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.
Ini anak saudara saya, Namanya Yahli. Dia berusia sembilan bulan. Ibunya doktor, ayahnya peguam. Ketika Yahli ke kolej, pekerjaan ibu bapanya akan tampak sangat berbeza.
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.
Tahun 2013, penyelidik Universiti Oxford lakukan kajian kerjaya masa depan. Kesimpulannya hampir satu dalam setiap dua kerjaya punyai risiko tinggi akan diautomasikan oleh mesin. Pembelajaran mesin ialah teknologi yang bertanggungjawab kepada gangguan ini. Ianya satu cabang paling berkuasa dalam kecerdasan buatan. Ia membenarkan mesin belajar dari data dan mimik beberapa perkara yang manusia lakukan. Syarikat saya, Kaggle, beroperasi pada kecanggihan pembelajaran mesin. Kami membawa ratusan ribu pakar untuk selesaikan masalah penting untuk industri dan akademik. Ini berikan kami perspektif unik apa yang mesin boleh lakukan, apa mesin tidak boleh dan pekerjaan boleh guna mesin atau mesin ancam.
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.
Pembelajaran mesin bermula dalam industri pada awal 90an. Ia bermula dengan tugasan mudah. Bermula dengan perkara seperti mengakses risiko kredit dari aplikasi hutang, menyusun surat dengan baca huruf ditulis dari poskod. Beberapa tahun lepas, kami telah melakukan kejayaan dramatik. Pembelajaran mesin sekarang mampu lakukan tugasan lebih kompleks. Pada 2012, Kaggle mencabar komunitinya membina satu algoritma yang boleh menilai esei sekolah menengah. Algoritma pemenang mampu memadankan gred-gred yang diberi cikgu manusia. Tahun lepas, kami isukan cabaran lebih sukar. Bolahkan anda ambil imej mata dan periksa satu penyakit mata dipanggil diabetic retinopathy? Sekali lagi, algoritma pemenang mampu memadankan diagnosis diberikan pakar oftalmologi manusia.
Now, given the right data, machines are going to 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.
Sekarang, dengan data betul, mesin boleh menandingi manusia untuk tugas seperti ini. Cikgu mungkin baca 10,000 esei sepanjang kerjaya 40 tahun. Pakar oftalmologi mungkin lihat 50,000 mata. Mesin boleh membaca jutaan esei atau lihat jutaan mata dalam beberapa minit. Kita tidak mampu melawan mesin pada tugasan kerap dan jumlah banyak.
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.
Tapi ada perkara kita boleh lakukan yang mesin tidak boleh. Di mana mesin lakukan kemajuan sangat perlahan iaitu menangani situasi baru. Mereka tidak boleh menangani perkara tidak terjadi banyak kali sebelumnya. Batasan asas pembelajaran mesin ialah ia perlu belajar dari jumlah data masa lalu yang sangat besar. Sekarang, manusia tidak perlu. Kita ada keupayaan menyambung bebenang yang kelihatan berbeza untuk selesai masalah tak pernah hadapi.
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.
Percy Spencer ialah pakar fizik bekerja untuk radar semasa Perang Dunia ke II, bila dia perasan magnetron cairkan bar coklatnya. Dia mampu sambung pemahamannya dalam radiasi elektromagnetik dengan ilmu memasaknya dalam proses untuk mereka -- cuba teka? --ketuhar gelombang mikro.
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.
Sekarang, ini ialah contoh kreativiti luar biasa. Tapi pendebungaan silang ini terjadi kepada kita dalam cara kecil beribu kali sehari. Mesin tidak boleh melawan kita berkenaan menangani situasi baru, dan ini batasan asas pada tugasan manusia yang mesin boleh automasikan.
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. But machines will shrink their ranks and make these jobs harder to come by.
Jadi apakah maknanya pada masa depan kerjaya? Masa depan mana-mana kerjaya bergantung kepada satu soalan: Setakat mana kerja itu dikaitkan dengan tugasan kerap, jumlah banyak, dan setakat mana ia menangani situasi baru? Tugasan kerap, berjumlah banyak, mesin semakin pintar. Sekarang mereka menilai esei. Diagnos beberapa penyakit. Masa depan, mereka akan lakukan audit kita, mereka akan baca klausa boilerplate dari kontrak undang-undang. Akauntan dan peguam masih diperlukan. Mereka akan perlu untuk penstrukturan cukai kompleks, litigasi luar kebiasaan. Tapi mesin akan kecilkan kedudukan dan buat tugas ini berkurangan.
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.
Seperti saya katakan, mesin tak buat kemajuan dalam situasi baru. Salinan di belakang kempen pemasaran perlu mendapat perhatian pengguna. Ia perlu menyerlah. Strategi perniagaan adalah cari jurang dalam pasaran, perkara yang tiada siapa buat. Manusia yang akan cipta salinan di belakang kempen pemasaran kita, dan manusia akan membangun strategi perniagaan kita.
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.
Jadi Yahli, apapun kamu mahu jadi, biar setiap hari bawa cabaran baru. Jika sebegitu, kamu akan berada di hadapan mesin.
Thank you.
Terima kasih.
(Applause)
(Tepukan)