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Beyond Language Models

June 2, 2025

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Beyond Language Models

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The Future of AI – From Language to Agents and Time Series Predictions

At Sphere Energy, we believe the landscape of artificial intelligence (AI) is undergoing a profound transformation. Over the past few years, Generative Pre-trained Transformer models used in large language models (LLMs) like GPT-3 and GPT-4 have captured the public's imagination with their impressive language processing capabilities. These models have showcased the power of transformer-based architectures, demonstrating their potential across a wide range of language-based tasks. However, we think the next major evolution in AI lies beyond language alone: it’s in agentic AI and foundation models.

Agentic AI represents the next step in AI’s evolution. Rather than just analyzing data, agentic AI is designed to execute full tasks autonomously, making decisions and taking actions in real time. On January 23rd, 2025 OpenAI released a new agentic application of generative AI, it goes beyond prediction and analysis called OpenAI's Operator, actively executing complex workflows and optimizing systems. These intelligent agents are capable of managing and acting on data across industries, from manufacturing to energy, creating end-to-end solutions for real-time decision-making.

While agentic AI will undoubtedly open new frontiers in automation and task execution, we think the true transformative potential of AI lies in foundation AI models designed to solve complex problems involving timeb series data. At Sphere Energy, we are working together with our partner @NVIDIA and @IBM Research, at the forefront of developing these models, which can process time-dependent data and generalize across industries. By addressing critical challenges in the field of battery technology, foundation AI models are poised to revolutionize industries far beyond energy storage, by not just predicting future events but also optimizing systems and improving decision-making in real-time.

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This white paper explores the next frontier of artificial intelligence beyond language-based models, focusing on agentic AI and foundation models designed for time series data. While large language models like GPT-4 have revolutionized natural language processing, the future of AI lies in autonomous agents capable of executing complex workflows and in general-purpose models that can interpret and act on dynamic, time-dependent data.

Sphere Energy, in collaboration with partners like NVIDIA and IBM Research, is at the forefront of applying these models to battery technology, developing scalable AI systems that predict battery aging, performance, and thermal behavior with unprecedented accuracy. The paper outlines the unique advantages of foundation models—such as contextual understanding, scalability, and data efficiency—while addressing the challenges of embedding, feature extraction, and long-term dependency handling in time series applications.

Real-world examples from weather forecasting, drug discovery, and battery lifecycle prediction illustrate the transformative potential of these technologies. The white paper also emphasizes the critical role of robust data infrastructure and governance in unlocking the full power of AI. Ultimately, it advocates for a future where AI not only processes language but also drives real-world decision-making across industrial domains through intelligent, autonomous, and context-aware systems.

‍Get the full picture — download our white paper now.

This white paper explores the next frontier of artificial intelligence beyond language-based models, focusing on agentic AI and foundation models designed for time series data. While large language models like GPT-4 have revolutionized natural language processing, the future of AI lies in autonomous agents capable of executing complex workflows and in general-purpose models that can interpret and act on dynamic, time-dependent data.

Sphere Energy, in collaboration with partners like NVIDIA and IBM Research, is at the forefront of applying these models to battery technology, developing scalable AI systems that predict battery aging, performance, and thermal behavior with unprecedented accuracy. The paper outlines the unique advantages of foundation models—such as contextual understanding, scalability, and data efficiency—while addressing the challenges of embedding, feature extraction, and long-term dependency handling in time series applications.

Real-world examples from weather forecasting, drug discovery, and battery lifecycle prediction illustrate the transformative potential of these technologies. The white paper also emphasizes the critical role of robust data infrastructure and governance in unlocking the full power of AI. Ultimately, it advocates for a future where AI not only processes language but also drives real-world decision-making across industrial domains through intelligent, autonomous, and context-aware systems.

‍Get the full picture — download our white paper now.

This white paper explores the next frontier of artificial intelligence beyond language-based models, focusing on agentic AI and foundation models designed for time series data. While large language models like GPT-4 have revolutionized natural language processing, the future of AI lies in autonomous agents capable of executing complex workflows and in general-purpose models that can interpret and act on dynamic, time-dependent data.

Sphere Energy, in collaboration with partners like NVIDIA and IBM Research, is at the forefront of applying these models to battery technology, developing scalable AI systems that predict battery aging, performance, and thermal behavior with unprecedented accuracy. The paper outlines the unique advantages of foundation models—such as contextual understanding, scalability, and data efficiency—while addressing the challenges of embedding, feature extraction, and long-term dependency handling in time series applications.

Real-world examples from weather forecasting, drug discovery, and battery lifecycle prediction illustrate the transformative potential of these technologies. The white paper also emphasizes the critical role of robust data infrastructure and governance in unlocking the full power of AI. Ultimately, it advocates for a future where AI not only processes language but also drives real-world decision-making across industrial domains through intelligent, autonomous, and context-aware systems.

‍Get the full picture — download our white paper now.

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