T
echnology has done wonders for this world. Since the start of the digital era, the world has experienced numerous technological advancements that have taken it to new heights. Artificial intelligence (AI) is among these technologies as it has the potential to change how people work and live. In its state and the current growth rate of technology advancement, it is only a matter of time before it starts impacting the society and the economy enormously.
To better understand AI, there is a need to identify the components that make it, and one such subset is machine learning. It is an application used for data analysis that focuses on the development of computer programs that can access data and use it to learn for themselves. It provides systems the ability to learn from data, identify patterns, and make decisions with little to no human intervention.
The subset has come a long way, and because it is dependent on huge amounts of data, it became quite popular in the 1990s as the World Wide Web was coming up. Thanks to the growth of the digital era, mass information is readily available now. Machine learning has taken advantage of this and evolved to be able to take in more complex data at a faster speed thus requiring less and less of human intervention. An example of how it is has developed is evident in the much-hyped self-driving cars.
However, far from this, machine learning is something most people use daily, for instance, in the spam filters of e-mails. It has become common in most industries that deal with large amounts of data such as health care, financial, and transportation as well as the government. It is also active in the surveillance field as well as web and mobile applications. In the financial sector, this AI application is useful as it helps financial institutions to identify the spending patterns of their clients and make better decisions. For example, banks can spot account closures before they occur and can also predict, when a customer receives credit, if they will default or repay. Additionally, in learning algorithms, the technology can detect fraud through data mining.
When it comes to the government, agencies are using machine learning in detecting fraud and minimizing identity threat. The technology can find similarities in terms of data to identify the criminal among billions on the Internet. Government is also using the information they collect from national activities such as census to increase efficiency in its public services. The technology also saves revenue and time as it assists in doing away with backlogs that are usually present in large numbers in most agencies.
In the health sector, the component is especially useful in developed countries where wearable devices are most common. Using these devices, medical professionals can use the information collected to diagnose a patient resulting in better medical treatment as well as assess one’s health in real-time. The technology is also being used to help in management of diseases such as cancer. For example, Google is training computers to accurately detect cancer with more than 75% accuracy.
The AI subset is also quite popular in web and mobile applications. Taking the example of FaceApp, which despite bringing up privacy and security concerns, was a popular application that uses machine learning to allocate features to an image and predict how it will look with a different filter. Web and mobile applications such as these are increasing in numbers as data becomes more easily and readily available.
Despite being helpful in most industries, some have criticized machine learning, portraying it as a growing source of unemployment. Machine learning in its definition is possible with minimal human intervention, many perceive that the technology will take away job opportunities. Apart from this, the technology is heavily dependent on large amounts of data meaning it can only handle situations that have already been experienced before at its current level. This indicates that the application will have it difficult when it comes to abstract reasoning where reasoning logically is required, which is crucial in the real world.
In a world where the population has no plans of decreasing, we need to find a balance especially by encouraging the youth in the importance of technology in the current era
Machine learning has a long way to go and its potential is massive. Every day we identify new ways of using it. The criticisms brought about it are also true; organizations are already shifting from human labor to robotics. In a world where the population has no plans of decreasing, we need to find a balance especially by encouraging the youth in the importance of technology in the current era.
Africa has seen the importance of technology and as a result, governments and organizations are already investing in AI to develop their countries. International organizations have given a helping hand to further the study of technology on the continent. Google, for example, opened the first AI lab in Ghana this year to provide better solutions for some of the challenges faced on the continent. Machine learning is going to be beneficial for Africa but in preparation, governments should also put in place measures to deal with its shortcomings.
