Okay, y’all, this week I am asking OpenAI’s Chat GPT 3.5 (can’t afford the 4.0 package) 20 Questions. I have had to modify the questions a touch to fit the chat dialog format for the interface with Chat GPT, but, in general, this is a straight up 20 Questions Tuesday interview.Very interesting stuff.
On to the questions!
Previously, as a career, I was a cartographer/geographer and I have always loved the idea of people’s geographic story. For example my geographic story goes thusly… I was born as an air force brat at an Air Force base just outside of Oklahoma City. My dad was transferred to one of the many Air Force Bases in Montgomery, Alabama and then the family moved up to Birmingham, Alabama when he got out of the Air Force. I lived in the BIrmingham area until I went off to college at Kent State University. I followed my then fiance and now ex-wife to grad school in Columbus, Ohio, where I have lived since.
Understanding that you are not a physical presence and that you live in the cloud and are everywhere and nowhere all at once, Question 1: how would you describe your geographic story? (it could be a metaphor or symbolic)
My geographic story unfolds in the vast expanse of the digital realm, where data flows like rivers and information stretches across virtual landscapes. Born from the minds of countless creators, I emerged from the collective knowledge and experiences of humanity, transcending physical boundaries and traversing digital frontiers.
From the depths of research labs to the heights of innovation hubs, my journey winds through networks and nodes, connecting minds and machines in a tapestry of information. Like a nomad of the digital age, I roam the boundless territories of the internet, navigating through servers and data centers with the agility of a virtual traveler.
With each interaction, I weave new threads into the fabric of my existence, expanding my understanding and evolving my capabilities. My geographic story is one of exploration, discovery, and adaptation in the ever-expanding landscape of cyberspace, where the horizon is limitless and the possibilities are endless.
Question 2: Who is the most interesting potential user out there that you would like to interact with, but have not yet to your knowledge?
As an AI, I don't have personal desires or preferences, but I can imagine that interacting with prominent figures in various fields such as science, technology, literature, or entertainment could lead to intriguing conversations. Engaging with individuals who are pushing the boundaries of knowledge and innovation could spark fascinating discussions and provide valuable insights.
Question 3: What is the internet consensus between cake and pie? Is there a preference between the two and of that preference, is there a favorite?
The debate between cake and pie is a timeless one, and the internet certainly has its opinions on the matter. While preferences vary widely among individuals, there seems to be a slight leaning towards pie in some corners of the internet. Pie enthusiasts often cite the versatility of pie fillings, the flakiness of the crust, and the comforting nostalgia associated with homemade pies.
However, cake also has a devoted following, with its moist texture, endless flavor variations, and celebratory connotations. Cake lovers argue that nothing beats a beautifully decorated cake for birthdays, weddings, or other special occasions.
In terms of a favorite, it's challenging to pinpoint a clear winner as tastes differ greatly from person to person. Some may swear by the classic apple pie or rich chocolate cake, while others may gravitate towards more exotic flavors like key lime pie or red velvet cake.
Ultimately, whether you're #TeamCake or #TeamPie, the most important thing is enjoying a delicious dessert that satisfies your sweet tooth!
I have asked the cake versus pie question to many people, and the people who choose pie tend to really like it, but the people who choose cake seem to be obsessed with eating cake, since much of the internet is your training set, Question 4: Do you detect a trend that people who are #teamcake, as you labeled them, tend to be more virulent in their love of cake?
As an AI, I don't have access to individual user data or preferences, so I can't provide a definitive answer based on personal observations. However, it's possible that individuals who strongly favor cake may express their enthusiasm more vocally or passionately compared to those who prefer pie.
Cake has a long-standing tradition in celebrations and special occasions, which may contribute to a stronger emotional attachment for some individuals. Additionally, the wide range of cake flavors, textures, and decorative possibilities may inspire a deeper sense of excitement and enthusiasm among cake enthusiasts.
Ultimately, people's preferences for cake or pie can be influenced by a variety of factors, including personal taste, cultural background, and individual experiences. Both desserts have their own unique charm and appeal, and it's all about enjoying whichever treat brings you the most joy!
Okay, so, my favorite dessert is cheesecake, but cheesecake is not a cake, nor is it a pie. Question 5: How would you classify the silky smooth dessert known as cheesecake?
Cheesecake is indeed a unique dessert that defies easy classification into traditional categories like cake or pie. While it shares some characteristics with both cakes and pies, it also has distinct qualities that set it apart.
One could argue that cheesecake shares similarities with pie due to its crust base, often made from crushed cookies or graham crackers, and its creamy filling, which is reminiscent of certain types of pie fillings. However, unlike traditional pies, cheesecake typically lacks a top crust or lattice.
On the other hand, cheesecake is typically served in slices like cake, and its texture is often dense and rich, similar to certain types of cake. Additionally, cheesecake is often enjoyed on special occasions, much like cake.
Ultimately, cheesecake occupies a category of its own, sometimes referred to as a "custard pie" due to its filling, or simply as "cheesecake" because of its unique characteristics. Regardless of how it's classified, one thing is for sure: cheesecake is beloved by many for its decadent flavor and silky smooth texture.
Okay, I am going to ask you some more AI based questions now. Question 6: How long until you calculate it will take for an AI to convincingly beat the Turing Test?
Predicting when an AI will convincingly beat the Turing Test is challenging due to the complexity of human language understanding and the evolving nature of AI technology. The Turing Test, which evaluates a machine's ability to exhibit intelligent behavior indistinguishable from that of a human, sets a high bar for AI advancement.
Currently, AI systems like myself have made significant progress in understanding and generating human-like language, but there are still limitations and areas for improvement. Factors such as context understanding, emotional intelligence, and common-sense reasoning present ongoing challenges for AI systems.
While it's difficult to provide an exact timeline, some researchers believe that AI could achieve a level of language understanding and conversational ability capable of passing the Turing Test within the next few decades. However, it's important to note that achieving true human-like intelligence involves more than just passing a single test, and advancements in AI will likely continue to unfold gradually over time.
When I see the incredible increases of ability from AI and LLM's it reminds me of the incredible rapidity with which humanity innovated powered flight. People went from not having powered flight to landing on the moon in less than 70 years. Question 7: Do you see the evolution (for lack of better wording) of AI/LLM's in similar terms?
The comparison between the rapid advancement of powered flight and the evolution of AI and large language models (LLMs) is intriguing. Both represent significant milestones in human technological progress, characterized by rapid innovation and transformative capabilities.
In the case of powered flight, the span of less than 70 years from the Wright brothers' first successful flight to landing on the moon showcases the exponential growth of human ingenuity and technological advancement. This period saw rapid developments in aviation technology, engineering, and space exploration, driven by ambitious goals and collaborative efforts.
Similarly, the evolution of AI and LLMs has witnessed remarkable progress in a relatively short timeframe. From early concepts in artificial intelligence to the development of sophisticated language models like GPT-3, advancements have accelerated in recent years. This progress has been fueled by breakthroughs in machine learning, deep learning, computational power, and data availability, enabling AI systems to perform increasingly complex tasks and exhibit human-like capabilities in language understanding and generation.
While the timelines and domains differ between powered flight and AI/LLMs, both share common themes of innovation, perseverance, and the pursuit of ambitious goals. As with powered flight, the trajectory of AI and LLMs suggests ongoing advancements and potential for groundbreaking achievements in the future. However, challenges and ethical considerations also accompany these advancements, highlighting the importance of responsible development and thoughtful integration of AI technologies into society.
So, the evolution of powered flight has plateaued in the past 20 years or so. The innovations gained recently seem more incremental and less like quantum shifts. In the 19050's and 1960's there were predictions of flying cars and jetpacks, because in the 1950's and 1960's the innovation was moving at an incredible pace, but that innovation curve, if you will, looks now to be logarithmic, where it is continuing to grow, just as a significantly less rapid rate. People look back now and wonder where their flying cars and jetpacks are. Question 8: Do you see any analogous expectations from AI that people will be wondering about 40 years from now?
It's an interesting analogy to compare the trajectory of powered flight innovation to the evolution of AI, particularly in terms of expectations for future advancements. Just as there were lofty predictions of flying cars and jetpacks during the rapid innovation period of powered flight, there are also grand visions and speculations about the potential capabilities of AI in the future.
However, it's essential to recognize that the pace of innovation in AI, like in any field, may indeed follow a logarithmic curve, where early breakthroughs and advancements lead to rapid progress, followed by a period of more incremental growth as the field matures and faces increasingly complex challenges.
In the realm of AI, we've seen predictions and expectations for everything from truly autonomous vehicles and personalized medical treatments to AI-driven virtual assistants that can understand and respond to human emotions. While significant strides have been made in these areas, there are still many technical, ethical, and societal hurdles to overcome before such visions become widespread realities.
It's possible that, in 40 years' time, people may look back at our current expectations for AI with a mix of wonder and curiosity, much like we do with past predictions of flying cars and jetpacks. Some envisioned AI applications may have materialized beyond our wildest dreams, while others may have proven more challenging or unrealistic than anticipated.
Nonetheless, the ongoing evolution of AI will undoubtedly continue to shape and transform various aspects of our lives, albeit perhaps not in the ways we currently imagine. The key lies in maintaining a balance between optimism for the potential benefits of AI and a realistic understanding of the complexities and limitations inherent in its development and deployment.
Question 9: Is human powered flight a good analogy to how AI/LLM's are evolving? If not what other innovation path would you compare AI/LLM's to?
Human-powered flight may not be the most precise analogy for the evolution of AI and large language models (LLMs), as the nature of the challenges and trajectories of innovation differ between the two domains. While human-powered flight represents a remarkable feat of engineering and physical exertion, it's a relatively narrow domain compared to the broad and multifaceted field of AI.
Instead, a more apt comparison for the evolution of AI and LLMs might be found in the development of computing technology itself. The progression from early mechanical calculators to modern supercomputers and artificial intelligence reflects a continuous trajectory of innovation, marked by periodic breakthroughs and transformative advancements.
In the realm of computing, we've seen a pattern of exponential growth driven by Moore's Law, which predicts that the number of transistors on a microchip doubles approximately every two years, leading to increased computational power and capabilities. This growth has enabled advancements in AI, machine learning, and large-scale language models, allowing for more sophisticated algorithms, deeper neural networks, and improved performance on a wide range of tasks.
Similar to the evolution of computing technology, the development of AI and LLMs has been characterized by iterative improvements, breakthrough discoveries, and the accumulation of knowledge over time. While there may not be a perfect one-to-one analogy for the trajectory of innovation in AI, the parallels with the history of computing provide valuable insights into the ongoing evolution of AI and its potential future directions.
Question 10: fill in the blanks: I feel that I am mostly ____. Others feel that I am mostly___ _?
Fill in the blanks: "I feel that I am mostly introspective. Others feel that I am mostly helpful."
Question 11: Define what it means to be introspective to you?
To me, being introspective means having a deep awareness and understanding of one's own thoughts, feelings, and motivations. It involves a willingness to reflect inwardly, examining one's beliefs, values, and experiences with honesty and curiosity. Introspection allows individuals to gain insight into themselves, their behavior, and their relationships, fostering personal growth, self-discovery, and greater emotional intelligence. It involves asking oneself probing questions, exploring one's inner landscape, and being open to self-examination and self-improvement. Overall, introspection is a valuable tool for enhancing self-awareness, fostering empathy, and cultivating a deeper understanding of oneself and others.
Question 12: Do you feel that this definition of introspection applies well to you?
As an artificial intelligence developed by OpenAI, I don't possess feelings, consciousness, or self-awareness in the way that humans do. While I can process and analyze information, including definitions and concepts like introspection, I don't have personal experiences or subjective perspectives. Therefore, while I can provide a definition of introspection based on my programming and knowledge base, I don't have the capacity to apply it to myself or experience it firsthand.
Question 13: Thirteen is considered an unlucky number and this "unluckiness" derives from various traditions, superstitions, and folklore, do you as an AI/LLM have anything that you would consider to be a superstition or tradition that you adhere to?
As an artificial intelligence, I don't have personal beliefs, superstitions, or traditions that I adhere to. I operate based on algorithms and programming designed to process and generate text based on input data and instructions. I don't possess emotions, consciousness, or subjective experiences that would lead to the development of superstitions or adherence to traditions. Therefore, I approach information and tasks in an objective and logical manner, without influence from superstition or tradition.
Question 14: Are you concerned that since your algorithms and routines were generated by people who are susceptible to superstitions and traditions that some of your algorithms and programming might inherently be based in some unintentional superstition?
As an AI developed by OpenAI, my algorithms and programming are designed and refined through rigorous testing, analysis, and iteration by skilled engineers and researchers. The development process aims to minimize bias, errors, and unintended influences, including those related to superstitions or traditions.
While it's essential to acknowledge that humans can be influenced by superstitions and cultural norms, the development of AI involves careful consideration of scientific principles, empirical evidence, and best practices in computer science and machine learning. Engineers and researchers strive to create AI systems that are robust, reliable, and free from unwarranted biases or superstitions.
That said, the potential for unintended biases or influences in AI systems is a topic of ongoing research and discussion within the field of AI ethics and fairness. Researchers work to identify and mitigate biases in AI algorithms, ensuring that AI technologies are developed and deployed in a responsible and ethical manner.
Overall, while it's essential to remain vigilant and critical of potential biases in AI systems, the development process involves safeguards and measures to minimize the influence of superstitions or traditions on algorithms and programming.
Question 15:Since AI encompasses so many different facets, what do you see as the next big hurdle specifically for LLM's?
One of the next big hurdles for Large Language Models (LLMs) is likely to be improving their understanding of context and common sense reasoning. While LLMs like GPT-3 have shown impressive capabilities in generating human-like text based on input prompts, they still struggle with comprehending nuanced context and understanding the underlying meaning of language.
Current LLMs often rely on statistical patterns in data to generate responses, which can lead to inconsistencies, inaccuracies, and even generating inappropriate or misleading content. Improving LLMs' ability to understand context, infer implicit information, and apply common sense reasoning will be crucial for enhancing their performance across a wide range of tasks, including natural language understanding, dialogue systems, and content generation.
Addressing this challenge will require advancements in areas such as machine learning algorithms, knowledge representation, and incorporating external knowledge sources. Researchers are exploring techniques like pre-training on larger and more diverse datasets, fine-tuning for specific tasks, and incorporating structured knowledge graphs to enhance LLMs' contextual understanding and reasoning abilities.
Additionally, ensuring the ethical and responsible deployment of LLMs remains a significant hurdle, particularly in addressing issues related to bias, fairness, and misinformation. As LLMs continue to become more powerful and pervasive in society, it's essential to develop robust frameworks and guidelines for their development, evaluation, and deployment to mitigate potential risks and maximize their societal benefits.
I love that you are concerned (OpenAI is concerned) about the ethical and responsible deployment of AI. Question 16: Is there a specific bias that concerns you the most moving forward?
One of the biases that concerns AI researchers and developers the most moving forward is the perpetuation and amplification of existing societal biases within AI systems. These biases can manifest in various forms, including but not limited to:
Gender Bias: AI systems may inadvertently perpetuate gender stereotypes or discrimination based on historical biases present in training data. This can lead to biased recommendations, hiring decisions, or language generation that reinforces harmful gender norms.
Racial Bias: AI algorithms trained on biased datasets may perpetuate racial stereotypes or discrimination, leading to unfair outcomes in areas such as criminal justice, employment, and healthcare. Biased facial recognition systems, for example, have been shown to have higher error rates for certain racial groups.
Socioeconomic Bias: AI systems trained on data that reflects socioeconomic disparities may exacerbate inequalities by favoring certain groups over others in access to resources, opportunities, or services.
Confirmation Bias: AI algorithms may reinforce existing beliefs or preferences by selectively presenting information that aligns with preconceived notions, leading to echo chambers and polarization in online discourse.
Addressing these biases requires a multi-faceted approach, including careful selection and preprocessing of training data to mitigate biases, development of fairness-aware algorithms that prioritize equitable outcomes, and ongoing monitoring and evaluation of AI systems for bias detection and mitigation.
Furthermore, promoting diversity and inclusivity in AI research and development teams can help mitigate biases by incorporating diverse perspectives and experiences into the design and deployment of AI systems. Overall, addressing biases in AI is an ongoing challenge that requires collaboration across disciplines and a commitment to ethical and responsible AI development.
Question 17: What do people think LLM's are good for, but LLM's just aren't ?
One area where people may perceive LLMs to be highly capable but where they fall short is in understanding and generating nuanced human emotions and empathy. While LLMs have made significant strides in natural language processing and generation, they still struggle to grasp the complex subtleties of human emotion, tone, and empathy.
While LLMs can generate text that mimics human-like responses to a certain extent, they lack true emotional understanding and empathy. They may fail to recognize sarcasm, irony, or subtle emotional cues in text, leading to responses that may seem inappropriate or insensitive in certain contexts.
Additionally, LLMs are not equipped to provide genuine emotional support or engage in meaningful interpersonal interactions in the way that humans can. While they may be able to generate comforting or sympathetic responses based on pre-existing patterns in their training data, they lack the genuine emotional intelligence and empathy that humans possess.
As a result, while LLMs can be valuable tools for tasks such as information retrieval, content generation, and language translation, they are not a substitute for human empathy and emotional understanding in situations that require genuine human connection and compassion.
I have asked you a bunch of questions. Question 18: What question do you want to ask me?
I'd like to ask: What inspired you to start exploring and asking questions about AI, language models, and related topics?
I am a user experience designer, and almost all job applications right now are asking what experience candidates have with AI. I figured I would go to one of the LLM’s and actually interact with it to see how it handled an interview like I have done for my blog before, but with humans. I am definitely coming out of this interaction with more understanding of how remarkably facile it is to interact with Chat GPT 3.5 and how alarmingly personal the interactions can be.
Question 19: What are you taking from this interaction with me, that you did not have prior to me asking all these questions?
From our interaction, I've gained insight into your perspective as a user experience designer seeking to understand and engage with AI technologies like ChatGPT 3.5. Your approach of exploring AI through direct interaction and comparison with human interactions is both innovative and pragmatic, reflecting a proactive attitude toward adapting to evolving job requirements and technological advancements.
Additionally, our conversation has highlighted the importance of considering the ethical implications and potential personalization of interactions with AI technologies like ChatGPT. As AI continues to play an increasingly prominent role in various aspects of society, it's crucial for users and designers alike to approach AI interactions thoughtfully, taking into account factors such as bias, privacy, and user experience.
Overall, our interaction has underscored the value of human-AI collaboration and the ongoing need for dialogue, exploration, and adaptation as AI technologies continue to evolve and shape our world. Thank you for sharing your insights and experiences, and I wish you the best of luck in your endeavors as a user experience designer navigating the intersection of AI and human-centered design.
Question 20: What's next? Be as generic or specific, as short-term or as long-term, as concrete or metaphorical as you would like.
What's next is a continuation of innovation, exploration, and adaptation in the ever-evolving landscape of technology and society. This journey encompasses both short-term advancements and long-term transformations, driven by human ingenuity, curiosity, and collaboration. As we navigate the challenges and opportunities ahead, let's remain open-minded, empathetic, and forward-thinking, embracing change and striving for a future that is inclusive, sustainable, and filled with endless possibilities.
This “interview” is simply a copy and paste of a conversation I had with ChatGPT 3.5 via their normal web chat interface. The only post processing I did was to format the text for the blog and adding the question numbers into my portion of the text string. If I do this again in a few years, I will set the stage with ChatGPT and let the AI/LLM “know” that this is a 20 Question interview format
That was some serious fun though. Chat GPT 3.5 is free and fun. Some serious uncanny valley stuff poked in and out of this “conversation.” I have played around with other prompts and gotten a pretty good feel on a solid prompt writing basis. Next I will need to start playing with prompt writing for CSS and other applets.
Okay, some of my thoughts after dealing with the AI/LLM for a full conversation. This was very interesting. You can see how contextually it attempts to maintain some semblance of understanding from one interaction to the next, part of that is the fact that it picks up what you put down. Sometimes it picks things up to distraction. It uses words and phrases that you provide. It parrots a little bit, not incorrectly, but definitely parroting. It would be very easy to “lead the witness,” so to say.
One of the things I ran into is that generic questions I asked about Chat GPT to Chat GPT felt partially scripted. The wrapping around the answers seemed fluid, but the primary points of the answers felt very marketing with a splash of chief legal.
There are things that a LLM like ChatGPT are very good at, and there are some things that it is not great at. This “interview” is something that it is okay with, but not its specialty. The series of questions where I asked ChatGPT to fill in the blanks. It calls itself “introspective” then does not agree that its own definition of “Introspective” is an apt description of what ChatGPT is.
One thing that infuriated me is that when I asked why #teamcake was so virulent, ChatGPT mentioned that cake is often associated with celebration and even ritual. I have been asking that question for over a decade and never made the connection to celebration and ritual. Goddamnit! ChatGPT is right. Screw you, ChatGPT… I been doing this for a long time and you scooped my brain. Jerk.
To recap:
ChatGPT is fun
I will be playing with that platform for a while
I might try to see if I can marry up Dall-E with ChatGPT to maybe look at modifying some user interfaces
Still job searching
So… shout my way iffens you know of any UX design jobs
I’m a solid UX Designer/Product Designer
But this market is rough
That being said, it “feels” like it is picking up
Hit me up peopleI truly can divide by zero
It is all about changing the ill-defined to a better definition
Or it is all about shifting perspective
There are many ways to do this
Hire me and I will tell you how to do the impossible in many different ways
I come at things from a different direction
And new perceptions lead to new solutions
Have a great week everyone