In the ever-evolving landscape of artificial intelligence, the potential for transformative impact is monumental. According to Goldman Sachs, AI has the potential to exert a staggering $7 trillion influence on the global GDP. However, as AI rapidly advances, a new challenge emerges: the delicate balance between generality and reliability in AI reasoning.
In the ever-evolving landscape of artificial intelligence, the potential for transformative impact is monumental. According to Goldman Sachs, AI has the potential to exert a staggering $7 trillion influence on the global GDP. However, as AI rapidly advances, a new challenge emerges: the delicate balance between generality and reliability in AI reasoning.
“Generative AI is just a phase. What’s next is interactive AI” said Mustafa Suleyman, the co-founder of Google’s DeepMind.
In 2017, Jensen Huan, CEO of NVIDIA, stirred up the tech world with his statement about “AI eating the software.” In recent years, AI code generation has gained significant traction, with OpenAI’s custom GPTs leading the way.
In 2017, Jensen Huan, CEO of NVIDIA, stirred up the tech world with his statement about “AI eating the software.” In recent years, AI code generation has gained significant traction, with OpenAI’s custom GPTs leading the way.
The AI transformation is a tangible reality now, and the pressing challenge is to democratize the power of AI for all, not exclusively for tech specialists. Over the last three years, our team at Lexie.ai has been dedicated to developing ‘Thought Graph’—an AI-native programming framework. This framework is grounded in the innovative “Visitor Workflow Model,” which we will explore in depth in this and subsequent blog posts.