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标签： AI Interaction

  • Meet the Future of AI: Aexa’s HoloConnect AI

    Meet the Future of AI: Aexa’s HoloConnect AI

    Imagine walking into a store and being greeted by an AI that can see, hear, and respond like a real person. No screens, no scripts, just natural conversation. Aexa’s HoloConnect AI is making this a reality.

    Recently, I came across a video where Aexa’s HoloConnect AI was deployed in a crepe restaurant, interacting with a customer in real-time. It was impressive to see how naturally the AI responded to the customer’s questions and requests.

    This technology has the potential to revolutionize the way we interact with AI in various industries such as hospitality, healthcare, retail, and enterprise. The fact that it can operate without goggles or headsets and run online or offline makes it even more versatile.

    Some of the key features of Aexa’s HoloConnect AI include:

    * Seeing and hearing like a human
    * Responding in real-time
    * Interacting naturally with customers
    * Operating without goggles or headsets
    * Running online or offline

    As I delved deeper into this technology, I was fascinated by the potential applications. For instance, in the healthcare industry, Aexa’s HoloConnect AI could be used to provide patients with personalized health advice and guidance. In retail, it could help customers find products and answer their queries in a more engaging way.

    The future of AI is looking more human-like than ever, and Aexa’s HoloConnect AI is at the forefront of this innovation. If you’re curious about what AI in the real world actually looks like, this is definitely worth exploring.

  • Maintaining Coherence in Large Language Models: A Control-Theoretic Approach

    Maintaining Coherence in Large Language Models: A Control-Theoretic Approach

    I’ve been reading about how large language models can lose coherence over long interactions. It’s a problem that doesn’t seem to be solved by just scaling up the model size or context length. Instead, it’s more about control. Most approaches to using these models focus on the input or data level, but what if we treated the interaction as a dynamic system that needs to be regulated over time?

    This is where a control-theoretic approach comes in. By modeling the interaction as a discrete-time dynamical system, we can treat the model as a stochastic inference substrate and use a lightweight external control layer to inject corrective context when coherence degrades. This approach doesn’t require modifying the model’s weights or fine-tuning, and it’s model-agnostic.

    The idea is to maintain a reference state – like the intent and constraints – and regulate the interaction using feedback. When coherence degrades, corrective input is applied, and when stability is achieved, intervention diminishes. In practice, this can produce sustained semantic coherence over hundreds to thousands of turns, reduce drift without increasing prompt complexity, and enable faster recovery after adversarial or noisy inputs.

    I think this is a fascinating area of research, especially for those working in control theory, dynamical systems, cognitive architectures, or long-horizon AI interaction. The key insight here is that intelligence in long-horizon interaction emerges from regulation, not from raw model capacity. By focusing on external governance and control, we might be able to create more coherent and stable interactions with large language models.