‘The luddites destroy the robots’ may make an attention grabbing headline, yet with all the hype around job losses caused by machine learning and Artificial Intelligence (AI) we need to gain some perspective on where we’re at and where we’re heading.
Ned Ludd certainly caused a stir in 1779 when he smashed a weaving machine in frustration at his own economic predicament. Indeed the ensuing industrial revolution in the late eighteenth and early nineteenth century, labour saving machinery actually contributed to job creation and economic demand and the newly established social science of economics argued that unemployment caused by machinery would be addressed by the upsurge in the economic growth that ensued.
The first reason to reject the Luddite fallacy is economic theory: companies introduce machines because they increase production and cut costs. This in turn builds economic wealth and demand for labour plus new job creation.
The second is history: from the start of the industrial revolution until now, there are more people employed today than ever before.
Simply put, if the Luddite fallacy was correct we’d all be unemployed now.
So what about the current ‘digital revolution’? As machine learning offers cognitive skills, will the robots steal jobs that we cannot replace?
One of the most powerful authors on AI and technological unemployment is by Martin Ford. In his books ‘The lights in the Tunnel’ and 'Rise of the Robots’, he argues AI systems are on the verge of wholesale automation of white collar jobs i.e. jobs involving pattern recognition and information processing.
In their 2015 research 'The four fundamentals of workplace automation' McKinsey consultancy identified 2,000 activities (greeting customers, demonstrating products) for a selection of occupations (e.g. retail sales) that were susceptible to automation. They noted that 5% of jobs were capable of full automation but 60% could have 30% of their constituent activities automated. Indeed the report concluded that people are to become more productive as machines enable human performance.
So this is where AI comes in. If you’ve ever seen AI play football, the team will not win the world cup on its own! It is important to note that AI did not rise naturally, but is created by humans. Indeed some prefer the term machine learning given that cars are not called artificial horses or planes artificial birds.
AI can now help humans with games and puzzles, map searches, self-driving vehicles, search, image and speech recognition. AI is also in the early days of emotion detection. In retail financial services AI is now helping:
- Auto-rebalancing and tax harvesting of investment portfolios
- Sync with your bank account providing guidance around your savings and spending behaviours
- Provide risk and audit management through regulation technology platforms
- Fight cybercrime through fraud detection
- Help drive customer centricity through ‘hyper personalisation’ services e.g. identifying customers spending patterns, financial transactions or travel behaviour
- There is also the issue of the current FCA machine executable reporting initiative, where they are looking at a call for input on how machines can interpret their rules, apply to the organisations in a highly relevant manner and report back accordingly.
So we are now at a the very start if a new age where machines will genuinely self learn and provide efficiency’s which will in turn provide cost and time savings. Yet, people buy people, and there are two key issues to take into account here:
- Whilst AI cannot match human capability, there is a need for a cyber approach where humans can blend machine with personal services
- As we have seen with the Luddite Fallacy, new technology creates a new space, new opportunities and new economic opportunities
As I keep telling my two children, when they reach the age for joining the workforce, many jobs they can apply for are yet to be created. With the rise of machine learning, there will be new opportunities arising and also a big need for humanities, soft skills and emotional intelligence that AI will always be challenged to replace.
The futures bright for humans, we just need to keep doing what we’re best at: adapting to working with AI driven machines.
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