A view on a very hot topic – My Job!
2017 just begun and 34 white-collar workers at Japan’s Fukuoka Mutual Life Insurance have already lost their job’s to AI.
The AI in question is IBM’s Watson Explorer which does the work of insurance claims workers by scanning hospital records and other documents to quantify insurance payouts.
Cost savings and increased productivity
The Watson Explorer costs Fukuoka Mutual 200 million yen (S$2.46 million) to install and takes 15 million yen (S$185,000) to maintain annually. Fukuoka Mutual is expected to save 129 million yen (S$1.59 million) on labour costs annually and will recoup its investment in just two years. The company expects productivity to increase by 30 per cent.
Knowledge work perfectly poised to be taken over by Artificial Intelligence
In an era where the word “productivity” is bandied around conjuring images of blue-collar workers being replaced by more efficient robots, the fact that knowledge-based workers being replaced by AI should not come as a surprise. But it still does.
For years, school-going children have been told to aspire towards knowledge-based professions that bring not only prestige, but also economic advantage over more mechanical-based jobs — you’d still need doctors, accountants and lawyers in a recession, but not factory workers.
The advent of bots, machine-learning, algorithms and AI seriously challenge this notion.
“But AI can’t replicate creativity and experience!”
There are two main arguments against Artificial Intelligence replacing knowledge-based workers: AI cannot make judgement calls, be creative or tap on experience; and creativity/experience/judgement are essential to these jobs.
Let’s take lawyers as an example. The workflow for lawyers involves receiving a case, gathering the necessary information/documents, looking up precedents, building the case, advising the clients, going to court, etc.
If we break down these tasks, not all of them require the ‘creativity’ or ‘experience’ deemed irreplaceable.
How about parliamentarians? …
read more
A view on a very hot topic – My Job!
2017 just begun and 34 white-collar workers at Japan’s Fukuoka Mutual Life Insurance have already lost their job’s to AI.
The AI in question is IBM’s Watson Explorer which does the work of insurance claims workers by scanning hospital records and other documents to quantify insurance payouts.
Cost savings and increased productivity
The Watson Explorer costs Fukuoka Mutual 200 million yen (S$2.46 million) to install and takes 15 million yen (S$185,000) to maintain annually. Fukuoka Mutual is expected to save 129 million yen (S$1.59 million) on labour costs annually and will recoup its investment in just two years. The company expects productivity to increase by 30 per cent.
Knowledge work perfectly poised to be taken over by Artificial Intelligence
In an era where the word “productivity” is bandied around conjuring images of blue-collar workers being replaced by more efficient robots, the fact that knowledge-based workers being replaced by AI should not come as a surprise. But it still does.
For years, school-going children have been told to aspire towards knowledge-based professions that bring not only prestige, but also economic advantage over more mechanical-based jobs — you’d still need doctors, accountants and lawyers in a recession, but not factory workers.
The advent of bots, machine-learning, algorithms and AI seriously challenge this notion.
“But AI can’t replicate creativity and experience!”
There are two main arguments against Artificial Intelligence replacing knowledge-based workers: AI cannot make judgement calls, be creative or tap on experience; and creativity/experience/judgement are essential to these jobs.
Let’s take lawyers as an example. The workflow for lawyers involves receiving a case, gathering the necessary information/documents, looking up precedents, building the case, advising the clients, going to court, etc.
If we break down these tasks, not all of them require the ‘creativity’ or ‘experience’ deemed irreplaceable.
How about parliamentarians? …
read more
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