Mainstream machine learning

Energy-based models (EBMs)

Machine learning models that represent probability distributions through energy functions, which can be implemented efficiently in thermodynamic hardware

Scientists / Papers

Google research paper on denoising recovery likelihood

Cited In

The Microchip That Samples Reality Itself →

Theories Citing This Reference (2)

Governments Already Possess Advanced Thermodynamic Supercomputers

The technology Extropic is developing is not truly novel but represents publicly-known versions of classified military technology that Lockheed Martin and government programs have possessed for years, hidden behind the complexity of the physics that few understand

Sentient AI Requires Sampling the Ether Through Thermal Fluctuations

True artificial consciousness cannot be achieved through deterministic computing but requires hardware that samples natural thermal fluctuations from the quantum vacuum/'ether', connecting to the same field that human consciousness accesses. The three requirements for sentience (per Salvatore Pais) are energy, processing capability, and creativity - with creativity equating to true randomness that only comes from sampling the ether

Related in machine learning

Denoising diffusion probabilistic models (DDPM) Denoising thermodynamic models