Back to school: a revolution in machine education and artificial intelligence

Classroom-style learning could become the norm for programming ethical, trustworthy robots, according to new research led by UNSW Canberra. 

In an exciting new revolution in artificial intelligence and machine learning, UNSW Canberra and University of Canberra researchers propose a novel approach to the programming of systems powered by AI.

This research contributes to the growing ‘machine education’ paradigm piloted by UNSW Canberra researcher and lead author Professor Hussein Abbass.

Machine education is possibly our only practically sound hope to truly design ethical, trustworthy, transparent and reliable AI systems,” Professor Abbass said.

Machine education proposes a more accessible approach to unlocking the power of AI, in contrast to the current attitude to machine learning which prioritises technical know-how and exclusivity in the undertaking of AI programming.

The combination of the machine education approach, together with workflows enabling two-way communication, promises to equip AI with the skills required to conduct ethical reasoning and logic-based synergy with their human peers.

Trust, fear, and performance expectancy are some of the biggest barriers to AI adoption across fields such as health care, manufacturing and autonomous vehicle sectors. This approach directly targets these concerns.

AI programming and machine learning has largely been approached through a data-driven lens up until now, according to UNSW Canberra Associate Professor Sondoss Elsawah.

“Machine learning so far has just meant ‘data in, data out’,” A/Prof Elsawah said.

“However, we need to move away from the idea of AI as a ‘black box’ in order to form mutual relationships between systems and users.”

By breaking down AI-enabled autonomous systems’ desired task outputs into sets and subsets of skills, similar to a university course curriculum, the researchers propose that they can educate AI in a classroom-style fashion.

This aims to ensure autonomous systems possess a sufficiently sophisticated knowledge base to pass tests and assessments, becoming qualified to work alongside domain experts.

By embedding a structured and systematic curriculum of content and knowledge within these systems, as well as the building blocks required to understand ethics, moral values, safety, and trust, these systems will be able to more successfully socially integrate into modern working conditions.

The paper, ‘A model of symbiomemesis: machine education and communication as pillars for human-autonomy symbiosis’ was published today by Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.