AI: The future of employment
AI: The future of employment
Perhaps we should all stop for a moment and focus not only on making our AI better and more successful but also on the benefit of humanity.
-Taken from a speech given by Hawking on making artificial intelligence benefit humanity
The first question arise when we hear the word AI is what exactly is AI, how it is going to shape the future and what concerns the most is the impact of AI on future of employment. Let’s understand these all one by one.
In computer science, artificial intelligence (AI), sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and other animals. Computer science defines AI research as the study of “intelligent agents”: any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals. More specifically, Kaplan and Heinlein define AI as “a system’s ability to correctly interpret external data, the term “artificial intelligence” is applied when a machine mimics “cognitive” functions that humans associate with other human minds, such as “learning” and “problem solving”.
We can trace back the evidences when Alan Turing published a landmark paper in which he speculated about the possibility of creating machines that think. He noted that “thinking” is difficult to define and devised his famous Turing Test. If a machine could carry on a conversation (over a tele printer) that was indistinguishable from a conversation with a human being, then it was reasonable to say that the machine was “thinking”. This simplified version of the problem allowed Turing to argue convincingly that a “thinking machine” was at least plausible and the paper answered all the most common objections to the proposition. The Turing Test was the first serious proposal in the philosophy of artificial intelligence.
When people talk about the impact that Artificial Intelligence will have on the labour market, the outlook is usually not positive, the fear of losing jobs to machine is somewhere dominates. One another fear is also that the companies will use only AI to increase efficiency but it is just opposite, the companies which are using AI and focusing on innovations they are most likely to increase the head count.
An initiative taken by Mckinsey Global Institute’s broad based research institute aimed at understanding what are the effects AI is providing in the field of economies, sectors and companies. It polled 20,000 AI –aware C level executives in 10 countries to compile a sample of more than 3000 companies and identified distinct clusters within that pool and ran different scenarios to project the effects on revenue, profitability and employment.
The research suggested that although AI will probably lead to less overall full time equivalent employment by 2030 it won’t inevitably lead to massive unemployment. Let’s see the major clusters which are going to effect the future of AI-
Enthusiastic Innovators– By 2030, we expect that profitability of enthusiastic innovators will grow 8% faster than that of the average company on an annual basis, their revenue will grow 4% faster, and their head count will rise 2.2% faster. For example google is using AI to drive innovation in search and efficiency of its servers by less energy consumptions. Chinese insurance launched a variety of CEO sponsored AI initiatives aimed at top line growth by hiring more than 600 data scientist to support their ventures.
Efficiency leaders- Efficient leaders are the early adopters of the AI technology intensively, the innovative technology with the aim to increase profitability .This include digitally savvy industries such as banking, insurance and manufacturing that are seeking to reduce manual processes. For example In 2010 Parkdale mills the largest buyer of raw cotton in the U.S., retooled its long shuttered South Carolina plants with smart robotics. This will provide efficiency in everyday operations and increase revenues.
AI resistors-AI resistors are the firms that believe in either don’t invest in AI at all or do so on a very limited scale. They are more concerned about the initial investments and fear for the failures. They are fearing the competitiveness which comes along with the innovation. It will bring the profitability pressure they will experience, they will have to cut costs which is directly indicating to reducing the head count. So their outlook for jobs may be more troubled than the analysis indicates.
As we compare the five clusters in terms of revenue, profitability, and employment, it’s important to consider a couple of things.
First, in the average scenario, the overall effect of AI between now and 2030 is significantly less substantial than you might expect. For instance, the impact on the labour/output ratio is about a 1% drop each year.
Second, Employment macro-dynamics will depend on AI activity within sectors and economies. More AI resistors and companies pursuing AI solely for efficiency will reduce the number of employees in an industry or economy.
So, it is not an inevitable conclusion that AI will ratchet up unemployment, as many have suggested — at least between now and 2030. Job losses will arise as the labour output ratio evolution suggests. But if the companies will not focus on this aspect they are going to lose their share of market. Which will eventually lead to loss in profit and revenue.
Kurzweil, who currently runs a group at Google writing automatic responses to your emails in cooperation with the Gmail team, had once famously said,
“It was fire that kept us warm, cooked our food, but also burnt our houses down. Technology is always a double-edged sword.”
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