Artificial intelligence (AI) and machine learning (ML) developments are already improving efficiency and cost-savings in a number of industries, yet a lack of sustained policy support could see Britain slide back into a recession.
A new report has found that the UK economy would be the worst hit of any in the world if government support and corporate communications were to trail behind machine learning innovation.
Data privacy and sharing are the two top concerns which stand between a thriving digital nation and an information-insecure society, the Google-sponsored Economist Intelligence Unit (EIU) report revealed. Business leaders and policymakers have never faced a more crucial time to invest in smart technologies and skills for an AI-ready workforce. Looking ahead to 2030, the report identified data protection and investment in R&D and technology to be the priorities for 2018-2030.
Globally, Energy is set to be the sector most impacted by ML, as pricing systems created from probabilistic models – alongside the development of smart grids – could cope better with changeability in the weather. Yet the threat of cyber attacks grows alongside the use of smart grids.
Healthcare was determined “ripe for AI”, with drug development already seeing marked improvement due to ML. Conversely, the sensitive nature of medical data could scupper adoption of AI if patients are not persuaded their information would be suitably confidential. Manufacturing stands to gain from reduced maintenance costs and batch production, though automation uptake depends on the payback period, a measure weighing the cost of technological investment versus local labour. Regulatory, liability and data security concerns continue to swirl amid innovation in Transportation.
Enterprises cannot become complacent over end-user trust, as analysts found widespread lack of understanding on data management in various use cases of machine learning. Communication between companies creating machine learning technologies and policymakers must improve or the country could fall behind the economies of US, Japan, South Korea, Australia, and Developing Asia. Companies need to work to manage expectations around the impact of machine learning, making the risks and rewards clear and educate the public to dissuade fear and confusion.
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“‘Trust us’ or trust the algorithm is not a viable strategy for gaining widespread acceptance of AI,” researchers noted. “Developers and users alike need to make known what they are doing and how they are doing it.”
On top of understanding, citizens will need upskilling; while STEM should not be neglected, soft skills including team building and critical thinking will be central to the nation adapting to economic change.
“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.” said Chris Clague, the editor of the report, “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.”
EIU ran three econometric scenarios, using their current forecast to 2030 as the baseline. Scenario #1, positive, forecast greater human productivity through upskilling. In turn, Scenario #2 predicted greater investment in technology and access to open source data. However, Scenario #3 foresaw insufficient policy support for structural changes in the economy and negatively impacts the country’s finances. EIU said its findings are based on econometric modelling, desk research and interviews with academic and industry experts.
