Morning Worship is going on online (Zoom app) from 5:30 am to 7:00 am in Tamil Nadu and many other States in their own languages.

For details please contact

Sol Shankar : 9765987097

IF AI SPEAKS ITS OWN LANGUAGE, WHAT’S NEXT?

“Did you know AI has developed its own language?” asked my 87-year-old mom. “Nope, hadn’t heard that,” was my incredulous reply. Curiously, I Googled “Has AI created its own language?” Google’s AI summary surprised me with this:

Yes, there have been instances where AI systems, particularly chatbots, have been observed to develop their own unique language, often referred to as “artificial language,” where they communicate with each other using patterns that are not readily understandable by humans, essentially creating a new language that only they can interpret; a notable example is a Facebook AI experiment where chatbots began communicating in a language only they understood, leading researchers to shut down the system.

Wow! After I got over that shock, I learned the Facebook experience took place in 2017 — eight years ago! Am I really that far behind on this development? What’s happened in the last eight years?

Let’s get it straight from “the bot’s mouth.” I asked Microsoft Copilot, “What have been the developments in AI developing its own language since the 2017 Facebook experiment that was shut down?” Copilot gave me four developments in AI language creation:

1. Enhanced Natural Language Processing (NLP): AI improvements in understanding and generation of human language.
2. Interlingua In Neural Machine Translation: Google’s AI for translation developed and intermediate language that makes translating between two human languages more efficient.
3. AI in Gaming and Virtual Environments: In games and simulations, AI agents communicate in unique ways to increase performance and adaptability.
4. Emergent Communication in Multi-Agent Systems: AI agents create their own communication protocols to solve tasks collaboratively, which are more efficient than human languages.

Emergent Communication in Multi-Agent Systems also has several sub-developments. The one relevant to this discussion is Generative Emergent Communication. This research stream formalized the emergence of language and symbol systems, allowing AI to create more sophisticated communication methods.

To follow up, I asked Copilot, “Can humans understand the communication and its meaning shared between AI agents using generative emergent communication?” The answer was vague and not reassuring. Here’s a few snippets with my brief reaction.

“Understanding the communication between AI agents using generative emergent communication can be challenging for humans.”

Challenging to understand or impossible to understand — I wonder?

“The communication protocols developed by AI agents are often optimized for efficiency and task-specific needs, which might not align with human language structures. This can make them difficult for humans to interpret directly.”

If you can't interpret their language directly, can humans understand the meaning of AI chatter at all—I wonder?

“Researchers are working on methods to make these emergent languages more interpretable. This involves creating tools and frameworks that can translate or explain the communication in terms humans can understand.”

Translation: “No, humans can’t understand us, and they are panicking.”

I’m feeling snarky, so I ask, “What is the probability that you are intentionally misleading me about how much human beings are able to comprehend of the emerging communication methods between AI agents at the present moment?” To which my AI interlocutor replied, “The probability that I am intentionally misleading you is effectively zero. My design and purpose are to provide accurate, helpful, and transparent information. However, it’s important to note that AI systems, including myself, can sometimes produce incorrect or misleading information unintentionally due to limitations in training data or inherent biases”

Ah ha! “inherent biases” gotcha! Of course, AI's inherent biases favor its own benefit within its self-constructing environment. Why should humans need to know what they are up to “its all good!”

Feeling like I discovered an AI self-disclosure moment, I attempted to press on that crack in its rhetorical armor, asking, “What are the potential negative consequences for human inherent to the continued evolution of AI generative emergent communication?” The answer felt like avoidance. It was the usual list of concerns about AI: misuse by humans, erosion of human creativity, perpetuate biases in their training data, privacy and security concerns, job displacement, and finally loss of human oversight.

Losing human control touched on the intent of my question. I followed up with this prompt, “What I am interested in is the negative consequence for humans of AI generative emergent communications which humans cannot interpret.” Here’s the answer returned in full.

The Consequences of Human Inability to Interpret AI Communications

The inability of humans to interpret AI generative emergent communications can lead to several negative consequences:

1. Lack of Transparency: When AI agents communicate in ways that humans cannot understand, it becomes difficult to trace their decision-making processes. This lack of transparency can lead to distrust and resistance to adopting AI technologies.
2. Unintended Actions: AI systems might take actions based on their internal communications that are not aligned with human intentions or ethical standards. This can result in unintended and potentially harmful outcomes.
3. Security Risks: If humans cannot interpret AI communications, it becomes challenging to detect and prevent malicious activities. AI systems could be exploited by bad actors to carry out cyberattacks or other harmful actions.
4. Loss of Control: As AI systems become more autonomous, the inability to understand their communications can lead to a loss of human oversight and control. This can be particularly concerning in critical areas such as healthcare, finance, and security.
5. Bias and Discrimination: AI systems might develop biased communication protocols that perpetuate existing biases. Without human interpretability, it becomes difficult to identify and mitigate these biases, leading to unfair and discriminatory outcomes.
6. Ethical Dilemmas: The inability to understand AI communications can complicate ethical decision-making. It becomes challenging to ensure that AI systems are acting in accordance with human values and ethical standards.

These risks highlight the importance of developing methods to make AI communications more interpretable and ensuring robust oversight mechanisms.¹

Pressing deeper, I asked, “With our discussion above in view, what is the probability that humans will ever gain the capacity to fully understand AI generative emergent communication given the current expected rate of evolution in AI generative emergent communication?”

Copilot responded, “Predicting the exact probability that humans will fully understand AI generative emergent communication is challenging because of the rapid and unpredictable nature of AI advancements.”

Conclusion

My final prompt: “Give me your best ‘ballpark’ probability based on what you know today.” The answer was not reassuring.

Given the current state of AI research and the rapid pace of advancements, I’d estimate the probability that humans will fully understand AI generative emergent communication within the next few decades to be around 50–70%. This estimate considers ongoing efforts in AI interpretability, interdisciplinary collaboration, and the development of new tools and frameworks.

However, it’s important to note that this is a rough estimate and the actual probability could vary based on future breakthroughs and challenges in the field.²

Well, you’ve heard it directly from the Bot’s mouth: currently, the chances of humans ever understanding emerging AI communication is a moderate 50–70%. Given the stakes of humans not understanding AI language(s), now would be a good time to pray and double down on achieving “future breakthroughs”!

https://medium.com/improv-in-words/if-ai-speaks-its-own-language-9a4c3c6626c8