Will Artificial Intelligence and Machine Learning Substitute Humans?
Development of automation enabled by technologies like artificial intelligence (AI) and machine learning in recent times have raised concerns around job, skills, wages and employment. While technologists have highlighted the promise of higher productivity and economic growth enabled by artificial intelligence and machine learning, considerable concerns on jobs, skills, wages and nature of work have been raised in the society. The hype around AI and machine learning sometimes has not projected the practical picture of adoption and a balanced view of its impact on jobs, skills and wages. For policy makers and workers, these technology shifts have created considerable uncertainty. Business leaders do see this as a combination of uncertainty and opportunity but many a times it is very easy to get lost in the noise and hype associated with such technology revolutions, whose frequency and pace is following a sharp upward curve.
Transformative power of AI and machine learning is hugely significant. Before AI, human intellect was programming the machine how to solve a problem. With AI, human intellect is programming the machine to be adaptable to learn and then providing the machine with the relevant data to learn. So, this has created non-linear advancement of an unprecedented scale. AI as it stands today is a general purpose technology that can be applied in a narrow vertical market e.g. trying to analyse an X-ray image, which can be compared symbolically with a one-track pony. AI is automation on steroids. However, AI is still a developing technology area and a lot more is yet to evolve. In such an evolving area, business leaders should work carefully and cautiously before they start making market announcements around their AI strategy or product roadmaps incorporating AI. It is important not to get carried away by the hype and clearly understand what can and cannot be done, based on what is available and proven today. The same is true for policymakers and the society when they display knee jerk reactions to the threats of AI replacing humans or worrying about artificial super intelligence taking over the human race. Very slow or almost no progress on general artificial intelligence combined with lack of understanding of its roadmap itself, makes it sensible not to be worried about artificial super intelligence taking over our lives very soon. However, we should be planning about near or short-term impacts like skills gap, changing work environment, pressure on wages, regulatory/ethical frameworks and above all explainability around regulations and regulators.
AI technology is concentrating wealth in the hands of a few. So, in a way it is further adding to an existing imbalance in the society. 99% of economic value created by an AI today is still in specialised narrow intelligence vertical markets. We should be looking at how AI can be used to distribute wealth across a wider section of the society. Application of narrow artificial intelligence will not substitute human intellect. It will automate a percentage of a majority of occupations but will not fully substitute human intellect. Human intellect will complement AI and as AI evolves, this complimentary role of humans will continue to go up the value chain. More people will work with technology. Highly skilled workers working with technology will benefit. While low-skilled workers working with technology will be able to achieve more in terms of output and productivity, these workers may experience wage pressure, given the potentially larger supply of similarly low-skilled workers, unless demand for the occupation grows more than the expansion in labour supply. However adoption of technology will enable a whole new set of opportunities for a section of society which would never had an opportunity to participate in the labour market. For example in India, Google is rolling out a programme called Internet Saathi (Friends of the Internet) in which rural women are trained to use the Internet, and then become local agents who provide services in their villages through Internet-enabled devices. The services include working as local distributors for telecom products (phones, SIM cards, and data packs), field data collectors for research agencies, helping local people access government services through an Internet-based device etc. On the other extreme, AI will create a significantly high volume of jobs around data science across geographies. Even at the current level of AI activity, such skills are in shortage. Hence, the bigger question for the industry, society and policy makers are to democratise education and skills development in these futuristic areas and support them with effective ethical and regulatory frameworks.
I would love to see technology companies taking a lead in broader development of this eco-system in the society as a whole. In fact, AI will spread much beyond just the technology sector, presenting opportunities both for the large companies as well as the start-ups. In the past, technology companies have hyped (sometimes overhyped) technological advancements and trends purely from demand creation and consumption point of view and have not thought about building a holistic eco-system. A case in point is social media, where unprecedented demand and consumption has turbo-charged, while ethical and regulatory frameworks have lagged behind significantly. Social and ethical awareness within disruptive organisations itself needs to be addressed comprehensively even now, let alone the wider society. Situation with skills and productivity challenges have similar connotations. Many organisations are partnering with educational institutions and the wider society to address challenges of skills and productivity. However, the sheer scale of these challenges leaves a wide gap between the direct and indirect interventions initiated by corporates and the future demand. Governments are supposed to fill that gap with their limited resources and inherent bureaucratic hurdles present within their operating model. Challenges around developing a consistent global approach on taxation of digital economy at the point of consumption do not help either.
As business leaders we need to have a holistic approach to AI and machine learning wherein corporate goals have to be embedded within the overall social and political goals with balanced commitment of resources. We need to display true and right emphasis on developing the society and the wider political system in the spirit of collaboration rather than confrontation, if we want to have a sustainable long-term business model. Failure to do so will only result in pitting technology against the society, which is not good for anyone!
Author: Raj Singh
Raj Singh is the Chairman for IoD Berkshire and member of a number of committees of techUK. Raj is a strategy and digital transformation leader with over 35 years of international experience and is CEO of Innotatio Limited.