Core Insights - Thinking Machines, valued at $12 billion, has released its first research blog focusing on overcoming nondeterminism in large language model (LLM) inference [1][51]. - The research emphasizes the challenge of reproducibility in LLM outputs, attributing it to batch non-invariance [3][12]. Group 1: Research Focus - The main theme of the research is "Defeating Nondeterminism in LLM Inference," which addresses why LLM inference results are often non-reproducible [3][8]. - The root cause identified is batch non-invariance, where the output of a single request is influenced by the number of requests in the same batch [14][15]. Group 2: Technical Findings - The research indicates that floating-point non-associativity and concurrent execution lead to different results in LLM inference, but this explanation is incomplete [9][10]. - The study reveals that the lack of batch invariance is the primary issue, as dynamic adjustments to batch sizes during deployment affect the computation order of key operations [15][16]. Group 3: Proposed Solutions - To achieve batch invariance, the research suggests fixing the reduction order in operations like RMSNorm and matrix multiplication, regardless of batch size [18][19]. - The proposed method involves compiling a unified kernel configuration for all input shapes to avoid switching parallel strategies due to batch size changes, even if it results in a performance loss of about 20% [22][21]. Group 4: Experimental Validation - Three types of experiments were conducted to validate the findings: inference determinism verification, performance verification, and real online policy reinforcement learning application verification [25]. - Results showed that using batch invariant kernels led to 1000 identical outputs, achieving deterministic inference, while non-invariant kernels produced 80 different results [27][28]. Group 5: Company Background - Thinking Machines was co-founded by Mira Murati, former CTO of OpenAI, and includes a team of notable figures from the AI industry, primarily from OpenAI [36][38][46]. - The company recently completed a $2 billion seed funding round, setting a record for AI funding, and is now valued at $12 billion despite not having any product yet [51][50].

Group 1 - The development of artificial intelligence (AI) has two clear trajectories: one represented by AIGC (Artificial Intelligence Generated Content) and the other by embodied intelligence [3][6] - AIGC is considered a technological revolution due to its foundational nature, its ability to significantly enhance productivity, and its profound impact on societal structures [10][11] - Embodied intelligence aims to replicate human sensory and action capabilities, but its impact on productivity is seen as limited compared to cognitive intelligence [11][13] Group 2 - The current stage of AI development emphasizes the quality of data and training strategies over sheer data volume and computational power [3][15] - The scaling law, which highlights the importance of large datasets and computational resources, is crucial for both AIGC and embodied intelligence [14][15] - The industry faces challenges in gathering sufficient high-quality data for embodied intelligence, which is currently lacking compared to language models [20][21] Group 3 - The future of embodied intelligence relies on its ability to understand and interact with human emotions, making emotional intelligence a core requirement for consumer applications [5][28] - The development of embodied AI is hindered by the complexity of accurately modeling human experiences and environmental interactions [30][32] - There is a need for innovative data acquisition strategies, such as combining real, synthetic, and simulated data, to overcome current limitations in embodied intelligence training [22][23]

Core Viewpoint - The article discusses the transformative impact of generative AI and embodied intelligence on technology, business, and society, emphasizing the need for a multi-faceted exploration of AI's opportunities and challenges [1]. Group 1: AI Development Trends - The development of AI in recent years has followed two clear trajectories: generative AI (AIGC) and embodied intelligence [5][9]. - Generative AI aims to equip machines with human-like cognitive abilities, while embodied intelligence focuses on enabling machines to mimic human sensory and action capabilities [10][11]. - The current AI landscape highlights the importance of data quality and training strategies over sheer data volume and computational power [6][19]. Group 2: Embodied Intelligence - The next phase of embodied intelligence is expected to involve mind-body coordination, reflecting the philosophical inquiry into how human-level intelligence arises [6][11]. - The application of embodied intelligence in consumer markets hinges on the machine's ability to empathize and understand human emotional needs [6][10]. - There is a significant gap in the data required for embodied intelligence to reach its potential, with current datasets lacking the scale necessary for generalization [7][24]. Group 3: AI as a Technological Revolution - Generative AI is characterized as a technological revolution based on three criteria: foundational nature, exponential productivity enhancement, and profound societal impact [13][14]. - The societal implications of AI's cognitive capabilities are vast, potentially affecting all human activities and leading to concerns about cognitive laziness among humans [14][16]. - In contrast, the impact of embodied intelligence on productivity is seen as limited compared to the cognitive advancements of generative AI [15][16]. Group 4: Data and Model Relationships - The relationship between model algorithms and data is crucial, with algorithms determining the lower limit of model performance and data defining the upper limit [20][21]. - The current focus in AI development is on enhancing data quality and training strategies, particularly in the context of embodied intelligence [19][22]. - The industry faces challenges in data acquisition for embodied intelligence, necessitating innovative approaches to data collection and synthesis [25][26]. Group 5: Future Directions - To overcome the data scarcity in embodied intelligence, strategies such as leveraging real, simulated, and synthetic data are being explored [25][26]. - The development of wearable devices capable of capturing real-world actions could provide a substantial data foundation for embodied intelligence [26]. - The complexity of human experience and environmental interaction presents significant challenges for the data-driven advancement of embodied intelligence [34][35].