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Alice - Automated Learning and Intelligence for Causation and Economics

Saw a Telegram post from "MJ12 Speaks @TS_SCI_MAJIC12"@therealmj12 about last night's Trump rally and the crummy RSBN broadcast of it. The reference to [ALICE] piqued my interest and, knowing how Q referenced the word in so many different ways, I checked qresear.ch for [ALICE] and for the acronym's explanation but didn't find any prior posts that related to the AI. TS_SCI_MAJIC12 had mentioned [ALICE] in relation to artificial intelligence in previous MJ12 posts (see picrel). There's also a hotel operations software by the name of ALICE, but it doesn't pertain to Q or Qefforts [ https://www.aliceplatform.com/ ]. Although Microsoft explains the AI's purpose is for economic decision making, I would think it would also extend to decisions about censorship; just different environments and parameters would be needed.

BTW, the search engine Qwant had very few references to Microsoft Alice [ALICE] so it's difficult to find. Also, https://github.com/Microsoft/EconML has an explanation of the relationship between ALICE and EconML (a Python package for estimating heterogeneous treatment effects from observational data via machine learning.).

https://t.me/s/therealmj12

MJ12 Speaks @TS_SCI_MAJIC12

[ALICE] is interfering with RSBN.

8.4Kviews 20:18

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https://www.microsoft.com/en-us/research/group/alice/

Automated Learning and Intelligence for Causation and Economics

Alice is a project to direct Artificial Intelligence towards economic decision making. We are building tools that combine state-of-the-art machine learning with econometrics – the measurement of economic systems — in order to bring automation to economic decision making. The heart of this project is a striving to measure causation: if you want to understand or make policy decisions in a complex economy, you need to know why the system moves the way it does.

Microsoft has a long tradition of work at the intersection of Economics and Computer Science, and this team brings together researchers from across Social Science, AI, and ML. The Alice project allows us to dramatically scale the success that we’ve had in adapting existing ML technology for economic applications and in developing new deep learning architectures for causal inference. All of our research is tied to concrete policy-relevant applications, including work on demand estimation and price optimization, on measuring effectiveness of advertising and sales strategies, and on the design of incentives for desirable healthcare and education outcomes. The goal is to use AI to improve and democratize economic research while taking from economic theory to push the frontier of AI.

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https://www.microsoft.com/en-us/research/group/alice/#!case-study

A Microsoft & TripAdvisor Case Study

Causal AI for Customer Segmentation

TripAdvisor is an online travel research company that empowers people around the world to plan and enjoy the ideal trip. TripAdvisor’s mission is to give travelers a platform to share their experiences to promote transparency in the travel industry and enable informed consumer choices.

The ALICE project at Microsoft Research New England1 applies Artificial Intelligence concepts to economic decision making. To make policy decisions in a complex economy, you need to know why the system moves the way it does. The ALICE team’s innovative tools make this kind of cause and effect analysis more reliable, scalable, and accessible for data scientists without extensive economic training. These tools are collected in the open source, production level quality EconML library.2

TripAdvisor’s ability to provide users with accurate information hinges on other travelers sharing their experiences on the platform. TripAdvisor and MSR joined forces to understand whether a membership model would improve user engagement on the website.

[Go to Part 2]

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Part 2 of 2

https://github.com/Microsoft/EconML

BTW, the search engine Qwant had very few references to Microsoft Alice [ALICE] so it's difficult to find. Also, https://github.com/Microsoft/EconML has an explanation of the relationship between ALICE and EconML

EconML: A Python Package for ML-Based Heterogeneous Treatment Effects Estimation

EconML is a Python package for estimating heterogeneous treatment effects from observational data via machine learning. This package was designed and built as part of the ALICE project at Microsoft Research with the goal to combine state-of-the-art machine learning techniques with econometrics to bring automation to complex causal inference problems. The promise of EconML:

o - Implement recent techniques in the literature at the intersection of econometrics and machine learning

o - Maintain flexibility in modeling the effect heterogeneity (via techniques such as random forests, boosting, lasso and neural nets), while preserving the causal interpretation of the learned model and often offering valid confidence intervals

o - Use a unified API

o - Build on standard Python packages for Machine Learning and Data Analysis

One of the biggest promises of machine learning is to automate decision making in a multitude of domains. At the core of many data-driven personalized decision scenarios is the estimation of heterogeneous treatment effects: what is the causal effect of an intervention on an outcome of interest for a sample with a particular set of features? In a nutshell, this toolkit is designed to measure the causal effect of some treatment variable(s) T on an outcome variable Y, controlling for a set of features X, W and how does that effect vary as a function of X. The methods implemented are applicable even with observational (non-experimental or historical) datasets. For the estimation results to have a causal interpretation, some methods assume no unobserved confounders (i.e. there is no unobserved variable not included in X, W that simultaneously has an effect on both T and Y), while others assume access to an instrument Z (i.e. an observed variable Z that has an effect on the treatment T but no direct effect on the outcome Y). Most methods provide confidence intervals and inference results.

For detailed information about the package, consult the documentation at https://econml.azurewebsites.net/.

For information on use cases and background material on causal inference and heterogeneous treatment effects see our webpage at https://www.microsoft.com/en-us/research/project/econml/

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This talk about simulation is missing the point. Focus on [ALICE] as a tool for censorship and thought manipulation, instead.