Low and middle-income states take AI adoption to new level
According to the ‘’ report, low and middle-income countries have been adopting newly-founded technologies such as e-banking and blockchain at a far faster rate than high-income nations, in recent years. This isn’t too surprising, given that so-called “third-world countries” like those found across Southeast Asia and the Asia-Pacific region feature simultaneously the most poverty, but also the highest level of tech investment and the highest number of technological hubs. Not only that but also soaring figures in terms of new Internet and smart device users on a daily basis.
The report highlights that health-based technologies will most likely follow the same trend, especially during a time when the creation and adoption of digital solutions are being accelerated, courtesy of the COVID-19 pandemic and the demand that it has created. With an increased need for isolation and less face-to-face contact, healthcare providers are in desperate need for viable solutions to avoid risking any further spread of illness ─ AI-enabled diagnostic technologies can bridge that gap. As a result, it’s expected that the market for solutions will experience exponential growth in the coming years.
“Many countries are ill-prepared to address a newly emerging disease such a COVID-19 in addition to the existing burden of infectious diseases and the ever-increasing tide of chronic diseases,” said Dr Ann Aerts, Head of the Novartis Foundation and Co-Chair of the Broadband Commission Working Group on Digital and AI in Health. “Digital technology and AI are essential enables to re-engineer health systems from being reactive to proactive, predictive, and even preventive.
“We have to develop a sustainable ecosystem for AI in health in the counties where it is most desperately needed [...] this has to happen while ensuring fairness and access for all. As health systems build back after the pandemic, technological innovation has to be a core part of the agenda.”
In many societies across the globe, supportive AI health tools could have an absolutely crucial role in the toolkits of nurses and community health workers; the tech-enhanced tools can help them diagnose and treat illnesses that would traditionally be dealt with by doctors. This will be an excellent development in nations, like those across the Southeast Asian region of the world, where large chunks of the population in rural areas lack access to surgeries and hospitals, and where there are shortages of healthcare workers.
UNICEF has, in recent times, developed its own AI-powered program which should help to reduce the social and economic burden of outbreaks through target population health measures. “Its Magic Box program both predicts when outbreaks are likely and allows health systems to reorganise their resources, alert health workers and launch public health campaigns, so they can respond better and faster to emergencies.”
The advantages and disadvantages of AI in cloud computing
Cloud computing offers businesses more flexibility, agility, and cost savings by hosting data and applications in the cloud. AI capabilities are now combining with cloud computing and helping companies manage their data, look for patterns and insights in information, deliver customer experiences, and optimise workflows.
We take a look at some of the benefits and drawbacks of AI in cloud computing.
The benefits of AI in cloud computing
A major advantage of cloud computing is that it eliminates costs related to on-site data centers, such as hardware and maintenance. Those upfront costs can be restrictive with AI projects, but with cloud enterprises you can access these tools for a monthly fee, making research and development related costs more manageable. AI tools can also gain insights from the data and analyse it without human intervention, reducing staff costs.
AI is able to identify patterns and trends in large data sets. Using historical data, AI compares it to the most recent data, which provides IT teams with well-informed, data-backed intelligence. AI tools can also perform data analysis fast so enterprises can rapidly and efficiently address customer queries and issues. The observations and valuable advice gained from AI capabilities result in quicker and more accurate results.
Improved data management
AI enables extensive data management, and cloud computing maximises information security, making it possible to deal with massive amounts of data in a programmed manner to analyse them properly, allowing the business to leverage information that has been “mined” and filtered to meet each need. AI can also be used to transfer data between on-premises and cloud environments.
Businesses use AI-driven cloud computing to be more efficient and insight-driven. AI can automate repetitive tasks to boost productivity, and also perform data analysis without any human intervention. IT teams can also use AI to manage and monitor core workflows. IT teams can focus more on strategic operations while AI performs the mundane tasks.
With businesses deploying more applications in the cloud, security is crucial in order to keep data safe. IT teams can use different AI-powered network security tools which can track network traffic, they can flag issues, such as finding an anomaly.
The drawbacks of AI in cloud computing
Enterprises need to create privacy policies and secure all data when using AI in cloud computing. AI applications require a large amount of data, which can include consumer and vendor information. While some data can be anonymous and can't be tied to personally identifiable information, knowing who the data belongs to makes it more valuable. When sensitive information is used, data protection and compliance is a major concern.
IT teams use the internet to send raw data to the cloud service and recover processed data. Poor internet access can hinder the advantages of cloud-based machine learning algorithms, as cloud-based machine learning systems need consistent internet connectivity.
While processing data in the cloud is quicker than conventional computing, there is a time lag between transmitting data to the cloud and receiving responses. This is a significant issue when using machine learning algorithms for cloud servers, where prediction speed is one of the primary concerns.