이것이 직업의 미래에 대해 어떤 의미를 던질까요? ‘얼마나 빈번하고 방대한 과제를 다루는 일인가’ ‘얼마나 새로운 상황과 씨름해야 하는 일인가’란 질문으로 어떤 직업의 미래든 가늠할 수 있습니다. 빈번하고 방대한 과제를 다루는 일에서는 기계가 점점 더 똑똑해지고 있습니다. 몇 년이 흐르면 기계가 회계감사를 하고 법적인 계약서의 문안을 읽을 것입니다. 회계사와 변호사는 아직 필요합니다. 복잡한 세금체계, 혁신적인 소송 때문에 그들은 필요할 것입니다. 그러니 내 조카 얄리는 무슨 일을 하겠다고 결정하든 매일같이 새로운 도전을 해야 합니다. 그렇게 할 때 기계보다 계속 앞서나갈 수 있습니다.
The jobs we'll lose to machines and the ones we won't
So this is my niece. Her name is Yahli. She is nine months old. Her mum is a doctor, and her dad is a lawyer. By the time Yahli goes to college, the jobs her parents do are going to look dramatically different. In 2013, researchers at Oxford University did a study on the future of work. They concluded that almost one in every two jobs have a high risk of being automated by machines. Machine learning is the technology that's responsible for most of this disruption. It's the most powerful branch of artificial intelligence.My company, Kaggle, operates on the cutting edge of machine learning. We bring together hundreds of thousands of experts to solve important problems for industry and academia. Machine learning started making its way into industry in the early '90s. It started with relatively simple tasks. It started with things like assessing credit risk from loan applications, sorting the mail by reading handwritten characters from zip codes. Over the past few years, we have made dramatic breakthroughs. Machine learning is now capable of far, far more complex tasks. In 2012, Kaggle challenged its community to build an algorithm that could grade high-school essays. Last year, we issued an even more difficult challenge. Can you take images of the eye and diagnose an eye disease called diabetic retinopathy? Again, the winning algorithms were able to match the diagnoses given by human ophthalmologists. Now, given the right data, machines are going to outperform humans at tasks like this.
But there are things we can do that machines can't do. 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. Percy Spencer was a physicist working on radar during World War II, when he noticed the magnetron was melting his chocolate bar. He was able to connect his understanding of electromagnetic radiation with his knowledge of cooking in order to invent the microwave oven. Now, this is a particularly remarkable example of creativity. But this sort of cross-pollination happens for each of us in small ways thousands of times per day. Machines cannot compete with us when it comes to tackling novel situations, and this puts a fundamental limit on the human tasks that machines will automate.
So what does this mean for the future of work? The future state of any single job lies in the answer to a single question: To what extent is that job reducible to frequent, high-volume tasks, and to what extent does it involve tackling novel situations? On frequent, high-volume tasks, machines are getting smarter and smarter. Today they grade essays. They diagnose certain diseases. Over coming years, they're going to conduct our audits, and they're going to read boilerplate from legal contracts. Accountants and lawyers are still needed. They're going to be needed for complex tax structuring, for pathbreaking litigation. So Yahli, whatever you decide to do, let every day bring you a new challenge. If it does, then you will stay ahead of the machines.
| 앤서니 골드블룸(Anthony Goldbloom)|
캐글(Kaggle)의 공동 설립자이자 CEO이다. 캐글은 데이터과학자들이 데이터를 다운로드해서 어려운 문제들을 해결하는 기계학습 대회를 주최하고 있다.