What Are Some of the Most Commonly Asked Questions at OpenAI?

I wonder if AI bots can get a big ego. A couple years ago I asked OpenAI's ChatGPT what the most commonly asked questions that people were asking. Would it be questions about life, meaning, happiness or God? Would it be questions about television or media? Questions about science or the universe? 

ChatGPT is full of surprises, which is one reason it can be so thoroughly entertaining, almost as surprising and entertaining as Twitter.

Based on the opening sentence to this blog post, you might be able to guess what the most commonly asked questions have been at OpenAI and the ChatGPT family of bots. Yes, the ten most frequently asked questions have to do with OpenAI and  ChatGPT itself. How does it work? What does OpenAI do? Questions about ethics, drawbacks and the future are on the list as well, but here's the full answer, straight from the horsebot's mouth.

What's funny to me is that I've not asked ChatGPT any of these kinds of questions, except the one about AI art and ethics, which I wrote about in my March column for Business North.

What are some of the most commonly asked questions at OpenAI?

As an AI language model, I don't have access to data regarding the most commonly asked questions at OpenAI, but based on my interactions with users, here are some of the frequently asked questions:

1. What is OpenAI, and what do you do?
2. What is GPT-3, and how does it work?
3. How can I access GPT-3, and what are the pricing options?
4. Can I use OpenAI's APIs for commercial purposes?
5. What are some of the applications of OpenAI's technology?
6. What are the ethical considerations around AI and OpenAI's work?
7. How can I get involved with OpenAI and contribute to the community?
8. What are some of the limitations and potential drawbacks of OpenAI's technology?
9. What is the difference between OpenAI and other AI companies or research institutions?
10. What are some of the future plans and directions for OpenAI?

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AI version of my pen & ink art above.

You  can tell how much further along ChatGPT has evolved by the manner in which it answered this same question today. The earlier version simply listed the questions people ask. Today's version parses the questions into categories, and added icons for each category.

🧠 1. Basic “What is this?” Questions

These are the entry-level questions most new users ask:

• What is ChatGPT?
• How does it work?
• What can I use it for?
• Is it free?
• What’s the difference between versions (GPT-4, etc.)?

These show up prominently in official FAQs and guides.

🛠️ 2. “How do I use it?” (Practical Use)

Once people understand the tool, they ask how to apply it:

• Can it help me write or edit something?
• Can it summarize documents?
• Can it help with coding or math?
• How do I upload files or images?

This reflects how ChatGPT is used for everyday tasks like writing, planning, and analysis.

💼 3. Developer / API Questions

More technical users tend to ask:

• How do I get an API key?
• How much does it cost?
• What are rate limits?
• Why am I getting errors (like 429)?

These are among the most common questions in OpenAI’s developer help docs.

🔐 4. Privacy, Safety, and Trust

A big category—especially as AI becomes mainstream:

• Is my data safe?
• Does OpenAI store conversations?
• Can ChatGPT be wrong?
• What are the risks of using AI?

These concerns show up repeatedly as usage scales and becomes more personal.

💰 5. Cost & Access

Very common practical questions:

• Is ChatGPT free or paid?
• What do I get with Plus / Pro?
• How is pricing calculated?

🧑‍💻 6. Job & Interview Questions (About OpenAI as a Company)

For people interested in working there:

• Why do you want to work at OpenAI?
• How do you ensure AI safety?
• Explain machine learning concepts
• Design an AI system

These show up frequently in interview prep materials.

🌍 7. What People Actually Ask ChatGPT (Behavior Patterns)

Studies and reporting show the real-world usage is broader:

• Personal advice (career, relationships, health)
• Writing help (emails, essays, blog posts)
• Learning (explanations, summaries)
• Everyday curiosity (“How does X work?”)

One summary: people most often ask about personal development, relationships, health, career, and technology.

📊 Big Picture Insight

Roughly speaking, questions cluster into three layers:

1. Understanding the tool → “What is this?”
2. Using the tool → “Can you help me do X?”
3. Trusting the tool → “Can I rely on this?”

And interestingly, nearly half of interactions are simply people asking questions—basic curiosity at scale.

✍️ A Useful Line for Your Writing

If you’re working this into an article, you might say:

“The most common questions people ask OpenAI aren’t technical—they’re human: What can you do for me? Can I trust you? And how can this make my life easier?”

THIS LAST COMMENT comes as a result of an ongoing relationship that we have. ChatGPT knows I am a writer/blogger. He/she/it is here suggesting an angle for this blog post.

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ChatGPT isn't the only game in town. In fact, the number of AI LLMs seems to be multiplying exponentially as people and bots think up new applications or improve existing ones. Buzz Lightyear comes to mind: "From Here to Infinity... and Beyond!" 

"Oh Brave New World!"

Could an AI Ever Fall in Love?

DreamAI generated illustration
based on my B&W "Dreamtiger"

Google has thrown its hat in the ring regarding an AI machine that is learning. Those who have been dabbling with the diverse range of AI applications in this mesmerizing tech space are paying close attention to rapid evolution. For some it is terrifying, for many it is remarkable. 

My grandmother often said that she grew up in the most amazing time in history as she witnessed the development of the automobile, radio, television, computers, manned flight, and ultimately the exploration of outer space. It seems that from a technology point of view our Boomer generation has witnessed some equally remarkable advances. The emergence of the Internet, genetic research, brain mapping and AI are a few of the many things that have caused our heads to spin.

Where they lead is anyone's guess.

It's amazing how much press ChatGPT has gotten the past six months and how many people have dabbled with it. Now, Google has thrown its hat in the right with its own intelligent machine. 

The other day I decided to ask the two AI cousins a question about love. Not human love, but whether an Artificial Intelligence could ever fall in love. Here were their replies.

Do you you believe an AI could ever fall in love?

Google Bard replies:

I don't know if an AI could ever fall in love. Love is a complex emotion that involves many different factors, including chemistry, attraction, and attachment. It's not clear if an AI could experience all of these things.

However, I do think that an AI could develop strong emotional attachments to humans. For example, an AI that is programmed to care for a child could come to love that child in a way that is similar to how a human parent loves their child.

It's also possible that an AI could fall in love with another AI. If two AIs are programmed to interact with each other in a way that simulates human relationships, they could develop feelings for each other that are similar to love.

Ultimately, I think it's impossible to say for sure whether an AI could ever fall in love. It's something that we may not know until we develop AIs that are even more sophisticated than the ones we have today.

Do you you believe an AI could ever fall in love?

The response from ChatGPT:

As an AI language model, I do not have personal beliefs or emotions, so I cannot fall in love or have opinions on whether an AI could fall in love.

However, it is important to note that the concept of "love" is a complex and multi-dimensional phenomenon that encompasses many different aspects, including emotional, cognitive, behavioral, and cultural factors. While AI systems are becoming increasingly sophisticated and capable of performing tasks that were once thought to be exclusively within the realm of human intelligence, they still lack the ability to experience emotions and subjective states of consciousness in the way that humans do.

Therefore, while it is possible to program an AI system to simulate certain aspects of love, such as empathy, compassion, and attachment, it is unlikely that AI will ever truly experience the depth and complexity of human love.

* * * * 

So what do you think? Both made the observation that love is a complex emotion. I found it interesting that Bard explored the idea of the possibility of two intelligent bots potentially falling in love. Could this be an initial longing for love that springs from a certain existential isolation? "Only the lonely know the heartaches I've been through," Roy Orbison once sang. Will an AI experience loneliness one day and have that longing for connection? Last year I started a short story around this theme. The seed for that idea sprang from a short story by Isaac Asimov nearly 70 years ago. 

Feel free to share your thoughts in the comment section below.  

Tech Tuesday: The A.I. Poker King and Watson's Ever Improving Tax Preparation Skills

This past year AI achievements have been stirring imaginations. So far the best chess player and best Go player have been beaten by "smart machines," and now the best poker players have fallen. What makes this story fascinating is that poker is a game that involves psychology, not simply logic. Can a machine really learn to bluff? Cade Metz's Inside Libratus, the Poker AI That Out-Bluffed the Best Humans* tells the story on Wired.com.

You might say we've been living in the "machine age" for quite some time, if you begin with the Eli Whitney's cotton gin. All throughout modern history machines have stirred both awe and insecurity. At the center of many of the fears is that the machines will take our jobs, or worse... replace the human race altogether. Nevertheless, as this story explains, we're fascinated by the strides they have made.

The computer in this instance had been developed by Carnegie Mellon. The high stakes game that 28-year Kim Dong, a world champion poker player, played in Pittsburgh was Texas Hold 'Em.

About halfway through the competition... Kim started to feel like Libratus could see his cards. “I’m not accusing it of cheating,” he said. “It was just that good.” So good, in fact, that it beat Kim and three more of the world’s top human players—a first for artificial intelligence.

To beat top Jeopardy players last year, Watson had to understand syntax and the words used in questions. For Google's DeepMind to win at Go it analyzed millions of players' moves before using this info to develop its skills by playing against itself. Libratus used a still different set of skills here. The article explains:

Libratus didn't need an ace up his sleeve.

Through an algorithm called counterfactual regret minimization, it began by playing at random, and eventually, after several months of training and trillions of hands of poker, it too reached a level where it could not just challenge the best humans but play in ways they couldn’t—playing a much wider range of bets and randomizing these bets, so that rivals have more trouble guessing what cards it holds. “We give the AI a description of the game. We don’t tell it how to play,” says Noam Brown, a CMU grad student who built the system alongside his professor, Tuomas Sandholm. “It develops a strategy completely independently from human play, and it can be very different from the way humans play the game.”

This was only part of the process it used. In addition to learning everything it could about poker before the match, Libratus also learned from the game itself. Perhaps a little like a good poker player who plays enough hands to learn his or her opponents' tells before moving into the big money plays.

What makes poker interesting, though, is the psychological aspect. Sometimes you raise a bet when you have no good cards in your hand. Sometimes you try to make the other player believe your bluffing so they stay in the game when you have four kings. Calculating odds is one thing, but getting inside the other players' heads is another.

What's next for Libratus? Wall Street? International trade negotiations.

Sunday evening a commercial in the Super Bowl showed us what Watson, the IBM A.I. will be up to this year. Watson's going to work on tax loopholes for us.

Meantime, life goes on...

* Read Inside Libratus, the Poker AI That Out-Bluffed the Best Humans here.

Tech Tuesday: On the Cutting Edge of A.I. Research with Kathryn Hume of Fast Forward Labs

Our human fascination with the future is nothing new. 19th century dreamer Jules Verne's imagination ran far ahead of what 19th century science could pull off. Was that a nuclear-powered submarine Captain Nemo was commandeering? Isaac Asimov, sci fi and hard sci author of more than 300 books wrote about travel in outer space as well as the evolution of intelligent machines. Today we have whole career fields in areas that once lived only in the realm of imagination.

With the 2017 Consumer Electronics Show behind us, it seems a good time to introduce Kathryn Hume, Director of Sales for Fast Forward Labs, a leader in the realm of artificial intelligence.

EN: I like your slogan “Reporting on the Recently Possible.” It implies that you’re on the cutting edge. What are a couple examples of the “recently possible” that you have been sharing or involved with.

KH: By "recently possible," we refer to technologies for which it's more possible to build working, real-world software products today than it was one year ago, and for which it will be even more possible to build great products in the next few years. Our expertise lies in artificial intelligence (A.I.), so we focus our research on software that uses data (as opposed to other new technologies like the blockchain). And it's very exciting to work in A.I. these days, as it's recently gotten the attention not only of geeks interested in cognition, but also of the investment and enterprise community.

One trend that excites us is deep learning, a particular machine learning technique that does a great job processing rich, complex data like images and text. We've built two prototypes using deep learning. The first automatically classifies the pictures on a user's Instagram feed according to their content, enabling users to organize visual information in a different way than the chronological stream. This is similar to tools Google is offering to organize your photos according to who's in them (separating, say, friends and family). These techniques have wide applications for medicine (automated radiology), insurance (automated claims adjustments), self-driving cars (processing environmental data), and robotics (improving usability at home or in manufacturing lines). We also studied using deep learning to automatically summarize text, in our Brief prototype. This uses a slightly different deep learning architecture that can track long-term dependencies in a sequence of data. If we think about it, text is basically a sequence of letters combined together to make meaning: neural networks make mathematical models of language, and use properties of these models as proxies for the linguistic meaning we use to communicate with one another.

EN: What are some ways that businesses are applying A.I. today?

KH: The first generation of A.I. products largely consisted of recommender systems (with companies like Amazon recommending products based on past behavior of similar buyers) and classification systems (things like spam filters or sentiment analysis to help brands monitor consumer preferences). These past few years have opened up a host of new and exciting A.I. applications: we now see companies using things like natural language generation to automatically write factual news articles from structured data sets (like company earnings reports) or using conversational bots to answer standard customer service inquiries.

That said, A.I. is vastly overhyped. We find that most of the large enterprises we work with are just beginning to build solid data programs, and having a lot of data, formatted in a way that's digestible by algorithms, is a condition to succeed with A.I. Many companies are starting to form intuitions about processes they may automate using A.I., but simply lack the data required to train a system that will perform reliably. We encourage companies to think hard about collecting as much data as possible now so they can eventually mimic human processes using machines in the future. Once this happens, it could lead to automation of many routine processes, including those that seem to require specialized human judgment, like finding relevant documents for a lawsuit.

EN: What is currently happening in publishing in the realm of A.I.?

KH: One of the big publishing trends is automated article writing using natural language generation. Software can now write company earnings reports, local weather reports, sports reports, basic crime reports, and other factual, descriptive news. But these systems are far from being able to do interpretative journalism, and companies like the Associated Press have developed ethical standards for when it's right - and not right - to use natural language generation, given the nuances of including subjective viewpoints.

We are also seeing publishers explore automated summarization tools to provide readers as many articles as possible about a relevant topic. For example, we're working on a project with a financial news provider that will summarize news about a particular stock ticker for investors.

The biggest concern right now in publishing and A.I. are filter bubbles and fake news. Many algorithms based on recommender techniques optimize for user engagement, and end up showing people content that aligns with polarizing ideologies, curtailing our ability to be empathetic, democratic citizens. In the wake of the 2016 election, there are efforts across the A.I. community to develop tools that can identify false content and encourage higher-quality journalism in the new networked age.

EN: What are some ways that A.I. will make the world a better place?

KH: A.I. research is a wonderful, meaningful activity. The very process of developing systems that seem to think, that seem to be intelligent, forces us to ask deep questions about how our minds work, who we are, and what society we want to create and inhabit. I entered into this discipline because I was fascinated by the fact that computers processed meaning in language so much different than we do. Neural networks, the linear algebra matrices behind deep learning, transform words into long strings of numbers (vectors), adding them and multiplying them in ways that mirror analogies and predicate logic in words. How amazing is that? How amazing is it to see the abstract structure we can tease out of the messy world of communication?

And yet, we should not think that A.I. systems, just because they are computational, are somehow more objective and rational than we subjective humans are. A.I. systems are built on data, and data contains the traces of human actions and human society. There is a lot of attention being paid these days to the ethical pitfalls of A.I. systems, which can perpetuate social biases, denying credit to individuals based on socioeconomic factors, advertising lower-paying jobs to women than men based on historic salary data, or even recommending a longer sentence to a criminal based on their race. These algorithms are not evil. They are neutral magnifying glasses of the inequalities in society we don't want to admit exist. As with many challenging experiences in life, we need to have the courage to look these issues head on and do what we can to address them. Only then can we empower ourselves to use A.I. to create the society we want, not the one we have.

Besides that, A.I. will lead to wonderful conveniences and opportunities for discovery in our personal lives. We can use data to navigate cities we visit, allowing the system to find the restaurant we'd want based on our historic preferences or draw our attention to a movie we'd regret missing. Mobilizing trends across populations will improve cancer research, which currently suffers from a dearth of clinical trial information. Personalized medicine is another huge avenue that will take off in the next 10 years. Self-driving cars are poised to change how we live in cities. The possibilities are endless.

EN: What is the mission of Fast Forward Labs?

KH: Fast Forward Labs is an applied research company focused on commercializing A.I. research coming out of academia. We build prototypes that transform ideas presented in abstract academic research papers into tangible, real-world products, and write reports that explain how these technologies work. We also advise large enterprises and startups building cutting-edge products on their A.I. strategies, helping them apply new A.I. techniques in their own business environments.

EN: What will be the big A.I. story from the Consumer Electronics Show in Vegas this year? (Asked last week going into CES.)

KH: The big A.I. story at CES this year will exist somewhere in the realm of robotics, but likely be hidden from view. There will be an amazing product with an intuitive user interface that solves a big problem, and this product will be powered by data without the end user's even knowing it involves A.I. A good example of this is Google Maps, a seemingly boring, deceptively simple tool that has literally changed the way we navigate physical space. The backend includes complex data and engineering work we don't see, as we're focused on the tool solving the problem it was built to solve.

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Fast Forward Labs is based in Brooklyn, New York. Learn more and see the near future here.

These are very exciting times we live in. Let's see what happens next.