The issue of artificial intelligence and its rapid, uncontrolled, and unreliable development has been a subject of much discussion and controversy. Research and studies have been published at a relatively high level on how AI contributes to changes in the sociology, law, and psychology of human beings. Some show wide-ranging effects with AI being a detriment to the functioning of the social sphere. Law enforcement, and relationships within business organizations (Nelson & Martony 2014). Overall, researchers conclude that the factors the robot creates upon taking humans roles would far outweigh the immeasurable strengths that AI tools would bring to the world of communication.
They caution against allowing AI to become a significant dynamic force in the social sphere as some reports deem it would, as AI is more an active part of its interface with humans instead of a passive entity at all. As these observations have been reviewed, it is important to also discuss the drawbacks of AI technologies and their effects in terms of employment and economic sustainability (Nelson & Martony 2014).
AI already exists as a robust technology and as a platform for applications. One key area that AI is currently being utilized in is via machine translation: AI is growing, it is impressive, and it can free human intelligence to be applied to different roles in the process of translation between words. AI, therefore, has an important role to play in the large field of translated text (Nelson & Martony 2014). Its fast performance and low cost allow it to act as a credible transliteration tool in languages not always listed deep (e.g. Arabic). Trained AI translations can be found being used in many ways. These range from applets such as the Google Translate app (Bermann et al. 2013), to libraries such as Google Books (Kandarani & Palmer 2018), or as a hard drive that stores translated text so as to overcome the black hole problem (Pfannenborg &
Laidlaw 2018). However, these simple, simplified solutions to translation still have their drawbacks. Currently, many machines are trained in programming languages (e.g. Python), which means that machine translation is hard to train the machines. The words “based on programming languages” have demonstrated themselves to not be a viable model for researching translation accuracy, as the hard technology behind the translation is not associated with programming languages. Examples of the application of “programming languages” have limited insight into how machine translation works to date.
Not only does machine translation have its inherent challenges, but “programming languages” also has other issues and flaws. Machine learning results are harder to interpret on their own than inputs due to the way they store and accumulate data. One of the potential problems to AI systems would arise if we as social beings become comfortable with machine translators. The huge disadvantage of translation to individuals for instance could be this: “Hello there” translates to “What’s the time?” which means “Hello there” would probably look like a YMCA camp command, and “Hello there” would look like “Hello there” and the user would probably accidentally say, “Hey Mike.”
Artificial intelligence hasn’t been given sufficient consideration. Many people see AI and chatbots as clunky and silly since they don’t portray human feelings, emotions, and culture. They can help companies respond to customer queries. However, the default to hide and hide their internal logic also creates a problem, as the translator, mainly, lacks context and the ability to learn what the user is saying. Also, the human being can discover different syntaxes at the necessary stage before implementation and those are still remaining unknown for machines. This leads to the fact that, in general, AI systems that are intended to handle tasks like translation, remain mostly in the unknown, with at best a small upper bound.
Applications of AI
In addition, when applying AI into several industries, like logistics, the Google-developed user interface that has led to great improvements is not something that is easily adapted to other industries. If Artificial Intelligence systems are ported into our lives. For example, there is a substantial challenge of bringing the end state into parity with the current reality. Due to AI not knowing what to do during flights, while its counterpart, the pilot, does know how to use the controls. Studies show that the AI process can be more rapidly integrated into human roles, but the end state of humans remains unsorted, and it remains difficult to adapt machine translation seamlessly into human lives. Based on this discussion, many people view AI as a colossal threat and in areas like public security, fire, and healthcare AI would certainly need regulation and modifications before such technology can be employed.