Researcher | Lifelong Learning AI | Neuromorphic Computing

ASHLEY GREEN

I specialize in leveraging GPT-4 fine-tuning to develop lifelong learning AI systems that mitigate catastrophic forgetting through neuromorphic hardware-inspired approaches and dynamic memory architectures. My research focuses on enabling AI to continuously acquire new knowledge while retaining and optimizing prior learning, fostering the development of efficient and robust long-term intelligent systems.

By integrating deep learning, brain-inspired computing, and reinforcement learning, I aim to advance AI applications in personalized education platforms and adaptive cybersecurity. I am passionate about the potential of federated learning, generative memory models, and self-supervised learning in enhancing AI adaptability and sustainable learning, striving to contribute both theoretical and practical advancements to the future of intelligent systems.

Integration of Neuromorphic Principles

Provide a blueprint for incorporating biological plasticity and dynamic memory architectures into AI systems, improving their ability to handle incremental tasks

Applications in Society

Enable personalized education platforms that adapt to individual learning needs and adaptive cybersecurity systems that evolve to counter emerging threats, addressing critical societal challenges.

A laboratory setting with several advanced machines, including a prominent white machine labeled 'SINIC-TEK' featuring a large monitor on top displaying technical graphics. Beside it, another machine displays the label 'GWEI' with a smaller screen. The setting has a high-tech and industrial appearance with a polished green floor and metallic curtains.
A laboratory setting with several advanced machines, including a prominent white machine labeled 'SINIC-TEK' featuring a large monitor on top displaying technical graphics. Beside it, another machine displays the label 'GWEI' with a smaller screen. The setting has a high-tech and industrial appearance with a polished green floor and metallic curtains.
Advancement of Lifelong Learning AI

Demonstrate how GPT-4 fine-tuning can enhance the development of AI systems capable of continuous learning without catastrophic forgetting, setting a new standard for adaptability.

Contribute to the development of AI systems that are not only technically advanced but also ethically sound and aligned with societal values.

Ethical and Responsible AI
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A stylized representation of a computer chip is integrated into a black and teal geometric background. The chip features a minimalist, futuristic face design along with the text 'CHAT GPT' in bold, glowing teal. The background consists of repeating black shapes outlined in teal, creating a high-tech, digital atmosphere.
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A laptop displaying a coding interface with lines of JavaScript code on the screen. Below the laptop, there is a black and orange gaming mouse with a honeycomb pattern, and its logo is illuminated. The laptop keyboard is backlit, emitting a soft purple glow.
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Increasing complexity and data-generation rates in cyber-physical systems and the industrial Internet of things are calling for a corresponding increase in AI capabilities at the resource-constrained edges of the Internet. Meanwhile, the resource requirements of digital computing and deep learning are growing exponentially, in an unsustainable manner. One possible way to bridge this gap is the adoption of resource-efficient brain-inspired “neuromorphic” processing and sensing devices, which use event-driven, asynchronous, dynamic neurosynaptic elements with colocated memory for distributed processing and machine learning. However, since neuromorphic systems are fundamentally different from conventional von Neumann computers and clock-driven sensor systems, several challenges are posed to large-scale adoption and integration of neuromorphic devices into the existing distributed digital–computational infrastructure. Here, we describe the current landscape of neuromorphic computing, focusing on characteristics that pose integration challenges. Based on this analysis, we propose a microservice-based conceptual framework for neuromorphic systems integration, consisting of a neuromorphic-system proxy, which would provide virtualization and communication capabilities required in distributed systems of systems, in combination with a declarative programming approach offering engineering-process abstraction. We also present concepts that could serve as a basis for the realization of this framework, and identify directions for further research required to enable large-scale system integration of neuromorphic devices.

Innovation

Hybrid algorithms for quantum and classical optimization.

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