the questions raised.
• Content Moderation: Targeted content SLMs can save time detecting and flagging inappropriate content in contextual scenarios.
• Text Summarization: Such models can help summarize long articles in a particular domain, such as law or medicine.
• Machine Translation: It would also be possible to concentrate on SLMs translating only specific language pairs or SLMs specific to certain domains.

Small Agent Models: Task-Aligned AI

What do we mean by Small Agent Models?

Small agent models take the idea of focused AI further. These models are oriented to focus on a language-processing unit and to be competent tools or act in some virtual environments performing particular functions. They are physical entities that can comprehend phrases in natural language and know what to do.

Distinctive Features of Small Agent Models:

• Task-Oriented Design: Such models are designed in structure-specific ways for certain interactions or kinds of interactions or tasks.
• Inclusion of Knowledge: It is not uncommon for small agent models to have reasonably advanced informational assets for their intended purpose.
• Initiative-Based: On the contrary, small agent models do not only embody linguistic abilities but are also capable of performing operations and making decisions based on the information obtained.
• Adaptability: They are also flexible and can be easily modified to fit within a behavioral domain for particular purposes.
• Economic Design: As with SLMs, small agent models are conceptualized and built so that they operate, to some extent, at lower computational power utilization.

Examples of Small Agent Models:

• Task Management Systems: Models function to plan and organize time, schedule reminders, and complete work simply.
• Wealth Managers: Realistic AIs provide simple investor support and offer investments under constraints.
• Healthcare Assistants: Consider them models who assist, including, but not limited to, symptom checkers, medication reminders, or even simple health advice.
• E-commerce Agents: E-commerce specialists with the capability of answering questions about products, receiving orders, or controlling stock of certain online stores.

Agent AI: The Next Stage in the Development of the Artificial Intelligence

What is Agent AI?

Agent AI can be considered a subset of a larger umbrella that allows both small and large models to be developed. The focus is on building AI systems that can function independently to complete certain tasks. Such agents are built to sense, think, and act to accomplish predefined goals or resolve certain challenges.

Key Features of Agent AI:

• Autonomy: Operating wholly independent of human interaction, making decisions, and performing the tasks that are expected from them is a reality for systems based on agent AI.
• Goal-Oriented Behavior: These agents are not mere robots that move around aimlessly but are equipped with a worthy plan and can think of examples of such plans.
• Adaptability: Through interactions with the real or artificial world, agent AI assimilates lessons and enhances its sophistication by modulating its behavior in relation to changing feedback and environment.
• Multi-Modal Interaction: Modality here refers to the multiple representations that agents (often termed multi-model) and AI interface variants can use to receive or convey specific information and understanding to users.
• Complex Decision-Making: Within Agent AI, a higher portion includes how computing offers technological and structural combinations in deep reasoning, planning, and solving tasks.

Applications of Agent AI:

• Self-Driving Cars: Voids AI agents that can drive cars and navigate in every set-up.
• Home Automation Systems: Smart Energy, home automation, and security systems are handled through agents.
• Game AI: NPC is envious of Shakespeare regarding judgment and decisions.
• Business Process Management: Invokes complex business process workflows automated by these agents.

Differences Between Small Language Models, Small Agent Models, and Agent AI

Essentially, these three concepts belong to one continuum, especially since they have some level of interrelationship but, in some cases, can stand out on their own:

Scope and Purpose:

• Small Language Models do not extend their boundaries beyond text-processing tasks.
• Small Agent Models perform precise activities in a specific area and generally include a decision-making component.
• Agent AI is a definition that prevails anything over the former two, where language processing or rather understanding is possible along with performing sophisticated tasks in different fields.

Autonomy and Decision-Making:

• SLMs generally do not claim the ability to make standalone decisions; they transform information into semantic/syntactic units and generate responsibility based on external inputs.
• Small Agent Models can only make simple decisions regarding the in-scope-restricted context.
• Agent AIs are much more intelligent in making decisions and using their abilities to carry out multiple tasks independently.

Interaction with the Environment:

• SLMs do not use any other media except for text that is written then though visually processed.
• Small Agent Models can connect to certain systems or databases specific to their task.
• Agent AI can usually connect to various systems and even physical structures.

Learning and Adaptation:

• SLMs remain static after training, although it is possible to make them do additional work to improve further.
• The small agent models may have only constrained learning instances in the specific domain.
• Advancement in Agent AI systems, with respect to Learning and Adaptation, is at a gradually upward trend.

Complexity and Resource Requirements:

• SLMs constitute the least degree of computational complexity and resources.
• Small Agent Models are small compared to SLMs, but they are less complicated and require fewer resources.
• In terms of complexity, even among agent AI systems, some agents are easy to complex, but in most cases, deep machines will always need more time computation to build such systems.

Collaboration of Little Models and the AI Agents: Revolutionizing the Concept of Working

The future of AI will evolve more efficiently if these diverse models synergistically integrate rather than if one model usurps all the others. In this section, I will provide an example of how small language models, small agent models, and agent AI working together enable the design of more powerful and flexible AI systems.

1. The Possibility of AI Systems Construction Based on Small Specialized Models

Building modular AI systems that are powerful and cost-effective by using smaller models that do specific tasks is possible. For instance:

In a customer service AI in SLM, an agent for natural language processing might be used, a small agent model for handling only a category of parts of queries, and a broad agent AI system for a multi-level query with several steps.

2. Composite Decision Making through Various Levels of the Agent AI Systems

Agent AI systems may employ small models as such models serve as part of other bigger decisions: Traffic sign recognition or detection of pedestrians might be small model tasks that an agent AI for an autonomous vehicle may have. However, the best agent overall makes routing and some decisions.

3. Dynamic Model Selection

Advanced AI systems may dynamically select the small model most suitable for the task at hand.

Such a virtual assistant can efficiently and accurately employ different SLMs or small agent models corresponding to the user’s request.

4. Continuous Learning and Improvement

When integrating different models, the AI systems acquire more capabilities and become able to learn continuously.

Could an agent AI system make use of the feedback portion from its interaction?

Please share your thoughts on the future of AI and its application in our world.