Current progress

  • Early 1940s: early computers
  • Late 1940s: cybernetics
  • Early 1950s: computational statistics
  • Mid 1950s: machine learning
  • 1956: artificial intelligence (AI)
  • Mid 1960s: natural language processing (NLP)
  • Late 1960s: computer vision
  • Late 1970s: robotics
  • 1990s – 2000s: data science and data mining
  • 2010s: deep learning
  • 2020s: neocybernetics

In 1956, Herb Simon, one of the “fathers of artificial intelligence”, predicted that within ten years computers would beat the world chess champion, compose “aesthetically satisfying” original music, and prove new mathematical theorems. It took forty years, not ten, but all these goals were achieved – and within a few years of each other! The music composed by David Cope’s programs cannot be distinguished, even by professors of music, from that composed by Mozart, Beethoven, and Bach. In 1976, a computer was used in the proof of the long-unsolved “four colour problem”.

Michael J. Beeson. The Mechanisation of Mathematics, in Alan Turing: Life and Legacy of a Great Thinker (2004).


We call the new science, built on the foundation of several modern and classical disciplines, neocybernetics. We are a neocybernetics company using the new science and technology to revolutionise finance, insurance, transportation, shipping, and medicine in the United Kingdom and worldwide.

Neocybernetics is built on:

  • data science
  • machine learning (ML)
  • deep learning (DL)
  • reinforcement learning (RL)
  • deep reinforcement learning (DRL)
  • artificial intelligence (AI)
  • big data
  • high-frequency data analysis
  • markets microstructure
  • quantitative finance
  • electronic trading
  • algorithmic trading
  • real-time computing
  • high-performance computing
  • reactive programming
  • message-driven architectures
  • low-latency messaging

Time series

Among other things, we apply neocybernetics to time series – sequences of timestamped updates, arriving in chronological order, on the state of a particular process evolving over time.

Example applications

  • stock prices
  • interest rates
  • currency exchange rates, including cryptocurrencies, such as bitcoin
  • micro- and macroeconomic data
  • states of a particular machine, e.g. in a car, on a ship, airplane, or on a space station
  • electrocardiogram (ECG) tests
  • electroencephalogram (EEG) tests
  • at the microscopic level, metabolic chain states
  • any medical test results as they evolve over time
  • fitband readings