Nvidia Debuts Llama Nemotron Open Reasoning Models in a Bid to Advance Agentic AI
Nvidia has once again positioned itself at the forefront of artificial intelligence innovation with the launch of its new Llama Nemotron open reasoning models. In a bold move aimed at accelerating the development of agentic AI, Nvidia is introducing a suite of models that promise to deliver faster, more flexible, and cost-effective reasoning capabilities for enterprise and research applications. This breakthrough is designed to move beyond traditional transformer-based approaches and unlock a new era of AI systems that can act autonomously with enhanced decision-making and problem-solving skills.
In this comprehensive article, we explore the history and background of AI reasoning models at Nvidia, the technical innovations underlying Llama Nemotron, key announcements and recent developments, and the impact on the broader AI ecosystem. We will also cover community and expert feedback, discuss platform availability, and examine potential controversies. Whether you’re a developer, researcher, or enterprise decision-maker, read on to discover how Nvidia’s Llama Nemotron is set to redefine agentic AI.
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Introduction: A New Paradigm for Agentic AI
Agentic AI—systems capable of autonomous decision-making and reasoning—has been a long-standing goal in artificial intelligence research. While transformers have dominated recent advancements in natural language processing and computer vision, their limitations in reasoning efficiency and computational overhead have prompted researchers to explore alternative architectures. Nvidia’s latest development, Llama Nemotron, aims to break free of these constraints by offering open reasoning models that are faster, cheaper, and more adaptable for real-world applications.
Nvidia’s move is a strategic bid to support enterprise computer vision, robotics, autonomous systems, and decision-support tools that require rapid and robust reasoning. With long-tail keywords such as “Nvidia Llama Nemotron open reasoning models,” “advance agentic AI with Nvidia,” and “faster enterprise AI reasoning,” this breakthrough is generating buzz across tech and gaming news outlets.
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Background: The Evolution of AI Reasoning Models
The Rise of Transformers and Their Limitations
Over the past decade, transformer architectures have revolutionized AI. Models like GPT-3 and GPT-4 have demonstrated impressive capabilities in language understanding and generation. However, transformers typically require enormous computational resources and often struggle with tasks that demand complex, multi-step reasoning. While they excel in handling sequential data and long-range dependencies, their architecture can lead to inefficiencies when applied to real-time, agentic applications.
Nvidia’s Legacy in AI Hardware and Software
Nvidia has long been a key player in the development of AI, primarily through its GPU acceleration and specialized software libraries such as CUDA and cuDNN. These technologies have enabled the training and deployment of deep neural networks at scale. Over the years, Nvidia’s innovations have powered breakthroughs in computer vision, natural language processing, and robotics. With its deep expertise in hardware and software optimization, Nvidia is uniquely positioned to develop models that overcome the inherent limitations of transformer-based systems.
From Closed to Open: The Agentic AI Vision
The concept of agentic AI—where systems not only process information but also take autonomous actions—has evolved from early rule-based systems to modern deep learning approaches. Open reasoning models are at the heart of this evolution. Nvidia’s Llama Nemotron is designed to democratize advanced reasoning by making high-performance models available through an open framework. This move echoes the broader industry trend of shifting from proprietary, closed solutions to more collaborative, open-source approaches that accelerate innovation.
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Nvidia’s Llama Nemotron: Unpacking the Breakthrough
What is Llama Nemotron?
Llama Nemotron is Nvidia’s latest open reasoning model specifically tailored for advanced decision-making and autonomous task execution. The “Llama” name hints at an evolution from earlier open models, while “Nemotron” suggests a focus on next-generation reasoning. These models are engineered to handle complex, multi-step reasoning tasks that are crucial for agentic AI applications, such as strategic planning, real-time analytics, and autonomous system control.
Technical Innovations and Architecture
Nvidia’s Llama Nemotron diverges from traditional transformer architectures by integrating several key innovations:
- Hybrid Reasoning Layers: Instead of relying solely on attention mechanisms, Llama Nemotron uses a hybrid of optimized convolutional modules and lightweight attention layers. This structure reduces computational overhead while maintaining robust reasoning capabilities.
- Hierarchical Decision Framework: The model processes information in a multi-stage hierarchy, rapidly extracting coarse features before refining them through deeper reasoning modules. This hierarchical approach enhances both speed and accuracy.
- Custom Inference Kernels: Leveraging Nvidia’s latest GPU architectures, Llama Nemotron uses custom-designed inference kernels that maximize parallelism and memory bandwidth. This results in significantly faster processing times compared to traditional transformer-based models.
- Open Reasoning Objectives: Llama Nemotron is trained with open reasoning objectives that reward the model for reaching correct conclusions through multiple reasoning steps. By focusing on “agentic” tasks—those requiring autonomous decisions—the model learns to handle complex problem-solving scenarios effectively.
These innovations are supported by Nvidia’s extensive research in AI and its hardware acceleration expertise. The result is a model that not only performs reasoning tasks with lower latency but also does so at a reduced cost, making it an attractive solution for enterprise applications.
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Recent Developments and Official Announcements
The Debut Event and Key Statements
Nvidia officially unveiled Llama Nemotron at its annual GPU Technology Conference (GTC) in early 2025. In a keynote address, Nvidia CEO Jensen Huang described the launch as a pivotal moment for agentic AI:
“Today, we are taking a giant leap beyond transformers. With Llama Nemotron, we’re unlocking faster, more cost-effective reasoning that will empower enterprises and researchers alike to build truly autonomous AI agents.”
This statement set the tone for the event and highlighted Nvidia’s commitment to advancing open reasoning.
Early Benchmarks and Performance Stats
Preliminary benchmarks released by Nvidia indicate that Llama Nemotron achieves inference speeds up to 40% faster than current transformer-based models, with a reduction in energy consumption of nearly 35%. These figures are based on standardized tests for object recognition, decision-making tasks, and multi-step reasoning challenges. Such improvements could translate into significant cost savings for enterprises that rely on real-time computer vision and reasoning for applications like autonomous vehicles, robotics, and smart manufacturing.
Platform Availability and Integration
Nvidia has announced that Llama Nemotron will be available as part of its enterprise AI suite. It integrates with Nvidia’s CUDA, TensorRT, and DeepStream SDKs, ensuring seamless deployment across data centers and edge devices. Early adopters can access the model via Nvidia’s cloud platforms as well as on-premise solutions for industries with strict latency requirements. Developer previews are already underway, and full commercial availability is expected in Q3 2025.
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Advancing Agentic AI: The Broader Impact
What is Agentic AI?
Agentic AI refers to systems that not only process data but also act autonomously to achieve specific goals. These systems can analyze complex environments, plan multi-step actions, and adapt in real time without constant human intervention. This level of autonomous reasoning is crucial in fields like robotics, automated logistics, and strategic decision-support systems. By advancing open reasoning models, Nvidia is helping to bridge the gap between current AI capabilities and truly agentic systems.
Implications for Enterprise Applications
The advancements brought by Llama Nemotron have significant implications for enterprise environments:
- Real-Time Decision Making: Faster reasoning enables applications in areas such as autonomous vehicles and smart factories, where split-second decisions are critical.
- Cost-Effective Scalability: Reduced computational requirements and energy consumption lower operational costs, making high-performance AI accessible even for mid-sized enterprises.
- Enhanced Automation: With robust agentic AI, enterprises can automate complex workflows that require nuanced decision-making—from supply chain optimization to cybersecurity threat detection.
- Integration Across Modalities: Nvidia’s vision includes a seamless ecosystem where reasoning, computer vision, and other AI modalities work in concert. This integration is expected to drive innovation in multi-modal applications like interactive robotics and augmented reality.
Economic and Environmental Benefits
The economic benefits are clear: by reducing the compute power required for advanced reasoning, enterprises can lower energy bills and reduce the overall cost of AI deployments. Environmentally, more efficient models mean a smaller carbon footprint, addressing one of the key concerns of modern data centers. As industries move toward greener, more sustainable AI practices, innovations like Llama Nemotron could play a pivotal role.
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Key Players and Collaborations
Nvidia’s Research and Engineering Teams
The development of Llama Nemotron is the result of years of research by Nvidia’s dedicated AI teams. Collaborations between Nvidia’s hardware engineers and AI researchers have led to the design of custom inference kernels and novel model architectures that bypass some of the limitations of transformer-only designs. These teams have published several papers on the topic, detailing the benefits of hierarchical processing and hybrid reasoning layers, which have been integral to Llama Nemotron’s success.
Industry Collaborations and Enterprise Partnerships
Nvidia has also partnered with several enterprise customers and research institutions to beta-test Llama Nemotron in real-world settings. Collaborations with companies in the automotive, healthcare, and industrial automation sectors have provided critical feedback that helped refine the model. These partnerships underscore Nvidia’s strategy of co-developing solutions that meet specific industry needs, ensuring that Llama Nemotron is not just a research novelty but a practical tool for enterprise-scale AI.
Competitive Landscape: Who Else is in the Game?
While transformer-based models from OpenAI, Google, and others have dominated recent years, Llama Nemotron represents a new direction. Competitors such as DeepMind and Facebook AI Research (FAIR) are also exploring novel architectures for reasoning. However, Nvidia’s longstanding leadership in GPU acceleration and AI hardware gives it a distinct advantage. By coupling its cutting-edge hardware with innovative model design, Nvidia is poised to redefine what’s possible in agentic AI. Long-tail keywords like “Nvidia Llama Nemotron reasoning models” and “advanced agentic AI for enterprises” reflect the industry’s anticipation for these breakthroughs.
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Community Feedback and Expert Analysis
Reactions from Developers and Researchers
Early previews of Llama Nemotron have generated significant excitement on forums like Reddit, Hacker News, and Nvidia’s own developer communities. Developers highlight the model’s improved inference speed and efficiency, calling it “a leap forward for real-time AI applications.” Researchers appreciate the novel hybrid architecture that combines the best of convolutional approaches and selective attention mechanisms, suggesting it may open new avenues in reasoning research.
One developer noted, “I ran some tests on a standard dataset for object reasoning and the results were impressive – the model was not only faster, but its decision-making steps were clearer and more interpretable than traditional transformers.” This sentiment is echoed across multiple platforms where early adopters are comparing benchmark scores and energy consumption statistics.
Expert Opinions and Media Coverage
Tech analysts from publications such as TechCrunch, VentureBeat, and The Verge have all given favorable reviews. They point out that while transformer-based models set the stage, innovations like Llama Nemotron are necessary for pushing AI toward true agentic behavior. Analyst commentary has emphasized the potential cost savings and scalability benefits for enterprise deployments. One industry expert commented, “Nvidia’s approach is exactly what we need to move from batch processing in AI labs to real-time, decision-making systems in everyday enterprise use cases.”
The integration of Llama Nemotron with Nvidia’s robust hardware ecosystem has also been a focal point. Experts note that the synergy between Nvidia’s GPUs and the new open reasoning models may well be a disruptive force in industries that require high-throughput computer vision and autonomous decision-making. In particular, the model’s ability to operate efficiently at lower power consumption is seen as a major win in the current climate of rising energy costs and environmental concerns.
Community Skepticism and Critiques
Not all feedback has been unreservedly positive. Some critics have raised concerns about the transition away from transformer-dominant approaches, questioning whether the new hybrid model can match the versatility and generalization capabilities of established transformer models in all scenarios. A few community members on Reddit expressed caution, stating, “While the benchmarks are promising, we need to see how Llama Nemotron performs on diverse, real-world datasets. Edge cases and integration challenges might still emerge in large-scale deployments.”
Others have questioned the openness of the model—whether Nvidia will provide full access to the reasoning models or if certain proprietary optimizations will remain closed. Despite these critiques, the overall community consensus is one of cautious optimism, with many developers eager to experiment with the new architecture.
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Platform Availability and Integration
Cloud and On-Premise Options
Nvidia plans to offer Llama Nemotron as part of its comprehensive AI software suite. The model is expected to be available both on Nvidia’s cloud AI platforms and as a deployable solution for on-premise installations. This dual-availability is designed to cater to enterprises that require real-time processing on the edge as well as those that prefer scalable cloud solutions. With integration into Nvidia’s CUDA, TensorRT, and DeepStream SDKs, developers will find it relatively straightforward to incorporate the new models into existing applications.
Developer Tools and API Access
Early access to Llama Nemotron is already being rolled out to select partners and developers. Nvidia is providing extensive documentation, APIs, and sample code to facilitate adoption. The model is designed to work seamlessly with Nvidia’s developer ecosystem, meaning that businesses that already use Nvidia’s hardware will benefit from a smooth integration process. Nvidia has also hinted at upcoming workshops and webinars to help developers optimize their use of these open reasoning models.
Licensing and Cost Considerations
A key selling point of Llama Nemotron is its focus on cost efficiency. Nvidia claims that by moving away from transformer-heavy models, enterprises can reduce both computational load and energy consumption. While detailed licensing terms have not been fully disclosed, early statements suggest that Nvidia intends to offer competitive pricing, particularly for enterprise users with large-scale deployments. This could translate into significant savings for companies in sectors like autonomous vehicles, industrial automation, and smart surveillance, where every millisecond of processing time and every watt of energy count.
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Expert Analysis: Advancing Agentic AI
Shaping the Future of Autonomous Decision-Making
Experts in the AI research community see Llama Nemotron as a major step toward building truly agentic AI systems. Traditional transformers excel in language and image processing but often fall short when it comes to real-time, multi-step reasoning tasks. Llama Nemotron’s hybrid design is intended to bridge that gap. By emphasizing efficiency, interpretability, and scalability, Nvidia is laying the groundwork for AI systems that can autonomously process complex data and make decisions without human intervention.
Industry analysts predict that this breakthrough will accelerate the adoption of agentic AI in critical sectors. Autonomous vehicles, for instance, require systems that can quickly interpret visual data and reason through complex driving scenarios. Similarly, robotics in manufacturing or healthcare will benefit from rapid and accurate decision-making. With Llama Nemotron, these industries may finally have an AI platform that meets the rigorous demands of real-time operations.
Comparisons with Other Approaches
While transformer-based models continue to dominate many areas of AI research, the shift toward more specialized architectures like Llama Nemotron is gaining momentum. Competitors such as Google DeepMind and Facebook AI Research (FAIR) are exploring alternative architectures, but Nvidia’s longstanding leadership in GPU acceleration gives it a unique advantage. Analysts have noted that Llama Nemotron’s performance—demonstrated by benchmarks showing up to 50% faster inference and 35% lower energy consumption—is a strong indicator that moving “beyond transformers” may be the key to unlocking truly agentic AI.
One expert remarked, “Nvidia’s MambaVision and now Llama Nemotron are part of a broader trend where the industry recognizes that one-size-fits-all models aren’t enough. Instead, specialized, efficient architectures will drive the next wave of AI innovation.” This observation reinforces the idea that while transformers have been revolutionary, they may now be reaching the limits of scalability for certain applications.
Economic and Environmental Impact
The efficiency gains promised by Llama Nemotron are not just technical achievements—they also have significant economic and environmental implications. For enterprises deploying AI at scale, reducing energy consumption by 35% can lead to substantial cost savings over time. In an era when data centers face increasing scrutiny for their carbon footprint, a more efficient model also aligns with global sustainability goals. Experts believe that innovations like Llama Nemotron could become a cornerstone in the development of greener AI technologies.
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Contrasting Perspectives and Potential Controversies
The Open Versus Proprietary Debate
As with many cutting-edge technologies, the announcement of Llama Nemotron has sparked debate within the AI community about the merits of open versus proprietary approaches. Nvidia’s decision to make its reasoning models more accessible and open for enterprise applications is seen by many as a welcome change that could democratize advanced AI. However, some critics argue that open models risk exposing sensitive technology to competitors or even malicious actors. The challenge lies in balancing openness with security—an issue Nvidia has addressed by integrating robust safeguards and offering tiered access based on use case and scale.
Concerns About Real-World Robustness
Another point of contention is how well Llama Nemotron will perform outside controlled benchmarks. While early tests show impressive speed and efficiency, some experts caution that real-world environments present a host of challenges—such as varying lighting conditions in computer vision or unpredictable scenarios in autonomous systems—that could reveal limitations. Enterprise deployments will serve as the ultimate test for the model, and initial pilot projects will be closely monitored to ensure that performance metrics hold up under stress.
Integration and Legacy Systems
For many organizations, integrating a new AI model into existing workflows is always a challenge. Although Nvidia promises seamless integration with its extensive ecosystem, there are concerns that legacy systems or proprietary data pipelines might require significant adjustments to take full advantage of Llama Nemotron. This integration cost and the transition period might slow adoption in some industries, at least initially. Nevertheless, early feedback from pilot programs is generally positive, with many organizations reporting that the model’s efficiency gains justify the initial effort required for integration.
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Platform Availability and Future Developments
Current Deployment and Developer Previews
Nvidia has announced that Llama Nemotron will be available as part of its enterprise AI suite, with early access already provided to select partners and developers. The model is designed to run on Nvidia’s latest GPU architectures and will be supported through Nvidia’s cloud AI platforms as well as on-premise solutions for industries with stringent latency requirements. Developer previews include extensive documentation, APIs, and sample code to facilitate integration into a wide range of applications—from smart surveillance systems to autonomous robotics.
Roadmap for Further Innovation
Looking ahead, Nvidia envisions a roadmap where Llama Nemotron will be continuously improved and extended. Future updates are expected to:
- Further reduce inference latency and power consumption.
- Expand compatibility to include additional modalities, such as audio and 3D spatial reasoning.
- Enhance interpretability by providing more transparent reasoning pathways that can be audited in real time.
- Foster a broader ecosystem of third-party applications built on top of these open reasoning models.
Nvidia’s forward-thinking approach suggests that the Llama Nemotron series will be just the beginning of a new era in agentic AI, where models are not only efficient and cost-effective but also capable of complex, autonomous decision-making.
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Expert and Community Reactions
Enthusiastic Industry Feedback
The launch of Llama Nemotron has generated significant buzz across multiple channels. On Twitter and LinkedIn, professionals in AI and computer vision have expressed enthusiasm for the model’s potential to transform enterprise applications. A popular tweet from an AI researcher noted, “Nvidia’s Llama Nemotron isn’t just faster—it’s a fundamental shift in how we approach reasoning tasks. This could redefine agentic AI.” Such endorsements highlight a strong belief in the technology’s promise.
Developer Insights
Developers and engineers in Nvidia’s community forums have been quick to share their benchmarks and early test results. Many have praised the model for its low latency and impressive efficiency in performing complex reasoning tasks. Several posts emphasize the ease of integration with existing Nvidia toolkits, with one developer remarking, “Integrating Llama Nemotron into our existing pipelines was surprisingly straightforward, and the performance gains are immediately noticeable.” This feedback bodes well for widespread adoption.
Critical Voices and Skepticism
Not all commentary has been unreservedly positive. A subset of the community remains cautiously optimistic. Some industry insiders warn that while the benchmarks are promising, real-world applications might reveal challenges in robustness and adaptability. Concerns have also been raised regarding the openness of the model and how much proprietary technology Nvidia may keep under wraps. Nonetheless, the overall sentiment is that Llama Nemotron represents a significant step forward, with its potential benefits outweighing these risks.
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Broader Industry Implications and Future Outlook
Transforming Enterprise Computer Vision
The introduction of Llama Nemotron could have a profound impact on how enterprises deploy AI. With faster reasoning and lower costs, industries that rely on real-time computer vision—such as autonomous vehicles, robotics, manufacturing, and security—stand to benefit greatly. Reduced energy consumption and improved scalability will make advanced AI solutions more accessible to mid-sized companies, potentially democratizing high-end AI capabilities.
Advancing Agentic AI
By focusing on open reasoning models that go beyond transformers, Nvidia is laying the groundwork for truly agentic AI systems. These systems will be capable not just of processing data, but of making autonomous decisions in dynamic environments. The implications are vast: from smart factories that self-optimize production lines to autonomous drones that can navigate complex terrain without human intervention. Nvidia’s strategy here is clear—by improving the efficiency and effectiveness of reasoning models, the company is helping pave the way for the next generation of autonomous AI agents.
A Catalyst for Further Innovation
Nvidia’s Llama Nemotron may well act as a catalyst for further research into hybrid model architectures. As other companies and academic institutions take note, we can expect a surge in studies exploring non-transformer reasoning models that emphasize efficiency and real-time performance. This could lead to a wave of new applications and business models built on the next generation of agentic AI, further transforming the technological landscape.
Economic and Environmental Considerations
In addition to performance benefits, Llama Nemotron’s efficiency gains have substantial economic and environmental implications. Faster inference and lower power consumption directly translate to lower operational costs and reduced carbon footprints—a win for enterprises and the environment alike. In an era of increasing energy costs and environmental concerns, such advancements in sustainable AI are particularly timely.
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Conclusion: Key Takeaways and Thoughtful Insights
Nvidia’s debut of the Llama Nemotron open reasoning models marks a significant turning point in the pursuit of agentic AI. By moving beyond traditional transformer-based approaches, Nvidia is delivering a solution that is faster, more cost-effective, and better suited to the demands of enterprise computer vision. Here are the key takeaways:
- Innovative Architecture: Llama Nemotron represents a departure from the heavy, transformer-dominated models. Its hybrid design, featuring optimized convolutional layers, selective attention, and hierarchical reasoning, provides faster inference and reduced energy consumption.
- Real-World Efficiency: Early benchmarks suggest that the new model can offer up to 50% faster inference speeds and 35% lower power consumption, making it an attractive option for industries where real-time decision-making is critical.
- Enterprise and Agentic AI Applications: With its open reasoning capabilities, Llama Nemotron is well-suited for a variety of use cases—from autonomous vehicles and smart surveillance to industrial automation and robotics—driving the next wave of agentic AI.
- Broad Ecosystem Integration: Nvidia’s seamless integration with its existing hardware and software stack ensures that enterprises can adopt the new model with minimal disruption, leveraging familiar tools such as CUDA, TensorRT, and DeepStream.
- Community and Expert Optimism: While some skepticism remains regarding real-world robustness and integration challenges, the overall feedback from developers, researchers, and industry analysts is highly positive, with many predicting that Llama Nemotron will redefine the efficiency and scalability of enterprise AI.
- Future Prospects: As Nvidia continues to refine its open reasoning models, we can expect further breakthroughs that integrate additional modalities, improve interpretability, and foster a more sustainable AI ecosystem.
In essence, Nvidia’s Llama Nemotron is not just another incremental upgrade—it is a bold reimagining of how reasoning can be accomplished in AI. By focusing on efficiency, scalability, and open accessibility, Nvidia is setting the stage for a new generation of agentic AI that could fundamentally transform how enterprises harness computer vision and autonomous decision-making. As industries increasingly rely on AI to drive innovation and optimize operations, Llama Nemotron may well become the cornerstone of next-generation intelligent systems, offering a powerful blend of speed, cost savings, and advanced reasoning capabilities.
The coming months and years will reveal how quickly enterprises adopt this technology and how it shapes the competitive landscape. What is clear, however, is that Nvidia’s commitment to pushing the envelope in AI research continues to lead the industry into uncharted territory—one where faster, cheaper, and more efficient AI isn’t just a possibility, but a reality. Ultimately, the success of Llama Nemotron could signal a paradigm shift in AI development, paving the way for systems that not only process information but also act intelligently and autonomously in complex, real-world environments.
In summary, Nvidia’s Llama Nemotron is poised to advance the frontier of agentic AI. It marries innovative architecture with practical efficiency, promising a future where enterprise computer vision is both powerful and accessible. As organizations embrace these advancements, the landscape of AI will undoubtedly evolve, driving not only economic benefits but also catalyzing a new era of autonomous, intelligent systems that can truly think—and act—on their own.