Since the inception of computers and computer programming less than 100 years ago, computing devices and computing has become key to the modern world in business, politics, social practices and more. And while the hardware is ever improving, software also has evolved to the needs of a modern population.
One of these innovations has been machine learning, a method of computing that emulates the human ability to observe a situation and choose the optimal method based on previous experience. What seems like such a simple trait is extremely difficult to imitate, requiring immense technical knowledge and coding skill.
To achieve this a number of factors must be considered:
First, an artificial intelligence must be able to receive and interpret data. This includes investigating data and discovering patterns within the wider information. Using this understanding of the data, an intelligence must then be able to apply that knowledge to find a solution to future issues.
Second, the system must be able to change its approach based on previous scenarios. Much like the human ability to learn new skills, the program should be able to adapt patterns, solutions and data interpretations to improve accuracy, efficiency and potency of the program’s result. For a powerful example, please click here and be amazed at just what can be done using machine learning.
Between these two factors artificial intelligence can emulate the abilities of human analysis. The advantages of a computerised analyst then become more prominent. By removing the human element, analysed segments can be larger and the detected patterns can be more complex. This is due to human error being largely removed from the analysis process and the ability of computing systems to both work faster and with a higher level of consistency. The last and perhaps most important advantage is the ability to detect patterns that human analysts may often miss due to complexity. This allows overlooked correlation and causations to be detected and considered in development of solutions.
The application of this technology is wide reaching and as the world has become more reliant on computers it has only become more commonplace. Some of the uses can be incredibly minor but have a major impact on the quality of consumer products. One of the most widespread usage is in the optimisation of software which for both large and small companies can be a process with a substantial cost in time and effort yet as anyone who has used exceptionally slow or buggy programs knows it severely impacts the consumers experience. By using machine learning, costs are reduced and the workforce can be spared the difficult and often heavy workload.
In the future we may see widespread usage in fields that have only just been explored in the current day. Such fields include medical diagnosis through interpretation of medical imaging, linguistics and counter-fraud systems also use machine learning AIs. Some of the most significant research may be in robotics which through analysing metrics such as balance, speed and consistency may ideally lead to the replacement of human workers in hazardous areas or jobs.