The jobs we'll lose to machines -- and the ones we won't | Anthony Goldbloom

Translator: Nurul Hanim Abd Karim Reviewer: Aznah Binti Arshad

Ini anak saudara saya,

Namanya Yahli.

Dia berusia sembilan bulan.

Ibunya doktor, ayahnya peguam.

So this is my niece.

Ketika Yahli ke kolej,

Her name is Yahli.

pekerjaan ibu bapanya akan tampak sangat berbeza.

She is nine months old.

Her mum is a doctor, and her dad is a lawyer.

Tahun 2013, penyelidik Universiti Oxford lakukan kajian kerjaya masa depan.

By the time Yahli goes to college,

the jobs her parents do are going to look dramatically different.

Kesimpulannya hampir satu dalam setiap dua kerjaya punyai risiko tinggi

In 2013, researchers at Oxford University did a study on the future of work.

akan diautomasikan oleh mesin.

Pembelajaran mesin ialah teknologi

They concluded that almost one in every two jobs have a high risk

yang bertanggungjawab kepada gangguan ini.

of being automated by machines.

Ianya satu cabang paling berkuasa dalam kecerdasan buatan.

Ia membenarkan mesin belajar dari data

Machine learning is the technology

dan mimik beberapa perkara yang manusia lakukan.

that's responsible for most of this disruption.

Syarikat saya, Kaggle, beroperasi pada kecanggihan pembelajaran mesin.

It's the most powerful branch of artificial intelligence.

Kami membawa ratusan ribu pakar

It allows machines to learn from data

and mimic some of the things that humans can do.

untuk selesaikan masalah penting untuk industri dan akademik.

My company, Kaggle, operates on the cutting edge of machine learning.

Ini berikan kami perspektif unik apa yang mesin boleh lakukan,

We bring together hundreds of thousands of experts

apa mesin tidak boleh

to solve important problems for industry and academia.

dan pekerjaan boleh guna mesin atau mesin ancam.

Pembelajaran mesin bermula dalam industri pada awal 90an.

This gives us a unique perspective on what machines can do,

what they can't do

Ia bermula dengan tugasan mudah.

and what jobs they might automate or threaten.

Bermula dengan perkara seperti mengakses risiko kredit dari aplikasi hutang,

Machine learning started making its way into industry in the early '90s.

menyusun surat dengan baca huruf ditulis dari poskod.

It started with relatively simple tasks.

It started with things like assessing credit risk from loan applications,

Beberapa tahun lepas, kami telah melakukan kejayaan dramatik.

sorting the mail by reading handwritten characters from zip codes.

Pembelajaran mesin sekarang mampu lakukan tugasan lebih kompleks.

Pada 2012, Kaggle mencabar komunitinya

Over the past few years, we have made dramatic breakthroughs.

membina satu algoritma yang boleh menilai esei sekolah menengah.

Machine learning is now capable of far, far more complex tasks.

Algoritma pemenang mampu memadankan gred-gred

In 2012, Kaggle challenged its community

yang diberi cikgu manusia.

Tahun lepas, kami isukan cabaran lebih sukar.

to build an algorithm that could grade high-school essays.

Bolahkan anda ambil imej mata dan periksa satu penyakit mata

The winning algorithms were able to match the grades

given by human teachers.

dipanggil diabetic retinopathy?

Sekali lagi, algoritma pemenang mampu memadankan diagnosis

Last year, we issued an even more difficult challenge.

Can you take images of the eye and diagnose an eye disease

diberikan pakar oftalmologi manusia.

called diabetic retinopathy?

Sekarang, dengan data betul, mesin boleh menandingi manusia

Again, the winning algorithms were able to match the diagnoses

untuk tugas seperti ini.

Cikgu mungkin baca 10,000 esei sepanjang kerjaya 40 tahun.

given by human ophthalmologists.

Now, given the right data, machines are going to outperform humans

Pakar oftalmologi mungkin lihat 50,000 mata.

at tasks like this.

Mesin boleh membaca jutaan esei atau lihat jutaan mata

A teacher might read 10,000 essays over a 40-year career.

dalam beberapa minit.

Kita tidak mampu melawan mesin

An ophthalmologist might see 50,000 eyes.

A machine can read millions of essays or see millions of eyes

pada tugasan kerap dan jumlah banyak.

within minutes.

Tapi ada perkara kita boleh lakukan yang mesin tidak boleh.

We have no chance of competing against machines

Di mana mesin lakukan kemajuan sangat perlahan

on frequent, high-volume tasks.

iaitu menangani situasi baru.

But there are things we can do that machines can't do.

Mereka tidak boleh menangani perkara tidak terjadi banyak kali sebelumnya.

Where machines have made very little progress

Batasan asas pembelajaran mesin

is in tackling novel situations.

ialah ia perlu belajar dari jumlah data masa lalu yang sangat besar.

They can't handle things they haven't seen many times before.

Sekarang, manusia tidak perlu.

Kita ada keupayaan menyambung bebenang yang kelihatan berbeza

The fundamental limitations of machine learning

is that it needs to learn from large volumes of past data.

untuk selesai masalah tak pernah hadapi.

Percy Spencer ialah pakar fizik bekerja untuk radar semasa Perang Dunia ke II,

Now, humans don't.

We have the ability to connect seemingly disparate threads

bila dia perasan magnetron cairkan bar coklatnya.

to solve problems we've never seen before.

Percy Spencer was a physicist working on radar during World War II,

Dia mampu sambung pemahamannya dalam radiasi elektromagnetik

dengan ilmu memasaknya

when he noticed the magnetron was melting his chocolate bar.

dalam proses untuk mereka -- cuba teka? --ketuhar gelombang mikro.

He was able to connect his understanding of electromagnetic radiation

Sekarang, ini ialah contoh kreativiti luar biasa.

with his knowledge of cooking

Tapi pendebungaan silang ini terjadi kepada kita dalam cara kecil

in order to invent -- any guesses? -- the microwave oven.

beribu kali sehari.

Now, this is a particularly remarkable example of creativity.

Mesin tidak boleh melawan kita

berkenaan menangani situasi baru,

But this sort of cross-pollination happens for each of us in small ways

dan ini batasan asas pada tugasan manusia

thousands of times per day.

yang mesin boleh automasikan.

Machines cannot compete with us

Jadi apakah maknanya pada masa depan kerjaya?

when it comes to tackling novel situations,

and this puts a fundamental limit on the human tasks

Masa depan mana-mana kerjaya bergantung kepada satu soalan:

that machines will automate.

Setakat mana kerja itu dikaitkan dengan tugasan kerap, jumlah banyak,

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:

dan setakat mana ia menangani situasi baru?

To what extent is that job reducible to frequent, high-volume tasks,

Tugasan kerap, berjumlah banyak, mesin semakin pintar.

Sekarang mereka menilai esei. Diagnos beberapa penyakit.

and to what extent does it involve tackling novel situations?

Masa depan, mereka akan lakukan audit kita,

On frequent, high-volume tasks, machines are getting smarter and smarter.

mereka akan baca klausa boilerplate dari kontrak undang-undang.

Today they grade essays. They diagnose certain diseases.

Akauntan dan peguam masih diperlukan.

Over coming years, they're going to conduct our audits,

Mereka akan perlu untuk penstrukturan cukai kompleks,

and they're going to read boilerplate from legal contracts.

litigasi luar kebiasaan.

Tapi mesin akan kecilkan kedudukan

Accountants and lawyers are still needed.

dan buat tugas ini berkurangan.

They're going to be needed for complex tax structuring,

Seperti saya katakan,

mesin tak buat kemajuan dalam situasi baru.

for pathbreaking litigation.

Salinan di belakang kempen pemasaran perlu mendapat perhatian pengguna.

But machines will shrink their ranks

and make these jobs harder to come by.

Ia perlu menyerlah.

Now, as mentioned,

machines are not making progress on novel situations.

Strategi perniagaan adalah cari jurang dalam pasaran,

The copy behind a marketing campaign needs to grab consumers' attention.

perkara yang tiada siapa buat.

Manusia yang akan cipta salinan di belakang kempen pemasaran kita,

It has to stand out from the crowd.

dan manusia akan membangun strategi perniagaan kita.

Business strategy means finding gaps in the market,

things that nobody else is doing.

Jadi Yahli, apapun kamu mahu jadi,

It will be humans that are creating the copy behind our marketing campaigns,

biar setiap hari bawa cabaran baru.

and it will be humans that are developing our business strategy.

Jika sebegitu, kamu akan berada di hadapan mesin.

So Yahli, whatever you decide to do,

Terima kasih.

(Tepukan)

let every day bring you a new challenge.

If it does, then you will stay ahead of the machines.

Thank you.

(Applause)