Applying for jobs online is one of the worst digital experiences of our time. And applying for jobs in person really isn't much better.
Memohon kerja secara online adalah antara pengalaman pahit, permohonan kerja secara berhadapan juga taklah begitu bagus.
[The Way We Work]
[Cara Kita Bekerja]
Hiring as we know it is broken on many fronts. It's a terrible experience for people. About 75 percent of people who applied to jobs using various methods in the past year said they never heard anything back from the employer. And at the company level it's not much better. 46 percent of people get fired or quit within the first year of starting their jobs. It's pretty mind-blowing. It's also bad for the economy. For the first time in history, we have more open jobs than we have unemployed people, and to me that screams that we have a problem.
Pengkaderan dipecahkan kepada beberapa peringkat. Ia menggerunkan ramai orang. Sekitar 75 peratus pemohon yang mengguna pelbagai cara pada tahun lepas mendakwa ketiadaan maklum balas majikan. Situasi di syarikat juga tak begitu baik. 46 peratus pekerja dipecat atau berhenti dalam tempoh tahun pertama bekerja. Ini amat mengejutkan. Ia tak bagus untuk ekonomi. Pertama kali dalam sejarah, pekerjaan melebihi penggangur, petanda wujudnya masalah.
I believe that at the crux of all of this is a single piece of paper: the résumé. A résumé definitely has some useful pieces in it: what roles people have had, computer skills, what languages they speak, but what it misses is what they have the potential to do that they might not have had the opportunity to do in the past. And with such a quickly changing economy where jobs are coming online that might require skills that nobody has, if we only look at what someone has done in the past, we're not going to be able to match people to the jobs of the future.
Saya yakin punca utama isu ini adalah resume. Resume pasti mempunyai maklumat berguna seperti: jawatan terdahulu, kemahiran komputer, penguasaan bahasa, tapi ia terlepas pandang akan potensi pemohon yakni peluang penambahbaikan yang mereka terlepas. Kepesatan perubahan ekonomi melahirkan kerjaya online yang perlukan skil baru namun calon pekerja sukar didapati jika pengalaman kerja yang lalu menjadi ukuran.
So this is where I think technology can be really helpful. You've probably seen that algorithms have gotten pretty good at matching people to things, but what if we could use that same technology to actually help us find jobs that we're really well-suited for? But I know what you're thinking. Algorithms picking your next job sounds a little bit scary, but there is one thing that has been shown to be really predictive of someone's future success in a job, and that's what's called a multimeasure test.
Di sinilah teknologi akan sangat membantu. Kita sedia maklum bahawa sistem algoritma semakin mahir memadankan citarasa pengguna. Mengapa tidak kita gunakan teknologi yang sama untuk membantu kita mencari kerjaya yang bersesuaian? Saya tahu kerisauan anda. Algoritma menentukan kerjaya anda? Kedengaran menakutkan, namun ada satu kaedah yang terbukti mampu menilai bakal kejayaan seseorang dalam pekerjaan. Kaedah ini dinamakan ujian aneka ukuran
Multimeasure tests really aren't anything new, but they used to be really expensive and required a PhD sitting across from you and answering lots of questions and writing reports. Multimeasure tests are a way to understand someone's inherent traits -- your memory, your attentiveness. What if we could take multimeasure tests and make them scalable and accessible, and provide data to employers about really what the traits are of someone who can make them a good fit for a job?
Ujian ini bukanlah sesuatu yang baru tapi kosnya agak mahal. Ia perlu pemilik PhD menyelia ujian anda, menjawab banyak soalan dan menulis laporan. Ujian aneka ukuran adalah satu cara untuk memahami perwatakan, daya ingatan, & daya perhatian seseorang. Mengapa tidak kita memanfaatkan ujian ini, meluaskan penggunaannya, dan menyediakan data kepada majikan tentang perwatakan yang sesuai bagi seseorang yang bakal mengisi jawatan itu?
This all sounds abstract. Let's try one of the games together. You're about to see a flashing circle, and your job is going to be to clap when the circle is red and do nothing when it's green.
Semua kedengaran abstrak? Mari kita cuba satu permainan. Anda akan melihat bulatan berkelip. Tugas anda, tepuk tangan tika bulatan berwarna merah & berdiam diri tika bulatan hijau.
[Ready?]
[Sedia?]
[Begin!]
[Mula!]
[Green circle]
[Bulatan hijau]
[Green circle]
[Bulatan hijau]
[Red circle]
[Bulatan merah]
[Green circle]
[Bulatan hijau]
[Red circle]
[Bulatan merah]
Maybe you're the type of person who claps the millisecond after a red circle appears. Or maybe you're the type of person who takes just a little bit longer to be 100 percent sure. Or maybe you clap on green even though you're not supposed to. The cool thing here is that this isn't like a standardized test where some people are employable and some people aren't. Instead it's about understanding the fit between your characteristics and what would make you good a certain job. We found that if you clap late on red and you never clap on the green, you might be high in attentiveness and high in restraint. People in that quadrant tend to be great students, great test-takers, great at project management or accounting. But if you clap immediately on red and sometimes clap on green, that might mean that you're more impulsive and creative, and we've found that top-performing salespeople often embody these traits.
Mungkin anda seorang yang menepuk tangan selepas beberapa detik bulatan merah muncul. Mungkin juga anda seorang yang mengambil sedikit masa untuk 100 peratus yakin. Mungkin juga anda bertepuk tangan tika bulatan hijau walau tak boleh. Yang menariknya, ini bukan ujian standard yang menentukan kelayakan seseorang untuk pekerjaan atau tidak. Sebaliknya ia mengenai pemahaman tentang kepadananan perwatakan anda dan kerjaya yang bersesuaian. Menurut ujian ini jika anda lambat tepuk tika merah dan tak tepuk tika hijau, anda mungkin mempunyai daya perhatian & kekangan yang tinggi. Orang dalam kelompok ini bakal menjadi pelajar dan calon ujian yang hebat, pakar menguruskan projek atau perakaunan. Namun jika anda tepuk cepat tika merah & kadang-kala tepuk tika hijau kemungkinan anda lebih mengikut gerak hati & kreatif. Kami mendapati jurujual yang cemerlang sering memiliki sifat ini.
The way we actually use this in hiring is we have top performers in a role go through neuroscience exercises like this one. Then we develop an algorithm that understands what makes those top performers unique. And then when people apply to the job, we're able to surface the candidates who might be best suited for that job.
Ujian ini diguna dalam pengkaderan dengan memberi ujian neurosains kepada calon terhebat seperti ini. Kemudian kita bina algoritma yang memahami faktor yang menjadikan mereka unik. Kemudian tika individu memohon kerja, kita dapat menapis calon yang sesuai untuk kerja itu.
So you might be thinking there's a danger in this. The work world today is not the most diverse and if we're building algorithms based on current top performers, how do we make sure that we're not just perpetuating the biases that already exist? For example, if we were building an algorithm based on top performing CEOs and use the S&P 500 as a training set, you would actually find that you're more likely to hire a white man named John than any woman. And that's the reality of who's in those roles right now. But technology actually poses a really interesting opportunity. We can create algorithms that are more equitable and more fair than human beings have ever been. Every algorithm that we put into production has been pretested to ensure that it doesn't favor any gender or ethnicity. And if there's any population that's being overfavored, we can actually alter the algorithm until that's no longer true. When we focus on the inherent characteristics that can make somebody a good fit for a job, we can transcend racism, classism, sexism, ageism -- even good schoolism.
Mungkin anda khuatir kemungkinan bahayanya. Dunia kerjaya kini taklah terlalu pelbagai. Jika algoritma dibina berdasarkan prestasi calon terhebat, bagaimana cara memastikan bahawa kita tidak meneruskan prasangka yang sudah wujud? Contoh, jika kita membina algoritma berdasarkan para CEO terhebat dan menggunakan S&P 500 sebagai set latihan, anda akan mendapati wujud kecenderungan untuk melantik lelaki mat saleh berbanding wanita. Itu gambaran realiti semasa jawatan tersebut. Begitupun teknologi sebenarnya mempunyai potensi yang menarik. Kita boleh cipta algoritma yang lebih saksama dan lebih adil daripada manusia. Setiap algoritma yang kami hasilkan telah diprauji untuk kepastian bahawa ia tak memilih kasih terhadap mana-mana jantina atau etnik. Sekiranya ada kelompok yang lebih disukai, kami boleh mengubah algoritma itu sehingga ia tak lagi benar. Apabila kita mengutamakan perwatakan yang sesuai untuk jawatan kosong tersebut, kita boleh mengatasi prejudis kaum, kasta kelas,jantina, umur -- bahkan sekolah yang bagus.
Our best technology and algorithms shouldn't just be used for helping us find our next movie binge or new favorite Justin Bieber song. Imagine if we could harness the power of technology to get real guidance on what we should be doing based on who we are at a deeper level.
Teknologi & algoritma terbaik tak sepatutnya hanya digunakan untuk mencari filem seterusnya atau lagu kegemaran Justin Bieber yang terbaru. Bayangkan jika kita dapat memanfaatkan potensi teknologi bagi mendapatkan petunjuk untuk tindakan yang bersesuaian dengan perwatakan sebenar kita.