Artificial intelligence and machine learning have become buzzwords that are extensively used by tech industry experts. By understanding the hype generated by these modern technologies, business leaders in multiple industries started implementing these technologies and experimenting with their potential. Now, several industries such as retail, manufacturing, construction, and e-commerce are using AI and ML-powered applications to streamline various business operations, enhance productivity, and provide a personalized customer experience. Looking at these benefits, business leaders may consider incorporating artificial intelligence and machine learning during software development and mobile app development to create robust business solutions. However, many business leaders may not know the key differences in artificial intelligence and machine learning and use these terms interchangeably. Hence, business leaders must understand these differences and the technicalities in artificial intelligence and machine learning before implementing them for their business.
John McCarthy, one of the pioneers of artificial intelligence, has defined it as “the science and engineering of making intelligent machines.” Artificial intelligence is a domain of computer science that aims to develop machines that are capable of imitating intelligent human behaviour. Hence, AI systems can perform tasks that need human intelligence, speech recognition, visual perception, and informed decision-making.
Software development for artificial intelligence involves a series of if-then statements and statistical models that can map raw data into various categories. The if-then statements are simple conditions that are programmed by developers to guide artificial intelligence models in specific scenarios. Such programs are called expert systems, rule engines, or symbolic AI. Together, all these programs and systems are known as Good, Old-Fashioned AI (GOFAI). Using such rules and programs, artificial intelligence systems can effortlessly mimic human behaviour and produce accurate results. For instance, the retail industry can utilize AI to notify business leaders about restocking popular products. AI collects and analyzes data such as sales history, promotions, location, and industry trends to predict which products will be in demand. In this manner, business leaders can utilize AI to streamline various business procedures.
Artificial intelligence can be categorized into two groups- Artificial General Intelligence and Artificial Narrow Intelligence. Artificial general intelligence can solve various problems in a domain intelligently. Whereas, artificial narrow intelligence can perform some specific tasks precisely. Therefore, applications of artificial narrow intelligence have a limited scope but outperform humans in certain tasks.
Machine learning is basically a subset of AI. The key aspect that differentiates AI and machine learning is that machine learning models are able to alter themselves after being exposed to more data. Hence, machine learning is dynamic and can make changes to output in different scenarios. Using this approach, machine learning systems become autonomous and require less human intervention.
The capabilities of machine learning were best demonstrated in 1959 by Arthur Samuel, who was one of the pioneers of research in machine learning. Arthur Samuel aimed to teach a computer how to play checkers better than humans. After years of research and learning, his program beat the checkers champion of Connecticut in 1962. To deliver such results, machine learning algorithms proactively try to minimize errors and maximize the odds of making correct predictions. For this purpose, machine learning models require a framework that can multiply several inputs and make predictions according to the given inputs. Hence, the results or predictions offered by machine learning models are always based on its inputs. Also, the data given to machine learning models must be accurate to generate precise results.
The initial predictions made by machine learning systems may be inaccurate. Therefore, developers correct their algorithms by understanding why the desired output wasn’t generated during software development and testing. With this approach, developers can identify their errors and modify the algorithms accordingly. After multiple iterations of measuring errors and altering algorithms, machine learning systems become capable of producing the right results.
There are some key differences between artificial intelligence and machine learning that must be addressed by business leaders and developers during mobile app development and software development.
Since machine learning is an integral part of AI, both of these modern technologies can work together. For example, in a chatbot, AI helps in deciding which responses would be appropriate and machine learning models learn from ongoing conversations to produce better responses in the future. Hence, business leaders must consider utilizing the abilities of artificial intelligence and machine learning together in software development and mobile app development.
Complicated programming, testing, and various resources are required for developing software that leverages artificial intelligence or machine learning. Hence, developing such software in-house can be increasingly complex and expensive. To address this issue, business leaders can outsource artificial intelligence and machine learning-based software development to reliable tech firms. They can reach out to Digital Fractal for personalized software development and mobile app development.