Core Viewpoint - The development of AI is fundamentally changing the software programming paradigm, leading to the emergence of Software 3.0, where natural language prompts are replacing traditional programming code, and large language models (LLMs) are becoming the new programming interface [2][3]. Group 1: Software Evolution - Software 1.0 was characterized by human-written code, while Software 2.0 shifted to neural networks, requiring data preparation and parameter training [2]. - Software 3.0 represents a significant transformation in software development, driven by the rise of large language models [2]. - The transition to Software 3.0 necessitates advancements in hardware, referred to as Hardware 3.0, to support new computational demands [2][3]. Group 2: Hardware Requirements - The dominance of CPUs in Software 1.0 has shifted to GPUs in Software 2.0 due to the need for parallel processing capabilities [3]. - The rapid development of transformer-based models in Software 3.0 has led to the increased adoption of Domain-Specific Architectures (DSA) [5]. - A balance between specialized efficiency and programming generality is crucial for the development of Hardware 3.0 [5][8]. Group 3: Challenges in AI Processor Design - Key challenges in designing AI processors include the lengthy time required to construct AI computing architectures, the prolonged development of instruction systems, and the long cycles for compiling software [9]. - Achieving widespread ecosystem support for self-built instruction systems presents significant hurdles [9]. Group 4: RISC-V and EVAS Architecture - RISC-V's open and modular design allows for the customization of AI acceleration instruction sets, making it a suitable foundation for DSA [8]. - The introduction of the Virtual Instruction Set Architecture (VISA) aims to bridge the gap between AI compilers and backend compilation, enhancing performance optimization [10][11]. - The EVAS architecture integrates VISA with RISC-V microinstructions, ensuring efficient execution of AI computations while improving user programming experience [12][16]. Group 5: Upcoming Innovations - The upcoming chip from the company will support various data types, including INT4, INT8, FP8, FP16, and BF16, with a focus on mixed-precision computing [17]. - The new architecture aims to provide advanced computing solutions for applications in autonomous driving, embodied intelligence, and other edge-cloud industry applications, contributing to the progress towards AGI [17].

Core Viewpoint - The emergence of "Software 3.5" signifies a paradigm shift in human-AI interaction, moving from traditional input-output models to cognitive collaboration, where AI acts as a transparent thinking partner rather than a mere tool [1][8][24]. Group 1: Evolution of Software Paradigms - Software 3.0 is considered outdated as it was based on the limitations of earlier AI capabilities, primarily focused on text generation and simple reasoning [6][20]. - The transition to Software 3.5 reflects a generational leap in AI capabilities, enabling true cognitive collaboration where AI understands not just commands but the underlying motivations and context [6][25]. - The new paradigm emphasizes that intelligence emerges from the interaction between humans and AI, rather than being a solitary attribute of either [7][37]. Group 2: Characteristics of Software 3.5 - Software 3.5 introduces a cognitive collaboration model, allowing for real-time interaction and adjustments, where users can intervene at any point in the AI's thought process [24][26]. - This model supports asynchronous collaboration, enabling AI to continue processing and exploring even when the user is offline, enhancing the overall efficiency of human-AI teamwork [26][27]. - The interface requirements for Software 3.5 necessitate a fundamental redesign to accommodate complex cognitive interactions, moving beyond simple Q&A formats [27][28]. Group 3: New Skills for Developers - Developers in the Software 3.5 era must acquire new skills, including cognitive modeling, intent engineering, and context management, to effectively design interactions that leverage AI's cognitive capabilities [28][30]. - Real-time interaction design and asynchronous collaboration architecture are essential skills for creating systems that allow for dynamic user engagement and cognitive transparency [30][31]. - The evolution from traditional programming to cognitive collaboration signifies that anyone can become a cognitive architect, emphasizing the democratization of software development [31][32].

Core Insights - The article discusses the evolution of software paradigms from Software 1.0 (traditional coding) to Software 2.0 (neural network weights) and now to Software 3.0 (prompts), emphasizing the significance of natural language as a programming language [3][8][11] - It highlights the emergence of Large Language Models (LLMs) as a new type of operating system (LLM OS), reshaping the computing ecosystem and enabling new forms of interaction with AI [5][8] - The article identifies the greatest opportunity in developing "partially autonomous" AI applications, which enhance human capabilities rather than aiming for full automation [10][11] Software Paradigms - Software 1.0 involves traditional coding with specific programming languages, while Software 2.0 utilizes neural networks where data sets are prepared to optimize parameters [3] - Software 3.0 introduces prompts as the programming language, allowing for a more accessible and intuitive way to interact with AI [3][8] LLM as an Operating System - LLMs are compared to a new operating system, where they act as the CPU, with their expanding context window serving as memory, and external tools functioning as peripherals [5][8] - The current state of LLMs is likened to the 1960s computing era, where they are primarily cloud-based and accessed through thin clients [6][8] Opportunities in AI Development - The article emphasizes the need to understand the "mental model" of LLMs, which exhibit human-like characteristics but also have limitations such as hallucinations and memory issues [7][10] - Successful AI applications should focus on creating a feedback loop where AI-generated content is quickly verified by humans, enhancing efficiency [10] Accessibility of Software Development - Software 3.0 lowers the barrier to entry for programming, allowing individuals without formal training to create software through natural language [11] - The future of software design must cater not only to humans but also to intelligent agents, necessitating new standards and tools for better interaction [11][12]