AI is poised to add more money to the world economy, if governments invest in data and upskilling employees to work with the technology.
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- Australia's economy will benefit the most from machine learning in a scenario where the labor force is upskilled and large investments are made in AI technology. — Economist Intelligence Unit, 2018
- To prepare for the rise of AI, policymakers in all countries must focus on investing in skills and training, keeping data safe, and promoting R&D and technology. — Economist Intelligence Unit, 2018
The actual impact of artificial intelligence (AI) on the world's economy and jobs will likely be somewhere between the utopian and dystopian futures that it is often discussed in terms of, according to a new report from the Economist Intelligence Unit.
The report, commissioned by Google, examined how AI will impact certain industries in the US, the UK, Australia, Japan, and Asia as a whole. The findings are based on econometric modelling, desk research, and interviews with academic and industry experts.
Firms developing and using machine learning need to better communicate among themselves as well as with the public and policymakers, the report stated. This means doing more to manage expectations around the impact of machine learning, acknowledging the potential risks and rewards, improving trust and transparency, and educating the public.
SEE: IT leader's guide to the future of artificial intelligence (Tech Pro Research)
"The debate over the impact of machine learning, and artificial intelligence, is an important one and like all important debates, it needs to be reasonable and informed," Chris Clague, editor of the report, said in a press release. "Our objective with this report is to help with that cause by charting a path between the techno-utopians who believe these technologies will solve all the world's problems and the pessimists who warn that they are dooming us to a jobless, dystopian future."
Researchers examined three different econometric scenarios, using the current forecast to 2030 as the baseline. Scenario 1 assumes more complementary work between humans and AI than baseline, and that governments will invest more in upskilling than they currently plan to. Should this be the case, Australia's economy will benefit the most from the adoption of machine learning, as growth in services become more important for economic growth than commodity exports, the report noted. Gains elsewhere would be relatively low by comparison, it also found.
Scenario 2 assumes investment in access to open source data, tax credits to encourage private sector adoption of machine learning, and advances in computing efficiency drive hardware costs down. This situation would yield the most economic growth across all nations, with Australia again faring best. All countries studied would see GDP rise by at least 1% above baseline between now and 2030.
For a more negative take, Scenario 3 assumes insufficient policy support for structural changes to the economy, with AI completely replacing human jobs. In this case, economic losses are substantial compared to the baseline: The UK and Australian economies would shrink compared to today, and while the US, Japan, and developing China's economies would grow, they would still fall below baseline.
Policymakers can work with businesses to do the following to prepare for the coming AI revolution, according to the report:
- Invest in skills and training. While a focus on STEM education is key, schools and businesses will also need to increasingly concentrate on teaching soft skills, such as team building, cooperation, and critical thinking.
- Deal with data. Policymakers need to focus on data protection and soothing citizens' concerns about privacy and security through more regulations. These measures need to be interoperable across borders to ensure data flow around the world.
- Invest in R&D and technology. Public sector investing in R&D is on the decline in many countries, and the private sector has stepped in to a degree. However, this could be unsustainable, and the public sector needs to be more involved if countries are truly going to capitalize on machine learning and other emerging technologies.
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