ChatGPT appeared to be the first popularized Artificial intelligence tool to the general public when OpenAI launch it on November 30, 2022. It was initially introduced in June 2020. ChatGPT builds upon the GPT (Generative Pre-trained Transformer) architecture, renowned for its ability to comprehend and generate human-like text across various contexts and tasks. ChatGPT's training process involves fine-tuning GPT-3.5 and GPT-4 models for conversational usage. ChatGPT allows users to engage in conversations and steer them towards desired lengths, formats, styles, levels of detail, and languages. It has become one of the fastest-growing consumer software applications in history, with over 100 million users by January 2023 (i.e. 1 month of its launch).
OpenAI, the organization behind ChatGPT, is dedicated to advancing artificial intelligence in a manner that benefits humanity. Founded in December 2015, OpenAI has since become a leading research institution in the field of artificial intelligence, pioneering breakthroughs in natural language processing, reinforcement learning, and other domains. Through its innovative research and development efforts, OpenAI has contributed significantly to the evolution of AI technology, fostering applications in diverse areas such as language understanding, image recognition, robotics, and more.
ChatGPT offers a wide range of features, making it a versatile chatbot. It can write and debug computer programs, compose music, teleplays, fairy tales, and student essays, answer test questions, generate business ideas, write poetry and song lyrics, translate and summarize text, emulate a Linux system, simulate chat rooms, play games like tic-tac-toe, and simulate an ATM. OpenAI has made efforts to reduce harmful and deceitful responses compared to its predecessor, InstructGPT . ChatGPT also remembers a limited number of previous prompts in a conversation, which has led to speculation about its potential use as a personalized therapist.
However, ChatGPT does have limitations. It may sometimes generate plausible-sounding but incorrect or nonsensical answers, a behaviour known as "hallucination". The model's reward system, designed around human oversight, can lead to over-optimization and hinder performance. Additionally, the knowledge base of ChatGPT is limited to events up to January 2022 for GPT-3.5 and April 2023 for GPT-4. The training data also suffers from algorithmic bias, which can result in misrepresentations of certain groups of individuals.
ChatGPT's release has sparked an AI boom, leading to rapid development in the field of artificial intelligence. However, concerns have been raised about the potential displacement or atrophy of human intelligence, plagiarism, and the spread of misinformation due to ChatGPT and similar programs.
Though there were many uncountable AI tools before it releases in November, 2022, ChatGPT’s released serves as eye-opener into the AI revolution. Tools like Grammarly and QuillBot have been valuable assets for writers, researchers, and students in enhancing their writing quality and efficiency even before it releases. Grammarly is a writing assistant that helps users improve their grammar, punctuation, style, and tone while QuillBot is a paraphrasing tool that utilizes AI to rewrite sentences and paragraphs while retaining the original meaning. They leverage AI technology to provide users with actionable insights and suggestions, facilitating the creation of high-quality written content.
As the prevalence of AI-generated writings and content increases, there is indeed a growing need for sophisticated AI detectors to identify instances of contents originating from AI-generated sources. This is what I call enemy within. AI tools for fighting AI users, not only AI-users but also from plagiarized contents. GPTZero, Copyleaks AI Content Detector, Crossplag AI Content Detector, Writer AI Content Detector, ZeroGPT, Writefull GPT Detector, GPTRadar, Winston AI, CopyScape, Content at Scale, Plagibot, Corrector App AI Content Detector, Sapling AI Content Detector, Grammarly, Writer, Sapling, Giant Language model Test Room (GLTR), Originality.AI, Undetectable, AI Detector Pro, GPTKit, Turnitin, SciSpace Academic AI Detector are included among the innumerable list depending on the context. For example, in academic settings, most higher institutions of learning use Turnitin as the tools to check for academic integrity and ensure authenticity and originality in students coursework. Also, Turnitin seamlessly integrates with various learning management systems (LMS) commonly used in educational institutions. This integration simplifies the submission and grading process, making it convenient for both students and instructors in getting and sending feedbacks therefore automating the academic processes workflow. These tools have proving useful in flagging AI-generated writings, helping to maintain transparency and authenticity in various domains by identifying instances where AI has been used to generate text. They also help in combating misinformation by enabling fact-checking and preventing the dissemination of false information. They also help protect the intellectual property rights of original authors and creators in a scenario where content is used without proper attribution or permission. Though the pressing issue is on text content, there are other AI detector tools that work for content in various context. AI detectors are not without challenges and limitations. Aside for the high cost of most of the tools, evolving AI technology and new techniques used in generating content call for AI detectors a must to stay updated to effectively identify AI-generated writings. Another challenge is the incidences of false Positives and Negatives as AI detectors may sometimes flag human-generated text as AI-generated or fail to detect heavily modified AI-generated content.
Human languages such as English and French have also seen significant impacts from advancements in AI technologies, particularly in natural language processing (NLP) and generation. As AI tools like ChatGPT become more integrated into everyday applications, the way we communicate, learn, and create content is evolving. AI models are increasingly being trained to understand and generate text in multiple languages. This capability allows users from diverse linguistic backgrounds to access information, engage in conversations, and create content in their native languages. For instance, ChatGPT can generate text not only in English but also in French, Spanish, German, and even indigenous languages like Yoruba, Hausa and Igbo, making it a valuable resource for global communication. AI-driven tools are transforming language learning by providing personalized assistance to learners. Applications like Duolingo utilize AI algorithms to adapt lessons based on a user’s progress, helping them master vocabulary and grammar more effectively. Additionally, chatbots can simulate real-life conversations, allowing learners to practice speaking and writing skills in a low-pressure environment. In the realm of content creation, AI tools facilitate the localization of materials for different markets. Businesses can use AI to translate marketing content, websites, and product descriptions while ensuring cultural nuances are respected. This not only saves time but also enhances the relevance of content for target audiences. Despite the benefits, there are challenges associated with using AI for language processing. One major concern is the potential for bias in language models. Since these models are trained on large datasets that may contain biased or unrepresentative samples of language use, they can inadvertently perpetuate stereotypes or produce insensitive content. Addressing these biases is crucial to ensure fair and equitable AI applications. Additionally, while AI can generate coherent text in multiple languages, it may struggle with idiomatic expressions or cultural references that require deeper contextual understanding. This limitation highlights the importance of human oversight in applications where nuance and sensitivity are paramount. As AI technologies continue to advance, their integration into human languages will likely deepen. Future developments may include improved contextual understanding, enhanced emotional intelligence in interactions, and more sophisticated translation capabilities that capture subtleties of meaning. Furthermore, as digital communication becomes increasingly prevalent, the role of AI in shaping language itself cannot be overlooked. The influence of AI-generated content may lead to the evolution of language patterns and usage among different demographics.
It was said that AI model such as ChatGPT have the ability to write and debug computer programs and coding across many programming or coding languages. Aside this, the emergence of more smart tools that enhancing and revolutionizing coding and sometimes integrated in IDEs. The concept of AI coding refers to using artificial intelligence to assist developers in writing and reviewing their code with the ultimate aim of enhancing code quality and boosting productivity. Someone called it a "second set of eyes," offering a fresh perspective and valuable assistance in programming. They are not intended to replace human engineers but rather augment their capabilities. The benefits of AI coding include: Boosted productivity and accelerated code development, saving time by automating repetitive and time-consuming tasks like code formatting and boilerplate code generation, providing valuable code suggestions to help developers overcome writer's block and jump-start the first draft of new code, ultimately meeting deadlines compared to those who didn't apply such tools. Another benefit is that AI coding improve code quality by enhancing code accuracy through real-time suggestions based on industry best practices, identifying issues, alerting on potential bugs or security vulnerabilities, and generally acting as a safeguard against common coding mistakes, thus helping developers create cleaner and more error-free code. Also, AI coding empowers non-developers and newcomers by making code development more accessible to them through contextual guidance, natural language comment responses, and simplified programming solutions, these solutions serve as mentors, breaking down complex concepts and providing step-by-step instructions and comprehensive assistance, fostering a supportive learning environment. AI coding tools usually features (1) AI code completion which suggests code snippets to developers as they type, predicting the next likely code based on context, thereby reducing manual input, enhancing code consistency, and minimizing errors, particularly beneficial for repetitive tasks. (2) AI code generation which transforms natural language prompts describing desired functionality into executable code by comprehending provided suggestions and offering contextually relevant code, considering established programming patterns. (3) Code translation which involves converting the source code into the desired programming language, enabling seamless integration of code from different teams within the organization or other open-source projects, and potentially reducing the effort and expense of updating legacy codebases written in ancient programming languages like COBOL or Ada. (4) Code review, which is an evaluation process leveraging AI to enhance code quality by identifying and fixing errors while generating comments and explanations in natural language, facilitates a clearer understanding of the review process, especially beneficial for developers seeking detailed feedback to improve their coding skills. (5) AI tools can evaluate code for consistency and readability to ensure that it adheres to established style guidelines and can be easily understood by other developers. some AI coding tools are also capable of code refactoring, code documentation, providing explanations, and searching for certain files and code snippets.
As a result of the influx of AI-generated content, uproar and opposition arose, which were combated by some. For instance, AI-generated answers have been temporarily banned on the coding Q&A site Stack Overflow due to concerns about the accuracy and reliability of these responses. After ChatGPT was launched, AI-generated results flooded the site with answers that appeared correct at first glance but were often incorrect upon closer examination, leading to the generation of "fluent bullshit," where the responses sound convincing but are ultimately incorrect. Experts in the AI community are currently debating the potential threats posed by these models. Some argue that while LLMs can generate bad output, they don't make the actual sharing of this text any easier, which is what causes harm. However, others believe that the ability of these systems to generate text at scale increases the risk of sharing incorrect information. However, some emerging developments have been integrating AI, though with stricter guidelines and measures in place to ensure the accuracy and reliability of the generated responses through improved spam filters, better moderation capabilities, or the use of AI tools specifically designed for code generation and optimization.
One downside of AI models, especially LLMs, is their ability to hallucinate, i.e., they give seemingly right but outrightly nonsensical answers. The GPT-4 version of ChatGPT has some features that could provide links to confirm information from web sources. Though there are many LLMs, only a few have the feature to provide links – these are referred to as AI search engines. AI search engines have emerged as a powerful alternative to traditional search engines, offering a more personalized and contextually rich experience. From my experience, I've used Perplexity.ai, you.com, Bing Chat, the web-search bot of poe.com, and sometimes Google Bard (now Gemini), though I have not yet found Bard reliable. Other examples that I have not tested but researched include Andi Search, Metaphor AI Search, Brave AI Search Summarizer, Phind.com, ZenoChat by TextCortex, Komo, Waldo.fyi, NeevaAI, Opera browser AI, and Yep.com. Unlike traditional search engines, when you ask a question, most AI-powered search engines like Perplexity don’t give back a list of links. Instead, they scour the web for you and use AI to write a summary of what they find. These answers are annotated with links to the sources the AI used, which also appear in a panel above the response. Some of the AI-powered platforms have extra features; for example, Perplexity features one impressive function known as “Copilot,” which helps a user narrow down a query by asking clarifying questions. It also features file upload, though few other (non-search engine) LLMs have the file upload feature. As I mentioned initially, "Before ChatGPT and other Large Language Models (LLMs), there was Google and other search engines." In my "Computer Basics and Digital Skills Foundation" course, I delved much into the utilization of search engines. A traditional search engine is powerful in finding things to some extent. Now, with the advancement in technology with AI, that's an added advantage.
Several platforms have emerged that offer multiple AI functionalities, exemplifying the trend toward multimodal integration in technology. Notably, platforms like Discord servers and poe.com, along with advanced iterations of the GPT-4 model in ChatGPT, Bing Chat, ClickUp, NeevaAI, and you.com, serve as multifunctional environments that amalgamate various AI tools into a single cohesive platform. This integration allows users to access diverse capabilities such as text generation, image processing, and data analysis all within one interface. Moreover, application software and web applications are increasingly striving to embed numerous AI features into their functionalities to maintain competitiveness in the marketplace. Messaging and social media applications like WhatsApp and X (formerly Twitter) are also incorporating large language model (LLM) features, such as MetaAI and Grok, enhancing their user interaction capabilities by providing intelligent responses and personalized content. This multifaceted approach not only enriches user experience but also positions these platforms at the forefront of AI innovation, allowing them to better meet the evolving demands of users in an increasingly digital world.
One of the most important topics in my course, "Computer Basics and Digital Skills Foundation," is the distinction between local and cloud computing. While AI technologies are increasingly prevalent in both environments, cloud computing is often regarded as the more effective option due to its scalability and accessibility. Historically, many AI models I've referenced operate online, leveraging the extensive computational resources available through cloud platforms. However, the landscape is changing, as there are now viable AI models that can run locally on personal devices, provided those devices have sufficient hardware capacity. For instance, local AI models such as localGPT and ComfyUI exemplify how users can harness AI capabilities directly on their machines without relying on internet connectivity. This shift towards local AI deployment offers several advantages, including enhanced data privacy and security since sensitive information does not need to be transmitted to external servers. Additionally, local models allow for greater customization and control over performance optimization, enabling users to tailor the technology to meet specific needs. The independence from internet connectivity also ensures that these models remain functional in areas with unreliable internet access. Another crucial discussion in my course, "Computer Basics and Digital Skills Foundation," revolves around the differences between free and paid services, particularly in the context of artificial intelligence (AI) models. In today's digital landscape, AI technologies are available in both free and paid formats, each offering distinct advantages and limitations. Free AI services often provide basic functionalities that allow users to explore and utilize AI capabilities without any financial commitment, making them accessible to a broader audience, including students and hobbyists. However, these free models may come with restrictions such as limited processing power, fewer features, or data usage caps. On the other hand, paid AI services typically offer enhanced functionalities, greater computational resources, and priority support, catering to users who require more robust solutions for professional or commercial applications. Some platforms adopt a freemium model, where users can access basic features for free while offering premium options that unlock advanced capabilities for a subscription fee. This model allows users to evaluate the service before committing financially, striking a balance between accessibility and functionality.