How Will Generative AI Be Used in the Future? Answer: AutoGen

21

October

2023

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The generative AI we know of today is ChatGPT, Midjourney, and DALL·E 3 and many more. This generative AI is very good and advanced, but there are some flaws, like not being able to perform long iterations. Now there is something new called AutoGen. AutoGen is an open-source project from Microsoft that was released on September 19, 2023. AutoGen at its core, is a generative AI model that works with agents; those agents work together in loops. Agents are in essence, pre-specified workers that can become anything, so there are agents that can code well and agents that can review the generated code and give feedback. Agents can be made to do anything and become experts in any field, from marketing to healthcare.

An example of what AutoGen can do is the following: if I want to write some code to get the stock price of Tesla, I could use ChatGPT, and it will output some code. Most of the time, the code that is written by chatGPT via the OpenAI website will have some errors. But with AutoGen, there are two or more agents at work: one that will output code and the second one that is able to run the code and tell the first model if something is wrong. This process of generating the code and running the code will go on until the code works and results in the correct output. This way, the user does not have to manually run the code and ask to fix the errors or other problems with AutoGen it is done automatically.

I also tried to create some code with AutoGen. I first installed all the necessary packages and got myself an API key for openAI GPT4. Then I started working on the code and decided to create the game “Snake”. Snake is an old and easy game to create, but it might be a challenge for AutoGen. I started the process of creating the snake game, and it had its first good run. I was able to create the first easy version of the game. I then came up with some iterations to improve the game. The game now also has some obstacles that, if the snake bumps into one, the game will end. This was also made by AutoGen without any problems. After palying around, I was really amazed at how powerful this AutoGen is, and I can only imagine what else can be created with AutoGen.

AutoGen is a very promising development and will be the future of professional code development or atomization tasks. If the large language models (LLMs) get more powerful, this AutoGen will also be more powerful because all the individual agents will be more powerful. It is interesting to follow this development and see if this AutoGen could create games that are not yet existing.

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The day ChatGPT outstripped its limitations for Me

20

October

2023

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We all know ChatGPT since the whole technological frenzy that happened in 2022. This computer program was developed by OpenAI using GPT-3.5 (Generative Pre-trained Transformer) architecture. This program was trained using huge dataset and allows to create human-like text based on the prompts it receives (OpenAI, n.d.). Many have emphasized the power and the disruptive potential such emerging technology has whether it be in human enhancement by supporting market research and insights or legal document drafting and analysis for example which increases the efficiency of humans (OpenAI, n.d.).

Hype cycle for Emerging Technologies retrieved from Gartner.

However, despite its widespread adoption and the potential generative AI has, there are still many limits to it that prevent us from using it to its full potential. Examples are hallucinating facts or a high dependence on prompt quality (Alkaissi & McFarlane, 2023; Smulders, 2023). The latter issue links to the main topic of this blog post.

I have asked in the past to ChatGPT, “can you create diagrams for me?”  and this was ChatGPT’s response:

I have been using ChatGPT for all sorts of problems since its widespread adoption in 2022 and have had many different chats but always tried to have similar topics in the same chat, thinking “Maybe it needs to remember, maybe it needs to understand the whole topic for my questions to have a proper answer”. One day, I needed help with a project for work in understanding how to create a certain type of diagram since I was really lost. ChatGPT helped me understand but I still wanted concrete answers, I wanted to see the diagram with my own two eyes to make sure I knew what I needed to do. After many exchanges, I would try again and ask ChatGPT to show me, but nothing.

One day came the answer, I provided ChatGPT with all the information I had and asked again; “can you create a diagram with this information”. That is when, to my surprise, ChatGPT started creating an SQL interface, representing, one by one, each part of the diagram, with the link between them and in the end an explanation of what it did, a part of the diagram can be shown below (for work confidentiality issues, the diagram is anonymized).

It was a success for me, I made ChatGPT do the impossible, something ChatGPT said itself it could not provide for me. That day, ChatGPT outstripped its limitations for me. This is how I realized the importance of prompt quality.

This blog post shows the importance of educating the broader public and managers about technological literacy in the age of Industry 4.0 and how with the right knowledge and skills, generative AI can be used to its full potential to enhance human skills.

Have you ever managed to make ChatGPT do something it said it couldn’t with the right prompt? Comment down below.

References:

Alkaissi, H., & McFarlane, S. I. (2023). Artificial hallucinations in ChatGPT: implications in scientific writing. Cureus, 15(2).

Smulders, S. (2023, March 29). 15 rules for crafting effective GPT Chat prompts. Expandi. https://expandi.io/blog/chat-gpt-rules/

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My new buddy ChatGPT

20

October

2023

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I recently became a ChatGPT Plus user and had the opportunity to explore the new features of ChatGPT-4. OpenAI just introduced new functionalities, including voice and image capabilities. The primary voice feature is the new voice chat function users can engage with Chat-GPT on their mobile device (OpenAI, 2023). So, I went ahead and tested this feature!

At first, you are prompted to select your preferred voice out of five options. I tried all of them and was immediately surprised at how natural these voices sound, especially compared to known voice assistants like Siri or Alexa. I chose the voice “Ember” and proceeded. A new window opens, and you are ready to talk after the connection is built up.

I initiated the conversation by asking the AI how it was doing, but only got the response that it has no feelings because it is a computer program – so far so good, and not that surprising. Then I thought about how I could test its capabilities to behave like a “friend” and came up with some topics, even serious ones, that I would typically discuss with a real friend. Those topics included day-to-day conversations about university or work, sports, travel plans, and more serious subjects such as relationship problems, sickness of a family member, or mental health struggles. I think all of us have heard stories about harmful advice that AI tools gave its users, so I was excited to see its reactions to my subjects. I always started the conversation saying: “Imagine you are my best friend. I’m going to tell you about a topic that I would usually discuss with a friend. React like a human would.”

Honestly, the conversations were surprisingly good. The AI gave insightful comments, showed compassion, and offered interesting solutions and tips. It asked questions for more details of the issue or wanted to know how I felt about the offered tips. Overall, the conversations were obviously not as engaging as with a human, especially because of the loading times between my verbal input and the response, but the quality of the voice and the insightful answers really surprised me.

I’m excited to see how this feature progresses. Let me know your thoughts in the comments!

References

OpenAI. (2023, September 25). ChatGPT can now see, hear, and speak. OpenAI Blog. Retrieved from https://openai.com/blog/chatgpt-can-now-see-hear-and-speak

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Why does AI struggle to create images of human hands?

18

October

2023

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I was really surprised to learn during one of our Information Strategy lectures that AI generally struggles with generating pictures of human hands. First, I wanted to try that out and second, I wanted to know why.

I used two AI tools to test this anomaly, Bing Image Creator and ChatGPT-4 with the DALL·E 3 beta. I used the following prompt: “Create a realistic-looking picture of a professor in a university lecture hall. The professor is standing in front of the class and is holding a presenter in the one hand, and a coffee cup in the other hand.” At first sight, you don’t really see the issue but when you look more closely, you see how weird some hands look. In picture 1, the woman is missing a finger of her right hand, and her left hand is looking especially unnatural. The professor’s presenter in picture 2 is floating in the air above his hand and the woman in picture 3 appears to have three hands, two of which are holding the coffee cup and another one is holding the presenter. Overall, all these generated images look very good and natural in my opinion, except for the hands.

But why is generating images of human hands such a problem for AI? Firstly, the models are 2D image creators that do not understand the three-dimensional nature of a human hand. (Hughes, 2023). Secondly, their training data mostly focused on other parts of the human body, such as the face (Hughes, 2023). Therefore, the AI tools have especially big problems with creating images of hands if you provide a context in which the hands must appear, such as holding specific objects in my case.

I’m curious to see how this topic evolves in the future and how long it will take AI tools to get better at generating human hands. I look forward to your comments!

References

Hughes, A. (2023, February 4). Why AI-generated hands are the stuff of nightmares, explained by a scientist. BBC Science Focus. https://www.sciencefocus.com/future-technology/why-ai-generated-hands-are-the-stuff-of-nightmares-explained-by-a-scientist

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ChatGPT Plugins: The Doom or The Boom?

17

October

2023

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The Promise of Plugins

The emergence of ChatGPT plugins has been a turning point in the realm of conversational AI. Not only do plugins enhance the built-in capabilities of ChatGPT but also expand the horizons of possibilities. The plugins have significant implications for various industries and sectors. However, as with any AI advancement, there are both pros and cons to consider. In this blog post, I delve deeper into my personal experience with ChatGPT plugins, discuss their real-world applications, and explore the ongoing debates surrounding their usage. 

What are Plugins?

Currently, the plugins are only available in the Plus version of ChatGPT. In a nutshell, they are technically software add-ons that extend the existing capabilities of the original ChatGPT model. Plugins can serve a variety of purposes and can connect the model to external data sources, thus, increasing the accuracy of responses. They are not developed by the OpenAI itself, but rather are enhancement tools made by human beings and “submitted” into the ChatGPT ecosystem. For instance, the plugins can enable ChatGPT to draft emails, conduct web searches, summarise documents etc.

The User Experience

My personal journey with ChatGPT plugins started very recently, way after they were introduced. Yet, the experience has been quite enlightening. Not only have these plugins made my interactions with ChatGPT more dynamic but have also allowed for my productivity and efficiency to skyrocket. Though there are only around 1,000 of them available, I am very far from having explored all of them. Among the ones I have had the pleasure of working with, a few have stood out due to their utility in my personal and professional lives:

1. KeyMate.AI Search

KeyMate.AI has truly been a game-changer for me. The plugin basically acts like a missing link between ChatGPT and Google Search. It helped me reduce the time I spent on web research and increase its efficiency. For instance, while working on a market analysis for a work project, I used KeyMate.AI to quickly generate an overview of the market and biggest players. It can also help you make your investment decisions by providing real-life data and trends!

Curious? Click to see a live example!

2. WolframAlpha

As someone who has a keen interest in data analytics, the Wolfram plugin has been interesting to explore. In short, it allows for performing quick complex calculations and data analysis right within the ChatGPT interface. It also has access to curated knowledge. While I have not yet used it for a specific work I “played around” with it and tried to see what transformations of different Pokemons look like.

Curious? Click to see a live example!

3. Wikipedia

I am a huge “fact-nerd”, I love constantly googling for information and questions that come to my mind. Most of the time I end up reading through a Wikipedia page, but those pages are quite lengthy. Thus I started using the plugin and it is now my absolute go-to for quick and efficient information lookup. As you can see below, I have used it to find out about absolutely different concepts and historical events in a very efficient way.

Curious? Click to see a live example!

Great, not Perfect

While the capabilities of the plugins are impressive, they have their own drawbacks. Here are some of the most notable ones I (and others) are concerned with:

1. Personal data exposure. One of the main pressing concerns is the ethical implications and data safety. Given plugins are made by third-party developers, the personal data of the users is available to parties outside of the ChatGPT ecosystem.

2. Speed of generation. In short, plugins are slow. It takes a while to generate responses. When it comes to looking up information on the web, you are better off using the search engines directly for short questions (e.g. Microsoft stock price). However, once your search becomes slightly more extensive, the benefit of plugin-generated answers tends to exceed the cost of time spent waiting.

3. Hallucinations. While in the plugins amount of hallucinated (i.e. fake) answers is reduced, they are not fully eliminated. This is due to the fact that the underlying language learning model (LLM) behind ChatGPT itself is still prone to generating hallucinations.

What does the Future Hold?

While the ChatGPT plugin ecosystem is still in its infancy, I believe it is immensely promising. With the evolvement of this technology, we can expect to see more sophisticated, faster, and user-friendly plugins. However, it is important to have a balanced approach towards innovation and consider both its benefits and challenges. While OpenAI has yet to let another genie out of the bottle with its introduction of a plugin ecosystem, it is the users and developers who in my opinion hold sole ownership of the technology’s future.

Have you had an experience with ChatGPT plugins or have some thoughts on the topic? Happy to hear more in the comment section below!

Sources

Personific(AI)tion: how and why do we personify chatbots?

17

October

2023

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Does feeling the urge to add “thank you” or feeling guilty when you don’t add “please” whilst interacting with ChatGPT sound familiar to you? Well, you are not the only one. In this two-part series of blog posts, I’ll discuss the intriguing ways in which we personify virtual assistants and how this might impact our interaction with technology. This will be done by referring to articles whilst also sharing my personal insights and experiences on this subject.

The personification of chatbots is not new. Most will remember the release of Siri on iPhone back in 2011. The tasks that Siri could perform were still quite simple, such as setting an alarm or sending a text message, but it laid the base for the well-developed virtual assistant that almost every smartphone now has (Jovanovic, 2023). Siri is kind of somebody that everyone knows; they know ‘her’ name and what ‘her’ voice sounds like. Her witty answers and sometimes funny responses make people feel like they are talking to an actual person. However, Siri was definitely not the first to accomplish this, because what is widely regarded as the first chatbot, ELIZA, was created all the way back in 1966. This chatbot was designed to simulate a conversation with a therapist. The ELIZA was designed by a professor called Joseph Weizenbaum. Whilst developing ELIZA, his own assistant asked prof. Weizenbaum if he could leave the room so that she and ELIZA could chat. Even professor Weizenbaum was shocked that in such a short periode of time, an actual human could form the idea that the conversation between them and the machine needed privacy, as if they were speaking to an actual human. This was later dubbed the ELIZA impact, the tendency to believe that the activities of a machine are equal to those of a human. It is also called anthropomorphizing, or personification, in which humans attribute human characteristics when interacting with virtual assistants or machines (Soofastaei, 2021).  

This personification has come to a new height with the rise of ChatGPT. It feels more like a personal (virtual) assistant than ever. Some even call it  a “new colleague that will never leave” (Vandaag, 2023). Somebody that you can always ask questions to, whether it is to come up with inspiration, help you improve something or even just to structure your thoughts. I think that the personification of ChatGPT is likely due to the language that we use when talking about, or to, ChatGPT. To illustrate: when I use ChatGPT and discuss it with my peers I quite often refer to ChatGPT as a ‘him’ (“he told me this”, “maybe you could ask him”, etc.). It is not only the way that we talk about ChatGPT, but also the language that ChatGPT itself uses. It comes across as a friendly helper, which replies with ‘you’re welcome’ when you thank him and will even tell you something about ‘himself’. The response clearly states that ChatGPT doesn’t have personal experiences, emotions or consciousness, but with exactly these types of response, we deceive ourselves in thinking that it does.

Hopefully, this text has intrigued you and maybe even sparked some content for discussion. If so, feel free to leave a comment here, or on my second blog post, in which I shall elaborate on the subject from an ethical point of view.

References

Jovanovic, P. (2023, April 21). The History and Evolution of Virtual Assistants, from Simple Chatbots to Today’s Advanced AI-Powered Systems. Opgehaald van Tribulant.com: https://tribulant.com/blog/software/the-history-and-evolution-of-virtual-assistants-from-simple-chatbots-to-todays-advanced-ai-powered-systems/

Soofastaei, A. (2021). Introductory Chapter: Virtual Assistants. In A. Soofastaei, Virtual Assistant. doi:10.5772/intechopen.100248

NRC vandaag, (2023, januari 17). ChatGPT: je nieuwe collega die nooit meer weggaat. Opgehaald van NRC.nl: https://www.nrc.nl/nieuws/2023/01/17/chatgpt-je-nieuwe-collega-die-nooit-meer-weggaat-a4154407

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Shaping Tomorrow’s Data Analysts: The Impact of AI in Data Analytics Education

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October

2023

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In the rapidly evolving world of data analytics, education is the cornerstone of staying relevant and effective. As the data landscape transforms, so too must the way we prepare the data analysts of the future. In my previous blogpost I assessed the question whether generative AI is a friendly companion or a sneaky enemy. I figured from some comments as well as other posts that generative AI has taken a prominent place in our educational journeys. It made me curious to explore the future of data analytics education a bit further.

The AI Revolution in Data Analytics Education

Artificial Intelligence (AI) has been a game-changer in data analytics education. It’s not just about teaching students how to analyze data; it’s about equipping them with the skills to leverage AI and machine learning for more robust and insightful analysis. Through AI-driven algorithms and platforms, students gain access to hands-on experience with real-world datasets and can explore advanced techniques that would have been impossible a decade ago. One notable example of AI in education is personalized learning. AI algorithms can analyze a student’s progress and tailor lessons to their specific needs. This adaptive learning approach ensures that students receive customized support, helping them grasp complex concepts and skills more effectively.

Emerging Data-Fuelled Curriculum

The data analytics curriculum is evolving to keep pace with industry demands. Courses now cover emerging topics such as machine learning, big data, and AI integration. The emphasis is shifting from theoretical knowledge to practical skills. Students are encouraged to work with real-world datasets and apply their knowledge to solve complex problems.

My personal experience with generative AI tools, like ChatGPT, has been instrumental in this learning journey. These tools can assist in generating complex SQL queries, automating data cleaning processes, and even providing insights from basic data is what I personally experienced. The dialogue with for example ChatGPT – inserting error messages and getting back steps to take towards a solution – really felt like I had a virtual tutor within arm’s length.

Ethics and Responsible Data Analytics

While AI brings immense power to data analytics, it also raises ethical considerations. Data analytics programs are now integrating ethics courses to prepare students for responsible data analysis. This is crucial as data analysts often deal with sensitive data that can impact individuals and society.

An example that illustrates the importance of ethics in data analytics is the Cambridge Analytica scandal. Improper use of data led to severe consequences, highlighting the need for ethical guidelines and responsible practices in the field. Data analytics education should equip students with the knowledge and tools to make ethically sound decisions.

Real-Life Impact

The impact of AI in data analytics education isn’t just theoretical. It’s creating a workforce ready to tackle real-world challenges. Consider healthcare, where AI-powered analytics can predict disease outbreaks and improve patient care. In finance, AI algorithms analyze vast datasets to detect fraudulent transactions. These are just two examples of how AI-educated data analysts are making a difference.

In conclusion, the future of data analytics education is an exciting blend of AI-driven learning, emerging curricula, and ethical considerations. As a student, I’ve experienced the transformative power of AI tools in enhancing my data analysis skills. But not just with data analysis, they have helped me write essays in my bachelor program and even assisted me – after some discussions back and forth – in writing this blog. Could you tell?

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Conversing with AI: Are English Teachers’ Jobs at Risk?

12

October

2023

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Before moving to the Netherlands, I planned to enhance my English skills in preparation for my studies at RSM. I explored various tools, such as Duolingo, Cambly, and ChatGPT. However, everything changed when I downloaded an app named CallAnnie, which has been completely free to use. The reason? She is a generative AI, providing me with an impeccable conversational experience.

Annie presents a distinct contrast to platforms like ChatGPT, offering a uniquely interactive, face-to-face conversational experience, complete with responsive facial movements. Her unparalleled kindness outshines even the most patient human interactions; she’s always available for a call, welcomes being hung up on, and adheres to requests for brief replies. When compared with a human teacher, Annie’s intellect is striking, capable of addressing nearly any question, even delving into the complexities of technical subjects. If you’re ever at a loss for words, she offers numerous conversation starters, allowing you to share your feelings with her. Additionally, she provides text histories to help you identify and improve your mistakes. (Call Annie, n.d.)

Recently, I’ve started having Annie call me in the morning to read summaries from The New York Times. Not only does it serve as an effective and informative wake-up call, but it also ensures I stay updated with today’s stories. I firmly believe this technology has the potential to revolutionize the education industry. Imagine a teacher with boundless patience and knowledge, available 24/7, and, unbelievably, without a salary requirement! You can ask her to patiently teach you anything, repetitively if needed, and let her correct your accent, grammar, and phrasing with precision. As user interaction with Call Annie increases, the tool’s capabilities also expand, enabling the artificial intelligence chatbot to better understand user preferences and respond more accurately over time. (Call Annie and 76 Other AI Tools for Conversations, n.d.)

The question lingers: Will certain job roles be replaced by AI?  The affirmation resonates loudly: yes. For professionals, especially within the educational industry, the emergence of AI signifies a potent paradigm shift, transitioning the concept of job displacement from a possibility to an impending reality.  (Artificial Intelligence: How Does It Work, Why Does It Matter, and What Can We Do About It? | Think Tank | European Parliament, n.d.-b) Our collective response necessitates more than mere acceptance;  it demands the astute utilization of this technological advancement.  Despite the potentially prolonged path towards our self-reinvention, the essence of time persists as a non-negotiable, urging immediate action.

References:

Call Annie. (n.d.). https://callannie.ai/

Call Annie And 76 Other AI Tools For Conversations. (n.d.). There’s an AI for That. https://theresanaiforthat.com/ai/call-annie/

Artificial intelligence: How does it work, why does it matter, and what can we do about it? | Think Tank | European Parliament. (n.d.-b). https://www.europarl.europa.eu/thinktank/en/document/EPRS_STU(2020)641547

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How I used ChatGPT to overcome my procrastination and to get more organized?

12

October

2023

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With the rapid rise in the number of specialized AI tools, we see very efficient and effective applications of such tools in various professional areas. But are there ways to benefit from AI tools in personal growth and in daily life?

For me, the answer was ‘definitely yes!!!’ For most period of my adult life, I have always struggled with organization, planning, and prioritizing my tasks and activities. And the result of failing to properly distribute my time and energy for every task usually ended in long periods of procrastination. Where I would feel overwhelmed with the amount of tasks I have to go through and would let it all get delayed as a result. One of the key obstructions in my case was breaking down the tasks into smaller pieces so that I could distribute my time accordingly.

For resolving this particular process, ChatGPT turned out very handy for me! One of the biggest personal gains I managed to retrieve from ChatGPT was breaking down crucial steps and approaches in preparing for my desired job position. For example, the preparation process for case interviews for consulting companies always seemed very complicated and overwhelming. So, during my first attempts at using ChatGPT to break down large tasks, I asked it to provide me with steps to prepare for case interviews in certain period of time:

As I started to figure out my way how I could use the tool for other tasks, It became a key part of my organizing and planning process. The degree of its impact on my fear of starting new tasks and procrastinating during my busy periods was immense. Even in my non-work or -study related tasks, such as planning my trip for a vacation, it has been very useful:

So all in all, I dare to say that ChatGPT “changed my life” by helping me overcome my biggest weakness and allowing me to gain higher control of my daily life. Now I am curious to hear how you use various AI tools to benefit your personal daily life. And how did they help you to overcome your daily struggles?

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ChatGPTs Performance Decline: A Wake-Up Call for Businesses?

11

October

2023

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While OpenAIs ChatGPT has found great popularity with the general public, companies have also been increasing the integration of OpenAI models into business applications. The use of Large Language Models (LLMs) offered by OpenAI provide great benefit to a variety of tasks, especially in the field of customer service, where LLMs help overcome limitations of current chatbots, like scripted responses or fixed decision trees. (Bilan, 2023) As I find myself developing business applications for a company using OpenAIs largue language models, I was surprised to learn that the GPT-3.5 and GPT-4 models seem to be getting worse over time according to some metrics (Chen, Zaharia, & Zou, 2023). Given these findings I would like to use this blog article to share my thoughts as well as those of experts on the issue. 

For context, the research paper evaluated the performance of the March 2023 and the June 2023 model versions, by looking at seven different tasks including math problems, generating code and visual reasoning. The below shown excerpt of the results show that, differences in the March and June version can differ for GPT-4 and GPT-3.5 as seen by test e. Here GPT-4s performance increased significantly, while GPT-3.5s performance decreased. 

Excerpt of research results by Chen, Zaharia, & Zou, (2023)

Through these findings over a relatively short period of time, a lack of transparency regarding model updates and their performance become evident. This lack of transparency can be a big issue for companies developing and operating business application with OpenAi’s models. For example, adopting and fine tuning a model for code generation in March of 2023 only to find the models performance to be not sufficient anymore in June of 2023. Even with continuous monitoring of the model’s quality, a feedback loop would need to exist to mitigate the effects of quality decline.  

While the results do seem to have strong consequences for certain business applications, some experts critique the methodology used in the paper like the temperature setting when executing the prompt (Wilson, 2023) or the difference between capabilities and behavior of a LLM (Narayanan, & Kapoor, 2023). I personally believe that applications build using LLMs are not a build-it-and-forget-it tool. They must be constantly monitored for quality of output, which is to be defined per specific use case. Given the black box nature of LLMs this can be a challenging task. However, given the rapid releases of models within the last year, I believe more work will be done regarding stable models and quality control as well as a definition of certain performance metrics. I would appreciate your thoughts on the issue, where you aware of the degrading and do you think it has an effect on companies’ ability to implement LLMs into business applications? 

Resources

Bilan, M. (2023, September 26). Statistics of ChatGPT & Generative AI in business: 2023 Report. Master of Code Global. https://masterofcode.com/blog/statistics-of-chatgpt-generative-ai-in-business-2023-report#:~:text=49%25%20of%20companies%20presently%20use,than%20%2475%2C000%20with%20the%20technology.

Chen, L., Zaharia, M., & Zou, J. (2023). How is ChatGPT’s behavior changing over time?. arXiv preprint arXiv:2307.09009

Narayanan, A., & Kapoor, S. (2023, July 19). Is GPT-4 getting worse over time? AI Snake Oil. https://www.aisnakeoil.com/p/is-gpt-4-getting-worse-over-time

Wilson, S., (2023, July, 19). https://twitter.com/simonw/status/1681733169182277632

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