From GenAI To AGI
Published on January 24th 2025 by Dr DJ Finnegan
As many of us move forward during 2025 in embracing the design and development of cutting-edge Generative Artificial Intelligence (GenAI) systems, leveraging ML, ANN, NLP, XAI, EDGE AI and other methodological approaches, with a focus on anomaly detection, live profiling, and event forecasting & prediction, the accelerating landscape needs a socio-ethical barometer check.
Whilst the AI ecosystem is designed and taught to focus on not any individual task, but rather learning to use a set of tools that allow it to complete multiple different tasks with minimal additional teaching. The nature of the self-learning, self-reflecting, and self-correcting AI entity means that it learns as it evolves, both from experience and human input. This progressive learning moving rapidly into the Self Cognitive space of AGI (General Artificial Intelligence), akin to human brain needs to be harnessed with specific topics in mind. Letting go full complete control may have unwanted repercussions towards mankind.
Nonetheless, benefits of fully autonomous AI are endless. Our AI quest is taking a giant leap from the existing machine learning-centred Large Language Model (LLM) systems, which require large contextual datasets. Self-learning and Autonomous learning AI is not dependent on extensive contextual datasets, thereby reducing the cost of requiring expensive data centres.
The very nature of our AI entity progress along with how it is taught, means that it is constantly evolving and adapting itself cognitive ability to learn from any context, task, and the requirements of its end-users seamlessly.