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4 Everyday Examples of How AI Is Changing Our Future

Computer science continues to shape our digital future, particularly through innovations in artificial intelligence. In 2019, companies specialising in artificial intelligence (AI) received nearly $40 billion globally in investment.  From business to governments to the education sector, AI has become a reliable tool for organisations looking to enhance their competitiveness, efficiency and innovation. 

AI is impacting almost every industry and is expected to reach every corner of our lives and shared futures. In light of this, we’ve examined four areas of our lives where AI is driving dynamic change.  

alt=""1) How artificial intelligence is improving healthcare 

Worldwide, healthcare systems are facing strain from the growing demand for their services, rising costs, an ageing population and the damaging effects from COVID-19. To meet these challenges, AI is driving innovation in clinical environments, including the development of treatment as well as strengthening the patient-doctor relationship.  

For example, AI algorithms are being employed to diagnose diseases faster and more accurately than doctors. VoxelMorph, an AI algorithm developed by MIT researchers, can examine biopsy images 1,000 times more quickly than standard techniques with an 87% accuracy rate. This is promising as it can lead to the detection of certain types of cancer that are harder to detect at the early stages, such as melanoma. 

AI is also assisting in the roll out of the COVID-19 vaccine. Researchers at the University of Southern California have created an AI framework that can quickly analyse COVID vaccine candidates – allowing scientists to pinpoint future mutations of the virus and find the most appropriate preventative medical therapies. 

The ability of AI to positively impact healthcare is welcomed, and by the end of 2021, the market value of AI in healthcare is expected to reach $6.6 billion with spending expected to rise from $463 million in 2019 to over $2 billion over the next five years. 

alt=""2) How AI is transforming your shopping experience  

High street retail has been struggling for years, and the closure of non-essential stores as a response to the COVID-19 pandemic has only further hurt the industry. Looking beyond the effects of COVID, over the past few decades more customers are opting for online shopping. As a result, AI is optimising e-commerce experiences and evolving the high-street experience. 

Retailers use AI to analyse consumers purchase history and offer personalised product recommendations based on the data collected. Google Cloud, for instance, recently announced the launch of a new suite of tools powered by a ‘Recommendations AI’ which draws real-time insights and enhances customer engagement to offer a more personalised shopping experience. 

Augmented reality and visual search are also being driven by developments in AI to provide better customer experiences. Visual search uses AI to examine real-world images and return a list of related results. Retailers like Amazon employ it in homeware and fashion, where search engine keywords may be less impactful due to the visual nature of those areas. Amazon’s StyleSnap, for example, allows users to replicate their favourite fashion by uploading a photo and allowing AI to suggest related products. 

alt=""3) How AI is changing education 

COVID-19 has raised big questions about how we can effectively educate students, from primary all the way to tertiary education. Classes have moved partially online, which raises the issue of how AI can be used by schools, universities and parents to solve challenges regarding remote learning and flexibility. 

Some  psychology and AI researchers, such as Dr Michelle Tempest, believe that by 2050, machine learning will empower robots and programmes to 'outsource childrearing,’ effectively. This will lead to scenarios where daily parenting duties and education are entirely carried out by AI programmes. 

Closer to the present day, AI is making a tangible impact on education, with the global AI and education market expected to be worth $5.80 billion by 2025. Practical examples of AI working to improve education include AI-based language translation enabling students from different language backgrounds to participate in lectures. AI can also be employed by schools to offer a personalised-learning experience that overcomes challenges around learning styles, progress and special needs. AI systems can be used to provide personalised grading responses for students or to build a custom learning profile of each student and then develop bespoke training materials for them based on their ability and appropriate learning style. 

On average, teachers spend half their time on non-teaching tasks. AI can be used to automate administrative tasks, grading as well as through AI-powered chatbots which can answer questions from parents and students. 


4) How artificial intelligence is revolutionising manufacturing  

The manufacturing sector is driving much of the innovation in AI tech and has a wide impact on society. Known as the Fourth Industrial Revolution, Industrial Internet of Things (IIoT) or Industry 4.0, this revolution in industry refers to how digitisation is enhancing industrial output and innovation through data analytics and machine learning.  

Using AI to predict when machines are likely to require maintenance is one of its most popular uses within the industrial world. General Motors use AI to analyse images from cameras mounted on assembly robots to spot signs of faulty robotic components before they result in unplanned outages. Another example can be found in Nokia’s use of a machine learning video application that alerts assembly operators of inconsistencies in the production process, allowing for corrections in real time. 

Transform your future with Northumbria University 

If you’re interested in how artificial intelligence is changing the world, Northumbria University’s distance learning Computer Science MSc will help you develop a core understanding of artificial intelligence and machine learning. 

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