샘 알트만은 틀렸다. 온톨로지가 AI의 정답이다 #팔란티어

TL;DR

Ontology is crucial for AI's future, not just intuition.

Transcript

hi my name is Michael I want to throw my hat into the ring on this AI Revolution and join the conversation because I think I have something of value to add um I think there are a lot of misconceptions when it comes to Ai and I'm hoping that maybe this kind of conversation can help elucidate things for some people just as terms of background... Read More

Key Insights

• The current AI conversation is misguided by focusing on scaling superintelligence rather than understanding AI's limitations and proper applications.
• AI systems, like LLMs, lack true understanding and intuition, often making unintuitive decisions due to narrow, specific training.
• Human cognition involves complex understanding beyond vision, which AI currently lacks, as seen in autonomous driving challenges.
• AI's energy demands are unsustainable with current approaches, necessitating a pivot towards more efficient methodologies.
• Ontology, the philosophical study of being, is vital in computer science for defining and organizing data within AI systems.
• A systematic ontology can streamline AI solutions, reducing energy usage and enhancing AI's ability to make logical decisions.
• Palantir is highlighted as a key player in AI, offering tools for data organization and AI orchestration across enterprises.
• AI should be seen as a component within a broader system, requiring specific knowledge and logical relationships to function effectively.

Questions & Answers

Q: Why does Michael believe the current AI conversation is misguided?

Michael believes the current AI conversation is misguided because it focuses on scaling AI to superintelligence without addressing the fundamental limitations and challenges. He argues that AI systems lack true understanding and intuition, often making unintuitive decisions due to their narrow training. This approach leads to unsustainable energy demands and fails to address the need for a more efficient methodology.

Q: What role does ontology play in AI, according to Michael?

Ontology plays a crucial role in AI by providing a framework for defining and organizing data. In computer science, ontology helps create a systematic structure for AI systems to operate within, allowing for more efficient data processing and logical decision-making. Michael argues that ontology can enhance AI's capabilities by offering guardrails of definition and logical relationships, reducing energy usage and improving AI's ability to make informed decisions.

Q: How does Michael view the relationship between AI and human cognition?

Michael views AI as lacking the complex understanding and intuition inherent in human cognition. He points out that human cognition involves more than just vision, as seen in the challenges of autonomous driving, where AI systems struggle to replicate the nuanced decision-making processes of humans. He argues that AI needs to be part of a broader system that includes specific knowledge and logical relationships to function effectively, rather than relying solely on intuition.

Q: What challenges does AI face with energy usage, according to the analysis?

AI faces significant challenges with energy usage due to the current approach of scaling AI systems like LLMs without addressing their inherent limitations. Michael argues that this approach requires enormous amounts of energy, which is unsustainable. He suggests that a pivot towards more efficient methodologies, such as incorporating ontology for data organization and logical decision-making, is necessary to reduce energy demands and improve AI's overall efficiency.

Q: Why does Michael highlight Palantir in the context of AI development?

Michael highlights Palantir as a key player in AI development because of its focus on data organization and AI orchestration. Palantir offers tools that streamline the process of cleaning and organizing data, making it easier to integrate AI solutions across enterprises. By providing a systematic platform for data management, Palantir enables AI systems to operate more efficiently within a defined framework, aligning with Michael's emphasis on ontology and logical relationships.

Q: What is Michael's perspective on AI as a solution versus a component?

Michael argues that AI should not be seen as a standalone solution but rather as a component within a broader system. He emphasizes that AI requires specific knowledge, logical frameworks, and defined relationships to function effectively. By viewing AI as part of a larger system, organizations can leverage its intuitive capabilities while ensuring it operates within a structured and efficient framework, ultimately achieving more meaningful results.

Q: How does Michael propose to enhance AI's decision-making capabilities?

Michael proposes enhancing AI's decision-making capabilities by incorporating ontology to provide a framework for defining and organizing data. This approach allows AI systems to operate within a structured environment, offering guardrails of definition and logical relationships. By leveraging ontology, AI can make more informed decisions, reducing reliance on intuition alone and improving overall efficiency and effectiveness in achieving specific tasks.

Q: What is the significance of object-oriented data organization in AI, according to Michael?

Object-oriented data organization is significant in AI because it allows for the systematic structuring of data within AI systems. Michael argues that organizing data in this way enables AI to understand the relationships between objects and make logical decisions based on defined characteristics and properties. This approach aligns with the principles of ontology, providing a framework for AI to operate efficiently and effectively within a broader system, enhancing its decision-making capabilities.

Summary & Key Takeaways

• Michael argues that the current approach to AI is flawed, focusing too much on scaling intelligence rather than understanding its limitations. He emphasizes the importance of ontology in defining and organizing data, which can enhance AI's decision-making capabilities.

• The discussion highlights how AI systems, like GPT, often lack true understanding, making unintuitive decisions due to their narrow training. Ontology is proposed as a solution to provide AI with a framework for better logical relationships and efficient energy use.

• Michael suggests that companies like Palantir, which focus on data organization and AI orchestration, are crucial for AI's future. He advocates for viewing AI as a piece of a larger system, requiring specific knowledge and logical frameworks to achieve meaningful results.