Is the ChatGPT-4 the End-All-Be-All Information Resource?
James M Sims
Relentlessly Driven Executive | Transforming IT Teams to Propel Enterprise Strategies | Passionate Leader and Innovator Tackling Complex Challenges | AI Adoption Evangelist
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!
There are other reasons as well, for example:
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:
DIY Knowledge:
Coding:
领英推荐
Online Learning:
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:
Feedback?
I would be very interested to hear from you. What are your experiences and thoughts on this?
AI Leader|Agentic AI|Multi modal search|RAG|Generative AI| Neural Networks|Transformers
1 年Awesome article Jim! Very well written !
Global Vice President of IT at Gale Pacific
1 年The heart of the matter is discernment. Are we asking questions with enough criteria to guide the search that returns the answers we’re looking for? With the statistical relevance of our individual questions relative to those serviced by a much wider audience, ChatGPT-4 may deduce that the answer delivered is accurate. Therein lies the rub. Asking the question with enough clarity. Secondly, with the population of that delivered content becoming the source for subsequent queries, there is the danger of ‘Model Collapse’, where ChatGPT returns results from its own generated content. Those results become more statistically relevant as ChatGPT creates more content.