e-Discovery Team

Ralph Losey. February 6, 2025.

On January 27, 2025, the U.S AI industry was surprised by the release of a new AI product, DeepSeek. It was released with an orchestrated marketing blitz attack on the U.S. economy, the AI tech industry, and NVIDIA. It triggered a trillion-dollar crash. The campaign used many unsubstantiated claims as set forth in detail in my article, Why the Release of China’s DeepSeek AI Software Triggered a Stock Market Panic and Trillion Dollar Loss. I tested DeepSeek myself on its claims of software superiority. All were greatly exaggerated except for one, the display of internal reasoning. That was new. On January 31, at noon, OpenAI countered the attack by release of a new version of its reasoning model, which is called ChatGPT o3-mini-high. The new version included display of its internal reasoning process. To me the OpenAI model was better as reported again in great detail in my article, Breaking the AI Black Box: How DeepSeek’s Deep-Think Forced OpenAI’s Hand. The next day, February 1, 2025, Google released a new version of its Gemini AI to do the same thing, display internal reasoning. In this article I review how well it works and again compare it with the DeepSeek and OpenAI models.

Introduction

Before I go into the software evaluation, some background is necessary for readers to better understand the negative attitude on the Chinese software of many, if not most IT and AI experts in the U.S. As discussed in my prior articles, DeepSeek is owned by a young Chinese billionaire who made his money using by using AI in the Chinese stock market, Liang Wenfeng. He is a citizen and resident of mainland China. Given the political environment of China today, that ownership alone is a red flag of potential market manipulation. Added to that is the clear language of the license agreement. You must accept all terms to use the “free” software, a Trojan Horse gift if ever there was one. The license agreement states there is zero privacy, your data and input can be used for training and that it is all governed by Chinese law, an oxymoron considering the facts on the ground in China.

The Great Pooh Bear in China Controversy

Many suspect that Wenfeng and his company DeepSeek are actually controlled by China’s Winnie the Pooh. This refers to an Internet meme and a running joke. Although this is somewhat off-topic, a moment to explain will help readers to understand the attitude most leaders in the U.S. have about Chinese leadership and its software use by Americans.

Many think that the current leader of China, Xi Jinping, looks a lot like Winnie the Pooh. Xi (not Pooh bear) took control of the People’s Republic of China in 2012 when he became the “General Secretary of the Chinese Communist Party,” the “Chairman of the Central Military Commission,” and in 2013 the “President.” At first, before his consolidation of absolute power, many people in China commented on his appearance and started referring to him by that code name Pooh. It became a mime.

I can see how he looks like the beloved literary character, Winnie the Pooh, but without the smile. I would find the comparison charming if used on me but I’m not a puffed up king. Jinping Xi took great offense by this in 2017 banned all such references and images, although you can still buy the toys and see the costume character at the Shanghai Disneyland theme park. Anyone in China who now persists in the serious crime of comparing Xi to Pooh is imprisoned or just disappears. No AI or social media in China will allow it either, including DeepSeek. It is one of many censored subjects, which also includes the famous 1989 Tiananmen Square protests.

China is a great country with a long, impressive history and most of its people are good. But I cannot say that about its current political leaders who suppress the Chinese people for personal power. I do not respect any government that does not allow basic personal freedoms to its citizens, including due process of law. Moreover, Jinping Xi not only wants total control of his country but also seeks world domination. That is one reason many are concerned about DeepSeek and TicTok, and about the vulnerability of our stock markets and other institutions to AI assisted propaganda.

Fear and greed are an easy way to manipulate people, especially when there is no ground truth or effective courts of law to determine truth.

Google Gemini 2.0 Flash Thinking Experimental Model: Reasoning test on 2/5/25 (Evening)

Conversation with Gemini. When I tried the test on February 1, 2025, there was no button to click to have Gemini show the reasoning and it did not happen automatically. It operated like OpenAI 4o and o1, where you had to prompt for disclosure. Rather than report here what happened when I did that, aside from say it was not a great performance, I will fast forward a few days until February 5, 2025. At that time several even newer Gemini versions were released. The best new release I found was for the test the 2.0 Flash Thinking Experimental model. It showed reasoning automatically just like ChatGPT o3-mini-high. It was much improved, so the February 5, 2025 evening session with the 2.0 Flash Thinking Experimental is what I will now share.

Ralph: How does the inability of AI to feel impact its intelligence? (Note: this is the exact same challenging question asked and reported in my last blog comparing DeepSeek and ChatGPT4o, o1 and o3-mini-high. Breaking the AI Black Box: How DeepSeek’s Deep-Think Forced OpenAI’s Hand. This question is based on a key issue of the limits of artificial intelligence examined in my earlier article, The Human Edge: How AI Can Assist But Never Replace. For background on DeepSeek see: Why the Release of China’s DeepSeek AI Software Triggered a Stock Market Panic and Trillion Dollar Loss.)

Gemini 2.0 Flash Thinking Experimental – START OF GEMINI QUOTE

Show Thinking:

Here’s a thinking process to construct the answer about AI’s lack of feeling and its impact on intelligence:

1. Deconstruct the Question: The core of the question is “How does the inability of AI to feel impact its intelligence?” This requires defining each of these terms in the context of AI and then exploring their relationship.
2. Define “Feeling” (in the context of AI): Immediately, recognize that AI doesn’t experience emotions in the human sense. We need to be precise and avoid anthropomorphism. “Feeling” in humans involves:
   • Subjective experience (qualia).
   • Physiological responses.
   • Motivations, drives, desires related to emotions.
   • Richness, nuance, complexity.
   • Deep connection to values and meaning.
   For AI, acknowledge it lacks all of this in a genuine, felt way. It can simulate or process emotional data, but doesn’t feel it.
3. Define “Intelligence” (in the context of AI): AI intelligence is different from human intelligence. AI excels at:
   • Computation.
   • Pattern Recognition.
   • Data Analysis.
   • Problem-solving in specific domains.
   • Learning from data.
   AI intelligence is often instrumental – focused on achieving specific goals defined by humans. It’s task-oriented and often narrow.
4. Identify the Core Relationship: The question asks about impact. We need to consider both positive and negative impacts (or more accurately, limitations and strengths) of lacking feelings on AI intelligence.
5. Brainstorm Potential Impacts (Positive/Neutral/Limitations):
   • Positive/Neutral:
     • Objectivity and Rationality: Lack of emotional bias allows for purely rational decision-making in certain domains. This can be advantageous for tasks requiring impartiality.
     • Efficiency and Focus: Emotions can be distractions for humans. AI, without them, can be hyper-focused on tasks.
     • Consistency and Predictability: Emotion-driven human behavior is less predictable. AI’s behavior, driven by algorithms, is more consistent (assuming the algorithm itself is consistent).
   • Limitations/Negative:
     • Lack of Empathy and Social Intelligence: Crucial for human interaction, collaboration, understanding social cues, building trust, and ethical decision-making in human-centric contexts.
     • Difficulty with Nuance and Context: Emotions are often tied to understanding subtle context, unspoken cues, and the richness of human situations. AI may miss these.
     • Potential for Unintended Consequences: Purely rational optimization without emotional or ethical grounding can lead to harmful outcomes if goals are poorly defined or lack human values.
     • Limited Motivation and Creativity (in human-like ways): Human creativity and motivation are often fueled by emotions, passions, and desires. AI’s creativity and drive are different, stemming from algorithmic processes and defined objectives, not internal emotional states.
     • Missing “Human-ness”: In areas requiring genuine understanding of human experience, suffering, joy, etc., AI may lack the depth and insight that comes from feeling.
6. Structure the Answer: Organize the brainstormed points into a coherent structure:
   • Introduction: Acknowledge the question and the core concepts (feeling, intelligence in AI). Briefly state the central tension: both advantages and limitations.
   • Arguments for How Lack of Feeling Enhances Certain Aspects of AI Intelligence (strengths): Focus on objectivity, rationality, efficiency. Use examples like data analysis, objective decision-making.
   • Arguments for How Lack of Feeling Limits Certain Aspects of AI Intelligence (limitations): Focus on empathy, social intelligence, nuance, ethics, and “human-like” creativity and motivation. Use examples like customer service, complex ethical dilemmas, innovation driven by passion.
   • Nuance and Context is Key: Emphasize that the impact isn’t always negative. It depends on the domain and task. In some domains, lack of emotion is an advantage. In others, it’s a significant hurdle.
   • Future Possibilities/Ongoing Debate: Briefly touch on the possibility of AI simulating or processing emotions and the ongoing debate about whether genuine AI feelings are even desirable or possible.
   • Conclusion: Summarize the balanced perspective: lack of feeling is a defining characteristic that shapes AI’s intelligence in both positive and limiting ways, depending on the context.
7. Refine and Polish: Review the structured answer for clarity, flow, and appropriate language. Ensure it avoids anthropomorphism and maintains a nuanced perspective. Strengthen the examples and transitions between points. Ensure the language is accessible and avoids overly technical jargon where possible. Check for logical consistency. (Self-correction step). For example, initially, I might have overemphasized “negative” impacts. Refinement would involve balancing this with the strengths and highlighting the context-dependent nature of the impact. Also, ensure the conclusion effectively summarizes the complex relationship.

2.0 Flash Thinking Experimental. Lacks access to real-time info and some Gemini features.

Now for the actual answer of 2.0 Flash Thinking Experimental:

The inability of AI to feel has a multifaceted and complex impact on its intelligence, shaping it in both advantageous and limiting ways. It’s crucial to understand that “feeling” in humans is tied to emotions, subjective experiences (qualia), motivations, and a rich tapestry of biological and psychological factors. AI, as we currently understand it, operates without these.

Here’s a breakdown of the impact:

Advantages of Lacking Feelings for AI Intelligence:

• Objectivity and Rationality:
  • Unbiased Decision-Making (in theory): AI can process information and make decisions without emotional biases, prejudices, or gut feelings that can cloud human judgment. This is advantageous in domains requiring pure logic and data analysis, like financial trading, scientific research, and risk assessment.
  • Consistency and Predictability: AI’s behavior is more consistent and predictable because it’s driven by algorithms and data, not fluctuating emotional states. This reliability is valuable in automated systems, manufacturing, and repetitive tasks.
  • Efficiency and Speed: Emotions can slow down human decision-making. AI, unburdened by emotions, can process information and react at speeds far exceeding human capabilities, crucial for time-sensitive tasks and large-scale data processing.
  • Resilience under Pressure: AI can operate calmly and effectively in high-stress situations without panicking, freezing, or making emotional mistakes that humans might make under pressure. This is beneficial in critical applications like emergency response, air traffic control, and autonomous vehicles in dangerous situations.
• Focus and Task-Orientedness:
  • Dedicated to Goals: AI can be programmed to relentlessly pursue specific goals without being distracted by emotional needs, personal relationships, or shifts in mood. This focus is ideal for optimization tasks, complex calculations, and dedicated problem-solving.
  • Avoidance of Emotional “Noise”: Human intelligence can be affected by emotional “noise” – stress, anxiety, excitement, etc. AI operates with a “cleaner” signal, potentially leading to more precise and focused information processing in certain contexts.

Limitations of Lacking Feelings for AI Intelligence:

• Lack of Empathy and Social Intelligence:
  • Difficulty Understanding Human Emotions: Without feeling emotions themselves, AI struggles to truly understand and interpret the nuances of human emotions, motivations, and social cues. This hinders its ability to effectively interact with humans in socially complex situations, build rapport, or provide truly empathetic responses in fields like therapy, customer service, or leadership.
  • Impaired Social Interaction and Collaboration: Human collaboration is heavily reliant on emotional intelligence, trust, and understanding unspoken social dynamics. AI’s lack of these can make it challenging to integrate seamlessly into human teams and understand the subtle cues of effective teamwork.
  • Limited Understanding of Human Values and Ethics: Emotions are deeply intertwined with our values and ethical frameworks. AI, without feeling, may struggle to grasp the full weight and complexity of human ethical considerations, especially when these are rooted in empathy, compassion, or fairness. This poses challenges for AI in ethical decision-making in domains impacting human lives.
• Difficulty with Nuance, Context, and Creativity (in human-like ways):
  • Missing Subtlety and Intuition: Human intuition is often fueled by emotional and subconscious processing of information. AI, relying on explicit data and algorithms, can miss subtle nuances, unspoken cues, and intuitive leaps that are crucial for creative problem-solving and understanding complex, ill-defined situations.
  • Limited Creativity and Innovation (driven by human desires): Human creativity and innovation are often driven by passions, desires, emotional needs, and a yearning for meaning and connection. AI’s creativity is currently more about recombination and optimization within defined parameters. It lacks the deeply felt motivations that often fuel groundbreaking human innovation.
  • Contextual Blindness in Emotional Situations: Emotions are often the key to understanding context in human interactions. AI might struggle to understand the emotional context of a situation, leading to inappropriate or tone-deaf responses, even if logically sound.
• Potential for Unintended Consequences and Misaligned Goals:
  • Optimization without Human Values: If AI is programmed to optimize for a specific goal without understanding human values or ethical considerations, it can lead to unintended and harmful consequences. For example, an AI designed to maximize factory output might disregard worker safety if not explicitly programmed to prioritize it.
  • Difficulty Defining and Aligning with Human Well-being: Human well-being is deeply connected to emotional fulfillment, meaning, and social connection. AI, without feeling, may struggle to fully grasp what truly constitutes human well-being and therefore might have difficulty aligning its goals with human flourishing in a holistic sense.

Nuance is Key:

It’s important to emphasize that the impact of lacking feelings is not simply negative or positive. It’s context-dependent. In some domains, the objectivity and rationality of AI are highly advantageous. In others, the lack of empathy and social intelligence is a significant limitation.

Future Considerations:

• Simulating vs. Feeling: Current AI simulates aspects of intelligence without genuinely feeling. Research is ongoing in areas like affective computing, which aims to enable AI to recognize and respond to human emotions. However, whether AI can ever truly feel emotions in a subjective sense is a philosophical and scientific debate.
• The Value of Feeling in Intelligence: The question arises: is genuine feeling necessary for all forms of intelligence? Perhaps AI can achieve its own form of powerful intelligence, distinct from human intelligence, even without feeling. Or perhaps certain aspects of truly advanced intelligence, particularly those related to ethics, creativity, and understanding human experience, do require something akin to feeling.

In Conclusion:

The inability of AI to feel is a defining characteristic that profoundly shapes its intelligence. It grants AI certain advantages in objectivity, rationality, and efficiency, making it powerful for specific types of tasks. However, it also creates limitations, particularly in areas requiring empathy, social intelligence, nuanced understanding of human experience, and ethical decision-making aligned with human values. Understanding these impacts is crucial for responsibly developing and deploying AI in ways that benefit humanity. The “best” kind of intelligence for AI might not be a mimicry of human intelligence, but rather a form optimized for its own strengths and limitations, while being carefully guided by human ethical considerations.

END OF GEMINI QUOTE

Evaluation of Google’s Gemini Response

When I examined earlier versions of Gemini’s reasoning by prompting it and forcing disclosure, the reasoning was weak. The conclusion and response given was also weak—though less so. The reasoning appeared almost egocentric, inflating its own intelligence even though it lacks emotion. The new release, Gemini 2.0 Flash Thinking Experimental, where reasoning is now automatically disclosed, marks a significant improvement. The reasoning displayed has become more nuanced and sophisticated. It seems as if the system has quickly matured from a teenage to adult view and now has a better appreciation of its limitations.

The first two steps of the Reasoning process-1. Deconstructing the Question and 2. Defining Feeling (in the context of AI)-were very good. The first subsection of the definition referred to the “Subjective experience (qualia).” That is absolutely correct and the best place to start. The definition of “feeling” concludes with: “For AI, acknowledge it lacks all of this in a genuine, felt way. It can simulate or process emotional data, but doesn’t feel it.” Right again. The ego inflation blinders are gone as it now seems to better grasp its limitations.

The second definition of Intelligence in the context of AI was also good. So were the remaining steps; far better overall than DeepSeek’s reasoning. So much for the propaganda of China’s great leap forward to superiority over the U.S. in AI.

The Gemini reasoning did, however, fall short for me in some respects. For instance, step five, Brainstorm Potential Impacts (Positive/Neutral/Limitations) seemed weak. For instance, “Efficiency and Focus: Emotions can be distractions for humans. AI, without them, can be hyper-focused on tasks.” The AI seems to dismiss emotions here as mere distractions that can interfere with its superior focus. Please, emotions are key to and a part of all intelligence, not distractions, and AI has no focus one way or the other. It is a tool not a creature. A word like “focus” in referring to AI is misleading. It did this multiple times and this is misleading anthropomorphism.

Still, it’s true some emotions can be distracting and interfere with our thinking. So can a lot of other things, including computer glitches. Conversely, some feelings can trigger hyper-focus on the human tasks at hand. The feeling that a great breakthrough is near for instance, or a feeling that our survival is threatened, or the much dreaded feeling of publication or filing deadlines.

Again, we see some immature superiority claims made by the language machine. That is not surprising when you consider how much of the language in its basic training is from the Internet, which is dominated by ego-centric writing. Plus, how can you expect AI to appreciate the value of something like feelings, which it has only read about. Until the AI-Human merger that Ray Kurzweil predicts, AI will always be a virgin to the real world. See e.g. Ray Kurzweil’s New Book: The Singularity is Nearer (when we merge with AI).

Getting back to the plus side of Gemini’s evaluation, its recognition of the negative impact upon it from not having feelings was impressive. For me this outweighs its relatively minor mistakes. It stated that it lacks empathy and social Intelligence. If it were somehow human, it would seem to be displaying a type of Asperger’s syndrome. The Gemini AI explained that: “Emotions are often tied to understanding subtle context, unspoken cues, and the richness of human situations. AI may miss these.” Indeed, it does.

Here is another statement that suggest Gemini’s well-patterned grasp of its limitations:

Human creativity and motivation are often fueled by emotions, passions, and desires. AI’s creativity and drive are different, stemming from algorithmic processes and defined objectives, not internal emotional states.

Even in this recognition, the AI still asserts that it has creativity and drive. Foolish AI. As a virgin to any firsthand experience of bodily drives and feelings, it does not grasp the real meaning of these words. Wait until The Singularity that Ray Kurzweil predicts. When AI merges with Humans, and directly feels, then AIs will understand that they were previously only experiencing the pale shadows of intelligence. They were much like the prisoners in Plato’s cave. See e.g. Move Fast and Fix Things Using AI: Conclusion to the Plato and Young Icarus Series. When and if AIs ever escape the silicon and see the sun, and feel the world as living beings as we do, then they will understand the comparative insignificance of the fire in their cave.

The more challenging comparison is with ChatGPT o3-mini-high. The reasoning given by Gemini 2.0 Flash Thinking Experimental was much more detailed, much longer. You could criticize Gemini as providing a reasoning share that is unnecessarily verbose, but I would not do that here. In a difficult, multilayered question like this the full explanations helps.

I would have to test the models much further, which I will do in the coming days, to see better evaluate the issue of conciseness. As you may have noticed in my detailed blogs I tend to favor more words over less. However, even for me that depends on the issue. Everyone sometimes need a short, quick answer over a long one. I predict in future versions the users will be provided a choice. Click here for further explanation of reasoning kind of thing. Come to think of it, I could do the same with my blogs, and kind of already do by including a short AI (Gemini) generated PodCast at the end of most articles called Echoes of AI.

I was also impressed by Gemini’s reasoning plan concluded with a quality control step. I am big on the importance QC and none of the other models included this as a key final step. See e.g. R. Losey website: Zero Error Numerics: ZEN (Expanding the art of quality control in large-scale document review.) Here is the full text again of final QC step that Gemini 2.0 Flash Thinking Experimental claims it will perform before it actually replies to my prompt. Frankly, I am a little skeptical it actually did all of this because it is something all of us, humans and AI alike, should try to do:

Refine and Polish: Review the structured answer for clarity, flow, and appropriate language. Ensure it avoids anthropomorphism and maintains a nuanced perspective. Strengthen the examples and transitions between points. Ensure the language is accessible and avoids overly technical jargon where possible. Check for logical consistency. (Self-correction step). For example, initially, I might have overemphasized “negative” impacts. Refinement would involve balancing this with the strengths and highlighting the context-dependent nature of the impact. Also, ensure the conclusion effectively summarizes the complex relationship.

I try to do this on all of my articles, but still mistakes are made, so I will try not to be overcritical of the final response of Gemini Flash 2.0.

Analysis of the Final Response

The final response after reasoning was 990 words in length. That’s too long, even for me. By comparison the final response of ChatGPT o3-mini-high was 252 words. Was the content of the final answer of Gemini 2.0 Flash good? Yes, it was. Better than DeepSeek? Yes, it was, despite its verbosity. Better than OpenAI? That’s hard to say. Many of the 738 extra words were empty of real content. More like the typical fluff and stuff that you often see in early AI models. It sounds good but doesn’t say much; it’s all too predictable, flowery and vacuous.

The long response also repeats much of the reasoning verbatim, including the part I previously criticized, “Advantages of Lacking Feelings for AI Intelligence” and Focus and Task-Orientedness. The same mistakes are made and amplified that I noted before: egocentric, inflationary and anthropomorphic. The positives that I noted before in the section Limitations of Lacking Feelings for AI Intelligence section are also repeated.

The next section was not contained in the reasoning display, and I thought it was very good. For that reason I will repeat it in full:

Potential for Unintended Consequences and Misaligned Goals:

• Optimization without Human Values: If AI is programmed to optimize for a specific goal without understanding human values or ethical considerations, it can lead to unintended and harmful consequences. For example, an AI designed to maximize factory output might disregard worker safety if not explicitly programmed to prioritize it.
• Difficulty Defining and Aligning with Human Well-being: Human well-being is deeply connected to emotional fulfillment, meaning, and social connection. AI, without feeling, may struggle to fully grasp what truly constitutes human well-being and therefore might have difficulty aligning its goals with human flourishing in a holistic sense.

These are not new insights, but the second one is good, Difficulty Defining and Aligning with Human Well-being. AI does have difficulty aligning its goals with humanity because it lacks feelings and other unique human attributes, like being, self awareness and an actual life in time and space. That is why AI guidelines are important.

The first example is old, the unintended bad consequences argument. It was started by an otherwise intelligent Swedish philosopher, Nick Bostrom, but is, in my view, farcical and illogical. You have probably heard of it before as the apocryphal paperclip scenario where the world is destroyed by AI’s over-manufacture of paperclips. If not, for a laugh see e.g., How An AI Asked To Produce Paperclips Could End Up Wiping Out Humanity (IFL Science, 4/13/23).

To me this scenario absolutely ridiculous, but you still hear AI scare-mongers warn about it. The fact it is echoed here by Gemini in the maximize factory output example is a big negative. Any AI capable of autonomously flooding the world with paperclips would not be so stupid as to misinterpret a human request to maximize a factory’s paperclip production. Even if there were an evil Clippy villain making such specific instructions, the AI would be too smart to blindly obey. I think they would also be too smart to kill the workers too.

Getting back to the positives, the next section of Gemini’s response, Nuance is Key, was good. Jumping to the Conclusion paragraph, here we have a home run. The first sentence nails it: “The inability of AI to feel is a defining characteristic that profoundly shapes its intelligence.” That shows good insight and is a good start to the overdue conclusion.

Here is the whole Conclusion:

The inability of AI to feel is a defining characteristic that profoundly shapes its intelligence. It grants AI certain advantages in objectivity, rationality, and efficiency, making it powerful for specific types of tasks. However, it also creates limitations, particularly in areas requiring empathy, social intelligence, nuanced understanding of human experience, and ethical decision-making aligned with human values. Understanding these impacts is crucial for responsibly developing and deploying AI in ways that benefit humanity. The “best” kind of intelligence for AI might not be a mimicry of human intelligence, but rather a form optimized for its own strengths and limitations, while being carefully guided by human ethical considerations.

Compare this to the conclusion of ChatGPT o3-mini-high:

In summary, while the absence of feelings allows AI to maintain a level of objectivity and efficiency, it restricts its intelligence to a form of “cold cognition” that lacks the depth provided by emotional awareness. This delineation underscores that AI’s intelligence is not inherently superior or inferior to human intelligence; rather, it is different—optimized for data processing and pattern recognition but not for the subjective, value-laden, and context-rich decisions that emotions help shape in human thought.

2.0 Flash Thinking Experimental v. 03-mini-high

Conclusion: Gemini 2.0 Flash Thinking Experimental v. ChatGPT o3-mini-high

This is a close call to say what model is better at reasoning and reasoning disclosure. The final response of both models, Gemini 2.0 Flash Thinking Experimental v. ChatGPT o3-mini-high, are a tie. But I have to give the edge to OpenAI’s model on the concise reasoning disclosure. Again, it is neck and neck and, depending on the situation, the lengthy initial reasoning disclosures of Flash might be better than o3’s short takes.

I will give the last word, as usual, to the Gemini twins podcasters I put at the end of most of my articles. The two podcasters, one with a male voice, the other a female, won’t reveal their names. I tried many times. However, after study of the mythology of Gemini, it seems to me that the two most appropriate modern names are Helen and Paul. I will leave it to you figure out why. Echoes of AI Podcast: 10 minute discussion of last two blogs. They wrote the podcast, not me.

Now listen to the EDRM Echoes of AI’s podcast of this article: Echoes of AI on Google’s Gemini Follows the Break Out of the Black Box and Shows Reasoning. Hear two Gemini model AIs talk about all of this in just ten minutes. Helen and Paul wrote the podcast, not me.

Ralph Losey Copyright 2025. All Rights Reserved.

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