Our vision is helping democratize artificial intelligence through
program synthesis & machine learning.

INTRODUCING

BayesLearn Systems provides businesses with a platform for building, automating and deploying data & machine learning models to help them solve challenges by finding the best predictive model for their data and offering probabilistic answers to ad hoc queries.

Our mission is building machine intelligence for advanced AI using Bayesian program synthesis & machine learning.

Indeed, we build probabilistic models coupled with simulation-based inference using Bayesian program synthesis while bringing human knowledge into the machine learning system. We are making it possible for everyone to do rigorous statistical analysis, even when their data is expensive, sparse, or complex.

SIMULATING SYSTEMS

The idea is to build Bayesian probabilistic programs that model the data and play the role of simulators allowing randomness & simulating systems but also the generative story of your data and how your data came to be via  probabilistic programs & Bayesian synthesis

Simulation is a key technology in the modern world to understand business problems of any kind.

Each program will try as many possibilities through random variable in the model as it can to try to match the data which gives the posterior distribution as output. Determinism is also part of our models especially when we have pretty good idea of the story that created the data.

AI WITH HUMAN-LIKE UNDERSTANDING

The idea is that we can take knowledge, put it into a machine learning system and then start getting results in the most meaningful ways possible. It is more human-like to understand things that learn and adapt in ways that people do, and not in ways that require you to get all kind of data and put it into the system. Put all these things together with push for explainability and a push for de-biasing to get what we are doing. It takes minutes not once or days to install it and mostly, it doesn't take millions of data points.

Common-sense knowledge that offers breakthrough improvements in accuracy & efficiency and creates a system where human intelligence can easily guide the program.

Our machine learning, is a next-generation system that represents a fundamental improvement in how machines can understand the human language and the knowledge expressed through it. It gives you the benefits of unsupervised learning (limited to no training data, fast implementation) while solving some of the real world problems with current offerings.

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MODEL AS A CODE

Automatic data modeling  using Bayesian synthesis of probabilistic programs.

Data is the basic building block of everything you do in modeling. We are specialized in building large scale Bayesian models and systems learning essentially the story about how the data came to be. In other words  we do automated data modeling via Bayesian synthesis of probabilistic programs from the observed data using Python and/or a bunch of Domain-Specific Languages (DSLs).

BAYESIAN INFERENCE

Using Bayesian probability as a mathematical framework to refine predictions about the world by means of acquired experience.

Bayesian methods remain the best way to express the probability theory & the theory of uncertainty. Probabilistic programming coupled with a Bayesian approach is the key to build solid data & machine learning models and make great approximations & accurate predictions by adjusting parameters, as far as real world data are concerned.

What can BayesLearn do for you? Learn how you can create new and innovative business opportunities that are fueled by data.

DATA GENERATIVE PROCESS

Allowing randomness and  simulating systems via  probabilistic programs & Bayesian synthesis.

The idea is to build Bayesian probabilistic programs that model the data and that play the role of simulators able to simulate the generative story of your data & how your data came to be. Each program will try as many possibilities through random variable in the model to match your sparse data which also gives the posterior distribution as output. Determinism will also be part of the model when we have pretty good idea of the story that created the data.