Anthony Goldbloom is the co-founder and CEO of Kaggle, a company hosting machine learning competitions, where data scientists download data and upload solutions to difficult problems. Kaggle has a community of over 600,000 data scientists and has worked with companies ranging from Facebook to GE on problems ranging from predicting friendships to flight arrival times. Goldbloom works extensively to crowd-source solutions to difficult problems using machine learning.
Before Kaggle, Goldbloom worked as an econometrician at the Reserve Bank of Australia, and before that the Australian Treasury. In 2011 and 2012, Forbes named Anthony one of the 30 under 30 in technology; 2013, the MIT Tech Review named him one of top 35 innovators under the age of 35, and the University of Melbourne awarded him an Alumni of Distinction Award. In addition, he holds a first call honors degree in Econometrics from the University of Melbourne.
Robots and Our Jobs
In his recent TED Talk, “The Jobs We’ll Lose to Machines and the Ones We Won’t,” Goldbloom says machine learning isn’t just for simple tasks anymore like assessing credit risk and sorting mail — today, it’s capable of far more complex applications, like grading essays and diagnosing diseases. With these advances in mind, Goldbloom addresses the uneasy question: Will a robot do your job in the future?
“In 2013, researchers at Oxford University did a study on the future of work and they concluded that almost one in every two jobs have a high risk of being automated by machines,” says Goldbloom. “Given the right data, machines are going to outperform humans at simple tasks. A teacher might read 10,000 essays over a 40-year career. An ophthalmologist might see 50,000 eyes. A machine can read millions of essays or see millions of eyes within minutes. We have no chance of competing against machines on frequent, high-volume tasks.”
However, Goldbloom says there is hope for us still.
“But there are things we can do that machines can’t do,” says Goldbloom. “Where machines have made very little progress is in tackling novel situations. They can’t handle things they haven’t seen many times before. The fundamental limitations of machine learning is that it needs to learn from large volumes of past data. Now, humans don’t. We have the ability to connect seemingly disparate threads to solve problems we’ve never seen before.”
Goldbloom says the future state of any work relies in one single question: To what extent is that job reducible to frequent, high-volume tasks, and to what extent does it involve tackling novel situations?
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