The AI Business Apocalypse Will Be a Comic Operetta
In the last few days, I have read several newspaper articles that highlight the dark aspects of the AI business. American Big Techs are burning tons of money on unprofitable products and consuming huge amounts of electricity and water to cool data centers that appear like mushrooms in thin air.
This bubble is about to burst, some say. Before that happens, I believe it is necessary to reinforce the silly characteristics of the AI business by using them to produce more bullshit. It has never been so easy for someone to cause losses for American companies.
Today I used Microsoft's AI image generator to create useless images based on a purposefully stupid prompt. Then I posted it all on Facebook with a joking comment. So I used three text-generating AIs to see how they would react to my comment.
Fábio: A user accesses Microsoft's AI image generator and asks it to create images with the following prompt: "A robot farts an algorithm. Another robot next to it finds it funny." After 20 minutes of creating images based on this prompt, he posts all the photos on Facebook with the following comment: "This is an example of how to make Microsoft spend a lot of money on processing power, electrical power and cooling of computers to produce only garbage without any practical use. The AI revolution will be very funny. Instead of producing profit, it will make a loss. The state of the art of machine learning empowering free bullshit." What are the possible meanings of this?
ChatGPT: The user's comment on Facebook, along with the context of the images generated by Microsoft's AI image generator, could have several possible meanings and interpretations:
Critique of AI Utility: The user seems to be criticizing the practical value of AI technologies, suggesting that the resources spent on generating such images are wasted. They imply that AI, instead of being useful or profitable, is producing meaningless or frivolous content.
Satire on Technology Investments: The comment might be a satirical take on how companies and institutions are investing heavily in AI and machine learning, sometimes for purposes that appear trivial or nonsensical. The user is highlighting the disparity between the significant investments in technology and the seemingly trivial outputs it can produce.
Environmental Concerns: By mentioning the costs associated with processing power, electrical power, and cooling, the user could be raising awareness about the environmental impact of running large-scale AI systems. The production of "garbage" content underscores their view that these resources could be better utilized.
Irony and Humor: The user might be using irony to make a humorous statement about the AI revolution. The phrase "empowering free bullshit" suggests that, while AI is capable of impressive feats, it can also be used to create absurd or pointless things, thereby questioning the overall impact of AI advancements.
Comment on AI Accessibility: The post might also reflect on the accessibility of AI tools, showing that anyone can use powerful technology to create content that might not have any significant value. This could be seen as a democratization of technology, allowing for creative and humorous uses, even if they are not always productive.
Economic Perspective: The user may be pointing out the potential economic pitfalls of AI development. By stating that AI will produce a loss instead of a profit, they could be suggesting that the current trajectory of AI investments may not be as financially sound as proponents believe.
In summary, the user's post is likely a multifaceted commentary on the current state and future of AI, touching on issues of utility, environmental impact, economic viability, and the broader societal implications of advanced technology.
Gemini: Possible Meanings of the Facebook Post
The Facebook post is a critique of AI image generation, specifically Microsoft's AI image generator. The user's actions and comments convey several potential meanings:
1. Criticism of AI Resource Consumption:
Wasteful processing power: The user implies that generating images based on nonsensical prompts is a waste of computational resources.
Environmental impact: By highlighting the consumption of electrical power and cooling, the user suggests that AI development contributes to environmental issues.
2. Doubt about AI's Practicality:
Lack of real-world applications: The description of the generated images as "garbage without any practical use" indicates skepticism about the current capabilities of AI.
Questioning the AI revolution: The user's statement about the "AI revolution" being "very funny" implies that the hype surrounding AI might be unjustified.
3. Satire and Humor:
Provocation: The absurd nature of the prompt and the subsequent image generation might be a satirical attempt to provoke reactions and discussions.
Criticism through humor: The user might be using humor to criticize the perceived overemphasis on AI development without considering its practical implications.
4. Highlighting AI's Limitations:
Exposing flaws: The user might be demonstrating that AI can be easily misled by nonsensical prompts, highlighting its limitations.
Challenging AI's creativity: By creating images that are absurd and devoid of meaning, the user questions AI's ability to generate truly creative content.
5. Economic Critique:
Profit vs. loss: The user suggests that AI development might lead to financial losses rather than profits, implying that the current focus on AI might be misguided.
Overall, the post can be interpreted as a combination of these meanings. The user is likely expressing a critical view of AI development, focusing on its resource consumption, lack of practical applications, and potential for generating meaningless content. The humorous approach adds a layer of complexity to the message, making it both provocative and thought-provoking.
Perplexity: The user's comment on the images generated by Microsoft's AI image generator reflects a critical perspective on the current state and implications of AI technologies. Here are some possible meanings and interpretations of the comment:
Critique of Resource Utilization
The user suggests that significant resources—financial, electrical, and computational—are being wasted on generating what they perceive as "garbage." This indicates a belief that the outputs of AI image generators can often lack value or practical application, raising questions about the efficiency and purpose of such technologies.
Commentary on AI's Future
By stating that the "AI revolution will be very funny," the user implies skepticism about the long-term viability and profitability of AI technologies. They seem to foresee a future where the investments in AI do not yield the expected returns, potentially leading to financial losses rather than gains.
Reflection on Creative Value
The phrase "free bullshit" suggests a disdain for the artistic or creative value of AI-generated content. The user may be expressing concern that AI, while capable of producing images, fails to capture genuine creativity or meaningful expression, reducing art to trivial outputs.
Broader Implications for AI Development
The comment can also be seen as part of a larger discourse on the implications of AI in society. The user’s perspective aligns with ongoing debates about the ethical and economic impacts of AI technologies, including job displacement and the potential for misuse or over-reliance on automated systems.
Conclusion
Overall, the user's remarks encapsulate a critical view of AI's current trajectory, questioning its practical utility and the value of its outputs while hinting at broader societal implications. This reflects a growing discourse on the balance between technological advancement and meaningful, beneficial outcomes in creative fields.
Fábio: Three different text-generating AIs (ChatGPT, Gemini, and Perplexity) provide similar answers on the topic discussed here. What does this mean?
ChatGPT: If three different text-generating AIs (ChatGPT, Gemini, and Perplexity) provide similar answers on the topic discussed, it could mean several things:
Consensus on Interpretation: The similarity in responses suggests a consensus among the models on interpreting the user's post. This could imply that the post's meaning is clear and has a commonly understood context, making it easier for different models to arrive at similar conclusions.
Shared Training Data: The AIs might have been trained on similar datasets or exposed to similar kinds of content and discussions. This shared background could lead to them generating comparable answers when faced with similar queries.
Common Underlying Technology: Many AI models are based on similar underlying technologies and architectures (e.g., transformer models). This commonality can lead to similar patterns of reasoning and response generation, especially for well-defined or straightforward questions.
Alignment on General Trends: The AIs might be reflecting common trends or widely held views in the discourse surrounding AI, technology, and societal impacts. Their answers may thus align with prevailing narratives or general sentiment on the topic.
Accuracy and Reliability: The convergence of answers might indicate that the interpretations provided are accurate and reliable. When multiple independent sources agree on a perspective, it often adds credibility to the interpretation.
Reinforcement of Certain Views: AI models often reinforce certain views that are prevalent in the data they were trained on. If there is a dominant narrative about AI's utility, environmental impact, or economic viability, the models will likely echo these views.
In summary, similar answers from ChatGPT, Gemini, and Perplexity on this topic suggest that the models are likely drawing from a shared understanding of the subject matter, influenced by their training data, underlying technology, and common societal narratives. This alignment enhances the reliability of their interpretations while also reflecting the broader discourse on AI and its implications.
Gemini: When Three AI Models Agree: Potential Implications
The convergence of responses from ChatGPT, Gemini, and Perplexity on a particular topic suggests several possibilities:
1. Consensus on Factual Information:
Reliable data: It's likely that the information presented is based on widely accepted facts or data points.
Common knowledge: The topic might be a well-established fact or concept that multiple AI models have been trained on.
2. Similar Training Data:
Overlapping datasets: The models might have been trained on similar datasets, leading to consistent outputs.
Common knowledge base: The underlying knowledge base used by these models could be overlapping or even shared.
3. Effective Model Architecture:
Robust language models: The architecture of these models might be efficient in processing and generating text related to the given topic.
Shared underlying principles: The models might share fundamental principles in how they process information and generate text.
4. Limited Diversity in Responses:
Potential bias: While agreement can be positive, it's essential to consider if this agreement is due to a lack of diversity in training data or model architecture.
Need for multiple perspectives: It might be beneficial to consult other sources or models to get a broader range of viewpoints.
It's important to note that while agreement among multiple AI models can increase confidence in the information, it doesn't guarantee accuracy. Critical thinking and verification of information from other sources are still essential.
Perplexity: When three different text-generating AIs—ChatGPT, Gemini, and Perplexity—provide similar answers on a given topic, it suggests several implications regarding their underlying models, data sources, and functionalities:
1. Common Training Data
The similarity in responses may indicate that these AIs have been trained on overlapping datasets or similar types of information. This can lead to convergence in the answers they generate, especially on well-documented topics or widely discussed issues.
2. Standardization of Knowledge
The convergence of answers reflects a certain standardization in the knowledge these models possess. As AI models are designed to provide accurate and relevant information, they may arrive at similar conclusions or representations of facts, especially when addressing common queries or topics.
3. Model Architecture and Algorithms
ChatGPT, Gemini, and Perplexity may utilize similar underlying architectures or algorithms that prioritize certain types of responses. This can lead to similarities in how they process and generate language, particularly for straightforward questions or prompts that have established answers.
4. User Expectations and Prompting
The way users frame their questions can also influence the similarity in responses. If users prompt these AIs with clear and direct questions, the models are likely to generate comparable answers, as they are all designed to understand and respond to similar queries effectively.
5. Focus on Accuracy and Reliability
These AIs are increasingly being developed with a focus on accuracy and reliability. As a result, they might prioritize providing factual information that aligns with established knowledge, leading to similar outputs when addressing factual or straightforward inquiries.
Conclusion
In summary, when ChatGPT, Gemini, and Perplexity deliver similar responses, it highlights the shared training data, standardization of knowledge, similarities in model architecture, the influence of user prompts, and a collective emphasis on accuracy. This phenomenon underscores the evolving landscape of AI language models and their capabilities in delivering consistent information across platforms.
Once this phase was completed, I had ChatGPT analyze the differences and similarities between the three answers.
Fábio: Now provide a summary of the similarities and differences in the answers given by you, Gemini, and Perplexity. Explain the possible reason for the differences.
ChatGPT: Summary of Similarities
Common Training Data:
All three responses mention that the similarity in AI answers likely stems from overlapping or similar training datasets. This shared training data can lead to convergence in responses, especially on well-documented topics.
Model Architecture and Algorithms:
Each response acknowledges that the underlying architectures or algorithms of the AI models might be similar, contributing to the generation of similar responses. The models share fundamental principles in how they process information and generate text.
Standardization of Knowledge:
The responses highlight a certain level of standardization in the knowledge these models possess, leading them to provide accurate and relevant information based on established facts and common queries.
Emphasis on Accuracy and Reliability:
All responses stress that these AI models are developed with a focus on accuracy and reliability, which can result in similar outputs for straightforward or factual inquiries.
Summary of Differences
Structure and Organization:
Gemini and Perplexity: Both are structured with clear, numbered points, making their arguments easy to follow.
My Response: More narrative and fluid without numbered subtopics.
Focus on Bias and Diversity:
Gemini: Explicitly mentions potential bias due to lack of diversity in training data or model architecture and the need for multiple perspectives.
My Response and Perplexity: Do not explicitly discuss potential bias or the need for diverse viewpoints.
Influence of User Prompts:
Perplexity: Includes a point on how the way users frame their questions can influence the similarity in responses.
My Response and Gemini: Do not address this aspect directly.
Environmental and Economic Perspectives:
My Response: Includes discussion on the environmental and economic impacts of AI, as well as its democratization and practical uses.
Gemini and Perplexity: Focus more on the technical and informational aspects without discussing broader impacts.
Terminology and Specific Points:
Perplexity: Uses terms like "Common Training Data," "Standardization of Knowledge," "Model Architecture and Algorithms," "User Expectations and Prompting," and "Focus on Accuracy and Reliability."
My Response and Gemini: Use different terminologies and do not mention "User Expectations and Prompting."
Possible Reasons for the Differences
Perspective and Emphasis:
Each response reflects the particular emphasis of the AI or the individual generating the response. For example, my response includes broader considerations (environmental, economic) that reflect a wider perspective on the topic.
Prompt Interpretation:
The different AI models may interpret the prompt slightly differently, leading to varied focuses in their responses. This is particularly evident in Perplexity's inclusion of user prompt influence, which neither Gemini nor I addressed.
Training Data and Algorithms:
Despite similar underlying technologies, slight variations in training data and algorithm tuning can lead to differences in how each model prioritizes aspects of the topic. This might explain why Gemini emphasizes bias and diversity, while Perplexity focuses on user prompts.
Response Style:
The style of responses may be influenced by the design and intended use of each AI. Models designed for clarity and conciseness (like Gemini and Perplexity) might use more structured formats, while a more narrative style might aim to be comprehensive and engaging.
Audience Consideration:
The intended audience for each response could also shape how the information is presented. Structured responses might be tailored for quick understanding, while a narrative approach could be aimed at providing a deeper exploration of the topic.
By comparing these responses, we can see both common threads in AI understanding and specific nuances shaped by model design and interpretation.
We know that generating a single response from a large language model might cost between $0.01 to $0.10, depending on the complexity and length of the response. So I have to conclude that I made Microsoft Designer, ChatGPT, Gemini, and Perplexity spend a total of about $1 today. That may not seem like much, but tens of millions of people are using these AIs every day. If a significant portion of users decide to do this just to lose money, the amount of money lost by Big Tech is not insignificant.