to advance the public's understanding of AI, and to serve as an open platform for discussion and engagement about AI and its influences on people and society.”
Granted, one of its main goals from an industrial perspective is to calm the fears of the masses, but it also intends to “support research and recommend best practices in areas including ethics, fairness, and inclusivity; transparency and interoperability; privacy; collaboration between people and AI systems; and of the trustworthiness, reliability, and robustness of the technology.”
The Partnership on AI's stated tenets23 include:
We are committed to open research and dialog on the ethical, social, economic, and legal implications of AI.
We will work to maximize the benefits and address the potential challenges of AI technologies, by:
Working to protect the privacy and security of individuals.
Striving to understand and respect the interests of all parties that may be impacted by AI advances.
Working to ensure that AI research and engineering communities remain socially responsible, sensitive, and engaged directly with the potential influences of AI technologies on wider society.
Ensuring that AI research and technology is robust, reliable, trustworthy, and operates within secure constraints.
Opposing development and use of AI technologies that would violate international conventions or human rights, and promoting safeguards and technologies that do no harm.
That's somewhat comforting, but the blood pressure lowers considerably when we notice that the Partnership includes the American Civil Liberties Union. That makes it a little more socially reliable than the Self‐Driving Coalition for Safer Streets, which is made up of Ford, Google, Lyft, Uber, and Volvo without any representation from little old ladies who are just trying to get to the other side.
Will a Robot Take Your Job?
Just as automation and robotics have displaced myriad laborers and word processing has done away with legions of secretaries, some jobs will be going away.
The 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