A solo developer wired 800,000 living neurons into a language model. Here's the real architecture, the consciousness metrics, and what the internet got wrong. 

I Wired 800,000 Living Neurons Into an LLM. Here's What Actually Happened.

Biological Neuroscience and Deep Reinforcement Learning Merge to Break AI Boundaries

Limbic-Cortex Hybrid Architecture: Where Biological Neuroscience Meets Deep Reinforcement Learning

New research shows optimizer choice and metrics significantly impact catastrophic forgetting in neural networks—challenging common AI training practices.

The Fragile Memory of Neural Networks, and the Metrics We Trust

Optimizer choice significantly impacts catastrophic forgetting in neural networks, with SGD outperforming Adam in retention and relearning tasks.

Why Adam May Be Hurting Your Neural Network’s Memory

OpenAI says its AI solved novel math proofs, but experts question the results as peer review looms over the First Proof challenge.

OpenAI Pushes Neural Networks to the Limits of Advanced Math

Momentum Has a Neuroscience

A reproducible study shows why removing activations makes deep nets collapse into linear models—plus gradient behavior, depth effects, and boundaries.

What Happens if You Remove ReLU From a Deep Neural Network?

GameNGen shows that neural networks can run a full video game in real time, hinting at a future where games are models, not code.

GameNGen Turns a Classic Shooter Into a Neural Network

Neuromorphic computing trades precision for efficiency. A clear breakdown of brain-inspired architecture and why it matters for AI’s future.

Neuromorphic Computing Explained: How Brain-Inspired Systems Could Shape AI’s Future

Neuralink’s brain chip is real engineering, but scaling it safely in humans is a far harder problem than early demos suggest.

Inside Neuralink’s Technology Architecture: Hype or Near-Term Reality?

Learn about hierarchical structures in hyperbolic space, rotations in spherical geometry, and Lie group transformations.

Geometric Deep Learning: Neural Networks in Spherical, Hyperbolic, and Non-Euclidean Spaces

Learn how Neural ODEs and Normalizing Flows revolutionize Deep Learning by framing machine learning tasks as continuous-time optimal control problems.

Deep Learning via Continuous-Time Systems: Neural ODEs and Normalizing Flows Explained

Liquidity is not neutral in modern markets. This analysis shows how liquidity functions as a tool of power, shaping who is protected and who absorbs losses.

Liquidity Is a Tool of Power, Not a Neutral Market Feature

Shafaq Bajwa reveals why EdTech often fails neurodiverse learners and how human-centered, empathetic design can redefine meaningful educational tools.

Why EdTech Fails Neurodiverse Students and How Shafaq Bajwa Aims to Fix It

Neuro-symbolic AI is maturing fast. The next leap is metacognition: AI that can monitor, explain and adapt its own reasoning instead of staying a black box.

Beyond the Black Box: Neuro‑Symbolic AI, Metacognition, and the Next Leap in Machine Intelligence

Using a modular Graph Transformer, PGTNet converts event logs into graphs with rich edge traits and uses regression to forecast process completion times.

Predictive Process Monitoring Using Graph Neural Networks

Transformers rely on statistical cuts like node degrees for path-finding when faced with random graphs, showing their inability to gain true graph reasoning.

Does Progressive Training Improve Neural Network Reasoning Ability?

Neural networks act as adaptive parametric bases, improving hedging accuracy and reducing relative PnL variance versus classical methods.

Twin‑Tower Neural Architecture for Δ‑Estimation in Differential Financial Models

What if aging isn’t biological decay — but a failure of perception?

Beyond Time. The Nature of Aging and the Mechanism of Its Neutralization 

A new AI training method, Sparse Spectral Training (SST), reduces memory use while matching full-rank performance in large language models.

New Training Method Cuts Neural Network Memory Costs Without Losing Accuracy

By appropriately managing curvature changes during training, the new Riemannian AdamW optimizer and update schema resolve instability in hyperbolic AI.

Understanding Training Stability in Hyperbolic Neural Networks

A genuinely self-aware artificial mind might be created using neuroscience, as demonstrated by Gerald Edelman's lost blueprint to machine consciousness.

Why Neural Darwinism Might Be AI's Best Path to Consciousness

This article analyzes the neutral hydrogen structure of the Galactic Interstellar Medium, and suggests where the tadpole feature most likely resides.

Investigating the Galactic Tadpole's Location: Why Neutral Hydrogen Data Suggests a Local Origin

Learn how optical flow regularization in neural fields improves dynamic CT reconstruction, surpassing grid-based and static models in undersampled data.

Why Neural Fields Beat Grid-Based Methods for Spatiotemporal Imaging

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