There will be dozens of new job titles emerging in the next few years that In a world where inequality seems to be on an inexorable rise, will the proliferation of AI be the solution that closes the gap between the privileged and underprivileged? If it's used ethically, then all the potential is there. Even though quality of life around the world has improved by almost all metrics over the last 50 years, from literacy to life expectancy, the divide between the'haves' and'have-nots' tends to dominate the news headlines, and it would be wrong to pretend that there are not endemic issues which need to be addressed if as a society we want to strive for true equality. It's most likely a fool's errand to try and'fix' the system from the top down. But to try and make sure that everyone gets access to the quality of life most of us are already lucky enough to enjoy, there's plenty that can be done from the bottom up. Primary amongst these is the potential of artificial intelligence to continue improving the quality of life for the minority who still suffer through living in poverty, without access to proper healthcare, or those who struggle to access the services society can and should provide.

The belief that robots, automation, and AI simply displace jobs and make humans irrelevant is not borne out in Asia, reports Chris Middleton. However, there are lessons to learn from the technologies' impact – in Asia and the rest of the world. Robots and automation are creating more jobs in Asia than they destroy, according to a new report from the Asia Development Bank (ADB). ADB analysis of a dozen Asian economies between 2005 and 2015 found that rising demand more than compensated for jobs lost to automation. The adoption of robotics and other connected systems stimulated higher productivity and economic growth, creating 134 million new jobs, compared with the 101 million lost to new technologies.

Natural language processing (NLP) is an important requirement for many AI systems. It's the same technology that powers Apple's Siri, Amazon's Alexa, and Facebook's M. But language can be problematic when you attempt to use machines to understand it. For example, if you say you want to meet at 2, it's important to know that you mean 2 p.m. and not 2 a.m. An NLP engine needs to learn the difference, and that requires large volumes of categorized data. Human expertise is required to create those training datasets.