| Recorded at | February 15, 2016 |
|---|---|
| Event | TED2016 |
| Duration (min:sec) | 04:23 |
| Video Type | TED Stage Talk |
| Words per minute | 192.01 fast |
| Readability (FK) | 49.97 difficult |
| Speaker | Anthony Goldbloom |
| Country | Australia |
| Occupation | businessperson |
| Description | Australian businessman |
Official TED page for this talk
Synopsis
Machine learning isn't just for simple tasks like assessing credit risk and sorting mail anymore -- today, it's capable of far more complex applications, like grading essays and diagnosing diseases. With these advances comes an uneasy question: Will a robot do your job in the future?
| 1 | 00:12 | So this is my niece. | ||
| 2 | 00:14 | Her name is Yahli. | ||
| 3 | 00:16 | She is nine months old. | ||
| 4 | 00:18 | Her mum is a doctor, and her dad is a lawyer. | ||
| 5 | 00:21 | By the time Yahli goes to college, the jobs her parents do are going to look dramatically different. | ||
| 6 | 00:27 | In 2013, researchers at Oxford University did a study on the future of work. | ||
| 7 | 00:32 | They concluded that almost one in every two jobs have a high risk of being automated by machines. | ||
| 8 | 00:40 | Machine learning is the technology that's responsible for most of this disruption. | ||
| 9 | 00:44 | It's the most powerful branch of artificial intelligence. | ||
| 10 | 00:47 | It allows machines to learn from data and mimic some of the things that humans can do. | ||
| 11 | 00:51 | My company, Kaggle, operates on the cutting edge of machine learning. | ||
| 12 | 00:55 | We bring together hundreds of thousands of experts to solve important problems for industry and academia. | ||
| 13 | 01:01 | This gives us a unique perspective on what machines can do, what they can't do and what jobs they might automate or threaten. | ||
| 14 | 01:09 | Machine learning started making its way into industry in the early '90s. | ||
| 15 | 01:12 | It started with relatively simple tasks. | ||
| 16 | 01:15 | It started with things like assessing credit risk from loan applications, sorting the mail by reading handwritten characters from zip codes. | ||
| 17 | 01:24 | Over the past few years, we have made dramatic breakthroughs. | ||
| 18 | 01:27 | Machine learning is now capable of far, far more complex tasks. | ||
| 19 | 01:31 | In 2012, Kaggle challenged its community to build an algorithm that could grade high-school essays. | ||
| 20 | 01:38 | The winning algorithms were able to match the grades given by human teachers. | ||
| 21 | 01:43 | Last year, we issued an even more difficult challenge. | ||
| 22 | 01:46 | Can you take images of the eye and diagnose an eye disease called diabetic retinopathy? | ||
| 23 | 01:51 | Again, the winning algorithms were able to match the diagnoses given by human ophthalmologists. | ||
| 24 | 01:57 | Now, given the right data, machines are going to outperform humans at tasks like this. | ||
| 25 | 02:01 | A teacher might read 10,000 essays over a 40-year career. | ||
| 26 | 02:06 | An ophthalmologist might see 50,000 eyes. | ||
| 27 | 02:08 | A machine can read millions of essays or see millions of eyes within minutes. | ||
| 28 | 02:14 | We have no chance of competing against machines on frequent, high-volume tasks. | ||
| 29 | 02:20 | But there are things we can do that machines can't do. | ||
| 30 | 02:24 | Where machines have made very little progress is in tackling novel situations. | ||
| 31 | 02:28 | They can't handle things they haven't seen many times before. | ||
| 32 | 02:33 | The fundamental limitations of machine learning is that it needs to learn from large volumes of past data. | ||
| 33 | 02:39 | Now, humans don't. | ||
| 34 | 02:41 | We have the ability to connect seemingly disparate threads to solve problems we've never seen before. | ||
| 35 | 02:46 | Percy Spencer was a physicist working on radar during World War II, when he noticed the magnetron was melting his chocolate bar. | ||
| 36 | 02:54 | He was able to connect his understanding of electromagnetic radiation with his knowledge of cooking in order to invent -- any guesses? -- the microwave oven. | ||
| 37 | 03:03 | Now, this is a particularly remarkable example of creativity. | ||
| 38 | 03:06 | But this sort of cross-pollination happens for each of us in small ways thousands of times per day. | ||
| 39 | 03:12 | 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. | ||
| 40 | 03:22 | So what does this mean for the future of work? | ||
| 41 | 03:24 | 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? | ||
| 42 | 03:37 | On frequent, high-volume tasks, machines are getting smarter and smarter. | ||
| 43 | 03:42 | Today they grade essays. They diagnose certain diseases. | ||
| 44 | 03:44 | Over coming years, they're going to conduct our audits, and they're going to read boilerplate from legal contracts. | ||
| 45 | 03:50 | Accountants and lawyers are still needed. | ||
| 46 | 03:52 | They're going to be needed for complex tax structuring, for pathbreaking litigation. | ||
| 47 | 03:57 | But machines will shrink their ranks and make these jobs harder to come by. | ||
| 48 | 04:00 | Now, as mentioned, machines are not making progress on novel situations. | ||
| 49 | 04:04 | The copy behind a marketing campaign needs to grab consumers' attention. | ||
| 50 | 04:08 | It has to stand out from the crowd. | ||
| 51 | 04:10 | Business strategy means finding gaps in the market, things that nobody else is doing. | ||
| 52 | 04:14 | It will be humans that are creating the copy behind our marketing campaigns, and it will be humans that are developing our business strategy. | ||
| 53 | 04:21 | So Yahli, whatever you decide to do, let every day bring you a new challenge. | ||
| 54 | 04:27 | If it does, then you will stay ahead of the machines. | ||
| 55 | 04:31 | Thank you. | ||
| 56 | 04:32 | (Applause) |