AUTHOR: Eddie S. Jackson, MrNetTek DATE: May 10, 2023, at 6:00 AM EST RESEARCH: Google search, current news, books, Copilot. EDITING: Grammarly
A large language model (LLM) is a type of artificial intelligence (AI) algorithm that uses deep learning techniques and massively large data sets to understand, summarize, generate, and predict content (some say it is new content; some say it is just theft). LLMs are based on neural networks, which are composed of multiple layers of nodes that process and learn from data (patented after how the brain works, at least that is the idea). LLMs use a specific neural network architecture called a transformer, which can read and analyze large amounts of text and identify patterns and relationships among words and phrases.
LLMs emerged around 2018 and have shown remarkable performance at a wide variety of natural language processing (NLP) tasks, such as answering questions, writing essays, composing poems, creating chatbots, and more. Some examples of LLMs are ChatGPT, Google Bard, and OpenAI Codex. These LLMs can produce human-like responses and outputs that are mostly indistinguishable from those written by humans. Useful, if you are trying to build a chatbot, or create content that would be closely equivalent to human writing. Controversial? Yes. And, oh, the opposing forces are gathering in large numbers.
LLMs are trained on large textual datasets that can range from millions to trillions of words (these words are tokenized, but that is a discussion for another day). Some commonly used textual datasets are Common Crawl, Wikipedia, Reddit, StackOverflow, and GitHub24. These datasets contain a wide variety of topics, genres, styles, and languages that enable LLMs to learn general knowledge and skills about the world. However, these datasets may also contain biases, errors, or harmful content that can affect the quality and ethics of LLMs’ outputs. Who watches the watcher? Good question.
LLMs have great potential to improve human lives and society in many ways, such as enhancing education, health care, entertainment, and business. LLMs can also help solve some of the world’s most pressing problems, such as climate change, poverty, and disease. However, LLMs also pose significant challenges and risks that need to be addressed and regulated.
Some of the challenges and risks are:
Privacy and security: LLMs can collect, process, and analyze large amounts of personal and sensitive data from various sources. This can enable beneficial applications such as personalized services, recommendations, and advertisements. However, it can also enable harmful applications such as identity theft, fraud, cyberattacks, manipulation, and exploitation. Moreover, LLMs can leak or reveal private or confidential information that they have memorized or inferred from the data they have seen.
Bias and discrimination: LLMs can inherit or amplify human biases and prejudices that exist in the data or the algorithms that they use. This can result in unfair or discriminatory outcomes or decisions that affect individuals or groups based on their characteristics such as race, gender, age, religion, or sexual orientation. For example, LLMs can be biased in hiring, lending, policing, health care, education, and criminal justice. I want to emphasize, that LLMs amplify human bias.
Human judgment: LLMs can challenge the role and value of human judgment in various domains and contexts. For instance, LLMs can influence or replace human decision-making in areas such as medicine, law, education, politics, and warfare. This can raise questions about the accountability, responsibility, and explainability of LLMs’ systems and their outcomes or actions. It can also raise questions about the autonomy, dignity, and agency of human beings who interact with or are affected by LLMs’ systems.
The question of, “What is a large language model?”, is not exactly definitive. It is a complex and evolving phenomenon that has both positive and negative implications for humans and society. All stakeholders involved in LLMs must adopt ethical principles and practices that ensure that LLMs are aligned with human values and goals. It is also important for society to educate itself about the benefits and risks of LLMs, and to engage in informed and responsible decision-making regarding their development and deployment. LLMs are here to stay. I do believe we are witnessing the infancy of LLM technology. So, expect the output, predictions, and content to only get better over time. At least, that’s the best we can hope for. As with most new technology, I’m cautiously optimistic.
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Tags: Artificial Intelligence, MrNetTek, Eddie Jackson Computer, AI
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