Language AI will take centre stage, with more start-ups getting funded in NLP than in any other category of AI.

Language is humanity’s most important invention. More than any other attribute, it is the defining hallmark of our species’ intelligence.

The field of natural language processing (NLP) has been upended and turbocharged in the past few years by a foundational new technology known as transformers, first introduced by Google researchers in a 2017 paper.

 “Responsible AI” will begin to shift from a vague catch-all term to an operationalized set of enterprise practices.

AI technology is improving faster than is our ability to deploy it responsibly, ethically and equitably.

A growing movement has emerged to advocate for the responsible use of AI. This push for more responsible AI spans a broad set of issues including AI bias, data provenance, model expandability and model auditability.

While awareness of these issues is growing, the topic remains sufficiently abstract that, by and large, AI practitioners do not build “responsible AI” practices into their day-to-day workflows.

Reinforcement learning will become an increasingly important and influential AI paradigm.

The dominant approach to AI today is supervised learning, which entails collecting a lot of data, labelling it, and feeding it into an AI model so that the AI learns useful patterns about the world. Unsupervised learning, a similar approach but without the need for human-generated labels, has also begun to gain traction in recent years.

In reinforcement learning, the AI is not trained on historical real-world data; it is not given the “answer key” and told what to pay attention to, as in supervised learning. Instead, it is allowed to open-endedly explore its environment, learning about the world as it goes, guided only by a particular objective that it seeks to optimize for.

As Forbes report Reinforcement learning may offer a path to a more sophisticated, flexible form of machine intelligence.