Next generation of AI

What are some other AI models (post LLM, not using LLM methods) that go beyond the current high-end LLMs and LLMs on the near horizon but are not proven to work yet?

Several experimental AI architectures and paradigms push beyond current post-LLM models but remain unproven at scale. These speculative approaches aim to address transformer limitations through radical structural innovations:

1. Quantum-Integrated Architectures

Early prototypes combine quantum computing principles with neural networks for optimization tasks.

Challenges include qubit instability and error correction, with hardware colocation in supercomputers being explored.

2. Liquid Neural Networks

Dynamic networks that continuously adapt weights during deployment, addressing transformers’ static parameter limitation.

Potential applications: Real-time control systems and robotics requiring continuous learning.

3. Self-Improving AGI Prototypes

Systems using automated synthetic data generation loops to refine capabilities without human intervention.

Early research shows promise in mathematical theorem proving but lacks real-world validation.

4. Neuro-Symbolic Integration

Hybrid systems combining neural networks with symbolic reasoning engines.

Theoretical benefits: Enhanced interpretability and causal reasoning for scientific discovery.

5. Brain-Inspired Computing Models

Spiking neural networks mimicking biological neuron communication.

Energy-efficient designs paired with neuromorphic chips (e.g., Intel Loihi) remain experimental.

6. Photonic Computing Designs

Optical neural networks using light instead of electrons for low-power processing.

Early prototypes achieve 10x energy efficiency gains but lack software ecosystems.

7. Model Collaboration Ecosystems

Federated systems where specialized models co-evolve through shared parameter spaces.

Example: Meteorological models combining weather prediction with energy grid optimization.

These approaches face significant hurdles in scalability, reproducibility, and integration with existing infrastructure. While promising in theory, none have yet demonstrated transformer-level performance across diverse tasks. Research priorities include developing standardized benchmarks for cross-architecture comparisons.

Key Insights:

1. Near-Term Focus: Model collaboration systems lead due to incremental improvements over existing federated learning frameworks, with early prototypes already deployed in weather prediction and supply chain optimization.
2. Reasoning vs Practicality: Neuro-symbolic approaches show strong reasoning potential but face integration challenges between neural and symbolic components.
3. Hardware Dependency: Photonic and quantum models remain constrained by current immature supporting infrastructure despite theoretical advantages.

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1. https://www.linkedin.com/pulse/future-large-language-models-llms-2025-beyond-rahul-chaube-cfkac
2. https://blogs.nvidia.com/blog/generative-ai-predictions-2025-humanoids-agents/
3. https://www.forbes.com/sites/robtoews/2023/09/03/transformers-revolutionized-ai-what-will-replace-them/
4. https://www.linkedin.com/pulse/future-ai-beyond-transformers-robyn-le-sueur-swtqf
5. https://www.gov.uk/government/publications/international-ai-safety-report-2025/international-ai-safety-report-2025
6. https://www.chartis-research.com/points-of-view/7947299/no-surprises-2025-will-be-another-big-year-for-ai

https://www.eurekalert.org/news-releases/1076166

Watch the animated musical story / prediction of where things could be going and how we get there. Trigger warning: It starts out dark.