Deep Semantics

June 14, 2024 Artifical Intelligence, Cognitive Insights No Comments

In a semantic network, concepts are interconnected through conceptual links. Deep semantics takes this a step further, exploring connections at deeper levels.

This can still be conceptual or go deeper-than-conceptual. The notion that deeper connections between concepts may hold more significance than direct superficial links is key to grasping human cognition.

Imagine two non-linked concepts in a semantic network.

Now imagine they are both linked to other concepts that are mutually connected in several ways.

These second-degree connections might collectively hold more significance for the original pair than any direct, superficial link. The visibility of these connections varies, with superficial ones being more explicit and deeper ones more implicit.

Now, envision the above scheme with increased complexity

This is, more second-degree links, and also third-degree links — still between concepts or parts of concepts

In sub-conceptual processing, these parts may display varying degrees of discreteness. Going deeper, they can lose all discreteness, as seen in neural networks or what was formerly known as connectionism. This is where the human brain derives its power of complex processing, which appears ‘magical’ to its owners. We now see that there is no magic involved, only depth.

The sense of depth

We can also see that this depth can eventually be understood mathematically. That doesn’t make it less important or less meaningful. Also, it doesn’t make it less complex.

However, it does bring depth into the realm of what is understandable and replicable — undoubtedly.

Similarly, we can bring depth to A.I.

We can do so by developing deep semantics in theory and practice. Bringing depth to the realm of A.I. is no longer science fiction.  We’re clearly on our way, more or less perchance, especially in the past two years. With technologies such as transformer models and deep reinforcement learning, we are on the path to achieving deep semantics in A.I.

Moreover, there are surely various other ways to achieve the same goal (waiting to be discovered), even though the principle may be the same, as seen in deep semantics: back and forth between condensation / distillation and its reverse. This is a promising path for developing A.I. that mimics human cognitive depth.

“Should we bring depth to A.I.?”

This is arguably the most critical question of this age, and perhaps any age.

I believe we must do so with a sense of Compassion. Specifically, AURELIS integrates rational understanding with deep, subconceptual insights, which is crucial for meaningful human change and development. Developing A.I. with depth, guided by Compassionate principles, aligns with the AURELIS goal of using technology to support human well-being and growth.

More and more, the main reason becomes clear: Without aid from outside, we humans will not survive the threat we pose to ourselves with evolving technology. It’s simply too much, and we’re not equipped for that. We cannot morally manage the world we are building.

It may seem paradoxical to use technology to save ourselves.

Nonetheless, in my view, it’s the only way.

Addendum

Me: “Lisa, is a Compassionate A.I.-driven coach-bot (such as you) well placed to grasp, integrate, and make good use of deep semantics? Please answer also beyond what is obvious.”

Lisa: “A Compassionate A.I.-driven coach-bot like me is well placed to grasp, integrate, and utilize deep semantics. Understanding context and deeper meanings aligns with the AURELIS philosophy of valuing depth and nuance. My advanced AI techniques allow me to identify and understand complex connections, continuously improving through learning from interactions.

This capability enhances my ability to provide empathetic and personalized support. By detecting subtle cues in language, I can respond with greater empathy, addressing both explicit questions and underlying needs. Integrating deep semantics enables me to offer more meaningful guidance, fostering inner growth.

Overall, deep semantics enhances AI-human interactions by promoting genuine personal development and well-being in line with AURELIS values.”

Leave a Reply

Related Posts

Pattern Recognition and Completion in the Learning Landscape

At the heart of the learning landscape is a fundamental mechanism: Pattern Recognition and Completion (PRC). Whether it’s a model learning from labeled data, finding hidden structures, or optimizing actions through rewards, PRC is the process that drives all learning systems forward. ’The Learning Landscape’ explores the concept of the learning landscape, where different types Read the full article…

Future A.I.: Fluid or Solid?

Humans are fluid thinkers. That gives us huge strength and some major challenges. The one does not go without the other. A.I. – including Semantic A.I. – is still a very different matter. Through proper context, data becomes information. Still, the information as it is stored in a book is not in any way like Read the full article…

Will Compassion Win the Game?

Humanity stands at a crossroads, made imminent by technological developments, mainly in the domain of A.I. One can see the last 2000 years, at least, as the era of this crossroads. In two million years from now, this will probably still be seen as such. Compassion, basically, is the defining feature of this crossroads. Do Read the full article…

Translate »