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Transduction Machine Learning
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Machine Learning can be seen in many different forms, whether that be Natural Language Processing (NLP), Artificial Intelligence, Regression Analysis, Computer Vision, and Artificial Neural Network, and Linux. All of these forms comprise of some sort of machine program that has no evolved into AI. Which was created to make life easier, these formulas either output words or graphs in some cases like Regression Analysis. It is not certain how AI will develop, continuing to change many jobs and way students learn in classes using machine learning platforms like "ChatGBT".
Machine Learning
"Machine learning is a branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy". Arthur Samuel invented the term "machine learning" after Robert Nealey lost checkers to an IBM 8094 computer in 1962. This does not seem that impressive compared to today's technology of smartphones and cars that drive themselves. Though, this was the beginning of artificial intelligence, especially in 1962 before AI was well known. A more modern example of machine learning is Netflix's recommendation engine. Suggesting shows the user might like based on previous shows that have been clicked on or have a similar genre. For instance, picking a comedy would likely suggest another comedy show. This prediction model of computing can also be seen in gambling and other video games. Thus increasing the need for data scientists as a career option.
Natural Language Processing
As we have been discussing all semester in Linguistics, Natural Language Processing (NLP) is an important tool to use that is becoming more and more relevant. By definition, "Natural language processing (NLP) combines computational linguistics, machine learning, and deep learning models to process human language." NLP is vital because it helps computers communicate with humans in the language that they recognize. It scales other language-related tasks. NLP bridges the gap from computers to languages that people speak. For example, NLP interprets text, and speech, as well as measures if it is important. NLP also recognizes the patterns of grammar and its various structures and syntax rules between languages. Such as how Spanish has a formal tense whereas English does not. Slang can also play a structure where a computer may not pick up the slang or if we stutter we may confuse the computer. Now, in the modern age, these elements are being improved even through Google Translate, it notices when someone stutters because it recognizes the English mode that I have it set to. It has long been thought that AI would not be able to advance to being as smart as the average human. Computers may not have an edge over humans with data-driven decision making. Now that AI can find code errors within minutes that would usually take a person hours to find. It can now assist with writing advanced code such as identifying text data assets with the latest technologies, reorganizing skill labor, and incorporating new language-based AI tools for different tasks.
What NLP Can Do
The best known natural language processing tool is GPT-3, from OpenAI, which incorporates statistics and AI to guess the next word in a sentence based on the words before it. We see the use of GPT-3 in businesses where the AI tool may be asked to summarize reports. In more advanced programs, even solving high school–level math problems. The latest version, called InstructGPT, has been modified by researches to output to responses that are much better than what the user input. For example, chat GPT, which can be modified in its settings to turn the text the user input to the chat bar to sound more professional or put it into simple terms. Having a tool that can rephrase what you are trying to say can be a great integration tool for improving writing. Making essays longer by bringing up topics that could have been overlooked, in the long run saving time to find more research questions. There is also computer vision that is a field of computer science that allows computers to identify and understand objects and people in images and videos. This speech recognition feature allows a person to talk into the microphone of their device and have it take what they say and write it out on that device in whatever language they want. Similar to other kinds of AI, the developer wants the computer to assist the human in replicating what a human would do in hearing what a person says toyouo, and writing it down.
Regression Analysis/Artificial Neural Network
Another couple examples being Regression analysis and artificial neural network. Regression analysis is "statistical method" that allows you to examine the relationship between two or more variables of interest. While there are many types of regression analysis, at their core they all examine the influence of one or more independent variables on a dependent variable. There is now types of software available to plot these points on a graph. Though there are more simple platforms like "Desmos" of graphing two points on a line. Next, Amazon's definition of an artificial network, "A neural network is a method in artificial intelligence that teaches computers to process data in a way that is inspired by the human brain". This type of machine learning process uses interconnected neurons and nodes in a structure which tries to replicate the human brain
Linux
Linux started off by people using it as a tool to explore coding, it then evolved into the work environments in the mid-1990s. Big organizations such as NASA gravitated towards Linux as a cheap alternative to their expensive machines. Dell and IBM followed with the hopes of escaping Microsoft's engulfment of the operating system market. Linux was the foundation to Embedded Systems, as a server installation such as LAMP application stack. The use of Linux distributions in home and enterprise desktops has been growing. Linux distributions have also become popular in the netbook market, with many devices shipping with customized Linux distributions installed, and Google releasing their own ChromeOS designed for netbooks. With its growing popularity, there has been lots of public funding despite the hurdles. Due to an earlier antitrust case forbidding it from entering the computer business, AT&T licensed the operating system's source code as a trade secret to anyone who asked. As a result, Unix. Linux is an open-source tool that people who code use. It is a Server OS for web servers, database servers, email servers, file servers, and any other server shared across platforms. The program dates[1] back to the 1990's by a man called Linus, Torvalds. Linus first released Linux on September 17, 1991. The overall concept of Linux is that it comes with a Linux distribution known as a distro, supporting system software such as libraries, and kernel. There are many libraries to use that all begin with Linux or GNU software. The history of Linux is particularly famous because of its reliability compared to other operating systems. It overall has fewer issues and fewer computer crashes compared to other interfaces. This success is due to the focus on background process management compared to other systems constantly running in the background which leads to frequent crashes.
Conclusion
In conclusion, there is a lot of unknown in the language processing world. All it takes is one bright mind to create the next big revolution in technology. You will never be able to replace the human interaction that managers have in jobs. However, for labor in jobs I think there is going to a steep decline in ware house types of jobs, where CEO's would rather save money and invest in technology to replace people. Rather than giving people bathroom and lunch breaks, and being required by laws to pay minimum wage and not overwork teens. Technology does seem like the cheaper option.
Citations
The Power of Natural Language Processing
Transduction Machine learning https://en.wikipedia.org/wiki/Transduction_%28machine_learning%29
- ^ Reinsalu, Riina; Vija, Maigi; Org, Andrus; Siiman, Ann; Remmik, Marvi (2023-06-07). "With or without Wikipedia? Integrating Wikipedia into the Teaching Process in Estonian General Education Schools". Education Sciences. 13 (6): 583. doi:10.3390/educsci13060583. ISSN 2227-7102.
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