Recent improvements in machine learning (ML) have enabled the application of artificial intelligence (AI) in many different areas, resulting in significant achievements in computational vision, speech recognition or protein analysis.
Being amazing, these techniques suffer significant limitations, presenting what some call 'diminishing returns'. In particular, machine learning often only recognizes a pattern it has seen before, 'catastrophic forgetting', overwriting past knowledge with new knowledge, lacking explanation of the train of its thoughts or basic common sense.
For all these reasons, in comparative terms, all of these results correspond to basic human perception hugely empowered with computational resources. Evolution of the artificial intelligence requires additional techniques such as neuro symbolic computations or causal inference.
In this short presentation, after a broader definition of artificial intelligence, causal inference is introduced, explaining its main features and methods, proposing a model for a causal inference engine and describing the main elements of a prototype implemented in Kotlin.