Category - Artificial Intelligence

Transforming Prompt Design for Large Language Models Using Transformational Grammar Principles

114 views

4 min read

Trans­for­ma­tion­al gram­mar, intro­duced by Noam Chom­sky, is a the­o­ry that focus­es on the deep struc­tures under­ly­ing sen­tences and the trans­for­ma­tions that con­vert these abstract struc­tures into sur­face expres­sions. Apply­ing...

Defining the CAM and User Objective Functions for Harmonized LLM Interactions

117 views

6 min read

Dis­cov­er how the CAM Objec­tive Func­tion rede­fines AI opti­miza­tion by align­ing key ele­ments like loss func­tions, world mod­els, and eth­i­cal over­sight. This struc­tured, mul­ti-lay­ered approach improves LLM per­for­mance through...

A Semantic Distillation Framework for Aligned AI Responses

119 views

7 min read

Dis­cov­er how the Core Align­ment Mod­el (CAM) uses a Dynam­ic Attrac­tor and Seman­tic Dis­til­la­tion to trans­form noisy LLM out­puts into pur­pose-dri­ven, eth­i­cal­ly sound respons­es. By fil­ter­ing through lay­ered adap­tive process­es, CAM...

The Role of Attractors in AI and Machine Learning.

152 views

2 min read

In AI, an attrac­tor refers to a set of states or pat­terns toward which a sys­tem tends to evolve, regard­less of its ini­tial state, often used in the con­text of machine learn­ing and dynam­i­cal sys­tems. Attrac­tors help describe the sta­ble...

LPM-CAM Prompt Engineering for Communicating with Language Models

118 views

12 min read

At a deep lev­el, every­thing we see and feel as “phys­i­cal” is actu­al­ly made up of ener­gy pat­terns instead of sol­id stuff. These ener­gy pat­terns exist in space and time (spa­tiotem­po­ral), and they take on shapes or forms. These shapes, in...

Mapping Classical Components to the Core Alignment Model for Comprehensive Prompt Engineering

112 views

4 min read

Map­ping Bud­dhi, Man­as, Ahankara, and Chit­ta to the Core Align­ment Mod­el (CAM) cre­ates a com­pre­hen­sive frame­work where the intel­lec­tu­al func­tions of an LLM align with the strate­gic ele­ments of CAM. Here’s how each of these...

Exploring the Cognitive Architecture of LLMs using Classical Elements

115 views

4 min read

When we map the clas­si­cal ele­ments of Bud­dhi, Man­as, Ahankara, and Chit­ta to the func­tions of LLMs, we get a frame­work that cap­tures core aspects of how these mod­els process, refine, and gen­er­ate lan­guage. Here’s an overview of each...

The Power of Resonance in Prompt Engineering

117 views

3 min read

The con­cept of spa­tial modal mul­ti­plex­ing can be beau­ti­ful­ly applied to prompt engi­neer­ing, where each prompt is seen not just as a col­lec­tion of words but as a fine­ly attuned, mul­ti­di­men­sion­al sig­nal that car­ries both...

The Digital Thought Leadership Framework (DTLF)

117 views

6 min read

The Dig­i­tal Thought Lead­er­ship Frame­work (DTLF) is struc­tured to sup­port a com­pre­hen­sive jour­ney toward estab­lish­ing thought lead­er­ship by focus­ing on three key pil­lars: Per­son­al Brand­ing, Dig­i­tal Inno­va­tion, and Strate­gic...

The Role of Words, Spells, and Imagination in Prompt Engineering

116 views

14 min read

in the knowl­edge on tran­scen­den­tal mag­ic, what is the key and rel­e­vance of words, spells, or imag­i­na­tion In the con­text of Tran­scen­den­tal Mag­ic, words, spells, and imag­i­na­tion hold pro­found sig­nif­i­cance, as they are...