Beyond Transformers: Four Secret AI Technologies Set to Make Current Models Obsolete

Central Theme

The video argues that despite an apparent slowdown in public-facing AI progress, major research labs are secretly developing breakthrough technologies that address the fundamental flaws of current transformer-based Large Language Models (LLMs). These four emerging architectures promise to create AI that is vastly more intelligent, efficient, and capable, potentially making today’s models obsolete within a few years.

Key Points & Arguments

1. Infinite Lifespan AI (Subquadratic Models):
   • Problem: Current transformers have a limited “lifespan” or context window. Their performance degrades as conversations get longer because they must re-process the entire history (a quadratically scaling problem), making true continuous learning impossible.
   • Solution: New “subquadratic” architectures, like Google’s conceptual “Titans,” integrate memory directly into the system. These models can selectively remember and forget information, enabling an effectively infinite context window and allowing an AI instance to learn and adapt indefinitely without starting over.
2. Yann LeCun’s Post-LLM Architecture (JEPA):
   • Problem 1 (Memorization vs. Understanding): LLMs waste too many parameters memorizing the exact words of the internet, which is unlike human intelligence. This leaves less capacity for learning higher-level abstractions and true generalization.
   • Problem 2 (Fake Thinking): Current models don’t truly “think”; they “talk out loud” by generating text in a thought-process format. This is inefficient and not true reasoning.
   • Solution: The Joint Embedding Predictive Architecture (JEPA) pushes models to think in the abstract space of ideas (vectors) rather than words. By predicting abstract representations of missing data (e.g., in an image), the model develops a deeper conceptual understanding, enabling true internal reasoning before generating a final output.
3. Advanced Synthetic Data & Self-Improvement:
   • Problem: The internet is running out of high-quality data to train ever-larger models.
   • Solution: Creating highly effective synthetic data through self-play. The “Absolute Zero” model from China is a key example. It not only learns to solve problems but also learns to *propose* new, challenging problems for itself, creating a scalable, self-evolving training loop that doesn’t rely on external data.
4. The “World Model” (DeepMind’s Vision for AGI):
   • Problem: Today’s AI is siloed by modality (text, image, etc.). The human brain, in contrast, is a general-purpose processor that can interpret any sensory input, a concept the video calls the “Potato Head” model.
   • Solution: A true “World Model,” which is the ultimate vision for Google’s Gemini. This is an “anything model” that learns from all modalities simultaneously (video, text, sound, action) to build a rich, unified internal representation of the world, independent of the input source. This is presented as a more authentic path to AGI than purely economic-based definitions.

Conclusion & Takeaway

The most significant advancements in AI are currently happening behind closed doors, focused on overcoming the architectural limitations of today’s models. The future of AI lies in systems with persistent memory, true abstract reasoning, the ability to self-improve with synthetic data, and a unified, multi-modal understanding of the world. These leaps are not incremental; they represent a fundamental shift that will redefine what AI is capable of.

Mentoring Question

The video outlines four distinct technological leaps: infinite memory, abstract reasoning, self-generating data, and unified world models. Which of these advancements do you believe will have the most disruptive impact on your field or daily life first, and why?

Source: https://youtube.com/watch?v=V8xAQrdeGoo&si=OMUGFVfUqnPVnceN