Building Tomorrow’s AI: The Rise of LLMs and Inference

By Michael Alan Prestwood

The progression from early AI systems to the hybrid future includes digital and organic elements symbolizing the evolution and integration of AI within society.
AI Approaches
Futurism < Philosophy
TAKE-AWAY: AI is just now getting to the point where it can mimic the cognitive abilities of human brains and surface human behaviors. These devices can and should be created to fill niche environments that enhance humanity and propel us forward to a better tomorrow.
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Building Tomorrow’s AI: The Rise of LLMs and Inference

Setting the Stage for AI’s Evolution

Artificial intelligence has come a long way from its early days as a rule-following machine. What started as a field focused on strict, logic-based systems has evolved into a powerful area of technology that now touches on more complex, life-like tasks. Today’s AI doesn’t just follow instructions; it processes, predicts, and even generates language in ways that seem eerily human. Central to this evolution are two key players: Inference AI and Large Language Models (LLMs). Each represents a distinct step in the journey of AI, with Inference AI working to mimic our cognitive abilities and LLMs working to mimic our behavior. While it’s possible a third AI originating ability will emerge, currently Inference AI’s brain-like processing and LLMs human-like behavior are key to the future of AI. Together, they shape the current landscape and point toward a future where AI could blend these two approaches to form a hybrid intelligence. Much like the evolution of animals, the likely future of AI is a combination of evolving raw cognitive abilities that facilitate new and complex behaviors.

The Birth of AI: Symbolic AI and Rule-Based Foundations

The earliest attempts at AI took shape in the form of Symbolic AI, a method born directly from existing coding techniques and rooted in algorithmic logic. Developers in the mid-20th century approached AI as a series of logical steps, encoding their systems with rules that machines could follow, much like a set of instructions for solving a math problem. These systems were groundbreaking, as they gave machines the ability to make “decisions” within defined constraints. Symbolic AI could analyze data, reach conclusions, and even diagnose specific problems within narrow fields. In essence, these machines were like early “thinking” systems, limited in scope yet capable of following structured logic to achieve set goals.

AI started with classic coding techniques including flowcharts with if-then-else statements, while loops, and other foundational logic structures.

Interestingly, the foundations of Symbolic AI echo the way evolution has shaped cognitive abilities over millions of years. Just as life on Earth began with simple neural networks and gradually evolved into more sophisticated brains, Symbolic AI emerged as a first step toward machine cognition. Early programmers understood that evolutionarily basic neural pathways performed specific, repeatable functions, such as pattern recognition or simple decision-making, and they sought to replicate these functions in their early AI designs. However, these systems faced the same limitations as primitive brains—they were confined to fixed patterns of thought, unable to adapt or learn beyond their programming.

Despite their initial success, the limitations of Symbolic AI quickly became apparent, and by the 1970s, researchers were looking for more. As powerful as rule-based systems were within narrow parameters, they lacked flexibility and couldn’t generalize to broader, more complex tasks. This rigidity highlighted the need for more adaptive AI approaches, ones that could process a range of data and learn from it, rather than being locked into fixed routines. This realization would pave the way for the next stage in AI evolution: models that didn’t just follow rules but could infer patterns and adapt—a significant leap that would bring AI closer to mimicking real-life cognitive processes.

Inference AI: A Cognitive AI Approach

With the limitations of Symbolic AI becoming clear by the 1970s, researchers began searching for ways to mimic brain-like cognitive ability. They wanted to find an AI approach that could adapt and generalize beyond rigid, rule-based logic. This shift led to Inference AI, a breakthrough that moved away from fixed instructions toward recognizing patterns in data. Unlike Symbolic AI, which was bound by strict if-then rules, Inference AI could analyze vast amounts of information and make predictions, a function that resembles the way brains—both animal and human—process experiences and form generalizations. Although the goal wasn’t necessarily to mimic all aspects of the human brain, this approach marked a major step toward creating systems with human-like cognitive abilities.

Cognitive AI is attempting to mimic animal brains starting with simple neural network structures and flowing data. Inference AI’s brain-like cognitive processing and pattern recognition attempt to mimic cognitive ability.

Inference AI fundamentally redefined AI’s capacity to “learn” by using data to refine its responses, rather than simply executing predefined commands. Much like animals draw from past experiences to make decisions, Inference AI draws from patterns to inform its outcomes. This ability to adapt and adjust based on new data brought AI closer to mirroring human cognitive processes. It’s as if, rather than just following instructions, AI was now developing a memory of past patterns that informed its decisions. With this more flexible approach, AI became capable of handling complex tasks where answers weren’t simply black or white, creating a new realm of possibilities.

An important extension of Inference AI is generative AI, which adds an element of creativity to cognitive processing. Generative AI doesn’t merely recognize patterns; it creatively blends them, filling in gaps and forming new ideas based on learned experiences. This ability to generate content—such as art, text, or even potential solutions to problems—is another example of cognitive ability, echoing how humans and animals creatively adapt to novel situations. With generative AI, Inference AI took on a cognitive depth that went beyond simple prediction, becoming capable of producing imaginative, human-like outputs. This ability to synthesize and create marked a critical evolution, allowing Inference AI to move beyond traditional analysis into the realm of creativity and innovation.

LLMs: A Behavior AI Approach

While Cognitive AI was making great strides in replicating brain-like processing, by around 2015, researchers began searching for ways to go beyond cognitive ability to model human behavior more directly. This led to the development of Large Language Models (LLMs), which emerged in their transformative form in 2018 with models like OpenAI’s GPT and Google’s BERT. These LLMs didn’t aim to replicate the intricate processes of the human brain but rather to emulate the way humans interact with language and communicate. By focusing on language patterns rather than strict cognitive functions, LLMs opened the door for AI that could engage in natural, human-like conversations across a wide array of topics, making interactions feel more relatable and intuitive.

Behavioral AI tries to mimic animal behavior. Behavioral approaches like LLMs and Reinforcement Learning mix visual elements that evoke language patterns, feedback loops, and adaptive learning.

LLMs rely on vast amounts of language data to “learn” patterns in human communication. Trained on enormous datasets, these models pick up on context, tone, and phrasing, allowing them to generate responses that sound remarkably human. It’s as though LLMs have read a library’s worth of human interactions and can now mimic styles, ideas, and expressions. This focus on behavioral replication, rather than brain-like processing, makes LLMs uniquely suited to language-based tasks where natural-sounding responses are essential, from answering questions to drafting emails or even creative writing.

Complementing LLMs is Reinforcement Learning (RL), an approach focused on improving behavior through feedback loops. In RL, an AI learns by trial and error, adjusting its actions based on rewards or penalties—much like how humans learn through experience. This feedback-driven approach fine-tunes AI behavior, making it more effective over time. While not exclusive to LLMs, RL enhances behavioral AI by allowing systems to adapt and improve based on outcomes, creating a dynamic, interactive learning process that more closely resembles human adaptive learning.

Together, LLMs and RL represent a Behavioral AI approach, mimicking how humans adapt behaviorally rather than focusing solely on underlying cognitive abilities. As AI systems that can mirror human conversation and refine their actions through feedback, LLMs and RL bring AI closer to the expressive, responsive behaviors that make interactions feel human, setting the stage for a future where AI can engage meaningfully in real-world conversations and tasks.

The Hybrid Future: Cognitive Ability Facilitates Useful Behavior

As AI continues to evolve, a hybrid approach combining Cognitive AI’s precision with Behavioral AI’s adaptability holds the most promise for creating versatile, robust systems. By blending the pattern-recognizing capabilities of Inference AI with the human-like interactions of LLMs, hybrid AI systems can seamlessly transition between broad, conversational tasks and specialized, detail-driven decisions. This allows for a balanced AI that’s both responsive and accurate, able to handle everything from open-ended conversations to precise technical tasks.

While some Hybrid AIs will look humanoid and collaborate with humans, most will not.

Much like animals and humans use cognitive ability along with their senses and physical skills to facilitate behavior in specific environments, hybrid AI will do the same, integrating cognitive and behavioral elements into a cohesive whole. Soon, Earth will be populated with plants, animals, and AI robots, each filling a niche within the ecosystem. This hybrid intelligence would be capable of reasoning deeply in areas requiring expertise while also engaging flexibly in more generalized interactions. Just as our own adaptive intelligence enables us to move between instinctive responses and thoughtful problem-solving, hybrid AI represents a new frontier where artificial systems could mirror these strengths.

In thinking about AI’s future, it’s natural to wonder about the risks of creating systems so powerful and complex. But it’s important to remember that, unlike evolution, which is shaped by survival pressures in unpredictable environments, the development of AI is in human hands. We, as its creators, are in control of its direction, evolution, and purpose. Dangerous predators and deadly diseases evolved as responses to specific niches in nature, but AI is not subject to these random forces. AI’s development is a product of careful engineering, design, and ethical decision-making, which means we have the power to create tools—not threats—purpose-built to serve humanity’s needs.

Niche AI devices don’t have to be humanoid. They can be more like really smart AI vacuum cleaners.

With this in mind, we can approach AI like any other tool meant for specific roles within our reality. Just as bees pollinate flowers and cattle graze grasslands to sustain ecosystems, we can design AI to fill particular niches and remain within those bounds. These intelligent systems can evolve through nurture, not nature, tailored to complement an entire ecosystem that benefits all life, particularly human life. By defining AI’s purpose and function carefully, we create a future where these systems act as companions in progress, supporting the Earth’s ecosystem while staying true to their intended roles, each limited to its unique niche.

Real-World Applications and Implications of Hybrid AI

The potential applications for hybrid AI are vast, spanning sectors from healthcare to customer service to autonomous systems. In healthcare, for instance, a hybrid AI could analyze medical data with high precision while also communicating effectively with patients, providing both expertise and empathy. In customer service, hybrid AI could handle routine inquiries in a conversational way, while offering deep, accurate insights when faced with complex issues. Autonomous systems, such as self-driving vehicles, could benefit by combining high-stakes decision-making with the flexibility to adapt to unexpected scenarios on the road.

Beyond these specific applications, hybrid AI brings significant societal implications. As AI takes on more roles, balancing cognitive precision with human-like behavior will be key to building public trust in AI systems. This hybrid model also raises ethical questions about AI’s growing influence, from privacy and transparency concerns to ensuring that AI systems act responsibly and in alignment with human values. The way we approach this hybrid future will play a major role in shaping how AI integrates into our daily lives, industries, and communities.

Conclusion: Building Tomorrow’s AI Together

From its beginnings with Symbolic AI to the rise of Cognitive and Behavioral AI, the evolution of artificial intelligence has been marked by continual growth toward greater adaptability and human-like abilities. Today, the convergence of Inference AI and LLMs suggests a future where AI systems achieve both precision and flexibility through a hybrid model. By embracing the strengths of each approach, we stand on the brink of creating AI that can think and respond in ways that feel truly intuitive.

Looking ahead, the potential of hybrid AI invites us to imagine a future where intelligent systems are not just tools, but collaborative partners in solving the challenges of our world. Now, more than ever, it’s essential to consider the roles we want AI to play in shaping a future that benefits everyone—one that encourages innovation while grounding it in trust and ethical responsibility. Together, we are building tomorrow’s AI—one with the power to transform how we live, work, and interact in a rapidly advancing world.

— map / TST —

Michael Alan Prestwood
Natural Philosopher

Mike’s throwback title simply means he writes about philosophy, science, critical thinking, and history with a focus on exploring boundaries and intersections. While his focus is on our rational ideas about empirical observations, he does enjoy dabbling in the irrational. His exploration of human thought led him to develop his Idea of Ideas which allows him to understand what is empirically true, rationally true, and irrationally false.

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