Machine Learning - Revision history

Tom: /* Learning */

2025-09-06T00:55:27Z

‎Learning

Tom: /* Educating an ML */

2025-09-06T00:28:51Z

‎Educating an ML

Tom: /* Contrasts with AI */

2025-08-21T23:17:02Z

‎Contrasts with AI

Tom: /* Contrasts with AI */

2025-08-21T23:12:16Z

‎Contrasts with AI

Tom: /* Educating an ML */

2025-08-21T21:02:33Z

‎Educating an ML

Tom: /* Educating an ML */

2025-08-21T20:59:14Z

‎Educating an ML

Tom: /* References */

2025-08-21T20:56:50Z

‎References

Tom: /* Problems */

2025-08-16T23:14:05Z

‎Problems

Tom: /* Problems */

2024-07-23T22:29:02Z

‎Problems

Tom: /* Problems */

2024-03-25T00:33:41Z

‎Problems

← Older revision		Revision as of 00:55, 6 September 2025
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	It is implicit — you “just know” how to do something, even if you can’t fully explain why.		It is implicit — you “just know” how to do something, even if you can’t fully explain why.

		+	Associative learning is the process of linking two previously unrelated pieces of information, such as learning to associate a bell with food in Pavlov's experiments. Intuitive learning, on the other hand, involves drawing conclusions based on feelings, patterns, and an understanding that is not necessarily conscious or explicit, like a musical prodigy who "just knows" what notes to play.
		+
		+	Associative Learning
		+	Definition:
		+	The process of forming connections or relationships between different pieces of information, stimuli, or events.
		+	Mechanism:
		+	It relies on the pairing of items or events to create a learned response. For example, classical conditioning links a neutral stimulus with a naturally occurring one, leading to the neutral stimulus evoking the response on its own.
		+	Examples:
		+	* Pavlov's Dogs: Associating a bell (neutral stimulus) with food (unconditioned stimulus) to the point where the bell alone triggers salivation.
		+	* Classroom Rewards: A student learning that good work (action) leads to high grades (reward).
		+	* Name and Face: Remembering the relationship between a person's name and their face.
		+	Intuitive Learning
		+	Definition:
		+	Learning through an underlying, often unconscious, understanding that provides a "feel" for a subject or skill, like a second nature or "know-how".
		+	Mechanism:
		+	It involves processing information and drawing conclusions without explicit reasoning or step-by-step analysis. This can involve recognizing patterns and making connections to broader experiences.
		+	Examples:
		+	Playing a Musical Instrument: A musician intuitively knowing what notes to play.
		+	Driving a Car: The procedural "know-how" of driving, which becomes almost automatic.
		+	Artistic Skill: An artist feeling their way through a creative process until they achieve a desired outcome.
		+	Key Differences
		+	Conscious vs. Unconscious:
		+	Associative learning often involves conscious effort to link items, while intuitive learning is more often a result of unconscious processing and pattern recognition.
		+	Explicit vs. Implicit:
		+	Associative learning can be explicit (declarative memory), like knowing facts, but it also encompasses implicit memory. Intuitive learning is a type of implicit or procedural memory, the "know-how" that doesn't require conscious recall of steps.
		+	Nature of Information:
		+	Associative learning focuses on relationships between specific, often unrelated, pieces of information. Intuitive learning involves broader patterns, context, and personal experience.

	==Educating an ML==		==Educating an ML==

← Older revision		Revision as of 00:28, 6 September 2025
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	If you get this far, well done. The journey is never over, but you’re in an excellent place and you understand ML as well as many experts.		If you get this far, well done. The journey is never over, but you’re in an excellent place and you understand ML as well as many experts.
		+
		+	==Learning==
		+	This section applies equally well to any sort of [[Artificial Intelligence]]
		+
		+	Machine learning models — especially deep learning — behave more like artificial intuition: they learn patterns from vast data without explicit rules.
		+	* They generalize from examples, make probabilistic predictions, and can “jump” to answers without a human‑style chain of reasoning.
		+	* Advantages: Can solve problems too complex for explicit logic (e.g., image recognition, natural language understanding).
		+	* Limits: Opaque “black boxes,” prone to unpredictable errors, and require large, representative datasets.
		+	* Example: A vision model that recognizes a cat without being told what “ears” or “whiskers” are — it just knows from training.
		+
		+	Experiential Learning Learning occurs through cycles of experience, reflection, conceptualization, and experimentation (Kolb).
		+
		+	intuitive learning is the process of gaining understanding or skill without explicit step‑by‑step instruction or conscious analysis of the underlying rules. It’s:
		+	It is implicit — you “just know” how to do something, even if you can’t fully explain why.
		+

	==Educating an ML==		==Educating an ML==

@@ Line 67: / Line 67: @@
 ==Contrasts with AI==
-{| border="2"
+{| border="1" padding="2" width="812"
 |-
 |Dimension||Artificial Intelligence (AI) || Machine Learning (ML)

@@ Line 83: / Line 83: @@
 |Human Analogy || Simulates cognition, emotion, and intent || Mimics learning from experience or exposure
 |-
-|Ethical Implications	Raises questions of autonomy, trust, and responsibility	Raises questions of bias, fairness, and explainability
+|Ethical Implications || Raises questions of autonomy, trust, and responsibility ||	Raises questions of bias, fairness, and explainability
 |-
 |Examples || Chatbots, autonomous vehicles, AI agents with delegation || Spam filters, recommendation engines, fraud detection

@@ Line 66: / Line 66: @@
 * I claim that our interactions with any [[Artificial Intelligence]] is be more like that of a parent than like that of a programmer.
-contrasts with AI
+==Contrasts with AI==
 {| border="2"
 |-
-|Dimension||Artificial Intelligence (AI)||Machine Learning (ML)
+|Dimension||Artificial Intelligence (AI) || Machine Learning (ML)
 |-
-|Definition	Broad field focused on building systems that simulate human-like intelligence	Subfield of AI focused on algorithms that learn patterns from data
+|Definition || Broad field focused on building systems that simulate human-like intelligence || Subfield of AI focused on algorithms that learn patterns from data
 |-
-|Goal	Emulate reasoning, perception, decision-making, and adaptation	Optimize predictive performance through data-driven models
+|Goal || Emulate reasoning, perception, decision-making, and adaptation || Optimize predictive performance through data-driven models
 |-
-|Scope	Includes ML, symbolic reasoning, planning, robotics, NLP, etc.	Primarily statistical modeling and pattern recognition
+|Scope || Includes ML, symbolic reasoning, planning, robotics, NLP, etc. || Primarily statistical modeling and pattern recognition
 |-
-|Methodology	Can use rules, logic, heuristics, neural nets, or hybrid systems	Uses supervised, unsupervised, or reinforcement learning
+|Methodology || Can use rules, logic, heuristics, neural nets, or hybrid systems || Uses supervised, unsupervised, or reinforcement learning
 |-
-|Agency Model	Can be designed to act autonomously or cooperatively	Typically passive unless embedded in agentic architecture
+|Agency Model || Can be designed to act autonomously or cooperatively || Typically passive unless embedded in agentic architecture
 |-
-|Human Analogy	Simulates cognition, emotion, and intent	Mimics learning from experience or exposure
+|Human Analogy || Simulates cognition, emotion, and intent || Mimics learning from experience or exposure
 |-
 |Ethical Implications	Raises questions of autonomy, trust, and responsibility	Raises questions of bias, fairness, and explainability
 |-
-|Examples	Chatbots, autonomous vehicles, AI agents with delegation	Spam filters, recommendation engines, fraud detection
+|Examples || Chatbots, autonomous vehicles, AI agents with delegation || Spam filters, recommendation engines, fraud detection
 |-
 |}

@@ Line 65: / Line 65: @@
 * An article https://arxiv.org/abs/2303.12712 claims that what GPT-4 does is not very human-like. Calling it intelligence is probably messing with our minds in strange ways.
 * I claim that our interactions with any [[Artificial Intelligence]] is be more like that of a parent than like that of a programmer.
 ==References==

@@ Line 9: / Line 9: @@
 * There is some question, starting in 2023, about whether machines will be programed or educated. Should the model be treated as a general library or as an instance of a particular session? Should the model remember past sessions, or immediately wipe them out?
 * Human bias and other weaknesses are embedded in the training data for AI like GPT. It is unclear how to accommodate for that without just introducing other biases.
 * Johns Hopkins researchers found that algorithms trained on manufactured data can be even better than the real thing for important surgical tasks like X-ray image analysis or giving a robot the ability to detect medical instruments during procedures. <ref name=JH >Catherine Graham, ''SYNTHETIC DATA FOR AI OUTPERFORM REAL DATA IN ROBOT-ASSISTED SURGERY'' (2023-03-20) https://hub.jhu.edu/2023/03/20/synthetic-data-outperform-real-data-robot-assisted-surgery/</ref>
 ===Probabilistic Responses===