20、 Obama
Int US 88398 Folder Collection
After playing the video, you can click or select the word to look it up in the dictionary.
Loading...
Report Subtitle Errors
So this is my niece.
Her name is Yahli.
She is nine months old.
Her mom 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.
It allows machines to learn from data,
and mimic some of the things that humans can do.
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.
This gives us a unique perspective on what machines can do,
what they can't do,
and what jobs they might automate or threaten.
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.
The winning algorithms were able to match the grades
given by human teachers.
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 gonna outperform humans at tasks like this.
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.
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 -- any guesses? -- 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 path-breaking litigation.
But machines will shrink their ranks
and make these jobs harder to come by.
Now, as mentioned,
machines are not making progress on novel situations.
The copy behind a marketing campaign needs to grab consumers' attention.
It has to stand out from the crowd.
Business strategy means finding gaps in the market,
things that nobody else is doing.
It will be humans that are creating the copy behind our marketing campaigns,
and it will be humans that are developing our business strategy.
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.
Thank you.
    You must  Log in  to get the function.
Tip: Click on the article or the word in the subtitle to get translation quickly!

Loading…

Loading…

Int US

【TED】Anthony Goldbloom: The jobs we'll lose to machines -- and the ones we won't (The jobs we'll lose to machines -- and the ones we won't | Anthony Goldbloom)

88398 Folder Collection
clara.english.0001 published on October 28, 2017    Su Kids translated    Mandy Lin reviewed

Learning Words

loading

Comments

Loading…
More Recommended Videos

  1. 1. Search word

    Select word on the caption to look it up in the dictionary!

  2. 2. Repeat single sentence

    Repeat the same sentence to enhance listening ability

  3. 3. Shortcut

    Shortcut!

  4. 4. Close caption

    Close the English caption

  5. 5. Embed

    Embed the video to your blog

  6. 6. Unfold

    Hide right panel

  1. Listening Quiz

    Listening Quiz!

  1. Click to open your notebook

  1. UrbanDictionary 俚語字典整合查詢。一般字典查詢不到你滿意的解譯，不妨使用「俚語字典」，或許會讓你有滿意的答案喔