{"id":24253,"date":"2026-06-09T10:53:54","date_gmt":"2026-06-09T14:53:54","guid":{"rendered":"https:\/\/carleton.ca\/scs\/?p=24253"},"modified":"2026-06-10T11:16:31","modified_gmt":"2026-06-10T15:16:31","slug":"categorizing-ai-from-a-computer-science-perspective","status":"publish","type":"post","link":"https:\/\/carleton.ca\/scs\/2026\/categorizing-ai-from-a-computer-science-perspective\/","title":{"rendered":"Categorizing AI from a Computer Science Perspective"},"content":{"rendered":"<p>Education is the most effective tool we have for separating fact from fiction, and fostering genuine AI literacy is a vital step toward a more informed and discerning society. While AI has only recently captured widespread public attention, it has been a serious and evolving field of computer science research for many decades. This distinction matters more than ever, a well informed public is essential to navigating the AI landscape responsibly. Understanding what AI actually is, rather than relying on sensationalized portrayals, empowers people to think critically about AI tech.<\/p>\n<p>The University of Toronto&#8217;s CANHEIT 2023 conference brought together leading voices in technology, including Dr. Hod Lipson of Columbia University, who delivered a keynote address over the course of the event. A prominent figure in computer science, Dr. Lipson&#8217;s work centers on robotics and artificial intelligence, and his talk offered attendees a compelling, Computer Science grounded framework for understanding and categorizing AI.<\/p>\n<p><span style=\"font-weight: 400;\">Dr. Lipson\u2019s waves organizes the AI tech in an order they are invented.<\/span><\/p>\n<p><\/p>\n<div class=\"slideme\"><dl class=\"slideme__list\"><dt class=\"slideme__term\"><a href=\"#slideme-wave-1-rules-based-symbolic-ai\" aria-expanded=\"false\" aria-controls=\"slideme-wave-1-rules-based-symbolic-ai\" class=\"slideme__heading slideme__trigger\">Wave 1: Rules Based \/ Symbolic AI<\/a><\/dt><dd class=\"slideme__description\" id=\"slideme-wave-1-rules-based-symbolic-ai\" aria-hidden=\"true\"><p><\/p>\n<p><strong>Timeframe<\/strong>: 1950&#8217;s\u20131980&#8217;s<\/p>\n<h4>Core idea<\/h4>\n<p>Hand\u2011crafted rules and logic. Systems do exactly what experts encode: if\u2011then rules, symbolic reasoning, decision trees, early planners.<\/p>\n<h4>Classic tech\/achievements<\/h4>\n<ul>\n<li>Early chess engines (Turing\u2011style algorithms, pre\u2011learning engines, even Deep Blue\u2019s core search + handcrafted eval).<\/li>\n<li>Expert systems like MYCIN and DENDRAL.<\/li>\n<li>Classic game \u201cAI\u201d: Pac\u2011Man ghosts, early FPS bots, RTS scripts, finite\u2011state machines, behavior trees.<\/li>\n<li>Limits: No learning; brittle outside predefined situations; cannot improve from data.<\/li>\n<\/ul>\n<p><\/p><\/dd><dl><\/div>\n<div class=\"slideme\"><dl class=\"slideme__list\"><dt class=\"slideme__term\"><a href=\"#slideme-wave-2-analytical-predictive-ai-big-data\" aria-expanded=\"false\" aria-controls=\"slideme-wave-2-analytical-predictive-ai-big-data\" class=\"slideme__heading slideme__trigger\">Wave 2: Analytical \/ Predictive AI \/ Big Data<\/a><\/dt><dd class=\"slideme__description\" id=\"slideme-wave-2-analytical-predictive-ai-big-data\" aria-hidden=\"true\"><p><\/p>\n<p><strong>Timeframe<\/strong>: 1990&#8217;s\u20132000&#8217;s<\/p>\n<h4>Core idea<\/h4>\n<p><span style=\"font-weight: 400;\">Statistical learning on large datasets to predict or rank<\/span><\/p>\n<h4>Tech Examples<\/h4>\n<ul>\n<li><span style=\"font-weight: 400;\">Web searches, credit scoring, fraud detection, ad click\u2011through prediction.<\/span><\/li>\n<li><span style=\"font-weight: 400;\">Classic Google Search core ranking and query understanding: heavy ML on logs and click data.<\/span><\/li>\n<li><span style=\"font-weight: 400;\">Big\u2011data stacks: Hadoop\/HDFS\/MapReduce\/YARN powering data lakes, offline feature generation, large\u2011scale ETL.<\/span><\/li>\n<li><b>\u201cBig data\u201d connection<\/b><span style=\"font-weight: 400;\">: The term <\/span><i><span style=\"font-weight: 400;\">big data<\/span><\/i><span style=\"font-weight: 400;\"> became mainstream in this era, cheap storage + distributed compute + huge logs = feeding predictive models.<\/span><\/li>\n<\/ul>\n<p><b>Limits<\/b><span style=\"font-weight: 400;\">: Great at prediction, weak at perception or open ended content generation; mostly works on structured or engineered features.<\/span><\/p>\n<p><\/p><\/dd><dl><\/div>\n<div class=\"slideme\"><dl class=\"slideme__list\"><dt class=\"slideme__term\"><a href=\"#slideme-wave-3-cognitive-perceptual\" aria-expanded=\"false\" aria-controls=\"slideme-wave-3-cognitive-perceptual\" class=\"slideme__heading slideme__trigger\">Wave 3: Cognitive \/ Perceptual<\/a><\/dt><dd class=\"slideme__description\" id=\"slideme-wave-3-cognitive-perceptual\" aria-hidden=\"true\"><p><\/p>\n<p><strong>Timeframe<\/strong>: 2010&#8217;s<\/p>\n<h4>Core idea<\/h4>\n<p><span style=\"font-weight: 400;\">Deep learning for perception, recognizing patterns in unstructured data: images, audio, video, sensor streams.<\/span><\/p>\n<h4>Tech Examples<\/h4>\n<ul>\n<li><span style=\"font-weight: 400;\">Image classification, object detection, speech recognition, medical imaging diagnostics. Enables applications like driverless cars and general \u201cunderstand what I\u2019m seeing\u201d capabilities.<\/span><\/li>\n<li><span style=\"font-weight: 400;\">Systems can recognize objects and patterns in unstructured data like images, audio, and video (distinguishing cats vs dogs, pedestrians vs road, cancerous vs benign lesions).<\/span><\/li>\n<\/ul>\n<p><b>Limits<\/b><span style=\"font-weight: 400;\">: Perceives well but doesn\u2019t inherently plan, act, or create still largely task\u2011specific.<\/span><\/p>\n<p><\/p><\/dd><dl><\/div>\n<div class=\"slideme\"><dl class=\"slideme__list\"><dt class=\"slideme__term\"><a href=\"#slideme-wave-4-generative-creative-ai\" aria-expanded=\"false\" aria-controls=\"slideme-wave-4-generative-creative-ai\" class=\"slideme__heading slideme__trigger\">Wave 4: Generative \/ Creative AI<\/a><\/dt><dd class=\"slideme__description\" id=\"slideme-wave-4-generative-creative-ai\" aria-hidden=\"true\"><p><\/p>\n<p><strong>Timeframe<\/strong>: Late 2010&#8217;s &#8211; 2020&#8217;s<\/p>\n<h4>Core idea<\/h4>\n<ul>\n<li><span style=\"font-weight: 400;\">Models that can <\/span><i><span style=\"font-weight: 400;\">generate<\/span><\/i><span style=\"font-weight: 400;\"> new content by learning high\u2011dimensional distributions: text, images, code, audio, video, designs.<\/span><\/li>\n<li><span style=\"font-weight: 400;\">Systems can generate new artifacts, text, code, images, video, designed by learning to \u201cfill in the blank\u201d in the media its working on.<\/span><\/li>\n<\/ul>\n<h4>Tech Examples<\/h4>\n<ul>\n<li>Large language models, diffusion models, and other generative architectures.<\/li>\n<li>Modern \u201cAI answers\u201d in search: Gemini\/LLM\u2011powered AI Mode summarizing and synthesizing results on top of classic retrieval.<\/li>\n<\/ul>\n<p><b>Impact<\/b><span style=\"font-weight: 400;\">: Enables creative workflows, code generation, and AI designed artifacts.<\/span><\/p>\n<p><b>Limits<\/b><span style=\"font-weight: 400;\">: Still often ungrounded, can hallucinate, can lack robust physical\/causal understanding without embodiment.<\/span><\/p>\n<p><\/p><\/dd><dl><\/div>\n<div class=\"slideme\"><dl class=\"slideme__list\"><dt class=\"slideme__term\"><a href=\"#slideme-wave-5-embodied-ai-robots\" aria-expanded=\"false\" aria-controls=\"slideme-wave-5-embodied-ai-robots\" class=\"slideme__heading slideme__trigger\">Wave 5: Embodied AI  \/ Robots<\/a><\/dt><dd class=\"slideme__description\" id=\"slideme-wave-5-embodied-ai-robots\" aria-hidden=\"true\"><p><\/p>\n<p><strong>Timeframe<\/strong>: 2010&#8217;s &#8211; 2020&#8217;s+<\/p>\n<h4>Core idea<\/h4>\n<p><span style=\"font-weight: 400;\">Intelligence connected to bodies operating in the physical world, robots that sense, plan, and act in messy, dynamic environments.<\/span><\/p>\n<h4>Tech Examples<\/h4>\n<ul>\n<li><span style=\"font-weight: 400;\">Self\u2011driving cars as embodied systems: perception (Wave 3) + prediction (Wave 2) + generative planning (Wave 4) + low\u2011level control on real hardware.<\/span><\/li>\n<li><span style=\"font-weight: 400;\">Manipulation robots, legged robots, drones, warehouse and logistics robots.<\/span><\/li>\n<\/ul>\n<p><b>Characteristics<\/b><span style=\"font-weight: 400;\">: Must handle dynamics, energy limits, safety, and irreversible physical errors; mistakes are expensive and sometimes dangerous.<\/span><\/p>\n<p><b>Limits<\/b><span style=\"font-weight: 400;\">: Hardware is hard: actuators, power, robustness and scaling beyond well structured environments is slow.<\/span><\/p>\n<p><\/p><\/dd><dl><\/div>\n<div class=\"slideme\"><dl class=\"slideme__list\"><dt class=\"slideme__term\"><a href=\"#slideme-wave-6-sentient-agi-like-ai\" aria-expanded=\"false\" aria-controls=\"slideme-wave-6-sentient-agi-like-ai\" class=\"slideme__heading slideme__trigger\">Wave 6: Sentient \/ AGI Like AI<\/a><\/dt><dd class=\"slideme__description\" id=\"slideme-wave-6-sentient-agi-like-ai\" aria-hidden=\"true\"><p><\/p>\n<p><strong>Timeframe<\/strong>: 2020&#8217;s+<\/p>\n<h4>Core idea<\/h4>\n<p><span style=\"font-weight: 400;\">Systems with explicit self\u2011models that can imagine themselves in the future, reason over those imagined futures, and adapt, taking on \u201cself\u2011awareness\u201d and AGI (Artificial General Intelligence).<\/span><\/p>\n<h4>Tech Examples (early\/prototypes)<\/h4>\n<ul>\n<li><span style=\"font-weight: 400;\">Self\u2011modeling robots that infer their own body plan from sensor data and learn to walk by simulating themselves.<\/span><\/li>\n<li><span style=\"font-weight: 400;\">\u201cMachine scientists\u201d that infer physical laws from data and autonomously generate and test hypotheses.<\/span><\/li>\n<li><span style=\"font-weight: 400;\">This is often associated with Artificial General Intelligence (AGI), which excites and alarms many observers.<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">Dr. Lipson expects this to be achievable and likely sooner than many think, seeing consciousness as an engineering problem rather than mysticism.<\/span><\/p>\n<p><b>Open questions<\/b><span style=\"font-weight: 400;\">: Safety, governance, interpretability, and how to \u201csteer\u201d such systems rather than fear them.<\/span><\/p>\n<p><\/p><\/dd><dl><\/div>\n<p><b>Author<\/b><span style=\"font-weight: 400;\">: Andrew Miles, Sr System Administrator, School of Computer Science, Carleton University<\/span><\/p>\n<p><b>Disclaimer<\/b><span style=\"font-weight: 400;\">: This document does not represent the official views of Dr. Hod Lipson. It is my personal interpretation and summary of his keynote presentation, and any errors or omissions are entirely my own. This posts creation was assisted using generative AI tools.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Education is the most effective tool we have for separating fact from fiction, and fostering genuine AI literacy is a vital step toward a more informed and discerning society. While AI has only recently captured widespread public attention, it has been a serious and evolving field of computer science research for many decades. This distinction [&hellip;]<\/p>\n","protected":false},"author":7,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_relevanssi_hide_post":"","_relevanssi_hide_content":"","_relevanssi_pin_for_all":"","_relevanssi_pin_keywords":"","_relevanssi_unpin_keywords":"","_relevanssi_related_keywords":"","_relevanssi_related_include_ids":"","_relevanssi_related_exclude_ids":"","_relevanssi_related_no_append":"","_relevanssi_related_not_related":"","_relevanssi_related_posts":"","_relevanssi_noindex_reason":"","_mi_skip_tracking":false,"_exactmetrics_sitenote_active":false,"_exactmetrics_sitenote_note":"","_exactmetrics_sitenote_category":0,"footnotes":"","_links_to":"","_links_to_target":""},"categories":[53],"tags":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v21.2 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Categorizing AI from a Computer Science Perspective - School of Computer Science<\/title>\n<meta name=\"description\" content=\"Education is the most effective tool we have for separating fact from fiction, and fostering genuine AI literacy is a vital step toward a more informed\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/carleton.ca\/scs\/2026\/categorizing-ai-from-a-computer-science-perspective\/\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"andrewmiles\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"4 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/carleton.ca\/scs\/2026\/categorizing-ai-from-a-computer-science-perspective\/\",\"url\":\"https:\/\/carleton.ca\/scs\/2026\/categorizing-ai-from-a-computer-science-perspective\/\",\"name\":\"Categorizing AI from a Computer Science Perspective - 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