Before the global pandemic struck in 2020 and the world was turned on its head, artificial intelligence (AI), and specifically the branch of AI known as machine learning (ML), were already causing widespread disruption in almost every industry
During this ongoing pandemic, we’ve seen first-hand the urgent need to quickly analyze and interpret data on the spread of viruses around the world. Governments, global health bodies, academic research centers, and industry have come together to develop new ways that information can be collected, aggregated, and worked with. We’ve become used to seeing the results of this on the news every night, when the latest infection or death rates are given for our own regions.
Automated Detection and Prevention
We have already seen the use of drones in several jurisdictions, including the US, to at least test the possibility that they can be used to monitor whether social distancing guidelines are being followed. More advanced applications are on the horizon – such as drones with the capability of detecting COVID symptoms such as high temperature in individuals within a crowd. These systems use computer vision technology to analyze data captured by cameras on the drones and inform authorities or local administrators of statistics and probabilities around the spread of the virus.
Another related growth area will be the use of facial recognition technology, also powered by computer vision algorithms. Somewhat more controversial as it focuses on the identification of individuals, rather than patterns among groups of people, facial recognition has been used by police to detect lockdown and quarantine-avoiders, as well as to track the movements of individuals displaying symptoms within a crowd.
The evidence seems to suggest that the public has become more tolerant of surveillance tactics that would previously have been considered overly draconian, due to the health risks posed by the virus. This tolerance is likely to be further tested over the coming 18 months as technologists become more adept at AI-driven surveillance and even enforcement.
Business on the rebound – predicting behavioral transformation
The way we live, work, and socialize has been hugely impacted by the spread of Covid-19. While there has been a steady, strong trend towards digital in many aspects of society, this year, we’ve witnessed a stampede. Amazon’s sales during the second quarter of 2020 were up 40% on the same period last year, as even those who have so far shunned online retail were forced to re-assess their options.
AI tools and platforms are already in place to help businesses understand the way their customers are adapting to a new reality. Organizations that were previously lagging in their uptake of digital channels for commerce and relationship nurturing have come to understand the urgency of the situation and are quickly getting to grips with concepts such as behavioral analytics and personalization. Tools providing organizations with self-service access to this technology will become increasingly prevalent throughout 2021, as small and medium-sized enterprises are seeking to establish their competitive edge.
Shutting down the next pandemic before it even starts
Most AI algorithms are geared towards prediction, and the holy grail of AI-assisted epidemiology will be to build systems that can accurately predict when and where future outbreaks will take place. This research has been ongoing for some time, and in fact, some of the earliest alerts about the current outbreak were generated by AI. Toronto-based BlueDot’s tool was already scanning 100,000 governmental and media data sources daily when it issued an alert about a potential outbreak in Wuhan, China, on December 31, 2019.
We can expect AI research to yield further breakthroughs over the coming 18 months that will increase our ability to spot and react to the danger of viral outbreaks. For this to happen, though, it will also require ongoing global cooperation between governments and private industry. How this plays out will most likely be affected by global politics and legislators, as well as the course of technological development. For this reason, issues such as access to medical datasets and barriers to the international exchange of information will also be hot topics over the coming year.