write a summary paragraph for this: 

Sizzle! Crack! Boom! I turned my head in the direction from which the sound came from. Ouch. Why is it so loud? Sitting on the riverbank, I was awfully close to the fireworks. Although it was my first time watching the fireworks, it didn’t occur to me that close proximity could impair my hearing. These fireworks, alongside other technologies, have usually added a new aspect to people’s lives. From the cotton gin to computing, there have always been benefits and drawbacks to new technologies. For example, arsenic in the form of chromated copper arsenate (CCA) was used to preserve the wood from insect and fungal attacks in furniture, buildings, and buildings. Ultimately, when a sufficient threshold of people realized its harms, it was banned for use in homes. Not too long ago, we started taking steps to prevent future harm. In 2002, the U.S. Environmental Protection Agency reached an agreement with the manufacturers of wood preservative products containing CCA to stop its use in virtually all residential applications, (CDC). The absence of regulations and safeguards has put the essence of humanity at risk. As a consequence of utilization of new technologies, the quantity and accessibility of students to teachers likewise has grown from pupil and master to individual tutoring to public education. As a direct consequence, the umbrella of resources and individual teacher time with students has decreased. Implementation of AI, the new frontier technology, will bring about numerous benefits alongside unforeseen consequences. In order to prepare future generations properly, we must look before we jump.
The main contentions ushering the implementation of AI in a variety of fields including education, are the speed and magnitude at which an AI application can generate a response. Additionally, said AI’s can be tuned specifically for one’s custom purpose with the promise to yield quality and pertinent information without the presence of bias or harm to the user. It’s like believing in medications which are 100% safe, effective, and free of side effects, being manufactured with said objectives in mind. Even the simplest medications have varied impacts on physiologies of biologically diverse people. For example, over-the- counter medications like Acetaminophen are the second most common cause of liver transplantation around the world and the most common cause of liver transplantation in America, in conjunction with frequent trips to the ER, (National Library of Medicine). Implementation of AI in education can produce similar detriments, but within its own parameters. With that being said, AI’s are trained by using mountains of data. AI training is in some regards a mimic of how humans naturally learn. As humans, we learn many things by recognizing patterns and through repetition. From elementary school to post collegiate learning, we learn from our environments, mentors, peers, and we change our behavior according to present circumstances. Often, when one experiences an unpleasant scenario, they may attribute it to others with hasty generalizations, leading to inherent bias. Similarly, AI models are plagued by bias due to either explicit programming, knowledge gaps, or deficiencies in transformers used to generate answers. Researchers have found bias in nearly 38.6% of facts used by AI in the GenericsKB dataset (USC Viterbi). Containing generic terms for AI training, the dataset has over one-third facts biased. Large Language Models (LLMs) trained with such data alongside web scraping petabytes upon petabytes of data to feed models make it near impossible for a human to sort everything into biased and fair information. In addition, modern innovations like OpenAI’s ChatGPT-4 model, are heavily trained from research papers, websites, and articles (Harvard University). According to universal expert belief, one of an LLM’s specialties is to condense gargantuan amounts of data and convert it into a response for the user. However, bias, misinformation, fake news, and hallucinations can and often are a portion of the product produced by an LLM. Putting this into perspective, some of the scientific data modern LLMs are trained from nowadays come from research papers and scientific journals, all in which humans make contributions and report their findings. But there is a slight problem. Some experimental findings reported are completely inaccurate. Before papers are published, they are peer reviewed to ensure integrity and validation. Theoretically, any scientist or competent individual should be able to read the procedure and reproduce the same if not similar results in accordance with the original study. But that isn’t the case. At best, around 50% of biomedical research had low levels of reproducibility, (National Library of Medicine). Not only in the medical field, but also in the realm of physics, 70% of scientists in the study couldn’t reproduce another one’s experiments, (Physics Today.) Furthermore, Only 24% of papers were successfully reproduced alongside 85% of chemistry 75% of biology research papers failed to be reproduced, (Nature.) Needless to say, it would be illogical to hope for pertinent and consistent truthful answers from an LLM with such data. Implementing a brittle foundation LLM in our children’s education would be like shooting oneself in the foot before improving upon AI.
Moreover, due to the way LLM transformers are structured, they generate a response in a predictable manner, one which can be picked up by AI detectors such as GPTZero. LLMs are tuned to predict word patterns. LLMs hope to fill in gaps by anticipating which word is the most probable to come after the previous with respect to the context, tone, and writing style. Because LLMs are not a biological being, and hope to imitate a human neural network, they seldomly lack construction of proper sentences hence “hallucinating”. “Hallucinations” occur when an AI model connects several data points, concocting a pattern which ultimately produces senseless conclusions, false facts, and contradictions. Hallucinations occur even in the latest models (OpenAI), leading one to be cautious. This is often a result from a lack of Reinforcement Learning Human Feedback (RLHF), or unintentional data polarization which forces the LLM to read between the lines to uncover the underlying motive. Feeding a model data from one side of the story polarizes its decision making process, planting the seeds for structural bias. One criterion to consider is that implementing personalized educative chatbots raises concerns in the minds of governing individuals, parents, and non profit organizations. For example, a large contingent of parents may not want a specific perspective implemented on a stated scenario but you, the developer, may want to notify the world. This would cause a conflict of interests, and may lead to uncomfortable scenarios. Before AI models are implemented into the very education systems which assist the molding of our present biological datasets, it’s imperative to ensure minimal bias and noise. In the absence of proper safeguarding, purification from misinformation, and unjust answers, LLM models can shift the thinking of our children towards the point of no return. Since they will be the ones to forge the world, we have to equip them with impartial knowledge and critical thinking skills.
Regardless of all the innovation coming from the promising prospect of AI, it’s useless if we can’t use it, or if we can only use it at the expense of needs. Not only has AI processing power been following the trend of Moore’s Law, it is coming at an increasingly climbing price on energy consumption. Our energy production is limited. As we approach the ends of fossil fuels, many hope to transition towards green and renewable energy. As population increases, and AI keeps making leaps, more energy is needed to sustain such processes. By 2027, which is nearly as close as the outbreak of  SARS-CoV-2, AI servers could use 85 to 134 terawatt hours annually. That’s close to what Argentina, Sweden, and the Netherlands use each year, (New York Times). As tech giants like Google, Apple, and Amazon push for advancement in AI, their energy consumption is also increasing. By 2030, we will reach one quintillion joules per year in energy consumption, reaching the world’s energy production cap soon in 2050, (AMD). Not only is AI training aggressively consuming our energy reserves, it’s making climate change worse. AI datacenters account for 2.5 to 3.7% of global greenhouse gas emissions, superseding the aviation industry, (Cho.) Before AI is implemented widely, it’s critical to decrease its detriments to ensure a solid foundation for future AI production.
While some may argue that implementation of AI in education will bring about a variety of benefits such as personalized learning, task automation, and all time connectivity, the drawbacks of implementing a wet behind the ears technology are more important to address first. It would be more difficult to uproot a foundational AI producing many benefits but also having inherent defects such as bias and lack of emotional intelligence that to build a proper AI from day one, keeping in mind possible repercussions along the way. Other than just seeing educational AI as a monetary investment, we have to make sure that AI in education isn’t a crutch, but maybe glasses. AI has the potential to create a near perfect environment where no one is required to exercise critical thinking. To clarify, after the World Wide Web was launched, over time, we tend to exercise our cognitive abilities less and less because more information is available at our fingertips. “Over-reliance: There's a risk that students might become overly reliant on GAI for answers, discouraging independent research or critical thinking,” (IBM.) Only a few generations ago, our parents had to learn everything by heart, by rote memorization. Through the implementation of technology and amenities, human faculty has dwindled. We should use tools to our advantage, not long lasting self destruction.