selling Prob.Comp to Bayes.Entrep #224
Replies: 10 comments 8 replies
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1. inference controller, path finding sequential MC algorithmsFact
Angie's Belief
Angie's actionsharing modularized prior and simulated re-action, asking for verification + further action modularized prior🎞️filming george's kidnapped robot simulate re-action to prompt
baseline controller might represent a standard approach to robot navigation, using observed data to infer the robot's position and move toward the goal. In contrast, the robust controller introduces particle filtering, a method that uses a set of "particles" to represent the distribution of possible states, to improve the agent's beliefs about its location, particularly when uncertainties or changes in the environment occur. concept of punctuated equilibrium from evolutionary biology—and by extension, evolutionary psychology—can offer intriguing insights into the processes of adaptation and innovation, such as those found in startup pivots or the development of robust controllers for localization algorithms. Punctuated equilibrium posits that evolutionary development is characterized by long periods of stability (equilibrium) interrupted by short, sudden changes (punctuations). In the context of startups, this can parallel the periods of steady growth or consistent business models that are periodically disrupted by significant shifts or 'pivots' in response to market changes, technological advancements, or new information. Applying this to your friend George's situation with his localization algorithm, the 'Kidnap the robot' animation might metaphorically represent a startup's unexpected shift in the market or a sudden realization that the current business model isn't viable—a 'kidnapping' of the startup's trajectory. George's difficulty with developing an inference controller could be akin to a startup struggling to find a new direction after such a disruption. The process of developing a robust controller could benefit from evolutionary principles in the following ways:
By drawing parallels between these evolutionary mechanisms and the challenges George faces, one can suggest that a more dynamic, adaptive, and resilient approach to algorithm development may emerge. This perspective might not only aid in refining his inference controller but also serve as an inspiration for startups navigating their pivots. |
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1. population modelJaao, Angie crosscat: https://jmlr.org/papers/volume17/11-392/11-392.pdf, http://probcomp.csail.mit.edu/software/crosscat/ |
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to answer charlie's question in halfway persuasion on PC(BE).txt, i prepared three summaries of vikash's talk: ted talk on AI That Understands the World, Using Probabilistic Programmingsummary: Probabilistic programs provide a new symbolic language for expressing uncertain knowledge about possible worlds and the processes to infer them. It's a new medium for knowledge representation. Examples demonstrate probabilistic programming outperforming machine learning systems on tasks such as perceiving 3D structure, reducing errors in perception (compared to Tesla's neural networks), cleaning millions of database records from US Medicare, and forecasting econometric time series. These probabilistic programs seem to understand the world more like humans do, in terms of symbolic representations. answer to your questions on its novelty + success case: Probabilistic Programming Tutorial Part 1, 2Part I
Part II |
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🙋♀️ queryQ1. among three query language Q2. what is the family tree of the three language? if it is BQL to IQL to GSQL what "need" for feature prompted birth of the latter ones? how would this be relevant to startup pivoting decisions? 👨🏽🏫 predicted answerbelow is short comparison of three languages from supply and demand's side (historic order) Supply Side Table
Demand Side Table
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casestudy building with prob.comp(matin and mattieu) on medtech company (medical device with >10times accurate imaging) that aims to find win win strategy that satisfies both ceo, vc, employee |
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meeting with zane. planning business is hard to quantify (adjustment from engineering to business focused)
presenting (prior beliefs on latent values; ability to input) |
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3. posterior space sampling algorithmsdifferent sampling algorithms that has implications for entrepreneurial learning (claude optimal learning modes)
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@chasfine asked definitions of three apis/architecture of vikash (prob.comp) so I guessed below to get @mugamma's verification tl;dr
1. Modular learning from human-scale data:This refers to a learning approach that breaks down complex problems into smaller, manageable components (modules) and learns from data that is comparable in scale to what humans typically encounter. In the context of probabilistic programming, this involves: a) Creating modular probabilistic programs that can be combined and reused across different tasks. This approach contrasts with traditional machine learning methods that often require massive datasets and monolithic models. 2. AI-human comparisons in comparable worlds:This concept involves creating simulated environments or "worlds" where both AI systems and humans can be evaluated on equal footing. In these comparable worlds: a) We define tasks and scenarios that are meaningful for both AI and humans. This approach allows us to make fair comparisons and gain insights into how AI systems can be designed to complement human intelligence. 3. World modeling and decision making:This refers to the process of creating computational models of the world (or specific domains within it) and using these models to make decisions. In the context of probabilistic programming: a) World modeling involves creating probabilistic programs that capture the causal structure, uncertainties, and dynamics of a given environment or problem domain.
d) The probabilistic nature of these models allows for reasoning about uncertainty in both the world state and the outcomes of actions. This approach to world modeling and decision making aims to capture the richness and uncertainty of real-world scenarios, allowing for more robust and adaptable AI systems that can reason in ways more similar to human cognition. cld.Leveraging Probabilistic AI for Entrepreneurial Decision-Making |
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#249
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target audience: BE evaluators
for PC evaluators, i summarized bayes.entrep in sister thread #234
this thread's purpose is two:
to put scalable auto-modeling using Bayesian synthesis and domain specific language on my Bayes in Business supporters' radar 📡
to share my willingness to bet to prob.comp with Vikash and seek path to officially join prob.comp project
format: Bob Horn's information mural with more structured as 𝌭
designing
and image sequence as 🎞️filming
. collaborated output of designer-value (𝌭), artist-vision (🎞️), scientist-understand (🔭), engineer-solve (⚙️) is architecture🏛️.keywords: domain specific language claude, inference controller, reverse engineering natural intelligence, population model, posterior sampling algorithms
𝌭designer part of vikash's prob.prog: world modeling, decision making, modular learning from human scale data, ai-human comparisons in comparable worlds
papers
🎞️filming automatic data modeling mural
Gen Tutorial
🎞️filming gen
angie's action
show Vikash this demo which is
stan
-based startup pivot simulation that tests existing hypothesis in entrepreneurship literature and align with prob.comp's vision to seek path in proceeding the project to develop startup education and prediction tool under computer human interaction expert's umbrellaplan developing education material with Academy of Management conference Professional Development Workshops team (led by Andreas Schwab) e.g. gen-finance gen-finance #177
model entrepreneurial growth IAI. innovative augmented intelligence 🤖 #174 (comment)
🎞️ filming entrepreneurial growth
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