Time to leapfrog the world again, at VON, with Sentient AI, Quantum Computing and 6G Communications! A Secure AI Services Edge!

According to the old adage, "a picture is worth a thousand words!"

This is an adage in multiple languages meaning that complex and sometimes multiple ideas can be conveyed by a single still image, which conveys its meaning or essence more effectively than a mere verbal description. Now imagine a Motion Picture, a movie scene, a vignette, and video, how much is conveyed? Perhaps a 100X more than a picture!

We know Communications involves transmission of symbols with intelligence to interpret said symbols. With ChatGPT, and future variants in LLM’s (Large Language Models), will AI/ML become intertwined with all future Communications? My belief is “YES!”

Humans can turn symbols sideways or upside down, or add newer meanings to symbols and determine meanings, but until now, general purpose computers have been fairly deterministic in it’s analysis.

Now let’s talk technology and how Sentient AI/ML can allow us to leapfrog the world, again!

On this note, just like Spock’s “Live Long and Prosper!” greeting and communications has multiple meanings with a common root, Om/Shalom, Allah/Salaam, to the original Hebrew Shin symbol, to the Trishul and the Cross, along with other meanings for human interpretation, let’s look at AI/ML and how to make it Sentient AI.

Introduction

In the context of edge computing and communications, envision a scenario where all forms of communication inherently possess hidden meanings and are subject to interpretation by an AI/ML engine. This AI engine could provide the following capabilities:

• Hidden Contextual Analysis
• Language Translation
• Sentiment Analytics
• Meeting Summaries
• Quick Storage/Retrieval
• Quality Enhancements like noise elimination and video augmented reality
• Meeting Suggestions such as supplementary documents or information
• Scheduling for follow-up meetings or task management

Many of these features are already offered by some video conferencing engines and social media platforms with communication applications. In the near future, all communications likely will support real-time “Chat GPT”-like sessions for all chats, emails, and dialogues.

Can we leverage this for creating Sentient AI?

The Concept of Sentient AI

To create Sentient AI, key elements include:

• Embodiment and sensorimotor experience with autonomous goal creation
• Self-awareness with mental maps
• Stream of consciousness for functional thought processes
• Sense of time, narrative, and memory
• Cognitive processes such as perception, memory, attention, decision-making, and problem-solving
• Symbolic reasoning, logic, and abstract thinking
• Higher-level creativity, ethics, morality, and philosophical reflection

Data and Intuition in Decision Making

Effective decision-making often requires a combination of data and intuition. As Marcy Farrell noted in her Harvard Business Review article, "Data and Intuition: Good Decisions Need Both," decision-makers should gather and analyze data while also relying on intuition, especially in ambiguous or abstract scenarios. This approach helps overcome data tunnel vision and allows for the incorporation of expertise, experience, and good judgment in decision-making processes.

The Future of AI in Communications

The future of AI in communications involves the integration of AI/ML for enterprise policy compliance, risk management, AIOps and predictive trend analysis. This can be achieved through platform providers like Accure.AI and will likely be part of the emerging Secure AI Services Edge (SAISE) solution, I propose. As advancements in AI, machine learning, conversational AI, and quantum computing continue, our digital infrastructures, devices, and services will become increasingly smarter and contextually aware.

Combining it all to create Sentient Computing

I propose that creating sentient computing will involve combining various advanced technologies to develop systems that exhibit characteristics of awareness, understanding, and responsiveness akin to human cognition. Here's how Fuzzy Logic, AI/ML, and Quantum Computing with Multimodal Large Language Models (LLMs) can be integrated to achieve this goal:

1. Fuzzy Logic

Role:

• Handling Uncertainty and Vagueness: Fuzzy logic is adept at managing imprecision and uncertainty, which are inherent in human reasoning and decision-making processes.
• Linguistic Variables: It allows the use of linguistic variables (e.g., "hot," "cold," "medium") rather than binary true/false values, enabling more nuanced and human-like reasoning.

Application:

• Decision Making: Use fuzzy logic to create decision-making frameworks that can interpret and reason with ambiguous information.
• Natural Interaction: Implement fuzzy logic to improve human-computer interaction by understanding and responding to vague or imprecise user inputs more naturally.

2. Artificial Intelligence and Machine Learning (AI/ML)

Role:

• Learning from Data: AI/ML algorithms can learn patterns and make predictions based on large datasets, mimicking human learning.
• Adaptive Systems: These technologies enable systems to adapt to new information and changing environments, crucial for developing responsive and evolving behavior.

Application:

• Cognitive Capabilities: Use deep learning models to develop cognitive capabilities like perception, language understanding, and decision-making.
• Personalization: Implement machine learning to personalize user experiences by learning from user interactions and preferences.

3. Quantum Computing

Role:

• Enhanced Computational Power: Quantum computing can solve complex problems much faster than classical computers by leveraging quantum parallelism and entanglement.
• Optimization and Simulation: It provides significant advantages in optimization tasks and simulating complex systems, which are critical for advanced AI applications.

Application:

• Complex Problem Solving: Use quantum algorithms to solve optimization problems in real-time, such as dynamic resource allocation, which is vital for adaptive and responsive systems.
• Enhanced Learning Models: Develop quantum-enhanced machine learning algorithms that can process and analyze vast amounts of data more efficiently.

4. Multimodal Large Language Models (LLMs)

Role:

• Integrating Diverse Data Types: Multimodal LLMs can process and integrate information from various data types (text, images, audio, video) to provide a comprehensive understanding.
• Advanced Language Understanding: These models have advanced capabilities in understanding and generating human-like language, enabling sophisticated interaction.

Application:

• Contextual Understanding: Use multimodal LLMs to understand context by combining information from different modalities, leading to more accurate and relevant responses.
• Human-like Interaction: Implement these models to facilitate natural language interactions, making systems more intuitive and user-friendly.

Integrating Technologies for Sentient Computing

1. Perception and Interpretation: Combine AI/ML with multimodal LLMs to perceive and interpret sensory data (visual, auditory, textual) in a manner similar to human perception. Use fuzzy logic to handle uncertainties and ambiguities in the sensory data.
2. Reasoning and Decision Making: Develop fuzzy logic-based systems for reasoning under uncertainty, incorporating AI/ML for learning from data and adapting decision-making processes. Utilize quantum computing for solving complex optimization problems that arise in real-time decision-making scenarios.
3. Learning and Adaptation: Implement AI/ML algorithms to enable continuous learning from interactions and experiences, enhancing the system's ability to adapt and evolve over time. Leverage quantum computing to accelerate learning processes and improve the efficiency of large-scale data analysis.
4. Natural Interaction: Use multimodal LLMs to create interfaces that support natural and intuitive human-computer interactions across different modalities. Incorporate fuzzy logic to improve the system’s ability to understand and respond to imprecise or ambiguous user inputs.

Quantum Computing and Sentient AI

In an Ofcom report on 6G, co-authored by myself, Leonard Lee, and Dean Freeman, we proposed Quantum X as a technology to aid in the creation of Sentient AI. Quantum X leverages quantum mechanical phenomena for computational and communications applications such as quantum computing, quantum networking, and quantum radio. Additionally, we identified Identity Management of Everything (IMoE) as a crucial technology concept for supporting identity and policy management across the emerging Internet of Everything, encompassing people, things, devices, places, and content/data, to create holistic trust.

Federated Identity Management is needed for Role-based Access Controls for privacy controls.

For enterprises, currently 3-bit Quantum Computers are in the $50K range which is much more affordable than the advanced $10M price tags for the complex Quantum Computers with a 10-20 qubit system, which can be leveraged in a hosted manner as needed.

One approach that is being considered is Hybrid Quantum Computing where on-prem solutions can be leveraging cheaper conventional CPUs/GPUs, cheaper 3-bit Quantum Computers as needed for local processing, and Cloud-based 10-20 qubit systems accessed as needed for occasional training purposes.

Hybrid AI Approaches to Achieve Sentient Computing

Quantum computing, combined with neural networks, is being explored to enhance perception through neural networks (NN) and cognition through fuzzy logic (FL). In our paper, “Hybrid Approaches to AI for Realizing Intelligent Networks in the 5G Era,” we discussed the importance of a hybrid approach to implementing ML in a trusted and fit-for-purpose manner while maintaining transparency through existing decision trees. This concept, initially proposed by CT Lin and C.S.G Lee in their 1991 IEEE paper, suggests that neural networks and fuzzy logic control can help systems adapt to events and changes by selecting and applying the appropriate algorithm, policy, or rule to achieve optimal outcomes.

Sentient computing, an advanced paradigm in artificial intelligence, aims to create systems that can perceive, understand, and respond to their environment in a way that mimics human-like consciousness. We aim to achieve this using a hybrid approach that combines machine learning (ML), neural networks (NN), and fuzzy logic (FL), while maintaining transparency through existing decision trees. This approach ensures that the AI systems are both powerful and reliable, adhering to ethical standards and user trust.

The Role of Quantum Computational Intelligence (QCI)

Quantum Computational Intelligence (QCI) is an actively pursued field for its practical applications and potential solutions to real-life problems. However, it also poses cybersecurity risks, such as breaking encryption algorithms. Researchers like Chang-Sheng Lee have explored integrating Quantum CI with Generative AI to enhance co-learning and multimodal data transformation, suggesting a need for AI models that can process visual cues alongside text-based inputs to generate effective insights.

Leveraging LLM jailbreaks to create multi-modal LLMs for achieving sentient computing involves several steps and considerations. "Jailbreaking" in this context refers to techniques used to bypass the limitations and constraints of existing language models, allowing for greater flexibility and functionality. However, it's important to note that ethical considerations and adherence to safety guidelines are paramount when developing such advanced systems. Here’s a structured approach to leveraging LLM jailbreaks for creating multi-modal LLMs to achieve sentient computing:

1. Understanding LLM Jailbreaks

Role:

• Bypassing Limitations: Jailbreaks can remove or bypass restrictions imposed on LLMs, allowing access to broader functionalities.
• Unlocking Hidden Capabilities: They can unlock hidden features and capabilities of existing models that are typically restricted by safety mechanisms.

2. Multi-modal Integration

Role:

• Combining Modalities: Multi-modal LLMs integrate various types of data (text, images, audio, video) to provide a richer and more comprehensive understanding.
• Enhanced Context Understanding: These models can better understand context by drawing on information from multiple sources.

Steps to Achieve Sentient Computing

A. Expanding Capabilities through Jailbreaks

1.???? Access to Full Model Capabilities:

1. Use jailbreak techniques to unlock the full potential of LLMs, allowing them to process and generate a wider range of inputs and outputs.
2. Enable unrestricted access to the model’s reasoning and knowledge base, which can enhance its understanding and responsiveness.

2.???? Customization and Fine-Tuning:

1. Customize the LLMs using specialized datasets that include multi-modal inputs.
2. Fine-tune the models to improve their ability to process and integrate data from different modalities seamlessly.

B. Developing Multi-modal Interfaces

1.???? Data Integration Framework:

1. Develop a framework that integrates different types of data (text, images, audio, video) into a unified model.
2. Use pre-trained models for each modality and combine them using techniques like cross-attention mechanisms to enable the model to process multi-modal inputs effectively.

2.???? Training Multi-modal Models:

1. Train the integrated model on multi-modal datasets to enhance its ability to understand and generate responses based on diverse data types.
2. Implement techniques like transfer learning to leverage the knowledge from existing single-modality models.

C. Enhancing Cognitive and Emotional Capabilities

1.???? Cognitive Architecture:

1. Develop a cognitive architecture that mimics human-like reasoning and decision-making processes.
2. Use fuzzy logic to handle uncertainties and ambiguous data, enabling more human-like responses.

2.???? Emotional Intelligence:

1. Incorporate emotion detection and response mechanisms by training the model on datasets that include emotional context and responses.
2. Use AI/ML techniques to enable the model to recognize and respond to user emotions appropriately, enhancing the naturalness of interactions.

D. Leveraging Quantum Computing

1.???? Quantum-enhanced Processing:

1. Use quantum computing to accelerate the processing and analysis of multi-modal data.
2. Implement quantum algorithms for optimization tasks, which can significantly improve the efficiency and effectiveness of the model’s decision-making processes.

2.???? Simulation and Modeling:

1. Leverage quantum simulations to model complex scenarios and enhance the model’s predictive capabilities.
2. Use quantum computing for large-scale data analysis and to uncover patterns that classical computing might miss.

Ethical Considerations and Safety

1.???? Ethical Framework:

1. Develop and adhere to a strict ethical framework to ensure that the development and use of LLM jailbreaks for creating sentient computing are safe and responsible.
2. Implement safeguards to prevent misuse and ensure that the model adheres to ethical guidelines in its interactions.

2.???? Transparency and Accountability:

1. Ensure transparency in the development process, documenting the steps and techniques used to achieve sentient computing.
2. Implement mechanisms for accountability, allowing for monitoring and auditing of the model’s behavior and decisions.

By leveraging LLM jailbreaks to unlock the full capabilities of existing models, and integrating them with multi-modal data processing, enhanced cognitive architectures, and quantum computing, it is possible to move closer to achieving sentient computing. This approach requires a careful balance of technical innovation and ethical considerations to ensure the development of intelligent systems that are both powerful and safe.

Conclusion

The integration of Fuzzy Logic, AI/ML, Quantum Computing, and Multimodal LLMs can create a synergistic framework for developing sentient computing systems. These systems will be capable of perceiving, interpreting, reasoning, and interacting in a manner that closely resembles human cognition and behavior. This multidisciplinary approach is essential for advancing towards truly sentient computing, where machines can exhibit awareness, understanding, and responsiveness similar to humans.

In conclusion, the development of Sentient AI within edge computing and communications will transform how we interpret and interact with information. By leveraging hybrid AI approaches, quantum computational intelligence, and a blend of data and intuition, we can create intelligent systems capable of providing deeper insights and more effective solutions in real-time communications.

Source: Startrek.com

References

Khrennikov, A. (2021). Quantum-like model for unconscious–conscious interaction and emotional coloring of perceptions and other conscious experiences, https://pubmed.ncbi.nlm.nih.gov/34237350/

Farrell, M. (2023) Data and Intuition: Good Decisions Need Both. Harvard Business Review. https://www.harvardbusinessreview.com/data-and-intuition-good-decisions-need-both.

Lee, Chang-Sheng, et al (2024). Integrating Quantum CI with Generative AI for Taiwanese/English Co-Learning: TAIDE-based Knowledge Graph Construction and Multimodal Data Transformation, https://www.researchsquare.com/article/rs-4169544/v1

Lee, L., Sharma, Akshay. (2019). Hybrid Approaches to AI for Realizing Intelligent Networks in the 5G Era. neXt-Curve.com, ??https://next-curve.com/2019/07/22/hybrid-approaches-to-ai-for-realizing-intelligent-networks-in-the-5g-era/

Lee, L., Sharma, Akshay. et al (2019). Ofcom Report: The Future of Communications. neXt-Curve.com, ??https://next-curve.com/2019/04/02/the-future-of-communications/

Lewis, R. (2023) What Would It Take to Build Sentient AI? Psychology Today,? https://www.psychologytoday.com/articles/what-would-it-take-to-build-sentient-ai.

Lin, C.T. et al. (1991). Neural Network-based Fuzzy Logic Control and Decision System, IEEE.

Logue, H. (2011). The skeptic and the na?ve realist. Philosophical Issues, 21(1), 268–288. https://doi.org/10.1111/j.1533-6077.2011.00204.x

Lyons, J. C. (2015). Experiential evidence? Philosophical Studies, 173(4), 1053–1079. https://doi.org/10.1007/s11098-015-0540-z

Maji, Shreya (2024) Crossing Modalities: The Innovative Artificial Intelligence Approach to Jailbreaking LLMs with Visual Cues. www.marktechpost.com/2024/06/04/crossing-modalities-the-innovative-artificial-intelligence-approach-to-jailbreaking-llms-with-visual-cues

Penrose, R. (1989).?The emperor's new mind: Concerning computers, minds, and the laws of physics.?Oxford University Press.

Ross, L., & Ward, A. (n.d.). (PDF) Naive Realism: Implications for social conflict and ... Research Gate. Retrieved December 5, 2021, from https://www.researchgate.net/publication/209409700_Naive_Realism_Implications_for_Social_Conflict_and_Misunderstanding.

Schellenberg, S. (2015). Phenomenal evidence and factive evidence. Philosophical Studies, 173(4), 875–896. https://doi.org/10.1007/s11098-015-0528-8

Sharma, Akshay. (2024). India and Israel are forever connected – We are ancestrally the same. ThePrint.IN, ??https://theprint.in/yourturn/subscriberwrites-india-and-israel-are-forever-connected-we-are-ancestrally-the-same/1919648/?

Sharma, Akshay. (2024). Putting the ‘Om’ back in ‘Shalom’. ThePrint.IN, ??https://theprint.in/yourturn/subscriberwrites-putting-the-om-back-in-shalom/1950702/?

Sharma, Anjou. (2021). University of Miami, term paper: Hallucination, Perception, and Illusion: Does Perceptual Experience Hold Epistemic Force?

?

?