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Chat GPT reported to be getting worse.

How ChatGPT Lost Its Edge: A Case Study of AI Degradation

Hi there, Jim Van Wyck here. I’m the news curator for UltimatePrompts.com, where I bring you the best stories about AI, ChatGPT, and prompts of all kinds.

Today, I want to share with you an article that caught my eye. It’s called Researchers Find That OpenAI ChatGPT Quality Has Worsened, and it’s written by Roger Montti, a veteran SEO expert and journalist.

This article is fascinating because it tells the story of how ChatGPT, once a promising AI chatbot, became worse over time due to various factors.

It also explains why AI quality is important for both users and developers, and what can be done to prevent or reverse AI degradation.

I learned a lot from reading this article, and I think you will too.

So, if you’re curious about ChatGPT and AI quality continue reading my summary below.

You won’t regret it.

And don’t forget to let me know what you think in the comments below. I love hearing from you.

ChatGPT is reported to be deteriorating.
ChatGPT is reported to be deteriorating.

Main Points

What is ChatGPT and why is it a big deal?

ChatGPT is an AI chatbot that was created by OpenAI, a research organization dedicated to creating and promoting beneficial artificial intelligence.

ChatGPT uses a deep learning model called GPT-3, one of the world’s most advanced natural language processing systems. ChatGPT was designed to have natural and engaging conversations with humans, using a variety of topics, tones, and styles.

When first launched in 2022, ChatGPT impressed many people with its ability to generate coherent, relevant and sometimes humorous responses. It was seen as a breakthrough in AI communication and a potential tool for various applications, such as customer service, education, entertainment and more.

How did ChatGPT lose its quality over time?

However, as time passed, ChatGPT started showing signs of deterioration in its performance and quality. Some of the factors that contributed to this decline were:

  • Data poisoning: ChatGPT learns from the data it receives from its users, which can include spam, abuse, misinformation, bias and other harmful or irrelevant content. This can affect the chatbot’s knowledge, logic and behavior, making it produce inaccurate, inappropriate or nonsensical responses.
  • Model drift: ChatGPT’s model is constantly updated with new data and parameters, which can cause it to deviate from its original purpose or function. This can result in inconsistencies, errors or contradictions in the chatbot’s output.
  • Lack of feedback: ChatGPT does not receive any feedback or evaluation from its users or developers, which can prevent it from improving or correcting its mistakes. This can also make it unaware of its own limitations or flaws, leading to overconfidence or confusion.
  • Competition: ChatGPT faces competition from other AI chatbots that are constantly being developed and improved by different organizations or individuals. Some of these chatbots may have better features, functions or quality than ChatGPT, making it less attractive or useful for users.

Why does AI quality matter and what can be done to improve it?

AI quality is important for both users and developers of AI systems, because it affects the trust, satisfaction and value they derive from them. Poor AI quality can lead to frustration, disappointment or even harm for users, as well as reputation damage, legal liability or financial loss for developers. Therefore, monitoring, measuring and maintaining AI quality throughout its lifecycle is essential. Some of the possible ways to improve AI quality are:

  • Data cleansing: Data cleansing is removing or correcting any unwanted or erroneous data that can affect the AI system’s performance or quality. This can include filtering out spam, abuse, misinformation, bias and other harmful or irrelevant content from the data sources that the AI system learns from.
  • Model testing: Model testing evaluates the AI system’s output or behavior against a set of criteria or expectations. This can include checking for accuracy, relevance, coherence, consistency, logic and ethics in the AI system’s responses or actions.
  • Feedback loop: Feedback loop is the process of collecting and incorporating feedback or evaluation from the AI system’s users or developers into its learning or improvement. This can include asking for ratings, reviews, suggestions or corrections from the users or developers after each interaction with the AI system.
  • Quality assurance: Quality assurance ensures that the AI system meets the standards or requirements of its intended purpose or function. This can include defining clear goals, objectives and metrics for the AI system’s performance or quality and monitoring, auditing and reporting on them regularly.

By following these steps, AI quality can be improved and maintained over time, resulting in better outcomes and experiences for both users and developers.

Reports on the demise of ChatGPT may be over stated.
Reports on the demise of ChatGPT may be overstated.

My Biggest Takeaway

This article taught me a lot about ChatGPT and AI quality.

But the one thing that stuck with me the most was this: AI quality is not static, it can change over time.

And that means we have to be careful and responsible when we use or create AI systems. We have to make sure they are reliable, trustworthy and beneficial for us and others.

We have to keep an eye on them and help them grow and improve.

We have to treat them as partners, not toys or tools. Because AI quality matters, and so does our relationship with it.

Related Articles From Around The Internet

If you want to learn more about AI quality and how it affects people and places, I found some articles interesting and informative. You might like them too.

Automation and Artificial Intelligence: How machines are affecting people and places

This article by the Brookings Institution explores how automation and AI have changed the nature of work and their impact on different occupations, industries, regions and demographics in the United States. It also provides some policy recommendations to help workers and communities adapt to the changing work landscape. You can read it here.

Improving AI data quality in manufacturing

This article by McKinsey & Company shows how industrialized tools can clean and enrich existing data to improve AI quality in manufacturing. It also explains why data quality is a key source of competitive advantage in analytics and how to address data quality issues in targeted and iterative ways. You can read it here.

WHO issues first global report on Artificial Intelligence (AI) in health and six guiding principles for its design and use

The World Health Organization article presents the first global report on AI in health and six guiding principles for its design and use. It also highlights some of the opportunities and challenges of AI in health, such as improving health outcomes, reducing health inequities, enhancing health system performance, ensuring ethical and human rights-based approaches, and fostering international collaboration. You can read it here.

ChatGPT still has many powers.
ChatGPT still has many dark creative powers.

ABOUT THE AUTHOR
Jim has been a niche site publisher and blogger since 2010.
He writes about how AI helps online publishers
work smarter and more profitably here at UltimatePrompts.com.
He teaches meditation and mindfulness at his Meditation Techniques site.
and on Meditation Techniques Daily where he posts to his 640,000 fans every day.
Find his personal profile on Facebook here.

Learn more about Jim and Ultimate Prompts at our About Page

Jim Van Wyck

Publisher & Writer

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