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Artificial Intelligence: Current and Future Trends in 2021
Artificial intelligence (AI) is at the forefront of transforming the world and the way we live at a breakneck pace. Artificial intelligence is already present in our digital life, from smartphones to chatbots. The Covid-19 pandemic has affected many areas of industry, but it hasn’t lessened the impact AI has on our lives. In reality, it’s becoming clear that self-teaching algorithms and intelligent machines will play a key role in the ongoing battle against this outbreak, as well as any potential outbreaks.
Many innovations have had a far-reaching impact that seems to outstrip the technology itself; cloud computing, for example, has enabled a generation of startups by providing a “rentable data centre. Artificial intelligence, on the other hand, is the only new technology that can radically alter human existence. AI is shaping everything from banking to healthcare to entertainment to education, all thanks to a single groundbreaking idea: that machines and systems can adapt and learn without the need for human intervention. When it comes to identifying the innovations that will transform how we live, function, and play soon, AI is undeniably a hot topic. So, here’s a rundown of what we should expect in the coming year as we rebuild our lives and reconsider our business strategies and goals.
Computerized Detection and the Prevention
Drones have already been used in many countries, including the United States, to at least test the feasibility of using them to track compliance with social distancing guidelines. Drones with the potential of detecting COVID symptoms like high temperature in individuals within a crowd are on the horizon. These systems use computer vision technology to analyse data collected by drone cameras to provide statistics and probabilities about the virus’s spread to authorities or local administrators.
Facial recognition technology, which is also driven by computer vision algorithms, will be a related growth field. Face recognition has been used by police to identify lockdown and quarantine-avoiders and to monitor the movements of individuals showing symptoms within a crowd. It is more controversial because it focuses on individual identity rather than trends within groups of people.
Owing to the health hazards posed by the virus, research suggests that the public has become more accepting of surveillance techniques that were previously considered excessively draconian. Over the next 18 months, as technologists become more adept at AI-driven monitoring and even compliance, this tolerance is likely to be put to the test again.
Analytics, Smart Insights and Big data
We’ve seen firsthand how important it is to rapidly evaluate and interpret data on virus spread around the world during this ongoing pandemic. We’ve grown accustomed to seeing the effects of this on television every night when the most recent infection or death rates for our respective regions are announced.
According to reports, more than 3 million people died due to COVID-19 until April 2021. The fact that this pandemic hasn’t (yet) killed as many people as, say, the 1918 Spanish Flu epidemic, which took up to 50 million lives, is due to technological progress. From advancements in medical technology and quality of care to advancements in communication technology that enabled outbreaks to be detected and lockdowns enforced more quickly. In the coming years, AI may be implemented in many other aspects of healthcare, not just in the fight against viruses.
We would be able to detect outbreaks more quickly, monitor communication between infected people, make a more accurate diagnosis, and create more efficient and long-lasting vaccines by improving our ability to apply machine learning problem-solving to these vast, real-time global datasets.
Digital Marketing, Chatbots and Automation
AI has transformed the digital world, from Google’s RankBrain, BERT, and Voice search to Amazon’s personalized recommendations and chatbots.
RankBrain is a Google algorithm that uses Artificial Intelligence to better comprehend the likely user intent of a search query. Google BERT, which stands for Bidirectional Encoder Representations from Transformers, is a core search algorithm update targeted at boosting Google’s language comprehension skills by using NLP. Natural Language Processing (NLP) is a subfield of artificial intelligence that includes linguistics and machine learning.
Through suggestions based on machine learning technologies and interaction with virtual assistants, AI may construct simulation models and customise shopping processes. Artificial Intelligence has been used by several companies to engage with their customers. Amazon, for example, utilises AI to suggest goods based on prior purchases, views, and searches.
Chatbots are becoming increasingly popular in digital marketing. Chatbots are most commonly employed for simple customer service and marketing, but they may also be integrated into operating systems as virtual assistants, or VAs.
Customers want an amazing experience every time they visit your website/app. Customers will get precisely what they want with chatbots. A chatbot, in other words, may serve as a link between your company and your customers.
A good digital marketing campaign is marked by an excellent user experience. I recently published an article on how to improve a website’s user experience you can refer to that. When customers can relate to the content or material, they are more likely to convert. It is what converts loyal consumers into brand ambassadors. This is where AI may assist improve the consumer experience. Marketers may use AI-generated data to identify what type of content is most relevant to their target audience. Past behaviour, historical data, and location may all be utilised to recommend the most valuable information to consumers. Online purchasing experiences are an example of this capacity. We’ve all seen how Amazon recommends things to purchasers based on their prior searches, views, and purchases. That is artificial intelligence in action!
Another app goes even farther, allowing users to digitally “try on” clothing without having to go to the store. This results in more engagement, as well as fewer product returns and angry customers.
Like these, Artificial Intelligence is going to change the future of Digital Marketing.
Predicting behavioural change in a recovering business
The dissemination of Covid-19 has had a major influence on how we live, function, and socialise. There has been a slow, solid movement toward digital in many facets of society.
Businesses now have AI technologies and platforms in place to help them realise how their clients are adjusting to a new reality. Organizations that were historically slow to adopt digital platforms for commerce and partnership nurturing have realised the importance of the situation and are increasingly grasping principles like behavioural intelligence and personalization. As small and medium-sized businesses aim to gain a competitive advantage, tools that enable companies to self-serve access to this technology will become more prevalent in 2021.
Putting an end to the next pandemic until it begins
The holy grail of AI-assisted epidemiology will be to create systems that can reliably predict when and where potential outbreaks will occur, and most AI algorithms are geared toward prediction. This study has been underway for some time, and AI was responsible for some of the first warnings about the current outbreak. On December 31, 2019, BlueDot, based in Toronto, released a warning about a possible outbreak in Wuhan, China. Coronavirus was discovered by Bluedot even before the rest of the world knew about it.
Over the next 18 months, we can expect further breakthroughs in AI research that will improve our ability to detect and respond to viral outbreaks. This will, however, necessitate continuing global collaboration between governments and private industry. Global politics and policymakers, and the pace of technological progress, would almost certainly influence how this plays out. As a result, problems like access to medical datasets and obstacles to international knowledge sharing will be hot topics in the coming year.
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