Before the global pandemic arrived in 2020 and the globe turned on its head, Artificial Intelligence ( AI), and especially the AI category known as Machine Learning
( ML), were already causing widespread disruption in almost every industry.
The Covid-19 pandemic has had an impact on many dimensions of how we do
life, but it has not diminished the impact that AI has on our lives.
In particular, it has become clear that self-teaching algorithms and smart machines will play a major role
in the ongoing battle against this epidemic, as well as others that we will face in the future.
Without a question, AI remains a crucial trend when it comes to choosing innovations
that will transform the way we live, function, and play in the near future.
So, here’s an outline of what we should expect in what’s going to
be a year of repairing our lives and rethinking business strategies and goals.
1. Smarter Big Data Data Analytics and Insights

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.

Technological innovation is the key reason that this pandemic has not (yet) killed as many people as, for example, the 1918 Spanish Flu epidemic, which took up to 50 million lives. From innovations in medical science and quality of treatment to developments in communication technologies that made it possible for diseases to be identified more easily and for locks to be placed. Over the next year, AI will be added to the list of technological advances that will allow us to deal more effectively with pandemics. The rise in the volume of science and medical literature alone is immense, with more than 28,000 papers published by April of this year on the topic of Covid-19.
A dedicated search engine powered by natural language processing (NLP) algorithms has already been made available, so anyone can get AI assistance when investigating this massive dataset.

Work is also ongoing to develop AI solutions to help deal with the huge backlog of other medical issues, such as cancer, where treatment has been affected as resources are diverted to fight Covid-19. Over the next year, we are likely to see the accelerated adoption of AI across many other areas of healthcare, not only related to tackling viruses.

By developing our ability to apply machine learning problem-solving to these massive, real-time global datasets, we will spot outbreaks more easily, track contact between infected people, enable more accurate diagnoses, and, by predicting ways that a virus might evolve in the future, develop more effective and lasting vaccinations.



2. 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 field of development would be the use of facial recognition technology, often driven by computer vision algorithms. Somewhat more controversial as it focuses on the detection of individuals rather than trends within groups of people, facial recognition has been used by the police to identify locking and quarantine-avoidances, as well as to monitor movements of individuals showing symptoms within the crowd.

The evidence seems to indicate that the public has become more accepting of surveillance techniques traditionally considered to be excessively draconian due to the health risks presented by the virus. This tolerance is likely to be further checked as a technician over the next 18 months.


3. 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% in 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.



4. 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 generate more breakthroughs over the next 18 months that will improve our ability to spot and respond to the risk of viral outbreaks. However, this will also entail continuing global collaboration between governments and the private sector. How this happens would most likely be influenced by global politics and politicians, as well as by the path of technological growth. For this reason, problems such as access to medical datasets and obstacles to the international exchange of information will also be hot topics for the coming year.

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