Disruption can be deceptive
To many observers, AI progress might appear stagnant or incremental, particularly from a consumer perspective. However, beneath the surface, profound advancements have been occurring quietly, poised to trigger substantial breakthroughs starting in 2025, especially in practical, real-world applications where AI tackles complex challenges.
Significant strides have emerged in specialized areas less visible to the general public. AI models have become exceptionally adept at addressing PhD-level questions and are driving accelerated research in fields like materials science. Remarkably, AI's capability to diagnose and rectify software issues has surged dramatically—from around a 5% success rate to over 70% on various benchmarks. Google, for instance, now attributes over 25% of its newly generated code to AI systems.
AI systems continue to evolve towards greater autonomy, frequently outperforming human experts in specialized tasks while remaining cost-effective. New research further indicates this trajectory will persist. Notably, models fine-tuned on weaker, less expensive synthetic data often surpass those trained on more robust but costly datasets. Surprisingly, simpler AI models that generate a broader range of solutions frequently yield superior overall performance. By creating numerous inexpensive samples, these models address a more diverse set of problems, covering more unique scenarios. Additionally, multiple valid solutions to the same problem enable models to develop richer reasoning abilities.
While simpler models may initially produce "false positives"—correct outcomes despite flawed intermediate steps—final downstream models still learn robust reasoning skills. Ultimately, these models exhibit logic consistency comparable to those trained on more expensive, high-quality data.
This demonstrates that even modest models, given appropriate scaffolding and aggregation techniques, can leverage synthetic data effectively to achieve impressive performance, effectively bootstrapping from limited resources to powerful outcomes.
Google’s recent "Titans" architecture represents another potential breakthrough, supporting radically larger models with substantially improved long-term contextual retention.
Traditional Transformer models, which attend to every token in a fixed-length "context window," slow down and lose focus as they handle more extensive inputs. Titans addresses this limitation with a hybrid approach: immediate context processing handled by traditional attention mechanisms (short-term memory), complemented by a novel "neural memory" module for retaining and dynamically updating historical context (long-term memory).
The neural memory module adapts continuously during inference through gradient-based updates, allowing it to respond dynamically to new inputs. An adaptive update mechanism selectively incorporates new data, guided by a "forget gate" that discards obsolete information. This system employs a sophisticated model of "momentary surprise"—the degree of deviation from the current model's understanding—and "past surprise," representing a decaying record of past unexpected events. Inspired by human memory, this approach prioritizes retention of unexpected or novel inputs.
Titans' innovative design represents a significant step forward for neural network architecture, especially in handling exceptionally long sequences. By clearly delineating short-term attention and long-term memory functions, it becomes feasible to scale efficiently beyond millions of tokens. Expect to see a much larger generation of models applying techniques similar to this before long.
Quiet advancements in AI are paving the way for substantial, transformative developments in the near future. These stealthy surges are like bubbles rippling on the ocean before a whale blasts through the surface. Sooner or later, the other shoe will drop.