Most studies about the impact of artificial intelligence (AI) on jobs and the economy have focused on developed countries such as the United States and Britain. Through my work as a scientist, technology executive and venture capitalist in the US and China, I have come to believe that the gravest threat AI poses is to emerging economies. In recent decades, China and India have presented the world with two different models on how countries can climb the development ladder. In the China model, the nation leveraged its large population and low costs to build a base of blue-collar manufacturing. The country then steadily worked its way up the value chain by producing better and more technology-intensive goods.

Most studies about the impact of artificial intelligence (AI) on jobs and the economy have focused on developed countries such as the United States and Britain. Through my work as a scientist, technology executive and venture capitalist in the US and China, I have come to believe that the gravest threat AI poses is to emerging economies. In recent decades, China and India have presented the world with two different models on how countries can climb the development ladder. In the China model, the nation leveraged its large population and low costs to build a base of blue-collar manufacturing. The country then steadily worked its way up the value chain by producing better and more technology-intensive goods.

Most studies about the impact of artificial intelligence (AI) on jobs and the economy have focused on developed countries such as the United States and Britain. Through my work as a scientist, technology executive and venture capitalist in the US and China, I have come to believe that the gravest threat AI poses is to emerging economies. In recent decades, China and India have presented the world with two different models on how countries can climb the development ladder. In the China model, the nation leveraged its large population and low costs to build a base of blue-collar manufacturing. The country then steadily worked its way up the value chain by producing better and more technology-intensive goods.

A good data scientist must be able to understand what the data is telling you, and to do that, you must have solid basic linear algebra, an understanding of algorithms and statistics skills. More advanced mathematics may be required for certain positions, but this is a good place to start. Machine learning is emerging as the buzzword but it is inextricably linked to big data. Machine learning uses artificial intelligence algorithms to turn data into value and learn without being explicitly programmed. Data scientists must know how to manipulate code in order to tell the computer how to analyse the data.