Wall Street Journal article, “The World's Largest Hedge Fund Is Building an Algorithmic Model from Its Employees' Brains,”24 reported on $160 billion Bridgewater Associates trying to embed its founder's approach to management into a so‐called Principles Operating System. The system is intended to study employee reviews and testing to delegate specific tasks to specific employees along with detailed instructions, not to mention having a hand in hiring, firing, and promotions. Whether a system that thinks about humans as complex machines can succeed will take some time.
A Guardian article sporting the headline “Japanese Company Replaces Office Workers with Artificial Intelligence”25 reported on an insurance company at which 34 employees were to be replaced in March 2017 by an AI system that calculates policyholder payouts.
Fukoku Mutual Life Insurance believes it will increase productivity by 30 % and see a return on its investment in less than two years. The firm said it would save about 140m yen (£1m) a year after the 200m yen (£1.4m) AI system is installed this month. Maintaining it will cost about 15m yen (£100k) a year.
The technology will be able to read tens of thousands of medical certificates and factor in the length of hospital stays, medical histories and any surgical procedures before calculating payouts, according to the Mainichi Shimbun.
While the use of AI will drastically reduce the time needed to calculate Fukoku Mutual's payouts – which reportedly totalled 132,000 during the current financial year – the sums will not be paid until they have been approved by a member of staff, the newspaper said.
Japan's shrinking, ageing population, coupled with its prowess in robot technology, makes it a prime testing ground for AI.
According to a 2015 report by the Nomura Research Institute, nearly half of all jobs in Japan could be performed by robots by 2035.
I plan on being retired by then.
Is your job at risk? Probably not. Assuming that you are either a data scientist trying to understand marketing or a marketing person trying to understand data science, you're likely to keep your job for a while.
In September 2015, the BBC ran its “Will a Robot Take Your Job?”26 feature. Choose your job title from the dropdown menu and voilà! If you're a marketing and sales director, you're pretty safe. (See Figure 1.3.)
Figure 1.3 Marketing and sales managers get to keep their jobs a little longer than most.
In January 2017, McKinsey Global Institute published “A Future that Works: Automation, Employment, and Productivity,”27 stating, “While few occupations are fully automatable, 60 percent of all occupations have at least 30 percent technically automatable activities.”
The institute offered five factors affecting pace and extent of adoption:
1. Technical feasibility: Technology has to be invented, integrated, and adapted into solutions for specific case use.
2. Cost of developing and deploying solutions: Hardware and software costs.
3. Labor market dynamics: The supply, demand, and costs of human labor affect which activities will be automated.
4. Economic benefits: Include higher throughput and increased quality, alongside labor cost savings.
5. Regulatory and social acceptance: Even when automation makes business sense, adoption can take time.
Christopher Berry sees a threat to the lower ranks of those in the marketing department.28
If we view it as being a way of liberating people from the drudgery of routine within marketing departments, that would be quite a bit more exciting. People could focus on the things that are most energizing about marketing like the creativity and the messaging – the stuff people enjoy doing.
I just see nothing but opportunity in terms of tasks that could be automated to liberate humans. On the other side, it's a typical employment problem. If we get rid of all the farming jobs, then what are people going to do in the economy? It could be a tremendous era of a lot more displacement in white collar marketing departments.
Some of the first jobs to be automated will be juniors. So we could be very much to a point where the traditional career ladder gets pulled up after us and that the degree of education and professionalism that's required in marketing just increases and increases.
So, yes, if you've been in marketing for a while, you'll keep your job, but it will look very different, very soon.
MACHINE LEARNING'S BIGGEST ROADBLOCK
That would be data. Even before the application of machine learning to marketing, the glory of big data was that you could sort, sift, slice, and dice through more data than previously computationally possible.
Massive numbers of website interactions, social engagements, and mobile phone swipes could be sucked into an enormous database in the cloud and millions of small computers that are so much better, faster, and cheaper than the Big Iron of the good old mainframe days could process the heck out of it all. The problem then – and the problem now – is that these data sets do not play well together.
The best and the brightest data scientists and analysts are still spending an enormous and unproductive amount of time performing janitorial work. They are ensuring that new data streams are properly vetted, that legacy data streams continue to flow reliably, that the data that comes in is formatted correctly, and that the data is appropriately groomed so that all the bits line up.
■ Data set A starts each week on Monday rather than Sunday.
■ Data set B drops leading zeros from numeric fields.
■ Data set C uses dashes instead of parentheses in phone numbers.
■ Data set D stores dates European style (day, month, year).
■ Data set E has no field for a middle initial.
■ Data set F stores transaction numbers but not customer IDs.
■ Data set G does not include in‐page actions, only clicks.
■ Data set H stores a smartphone's IMEI or MEID number rather than its phone number.
■ Data set I is missing a significant number of values.
■ Data set J uses a different scale of measurements.
■ Data set K, and so on.
It's easy to see how much work goes into data cleansing and normalization. This seems to be a natural challenge for a machine learning application.
Конец ознакомительного фрагмента.
“Will a Robot Take Your Job?” http://www.bbc.com/news/technology‐34066941.
Source: Personal interview.
