Deep learningAn Inquiry voyage Dr. Joseph Shapirajshapira@netvision.net.il What shall we talk about? About learning lawsand understanding systems What is scientific intuitionWhy motivationWhat is inquiry and creative engineering Dr Joseph Shapira Deep Learning1 Aug. 20152 What is understanding Understanding is building a story that links a new experience and inference to existing cognitive picture of the world -and expands it. Understanding is introducing a new animal to Alices Wonderland Understanding is actively solving new problems. Passive understanding is only the satisfaction of listening to teacher/reading textbook. The trust in the book/ teacher waives the need to question/ inquire. This is rote learning. Dr Joseph Shapira Deep Learning1 Aug. 2015 3 And what does it take to understand? A world of experienced memories and concepts encompassing the personal secured space. Linkage of the new experience to memories having some similarity Note: 95% of the memories are not verbal or written equations but audio, visual, haptics, smell, etc. The multimedia sensory memories provide multiple links to the new experience. Imagination visioning the scenario within the familiar world, examining and comparing. Critical thinking Independent tests by comparison to known scenarios Logic reasoning Imprinting in the cognition ( enhanced by motivation) 4 Dr Joseph Shapira Deep Learning1 Aug. 2015 Intuition Understanding is conceiving a model that predicts the phenomena. In search for the rules affecting the scene, and their balance, the brain prefers to guess and estimate similarity over running an infinite sporadic search. This guess, relying on previous similar experiences, is intuition - The unstructured thinking invoking relevant associations. Every human creation involves intuition,thus differing from machine thinking. 5 Dr Joseph Shapira Deep Learning1 Aug. 2015 When intuition meets a conflict A child is born with the guess instinct, but his intuition is built through accumulation of experiences. When the intuition conflicts with the observed situation: It is recalibrated if the conflict is surmountable. A richer intuition is developed. It is destroyed ( looses credibility) if the conflict is essential. Intuition is essential for understanding. How do we nurture intuition: By creating consecutive surmountable conflicts that tame the intuition to incorporate the new experiences and inferences. 6 Dr Joseph Shapira Deep Learning1 Aug. 2015 Motivation The brain, as a sentinel, observes situations, predicts threat/ opportunity and commands action. Motivation is the prize for performing a challenging command. It is an intrinsic function of a living system ( remember Pavlov?). Why is motivation essential for learning? Motivation energizes the body and brain systems and imprints the experience and the lesson learned. The challenge in science research: exploration, conception and then testing models is rewarded: by expansion of the comprehension of the world ( the enlightening) By being appreciated by close friends. 7 Dr Joseph Shapira Deep Learning1 Aug. 2015 Adapting to a Changing World The Committee on Undergraduate Physics Education Research and Implementation, National Academies An overarching theme has emerged from educational research: Learning improves when students are interactively engaged with their peers, their instructors, and the material being learned, and when they are integrating the newly learned concepts with their previous ideas, whether learned in a formal classroom or in everyday life. 8 http://www.nap.edu/catalog.php?record_id=18312 Dr Joseph Shapira Deep Learning1 Aug. 2015 Einstein and the importance of the mental picture From Einsteins Biography ( Walter Isakson 2007): During his 16s Einstein attended highschool based on Pasteluccis philosophy that encourages the pupils to imagine pictures, extract their own inferences from observations, develop intuitions, conceptual thinking and visual imaging The visual understanding of concepts became a substantial aspect of his genius this kind of visual Gedankenexperiments becamea symbol of his career 9 Dr Joseph Shapira Deep Learning1 Aug. 2015 Physics teaching: passive vs active Objectives:- Productive and creative Skills and values - Expertise in the field of knowledge Passive learning (teacher-board-class) Formal (mathematical)and general definition of laws of nature Exercise: Data (given), laws (known), results ( to be solved) Controlled imaginary scene incorporating few laws. The learning process serves operational skills, not creativity Non-real scenery and parameters impedes estimation, analogy and critic, and does not contribute to scientific intuition 10 Dr Joseph Shapira Deep Learning1 Aug. 2015 Richard Feynmann Nobelist physicist and an esteemed teacher I can't understand anything in general unless I'm carrying along in my mind a specific example and watching it go Understanding from the actual case to abstraction "Physics is not complete We do not know yet how the machine works There is no axioms that reality has to match to, But rules derived from observationsand analysis 11 Dr Joseph Shapira Deep Learning1 Aug. 2015 Inquiry-based/creative learning Inquiring familiar environment Observation (assembled), Controlling rules ( not known, needs creativity), Behavior ( observed) Conceptual inference of laws of nature Critic through analogy, estimation, validity Formal mathematical formulation The process proceeds from personal experience, related to past experience , through imaginative creation, estimation and critique (intuition). Observation of reality involves multiple details and multiple senses, that lead to previous relevant experience and inference. This is how scientific intuition is built. 12 Dr Joseph Shapira Deep Learning1 Aug. 2015 Inquiry of systems 13 Structure Top-down Bottom-up Backward Forward Logical/ causal axis Structural axis Rules Cause and effect Component and structure Dr Joseph Shapira Deep Learning1 Aug. 2015 Analysis of systems Complex systems are not transparent System inquiry is seeking the topology that links cause to the observed effect. Logic/ causal axis (balance and sequence of rules) Observed effect Possible source/ rule Structural/ geometrical axis Observed structurePossible building blocks The course of inquiry is a travel back and forth in both axes: From observation to laws, from the whole to components 14 Dr Joseph Shapira Deep Learning1 Aug. 2015 Physical laws and systems Laws are the building-blocksof the systems of nature, understanding of which is the ultimate goal Dr Joseph Shapira Deep Learning1 Aug. 2015 15 Learning and assimilating laws From experience to the concept From authentic case to generalization From living reality to abstraction Assimilation through the detailed inquiry process Exploring/developing systems The system is defined by the inquiry question, or by its operational objectives. Observation and analysis according to the impact on the systems performance. Intuitive guess is intrinsic in the process. Predictive estimates are essential for convergence. The inquiry tool-box Conservation laws Scaling (validity of) Parametric response ( structure, time-line), and limitsd on validity of the model. Dimensional analysis Sequential , hierarchical modeling, each approximated to the accuracy required by mother model. Analogy Imagination 16 Dr Joseph Shapira Deep Learning1 Aug. 2015 On hypotheses and estimatesThings should be made as simple as possible ,but not any simpler . Albert Einstein "Plurality should not be posited without necessityWilliam of Occam (Between equally plausible hypotheses, the one with minimum posits should survive) When things get too complicated, it sometimes makes sense to stop and wonder: Have I asked the right question? Enrico Bonbieri The buildup of a model is an iterative sequence of guess, estimatea model and test, correct. The guess is intuitive, based on relevant accrued memories. 17 Dr Joseph Shapira Deep Learning1 Aug. 2015 Physics is the platform Physics is the mother of sciences. The childs neighborhood familiarizes him with physical phenomena. A small number of rules enables analysis, modeling and prediction. Learning through authentic examples, and personal experience builds intuition, assessment, critic, and linkage between events. Technology is an integration of physics laws with a strategic goal. Physics learning is the platform to conceptual understanding of systems in nature and technology 18 Dr Joseph Shapira Deep Learning1 Aug. 2015 Training of inquiry-teachers The paradigm change from structured passive teaching to cooperative inquiry is a conceptual transformation, requiring mental readiness. The creation dominates. The process evolves through daring, exposure, cooperation and peers reflection. Exposure, integrity and trust are the key words. The value of the inquiry is important to the teacher as it is to the student. The teacher is empowered and creates, and enlightens the students. The teacher needs a supporting environment,similar to the engineer and the resercher. A reflecting and debating teachers community is needed. The involvement of experienced engineers, is essential, for bridging the cultural gap. 19 Dr Joseph Shapira Deep Learning1 Aug. 2015 Teachers communities The concept of teachers communities is spoken in the pedagogical community,Its implementation does not have a marvelous record. Why teachers communities? The role of a teacher as a curiosity, creativity and critical thinking exciter and empowerer, and knowledge organizer, and as a bridge to the world of science and technology is creative and demanding more than that of an average engineer or scientist. Yet, the teacher does not have a structured frame for communicating, updating, debating, peer reflection as the engineer and scientist have.A science-oriented peer community is needed to revive and maintain the teachers striving for excellence. 20 Dr Joseph Shapira Deep Learning1 Aug. 2015 Achievement goals - summary Assimilation of deep learning Development of critical thinking, analyzing, comparing, estimating and creating. Development of scientific intuition through continuous linking new experience and inference to the mental picture of the world. Broadening the tool-box of concepts and tools Physical, mathematical, engineering Creative development of a system Definition of purpose and its operating environment Acquaintance with the relevant laws, tools and integration processes Project planning for effective risk reduction Experiencing creativity Critical tests 21 Dr Joseph Shapira Deep Learning1 Aug. 2015 Thank youfor your attention Dr Joseph Shapira 0546 607088 j shapi ra@netvi si on.net .i l 22 Dr Joseph Shapira Deep Learning1 Aug. 2015