AlphaGeek AI -
Machine Intelligence at Developer Speed
Explore neural networks, symbolic reasoning, and neuro-symbolic AI through interactive visualizations. From transformers to theorem provers, master the mathematics behind modern AI systems.
Built for engineers who want to understand how AI really works -- rigorous technical deep dives into attention mechanisms, gradient descent, proof search, and hybrid architectures.
Core AI Topics
From attention mechanisms to program synthesis, explore the building blocks of modern AI.Click any topic to learn more and test your knowledge.
Self-attention, multi-head attention, and transformer architectures. How queries, keys, and values compute contextual representations that power modern language models.
Loss surfaces, saddle points, and local minima. Compare SGD, Adam, and RMSprop. Understand learning rates, momentum, and adaptive strategies.
Message passing on knowledge graphs. Learn node embeddings that preserve relational structure. Apply to molecular design and social networks.
Neural networks that generate code from specifications. Combine language models with formal verification to ensure correctness.
Pioneers of Computing and AI
The foundations of AI were laid by visionaries who defined computation, information, and intelligence.Click to explore each pioneer's contribution.
Alan Turing defined computation itself with his abstract machine. The Church-Turing thesis established the limits of what can be computed.
Claude Shannon founded information theory, defining entropy, channel capacity, and the fundamental limits of data compression and transmission.
John McCarthy coined "Artificial Intelligence" and created Lisp, the language of symbolic AI. His work on formal reasoning shaped the field.
Master AI Fundamentals
Six interactive modules covering neural networks, symbolic logic, theorem proving, and the emerging field of neuro-symbolic AI. Learn by doing.
Begin the JourneySelected References
Neural Networks
Goodfellow, Bengio, Courville. Deep Learning (2016)
Vaswani et al. Attention Is All You Need (2017)
Symbolic AI
Russell, Norvig. Artificial Intelligence: A Modern Approach (2021)
Robinson. A Machine-Oriented Logic (1965)
Neuro-Symbolic
Garcez et al. Neural-Symbolic AI: Third Wave (2023)
Marcus. The Next Decade in AI (2020)
Foundations
Turing. On Computable Numbers (1936)
Shannon. A Mathematical Theory of Communication (1948)
Related Topics in the Knowledge Network
Explore related subjects across the Global Knowledge Graph Network.