## 🗄️vikash1. Four Reasonings and Six Steps (REVED) | | Probabilistic Reasoning | Relational Reasoning | Social Reasoning | |---------------------------|------------------------|----------------------|-------------------| | Knowledge about the world | The winner of a match is whichever team is stronger. | A grandfather is the father of one's parent. | Depending on whether they have a bike, people can bike or walk. | | Generative world models | (define won-against (team-1 team-2) (> (team-strength team-1) (team-strength team-2))) | (define grandfather-of? (name_a name_b) (exists (lambda (x) (and (father-of? name_a x) (parent-of? x name_b))))) | (define actions (agent-id) (if (has_bike? agent-id) (list 'is_walking 'is_biking) (list 'is_walking))) | | Observations about the world | John and Mary faced off against Tom and Sue and won. | Charlie is Dana's grandfather. | Alex loves sushi but hates pizza; and he brought his bike to work today. | | Condition statements | (condition (won-against '(john mary) '(tom sue))) | (condition (grandfather-of? 'charlie 'dana)) | (condition (and (loves? 'alex 'sushi) (hates? 'alex 'pizza) (has-bike? 'alex))) | | Questions about the world | Is Mary stronger than Tom? | Which of Charlie's kids is Dana's parent? | What do you think Alex will do? | | Query statements | (query (> (strength 'mary) (strength 'tom))) | (query (filter-tree (lambda (x) (and (child-of? x 'charlie) (parent-of? x 'dana))))) | (query (get_actions 'alex)) | ## 🗄️vikash2. rule vs similarity | Aspect | Rule-based / Bayesian | Similarity-based / Evolutionary | |--------|------------------------|----------------------------------| | Focus | Optimization, precise solutions | Adaptation, flexible solutions | | Decision-making | Careful analysis, planning | Experimentation, action-oriented | | Uncertainty handling | Probabilistic inference | Trial and error, learning from experience | | Generalization | Narrow, specific | Broad, flexible | | Learning process | Convergence to optimal solution | Exploration of multiple possibilities | | Strength in entrepreneurship | Detailed analysis, risk assessment | Quick adaptation, innovation in dynamic environments | | Example algorithm | MIN algorithm | Weak Bayes | | Computational approach | Bayesian inference | Evolutionary algorithms | ## 🗄️vikash3. bayes rule (prior, likelihood, posterior)+ (example, hypothesis ) eight concepts on hypothesis, observation/example of concept, prior of hypothesis, likelihood of observations given hypothesis, posterior, size principle of likelihood (smaller hypothesis receives greater likelihood, more so as n increases), choice principle (natural over logic) of principle, hypothesis averaging for new observation (averaging prediction over all hypothesis weighted by posterior) | Concept | Description | Emoji | |---------|-------------|-------| | Hypothesis space (H) | Space of possible concepts |⬛️ | | Examples (X) | X = {x1, . . . , xn}: n examples of a concept C | ⚫️ | | Prior | p(h): domain knowledge, pre-existing biases | 🟩 | | Likelihood | p(X\|h): statistical information in examples | 🔴 | | Posterior | p(h\|X): degree of belief that h is the true extension of C | 🟦 | | Size principle | Smaller hypotheses receive greater likelihood, exponentially more so as n increases |📌 | | Choice principle | Choice of hypothesis space embodies a strong prior, favoring natural over logical possibilities |🌲>🎄 | | Hypothesis averaging | p(y ∈ C \| X): Probability that C applies to new object y, averaged over all h weighted by p(h\|X) | ⭐️| | Conservation of belief | Total probability must sum to 1; increased belief in some hypotheses necessitates decreased belief in others | ⚖️ | ## 🗄️vikash3. marr's three level | Level | Name | Description | | ----- | ---------------------------- | ------------------------------------------------------------------------------------------------------------------------------- | | 1 | Computational theory | - What are the inputs and outputs to the computation?<br>- What is its goal?<br>- What is the logic by which it is carried out? | | 2 | Representation and algorithm | - How is information represented?<br>- How is information processed to achieve the computational goal? | | 3 | Hardware implementation | - How is the computation realized in physical or biological hardware? | ## 🗄️vikash4. seminar modules | | Name | paper (vikash pick) | notes | | --- | -------------------------------------------- | ----------------------------- | ---------------------------------------------------------------- | | 1 | | | [[module1 scaling behavior of intelligence vs machine learning]] | | 2 | | | [[module2]] | | 3 | ![[Vikash Seminar Modeling Inference 1.txt]] | | [[module3-modeling and inference]] | | 4 | ![[vikash seminar_alex_automate_math.txt]] | | [[Space/Sources/Lab/🐅CompBayes/💻Mansinghka25_9.s/module4]] | | 5 | | | [[module5 automating math]] | | 6 | ![[vikash seminar learning probprog.txt]] | | [[module6]] | | 7 | language | [[📜syn_sem_ctrl_llm_smc]] | | | | image perception as inverse graph | [[📜vikash13_appx_bayes_img]] | |