Abstract:

Cultural evolution is the application of evolution principles (variation, transmission, selection) to culture, e.g. customs, norms, languages, laws, religions, sciences.
It is considered as a key factor in the evolution of the human species.
Agent-based simulation of cultural evolution has been performed for long.
It has been successfully and convincingly applied to the evolution of natural languages.
We apply it to knowledge and beliefs.

Nowadays, the topics is renewed by the perspective of the deployment of artificial systems in our societies.
In order to be accepted, these systems should be able to adapt and behave along a culture shared within such societies.
Providing them with cultural evolution capabilities would contribute to their acceptance.
This is not any more cultural evolution simulation, but artificial cultural evolution, like there is artificial intelligence.
Knowing the principles by which societies can evolve their knowledge and beliefs should contribute to this.

Within this talk I will present the results of cultural ontology evolution within agents and how it allow them t succeed in their tasks, to improve their knowledge and to preserve its diversity.
I will also discuss theoretical approaches that could cover cultural knowledge evolution.

Abstract

Building production systems with machine learning components is challenging and many projects fail when moving into production even when showing initial success with training machine-learned models. Unfortunately data science education focuses narrowly on data analysis, machine-learning algorithms, and model building but rarely engages with how the model may be used as part of a system. Engineering aspects beyond deploying models are often ignored or underappreciated, including requirements engineering, user experience design, planning and testing integration with non-ML components, and planning for evolution, leading to poor outcomes in many real-world projects. Software engineers and data scientists often clash in teams due to different goals, processes, and expectations, finding it hard to effectively coordinate and integrate work. In this talk, I argue for the important roles that software engineers have in machine learning projects that want to move beyond a prototype model. I argue that truly a system-wide perspective is needed if we want to have any hope at making meaningful progress on safety, usability, fairness, or security. I explore the common collaboration problems and discuss strategies to overcome them. This talk is a call for more and better education in this space at the intersection of software engineering and machine learning, as well as for more system-wide research on building software systems with machine-learning components.