Overcoming Current Limitations in Machine Learning Models and AI LLMs: What to Expect in the Next 24 Months

Claude Paugh
2 hours ago
3 min read

Machine learning models and large language models (LLMs) have transformed many fields, from natural language processing to image recognition. Yet, despite impressive progress, these models face several key limitations that slow their development and practical use. Understanding these challenges and the innovations on the horizon helps clarify what the next 18 to 24 months will bring for AI capabilities. This post explores the current bottlenecks in machine learning and LLMs, the timeline for overcoming them, and the hardware advances that will support this evolution.

AI computing hardware powering machine learning models

Current Limiting Factors for Machine Learning Models and AI LLMs

1. Data Quality and Quantity

Machine learning models depend heavily on large, high-quality datasets. However, gathering diverse, unbiased, and well-labeled data remains a challenge. Many datasets contain errors, biases, or lack representation of minority groups, which leads to models that perform poorly in real-world scenarios or reinforce harmful stereotypes.

2. Model Size and Complexity

LLMs like GPT-4 have billions of parameters, requiring enormous computational resources for training and inference. This complexity leads to:

High energy consumption
Long training times
Difficulties in fine-tuning for specific tasks

These factors limit accessibility to only well-funded organizations and slow down innovation cycles.

3. Interpretability and Explainability

Understanding why a model makes a certain prediction is crucial for trust and safety, especially in sensitive areas like healthcare or finance. Current models operate as "black boxes," making it hard to explain their decisions or debug errors.

4. Generalization and Robustness

Models often struggle to generalize beyond their training data. They can fail when exposed to new, unexpected inputs or adversarial attacks. This lack of robustness limits their reliability in dynamic environments.

5. Hardware Constraints

Training and running large models require specialized hardware such as GPUs and TPUs. These devices are expensive, consume significant power, and have physical limits on memory and processing speed. The gap between hardware capabilities and model demands restricts scalability.

When Will These Limitations Be Overcome?

The pace of AI research and development suggests many of these challenges will see significant progress within the next two years.

Data improvements will come from better data collection tools, synthetic data generation, and more rigorous dataset curation. Techniques like data augmentation and active learning will reduce the need for massive labeled datasets.
Model efficiency will improve through innovations in architecture design, such as sparse models and modular networks that reduce parameter counts without sacrificing performance.
Explainability will advance with new methods for model introspection, including attention visualization and causal inference tools.
Robustness will benefit from adversarial training and domain adaptation techniques that help models handle diverse inputs.
Hardware will evolve with new chips designed specifically for AI workloads, offering faster processing and lower energy use.

What to Expect in the Next 18-24 Months

More Efficient and Accessible Models

Researchers are developing smaller, more efficient models that perform comparably to large LLMs. For example, techniques like knowledge distillation allow large models to teach smaller ones, making AI more accessible to organizations without massive computing budgets.

Advances in Multimodal Models

Models that combine text, images, audio, and video will become more common. These multimodal models will better understand context and provide richer outputs, improving applications like virtual assistants and content generation.

Improved Fine-Tuning and Personalization

Fine-tuning models for specific tasks or users will become faster and require less data. This will enable more personalized AI experiences in education, healthcare, and customer service.

Enhanced Safety and Ethical AI

New frameworks and tools will help detect and mitigate bias, ensuring AI systems behave fairly and transparently. Regulatory attention will also increase, pushing developers to prioritize ethical considerations.

Hardware Innovations Supporting AI Growth

Several hardware products are set to accelerate model development:

Next-generation GPUs and TPUs with higher memory bandwidth and energy efficiency
AI-specific accelerators like Graphcore’s IPU and Cerebras’ wafer-scale engine designed for parallel processing of neural networks
Neuromorphic chips that mimic brain activity to improve learning efficiency and reduce power consumption
Quantum computing research aimed at solving optimization problems faster, though practical applications remain a few years away

AI accelerator chip designed for faster machine learning training

Practical Examples of Progress

OpenAI’s GPT-4 introduced improvements in reasoning and context understanding, showing how model architecture tweaks can enhance performance without just increasing size.
Google’s PaLM model uses sparse activation to reduce computation while maintaining accuracy.
NVIDIA’s H100 GPU offers significant speed-ups for training large models, reducing energy costs and time.
Meta’s research on data-centric AI focuses on improving datasets rather than just models, leading to better real-world results.

What This Means for AI Users and Developers

The next two years will bring AI models that are faster, cheaper, and more reliable. Developers will have tools to build customized AI solutions without requiring massive infrastructure. Users will benefit from AI that understands context better, adapts to their needs, and operates more transparently.

Organizations should prepare by: