{"id":686,"date":"2025-08-02T14:54:46","date_gmt":"2025-08-02T20:54:46","guid":{"rendered":"https:\/\/richardkershner.com\/?page_id=686"},"modified":"2025-08-02T14:54:47","modified_gmt":"2025-08-02T20:54:47","slug":"project-gaia","status":"publish","type":"page","link":"https:\/\/richardkershner.com\/?page_id=686","title":{"rendered":"Project GAIA"},"content":{"rendered":"<style>.kb-row-layout-id686_d4021e-ae > .kt-row-column-wrap{align-content:start;}:where(.kb-row-layout-id686_d4021e-ae > .kt-row-column-wrap) > .wp-block-kadence-column{justify-content:start;}.kb-row-layout-id686_d4021e-ae > .kt-row-column-wrap{column-gap:var(--global-kb-gap-md, 2rem);row-gap:var(--global-kb-gap-md, 2rem);max-width:600px;margin-left:auto;margin-right:auto;padding-top:10px;padding-right:0px;padding-bottom:10px;padding-left:0px;grid-template-columns:minmax(0, 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kb-row-layout-id686_d4021e-ae alignfull kt-row-has-bg wp-block-kadence-rowlayout\"><div class=\"kt-row-layout-overlay kt-row-overlay-normal\"><\/div><div class=\"kt-row-column-wrap kt-has-1-columns kt-row-layout-equal kt-tab-layout-inherit kt-mobile-layout-row kt-row-valign-top\">\n<style>.kadence-column686_267a35-66 > .kt-inside-inner-col{padding-top:10px;padding-right:10px;padding-bottom:10px;padding-left:10px;}.kadence-column686_267a35-66 > .kt-inside-inner-col,.kadence-column686_267a35-66 > .kt-inside-inner-col:before{border-top-left-radius:0px;border-top-right-radius:0px;border-bottom-right-radius:0px;border-bottom-left-radius:0px;}.kadence-column686_267a35-66 > .kt-inside-inner-col{column-gap:var(--global-kb-gap-sm, 1rem);}.kadence-column686_267a35-66 > .kt-inside-inner-col{flex-direction:column;}.kadence-column686_267a35-66 > .kt-inside-inner-col > .aligncenter{width:100%;}.kadence-column686_267a35-66 > .kt-inside-inner-col:before{opacity:0.3;}.kadence-column686_267a35-66{position:relative;}@media all and (max-width: 1024px){.kadence-column686_267a35-66 > .kt-inside-inner-col{flex-direction:column;}}@media all and (max-width: 767px){.kadence-column686_267a35-66 > .kt-inside-inner-col{flex-direction:column;}}<\/style>\n<div class=\"wp-block-kadence-column kadence-column686_267a35-66 inner-column-1\"><div class=\"kt-inside-inner-col\"><style>.wp-block-kadence-advancedheading.kt-adv-heading686_5beab5-00, .wp-block-kadence-advancedheading.kt-adv-heading686_5beab5-00[data-kb-block=\"kb-adv-heading686_5beab5-00\"]{margin-bottom:26px;text-align:center;font-size:var(--global-kb-font-size-xl, 3rem);line-height:64px;font-style:normal;border-top-left-radius:20px;border-bottom-right-radius:20px;}.wp-block-kadence-advancedheading.kt-adv-heading686_5beab5-00 mark.kt-highlight, .wp-block-kadence-advancedheading.kt-adv-heading686_5beab5-00[data-kb-block=\"kb-adv-heading686_5beab5-00\"] mark.kt-highlight{font-style:normal;color:#f76a0c;-webkit-box-decoration-break:clone;box-decoration-break:clone;padding-top:0px;padding-right:0px;padding-bottom:0px;padding-left:0px;}@media all and (max-width: 767px){.wp-block-kadence-advancedheading.kt-adv-heading686_5beab5-00, .wp-block-kadence-advancedheading.kt-adv-heading686_5beab5-00[data-kb-block=\"kb-adv-heading686_5beab5-00\"]{font-size:32px;line-height:36px;}}<\/style>\n<h1 class=\"kt-adv-heading686_5beab5-00 wp-block-kadence-advancedheading has-theme-palette-3-color has-text-color\" data-kb-block=\"kb-adv-heading686_5beab5-00\"><strong>Project <\/strong><br>\ud83c\udf86 <strong>G.A.I.A.<\/strong> \ud83c\udf86<\/h1>\n\n\n\n<p class=\"has-text-align-center\"><strong>General Artificial Intelligence Architecture.<\/strong> <\/p>\n\n\n<style>.wp-block-kadence-advancedheading.kt-adv-heading686_ffa56f-51, .wp-block-kadence-advancedheading.kt-adv-heading686_ffa56f-51[data-kb-block=\"kb-adv-heading686_ffa56f-51\"]{margin-bottom:40px;text-align:center;font-style:normal;}.wp-block-kadence-advancedheading.kt-adv-heading686_ffa56f-51 mark.kt-highlight, .wp-block-kadence-advancedheading.kt-adv-heading686_ffa56f-51[data-kb-block=\"kb-adv-heading686_ffa56f-51\"] mark.kt-highlight{font-style:normal;color:#f76a0c;-webkit-box-decoration-break:clone;box-decoration-break:clone;padding-top:0px;padding-right:0px;padding-bottom:0px;padding-left:0px;}<\/style>\n<p class=\"kt-adv-heading686_ffa56f-51 wp-block-kadence-advancedheading has-theme-palette-4-color has-text-color\" data-kb-block=\"kb-adv-heading686_ffa56f-51\">\ud83e\udd8b <br><br><strong>Exploration in Neural Network Design and Mapping<\/strong><\/p>\n\n\n\n<p><strong>GAIA<\/strong> is a custom neural network architecture designed to emulate <strong>human-like focus, attention, and memory<\/strong>. By iteratively analyzing complex inputs such as images, GAIA identifies regions of interest, dynamically resizes them, extracts both supervised and unsupervised features, and integrates this data across multiple cycles to produce <strong>context-aware, intelligent predictions<\/strong>.<\/p>\n<\/div><\/div>\n\n<\/div><\/div>\n\n\n<details class=\"wp-block-details is-layout-flow wp-block-details-is-layout-flow\"><summary>\ud83e\udde0 <strong>Overview &amp; \u2699\ufe0fTechnical Highlights<\/strong><\/summary>\n<p style=\"padding-top:0;padding-right:var(--wp--preset--spacing--40);padding-bottom:0;padding-left:var(--wp--preset--spacing--40)\">      As the next generation of <strong>artificial intelligence systems<\/strong> emerges, the <strong>architecture<\/strong> behind them becomes critical. GAIA explores large-scale, modular AI structures with components like:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li style=\"padding-right:var(--wp--preset--spacing--40);padding-left:var(--wp--preset--spacing--40)\"><strong>Vectorized memory systems<\/strong><\/li>\n\n\n\n<li style=\"padding-right:var(--wp--preset--spacing--40);padding-left:var(--wp--preset--spacing--40)\"><strong>Dynamic window attention mechanisms<\/strong><\/li>\n\n\n\n<li style=\"padding-right:var(--wp--preset--spacing--40);padding-left:var(--wp--preset--spacing--40)\"><strong>Floating-window CNNs<\/strong><\/li>\n\n\n\n<li style=\"padding-right:var(--wp--preset--spacing--40);padding-left:var(--wp--preset--spacing--40)\"><strong>Dual-mode learning (unsupervised &amp; supervised)<\/strong><\/li>\n\n\n\n<li style=\"padding-right:var(--wp--preset--spacing--40);padding-left:var(--wp--preset--spacing--40)\"><strong>Memory-guided decision loops<\/strong><\/li>\n<\/ul>\n\n\n\n<p style=\"padding-right:var(--wp--preset--spacing--40);padding-left:var(--wp--preset--spacing--40)\">     This architecture builds on concepts like <strong>deep feature abstraction<\/strong> and <strong>multi-pass attention<\/strong>, combining them into a flexible, trainable neural core. Rather than relying solely on pre-defined layers, GAIA introduces <strong>custom Keras layers<\/strong> with novel behaviors such as <strong>bell-curve activations<\/strong>, <strong>external training tracks<\/strong>, and <strong>adaptive memory storage<\/strong>.<\/p>\n\n\n\n<p style=\"padding-right:var(--wp--preset--spacing--40);padding-left:var(--wp--preset--spacing--40)\">\u2705 Below is the first published schematic of the GAIA model (main layer):<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"879\" height=\"361\" src=\"https:\/\/richardkershner.com\/wp-content\/uploads\/2025\/07\/general_architecture_layer_V25_07-13.jpg\" alt=\"\" class=\"wp-image-697\" srcset=\"https:\/\/richardkershner.com\/wp-content\/uploads\/2025\/07\/general_architecture_layer_V25_07-13.jpg 879w, https:\/\/richardkershner.com\/wp-content\/uploads\/2025\/07\/general_architecture_layer_V25_07-13-300x123.jpg 300w, https:\/\/richardkershner.com\/wp-content\/uploads\/2025\/07\/general_architecture_layer_V25_07-13-768x315.jpg 768w\" sizes=\"auto, (max-width: 879px) 100vw, 879px\" \/><\/figure>\n<\/details>\n\n\n\n<details class=\"wp-block-mamaduka-toggles wp-block-toggles\"><summary>\ud83d\udc68\u200d\ud83d\udcbb <strong>Creator\u2019s Note<\/strong><\/summary><div class=\"wp-block-toggles__content\">\n<p style=\"padding-right:var(--wp--preset--spacing--40);padding-left:var(--wp--preset--spacing--40)\">     I&#8217;ve been exploring neural networks since <strong>1993<\/strong>\u2014when computing power limited their practical use. Fast forward to today, tools like <strong>TensorFlow<\/strong>, <strong>Keras<\/strong>, <strong>PyTorch<\/strong>, and <strong>Scikit-learn<\/strong> enable exploration of everything from <strong>dense networks<\/strong> to <strong>transformers<\/strong>, <strong>CNNs<\/strong>, and beyond.<br>     Modern architectures like <strong>Large Language Models (LLMs)<\/strong> transform sequences into vector space memory, enabling complex predictions. Parallel networks now handle <strong>where to look<\/strong> and <strong>what to find<\/strong>\u2014inspired by biological attention systems. With GAIA, I aim to integrate these mechanisms into a cohesive, next-gen <strong>general-purpose neural architecture<\/strong>.<\/p>\n<\/div><\/details>\n\n\n\n<details class=\"wp-block-mamaduka-toggles wp-block-toggles\"><summary>\ud83e\uddea <strong>Development Timeline<\/strong><\/summary><div class=\"wp-block-toggles__content\">\n<h3 class=\"wp-block-heading\" style=\"padding-right:var(--wp--preset--spacing--40);padding-left:var(--wp--preset--spacing--40)\">\u2705 <strong>Phase 1: R&amp;D and Concept Design<\/strong><\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li style=\"padding-right:var(--wp--preset--spacing--40);padding-left:var(--wp--preset--spacing--40)\">Brainstorming and architecture drafting<\/li>\n\n\n\n<li style=\"padding-right:var(--wp--preset--spacing--40);padding-left:var(--wp--preset--spacing--40)\">Design of modular flow and component layering<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\" style=\"padding-right:var(--wp--preset--spacing--40);padding-left:var(--wp--preset--spacing--40)\">\u2705 <strong>Phase 2: Custom Keras Layers<\/strong><\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li style=\"padding-right:var(--wp--preset--spacing--40);padding-left:var(--wp--preset--spacing--40)\">Build first custom layer<\/li>\n\n\n\n<li style=\"padding-right:var(--wp--preset--spacing--40);padding-left:var(--wp--preset--spacing--40)\">Implement external training track<\/li>\n\n\n\n<li style=\"padding-right:var(--wp--preset--spacing--40);padding-left:var(--wp--preset--spacing--40)\">Create bell-shaped activation function<\/li>\n\n\n\n<li style=\"padding-right:var(--wp--preset--spacing--40);padding-left:var(--wp--preset--spacing--40)\">Combine bell activation with tracking<\/li>\n\n\n\n<li style=\"padding-right:var(--wp--preset--spacing--40);padding-left:var(--wp--preset--spacing--40)\">Initial testing and verification<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\" style=\"padding-right:var(--wp--preset--spacing--40);padding-left:var(--wp--preset--spacing--40)\">\u2705 <strong>Phase 3: Documentation &amp; Community<\/strong><\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Blog post: Lessons learned during development<\/li>\n\n\n\n<li>\ud83d\udcf7 Video demos and screenshots<\/li>\n\n\n\n<li>Source code, examples, and modular reuse ideas<\/li>\n<\/ul>\n<\/div><\/details>\n\n\n\n<details class=\"wp-block-mamaduka-toggles wp-block-toggles\"><summary><strong>Blogs, Posts and Videos<\/strong><\/summary><div class=\"wp-block-toggles__content\"><\/div><\/details>\n\n\n\n<details class=\"wp-block-mamaduka-toggles wp-block-toggles\"><summary><strong>Lessons learned blog<\/strong><\/summary><div class=\"wp-block-toggles__content\"><\/div><\/details>\n\n\n\n<details class=\"wp-block-mamaduka-toggles wp-block-toggles\"><summary>\ud83c\udfa5 <strong>Demos &amp; Screenshot<\/strong>s<\/summary><div class=\"wp-block-toggles__content\"><\/div><\/details>\n\n\n\n<p><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Project \ud83c\udf86 G.A.I.A. \ud83c\udf86 General Artificial Intelligence Architecture. \ud83e\udd8b Exploration in Neural Network Design and Mapping GAIA is a custom neural network architecture designed to emulate human-like focus, attention, and memory. By iteratively analyzing complex inputs such as images, GAIA identifies regions of interest, dynamically resizes them, extracts both supervised and unsupervised features, and integrates&#8230;<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"om_disable_all_campaigns":false,"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"_kadence_starter_templates_imported_post":false,"_kad_post_transparent":"","_kad_post_title":"","_kad_post_layout":"default","_kad_post_sidebar_id":"","_kad_post_content_style":"default","_kad_post_vertical_padding":"","_kad_post_feature":"","_kad_post_feature_position":"","_kad_post_header":false,"_kad_post_footer":false,"footnotes":""},"class_list":["post-686","page","type-page","status-publish","hentry"],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/richardkershner.com\/index.php?rest_route=\/wp\/v2\/pages\/686","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/richardkershner.com\/index.php?rest_route=\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/richardkershner.com\/index.php?rest_route=\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/richardkershner.com\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/richardkershner.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=686"}],"version-history":[{"count":16,"href":"https:\/\/richardkershner.com\/index.php?rest_route=\/wp\/v2\/pages\/686\/revisions"}],"predecessor-version":[{"id":787,"href":"https:\/\/richardkershner.com\/index.php?rest_route=\/wp\/v2\/pages\/686\/revisions\/787"}],"wp:attachment":[{"href":"https:\/\/richardkershner.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=686"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}