The world of Artificial Intelligence (AI) will eventually move from ‘narrow AI’ to ‘broad AI’. Simply put, machines now do narrow or specific tasks and they would, in the near future, graduate into doing broader tasks or those across many areas. IBM says, “Broad AI will be characterised by the ability to learn and reason more broadly across tasks, to integrate information from multiple modalities and domains, all while being more explainable, secure, fair, auditable and scalable.”
Dario Gil, the COO and VP of AI and Quantum at IBM Research recently put out a blog that lists both the advances in the company’s AI research in 2018 and what progress we could expect in 2019.
In 2018, for instance, “AI learnt to hear what you are saying”. Machines can better comprehend what humans say while they make arguments. Gil mentions that IBM researchers also “presented a first-of-its-kind framework and algorithm to enable AI agents to learn to teach one another and work as a team. By exchanging knowledge, agents are able to learn significantly faster than previous methods”.
Gil lists three trends for the future.
First, he says that “causality will increasingly replace correlations”. That’s very technical but he offers an example: “Everyone knows that the rooster’s crowing at dawn does not cause the sun to rise, and that conversely, flipping a switch does cause a light to turn on. While such intuitions about the causal structure of the world are integral to our everyday actions and judgments, most of our AI methods today are fundamentally based on correlations and lack a deep understanding of causality. Emerging causal inference methods allow us to infer causal structures from data, to efficiently select interventions to test putative causal relationships, and to make better decisions by leveraging knowledge of causal structure. In 2019, expect causal modeling techniques to emerge as central players in the world of AI.”
The second trend Gil points out is around trust. Trusted AI, he says, will take centre stage. “This year, a number of organisations responded to data breaches and consumer privacy concerns by establishing ethics advisory boards, and we’ve seen increased research investment in the pillars of trust (algorithmic fairness, explainability, robustness, transparency), along with increased efforts in deploying AI for social good. In 2019, we’ll begin to see these efforts become central to how companies build, train and deploy AI technologies. We expect to see special focus on transferring research advances in this space into real products and platforms, along with an emphasis on encouraging diversity and inclusion on technical teams, to ensure that many voices and perspectives guide technological progress.”
The last trend is around quantum computing. In 2019, Gil, writes, the world would see higher traction in quantum research – “on how quantum computing can potentially play a role in training and running AI models”.