15/06/2025
Types of AI Models Beyond Large Language Models (LLMs): Functions, Applications, and Limits
https://medium.com//types-of-ai-models-beyond-large-language-models-llms-functions-applications-and-limits-6058f5ee6c5d?source=rss-766367f66be3------2
Photo by Steve Johnson on Unsplash
AI models beyond LLMs serve distinct purposes — including CNNs for image analysis, RNNs/Transformers for sequential data, GANs for data generation, decision trees for tabular predictions, RL for trial-and-error learning, and unsupervised models for pattern discovery.
Key limitations persist — AI lacks true understanding, creativity, autonomous self-improvement, and consistent fact-checking; explainability, bias mitigation, and flexible objectives are areas of partial progress.
Future potential varies — some limitations (e.g., bias, fact-checking) may see technical progress, but challenges like sentience, genuine creativity, and full generalization will likely remain unresolved without breakthroughs in artificial general intelligence.
Types of AI Models Beyond LLMs
There are many types of AI models beyond large language models (LLMs). Each is designed with distinct architectures and purposes, addressing different kinds of tasks in science, industry, and everyday applications. Below is a structured overview of the key categories of AI models, their functions, and how they differ from LLMs.
Convolutional Neural Networks (CNNs)
Purpose:
Specialized for processing grid-like data, most notably images.
How they work: CNNs apply filters (or kernels) across an input (such as a picture) to detect features like edges, shapes, or textures. As layers are stacked, the network identifies increasingly complex patterns (e.g., from lines to faces).
Applications:
Medical imaging (detecting tumors in X-rays or MRIs)
Facial recognition
Autonomous vehicle vision systems (road sign detection)
Satellite image analysis
Example:
The AI system in Google Photos that groups similar faces uses CNNs.
Recurrent Neural Networks (RNNs) and Transformers
Purpose:
Designed for sequential data (RNNs) and efficient parallel processing of sequences (Transformers).
How they work:
RNNs process input one step at a time, retaining memory of previous steps (good for time series or speech).
Transformers process all elements of a sequence simultaneously while learning how different parts relate (used in LLMs but also independently).
Applications:
Speech recognition
Time-series forecasting (e.g., stock prices, weather patterns)
Machine translation (e.g., Google Translate)
Example:
Early voice assistants like Siri used RNN-like architectures. Transformers now dominate language and many sequence tasks.
Generative Adversarial Networks (GANs)
Purpose:
Produce new data that looks like a training dataset.
How they work:
GANs consist of two competing neural networks:
A generator creates fake data.
A discriminator tries to tell real from fake.
They improve by competing, so the generator gets better at producing convincing outputs.
Applications:
Creating realistic synthetic images or videos
Enhancing low-resolution images (super-resolution)
Simulating data where collecting real data is hard or expensive
Example:
GANs generate deepfakes or restore old photos.
Decision trees, Random forests, Gradient boosting machines (GBMs)
Purpose:
Structured, interpretable models for tabular data and decision making.
How they work:
A decision tree splits data by conditions (e.g., “if X > 5, go left; else, go right”).
Random forests build many trees and combine their results.
GBMs build trees sequentially, each correcting the last’s errors.
Applications:
Credit risk scoring
Medical diagnosis using patient records
Predictive maintenance in industrial systems
Example:
Your bank may use random forests to assess loan applications.
Reinforcement learning (RL)
Purpose:
Teach an agent to make decisions by trial and error to maximize a reward.
How they work:
An agent interacts with an environment, receives feedback (rewards or penalties), and learns which actions yield the best long-term gains.
Applications:
Robotics (teaching robots to walk or manipulate objects)
Game playing (e.g., AlphaGo, AlphaZero)
Industrial control systems
Example:
RL agents power warehouse robots that learn how to efficiently pick and place items.
Clustering and unsupervised models (e.g., K-means, DBSCAN, Autoencoders)
Purpose:
Find structure or patterns in unlabeled data.
How they work:
Clustering groups similar data points.
Autoencoders compress data into a smaller form, often used for anomaly detection.
Applications:
Customer segmentation in marketing
Anomaly detection in cybersecurity
Pattern discovery in genetics
Example:
A telecom company might use clustering to group users with similar usage patterns.
Symbolic AI (rule-based systems)
Purpose:
Encode human knowledge as rules and logic rather than learning from data.
How they work:
Symbolic AI uses if-then rules or logic statements to process information.
Applications:
Early expert systems (e.g., MYCIN for medical diagnosis in the 1970s)
Modern hybrid systems combining logic with machine learning
Example: Rule-based fraud detection systems in banking.
Evolutionary algorithms and genetic programming
Purpose:
Optimize solutions by simulating evolution.
How they work:
These models iteratively mutate and combine candidate solutions, keeping the best over generations.
Applications:
Engineering design (e.g., antenna shapes for satellites)
Automated code generation
Strategy development in complex games
Example:
NASA used evolutionary algorithms to design an efficient satellite antenna.
Photo by Growtika on Unsplash
What AI Models Cannot Do and Can These Limits Be Overcome?
Addressing whether the obstacles outlined can be overcome requires a careful, factual analysis. Each obstacle reflects both current technical limits and fundamental characteristics of AI as it exists today. Some may be mitigated with engineering advances, while others relate to the nature of AI itself and might not be fully surmountable. Let’s review each in detail, with realistic assessments of what is possible in the near or distant future.
No true understanding
Can it be overcome?
No, not with current or foreseeable AI architectures.
AI models, including LLMs and neural networks, function by detecting patterns in data, not by forming genuine comprehension or consciousness. While models may simulate understanding more convincingly (e.g., with better reasoning chains or multi-modal inputs), actual sentience or intent would require breakthroughs in artificial general intelligence (AGI), which remains speculative.
No fact-checking ability
Can it be overcome?
Partially.
Researchers are actively working on models that integrate external tools for fact-checking — for example, LLMs connected to real-time databases, scientific knowledge graphs, or search engines. These hybrids can validate or cross-reference claims during generation. However, ensuring universal truthfulness across all outputs is likely unachievable without tight human oversight because data sources themselves can contain inaccuracies or biases.
Limited generalization
Can it be overcome?
Partially.
Transfer learning, domain adaptation, and multi-modal models improve generalization. Efforts in building AI systems that can handle more diverse inputs (e.g., combining text, images, and structured data) are making headway. Yet, any model trained on finite data will face challenges with radically unfamiliar scenarios. Only true AGI, if it emerges, could fully generalize as humans do.
Poor explainability
Can it be overcome?
Partially.
Techniques like SHAP values, LIME, attention visualization, and interpretable model architectures help explain individual predictions. Entire subfields of AI (explainable AI or XAI) aim to make models clearer. However, full transparency in complex deep neural networks remains challenging because of their high-dimensional, non-linear nature. The deeper the network, the harder it is to provide precise human-interpretable explanations for every decision.
No generation of new science
Can it be overcome?
Unlikely in the fullest sense.
AI can assist in hypothesis generation, design simulations, and optimize experiments, but it will not replace human intuition, philosophical reasoning, or theory-building rooted in experience and creativity. That said, AI can greatly augment human discovery by proposing ideas that humans then validate.
Bias risk
Can it be overcome?
Can be mitigated, but not fully eliminated.
Bias can be reduced through careful dataset curation, adversarial training, fairness constraints, and post-processing audits. However, because all data reflects its context (including societal biases), some risk always persists. Achieving a bias-free AI is unrealistic, but responsible design can minimize harm.
Fixed objectives
Can it be overcome?
To a degree.
Advances in meta-learning, continual learning, and reinforcement learning allow AI to adjust to changing objectives within certain bounds. However, most models still require retraining or fine-tuning when objectives change significantly. True fluid adaptation across domains without retraining would require breakthroughs in AGI.
No physical agency
Can it be overcome?
Yes, through integration with robotics.
AI on its own is immaterial, but when paired with sensors, actuators, and physical platforms, it gains physical agency. Advances in robotics, edge computing, and control systems are steadily narrowing this gap. Many industrial and service robots today operate with embedded AI.
No true creativity
Can it be overcome?
Not in the human sense.
AI can simulate creativity — generating novel combinations of patterns — but it does not possess emotions, experiences, or intrinsic motivation that fuel human creativity. Tools may become better at mimicking the output of creative processes, but they will not feel or intend.
No autonomous self-improvement
Can it be overcome?
Partially.
There is active research into AI that can fine-tune itself during deployment (e.g., through online learning or self-supervised learning). But self-improvement without external feedback introduces risks of instability, unintended behaviors, or loss of alignment with human goals. Safe autonomous self-improvement remains a major open problem in AI safety and alignment research.
Some obstacles represent the current engineering and design limits of AI (e.g., fact-checking, generalization, bias mitigation) and may see substantial improvement. Others reflect fundamental characteristics of today’s AI (e.g., lack of understanding, true creativity) and are unlikely to be resolved without revolutionary advances toward AGI. Meanwhile, safe and ethical deployment will always require human oversight.
https://cdn-images-1.medium.com/max/1024/0*WsrvHd-r6pR6lCOl
