We've seen what's possible by gathering large amounts of data and training artificial intelligence to interpret it: computers that learn to translate languages, facial recognition systems that unlock our smartphones, algorithms that identify cancers in patients. Data sets that fail to represent American society can result in virtual assistants that don't understand Southern accents; facial recognition technology that leads to wrongful, discriminatory arrests; and health care algorithms that discount the severity of kidney disease in African Americans, preventing people from getting kidney transplants. Hiring tools that learn the features of a company's employees can reject applicants who are dissimilar from existing staff despite being well qualified--for example, women computer programmers. Mortgage approval algorithms to determine credit worthiness can readily infer that certain home zip codes are correlated with race and poverty, extending decades of housing discrimination into the digital age. Training AI indiscriminately on internet conversations can result in "sentiment analysis" that views the words "Black," "Jew," and "gay" as negative.

In the past decade, data-driven technologies have transformed the world around us. We've seen what's possible by gathering large amounts of data and training artificial intelligence to interpret it: computers that learn to translate languages, facial recognition systems that unlock our smartphones, algorithms that identify cancers in patients. Eric Lander is science adviser to the president and director of the White House Office of Science and Technology Policy. Alondra Nelson is deputy director for science and society at the White House Office of Science and Technology Policy. But these new tools have also led to serious problems.

AI is proliferating in every major marketing cloud in nearly every marketing point solution, but most organizations are still experimenting with it. There are many use cases for AI that span every role in marketing from advertising to social, web, content, analytics and more. How you apply AI to marketing should reflect how you do your marketing. If you do a lot of outbound email, there are AI engines to enhance list selection and segmentation, subject lines and dynamic content. If you do a lot of social, there are AI engines to analyze sentiment, identify influencers, and post content.

You've got a classroom filled with middle school students working out math problems on computers. Which students are knocking them out with ease? In a pilot test of math software at David E. Williams (DEW) Middle School in Coraopolis, Pa., emojis tell the smart-glasses-wearing teacher what she needs to know. A smiling emoji hovers over a student's head: That means the student is progressing nicely. A frown emoji indicates struggle.

Andrew Ng is a hero and a role model for everyone who is starting the machine learning journey. One of his earliest Machine Learning courses saw lakhs of students enrolling and getting a huge boost to their careers. He is now back with a course in Deep Learning specialisation supported by his company Deeplearning.ai. Andrew Ng, one of the foremost artificial intelligence experts, is working hard to train more AI experts on a larger scale who can work across a range of industries. Ng has been an early adopter of online learning with the creation of Coursera.

With virtual assistants answering our emails and robots replacing humans on manufacturing assembly lines, mass unemployment due to widespread automation seems imminent. But it is easy to forget amid our growing unease that these systems are not "all-knowing" and fully competent. As many of us have observed in our interactions with artificial intelligence, these systems perform repetitive, narrowly defined tasks very well but are quickly stymied when asked to go off script -- often to great comical effect. As technological advances eliminate historic roles, previously unimaginable jobs will arise in the new economic reality. We combine these two ideas to map out potential new jobs that may arise in the highly automated economy of 2030.