Is the ChatGPT-4 the End-All-Be-All Information Resource?

Are large language models (LLMs) like ChatGPT-4 ready to replace all other online resources we use for information-seeking tasks such as general research, DIY knowledge, online learning, and IT programming?

While LLMs like ChatGPT-4 are becoming increasingly advanced and can provide valuable support, it's unlikely that they will completely replace all other online resources in the near future. 

My short answer is that it is not yet there. Having said that, generative AI solutions are nearly always my first stop in doing research. I could easily say that my use of Google is down 95% from a year ago. LLMs like Bard, ChatGPT-4, and Perplexity.AI are becoming so broad and deep in their knowledge and increasingly articulate and comprehensive in their answers I mostly do follow up searches just to ensure the information I have collected is complete and accurate (i.e., no hallucinations).

So then, why are these AI tools not there yet? Simply stated, they just are not reliable at this time!

• Quality Control and Accuracy: AI models, even the most sophisticated ones, can occasionally provide incorrect or misleading information. This issue is especially critical in fields that require high precision and accuracy, such as academic research, advanced DIY projects, or complex coding problems.

There are other reasons as well, for example:

• Context and Personal Interaction: While LLMs are getting better at understanding context, they can still struggle with complex or nuanced queries that a human might easily understand. Learning often benefits from human interaction, personal experience, and social engagement—elements that AI can't fully replicate.
• Expertise and Authority: Many online resources are curated and maintained by subject matter experts whose extensive training and experience can't be directly equated to an AI's algorithmic understanding. This is particularly true for technical fields like medicine or law, where professional guidance is essential.
• Up-to-date Information: AI models like ChatGPT-4 have a training cutoff, which means they can't incorporate new information after that point. On the other hand, human-curated online resources can be updated in real-time to reflect the latest research, news, and developments.
• Depth of Information: While LLMs can provide high-level overviews and answer many questions across a wide array of topics, they may provide a different depth of information than specialized resources. This can be particularly true in academic research, complex DIY projects, or advanced coding challenges.

While there is no denying that AI LLMs like ChatGPT-4 are powerful tools that can provide instant information and support across many topics, they are best used to complement existing online resources rather than serve as a replacement at this time. A blended approach, utilizing the strengths of both AI and human-curated resources, can provide the most comprehensive and effective learning and support experience.

Here are just a few of the resources many of us currently use:

General Research:

1. Google: The 'go to' search engine for at least the last decade, Google provides quick access to an immense array of resources across all fields. Whether you're looking for academic papers, how-to guides for a DIY project, or troubleshooting help for a coding problem, Google is often the first stop for finding information online.
2. Wikipedia: A free online encyclopedia created and edited by volunteers worldwide. It covers a wide range of topics in numerous languages.
3. Google Scholar: A database of scholarly literature across many disciplines and sources, including theses, books, abstracts and articles.
4. Google Books: This service from Google searches the full text of books and magazines that Google has scanned, converted to text, and stored in its digital database.
5. JSTOR: Offers various academic journal articles, books, and primary sources from multiple disciplines.
6. PubMed: PubMed is helpful for medical research; this free search engine primarily accesses the MEDLINE database of references and abstracts on life sciences and biomedical topics.
7. PLOS ONE: An international, peer-reviewed, open-access, online publication for scientific research.
8. arXiv.org: A free distribution service and open archive for scholarly articles in physics, mathematics, computer science, and more.

DIY Knowledge:

1. YouTube: Numerous channels offer tutorials on almost any topic, from home repair to crafting.
2. Instructables: A community-based platform where people share their DIY projects and how they made them.
3. WikiHow: A comprehensive database of how-to guides.
4. Reddit's DIY section: A large community sharing DIY projects and advice.

Coding:

1. Codecademy: An interactive platform that offers free coding classes in various programming languages.
2. LeetCode: A platform for preparing technical coding interviews and improving your coding skills.
3. FreeCodeCamp: An open-source community that helps you learn to code, build pro bono projects, and get a job as a developer.
4. Stack Overflow: A community of professional and enthusiast programmers helping each other with code-related questions.
5. GitHub: A platform where developers can share their code. It's an excellent place to find real-world examples of how to use a programming language or framework.

Online Learning:

1. Coursera: Offers online courses from many of the world's top universities.
2. edX: Provides free online courses from the world's best universities and institutions.
3. Khan Academy: Offers practice exercises, instructional videos, and a personalized learning dashboard for self-paced learning.
4. Udemy: An online learning platform aimed at professional adults and students, with content provided by experts and thought leaders across the globe.
5. LinkedIn Learning: Offers video courses taught by industry experts in software, creative, and business skills. It was formerly Lynda.com.

Getting More Accurate Responses

One of the ways we might get more reliable answers is to apply the concept of Generative Adversarial Networks (GANs) to the output of LLMs such as LSTMs (Long Short-Term Memory) or Transformer-based models like GPT (Generative Pre-trained Transformer) to improve the quality and accuracy of the final output

GANs consist of two components: a generator and a discriminator. The generator generates samples, while the discriminator assesses whether the generated samples are real or fake. By training these components simultaneously in a competitive manner, GANs learn to generate high-quality and realistic samples.

In the context of language generation, GANs can be used to enhance the output of LLMs. The generator component can be an LLM, such as an LSTM or GPT, which generates text. The discriminator component can be trained to distinguish between real and generated text samples. By training the generator and discriminator together, the generator can learn to produce more accurate and realistic text as it tries to fool the discriminator.

Applying GANs to LMs can help address some limitations of LLMs, such as generating plausible but incorrect or nonsensical text. By incorporating the adversarial training of GANs, the generator can be guided to generate text that is not only coherent but also aligns better with the desired quality and accuracy.

It is important to note that this is not as easy as it sounds. GANs can be challenging to train and may require substantial computational resources and data. Additionally, finding a suitable architecture and training setup can be complex. Nonetheless, researchers are exploring the application of GANs to language generation tasks to improve the quality of output generated by LLMs.

ChatGPT-5

Putting all these issues aside, you can see how amazingly fast these tools are evolving. In fact, the soon-to-be-released ChatGPT-5 will very likely be a further game-changer. One of the capabilities of ChatGPT-5, which will be released later this year, is OpenAI Academy, where you can build a personalized curriculum that is tailored to your specifications. Even more importantly, ChatGPT-5 will accept unlimited tokens in your prompt. This will enable more extensive prompts and the retention of a more comprehensive, complex and nuanced context.

Further Reading

Here are some additional articles that further explore this topic:

• Wikipedia’s Value in the Age of Generative AI
• Why AI Can't Take Over The World?
• The Top 10 Limitations Of ChatGPT
• Top 11 Limitations of ChatGPT
• Overcoming ChatGPT Limitations with ChatGPT Plugins & Mantium

Feedback?

I would be very interested to hear from you. What are your experiences and thoughts on this?