Machine Learning X Doing

Machine Learning X Doing

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Machines that learn as we do. Solving the human condition and the economy so we can live better lives

10/24/2024

You never outgrow the challenge of learning.
You just discover its sweetness.
Knowledge isn't a destination, it's a journey.
Because stagnation is the real enemy.
Not the difficulty of the path ahead.
But the comfort of standing still.
Ready to start your AI journey?

Machine Learning X Doing

06/01/2024

Where AI meets ROI in healthcare.

Soccer Video Game Economics – Machine Learning X Doing 04/16/2024

How can leading soccer video game franchises have dramatically improved player experiences with economics? Read our .
Machine Learning X Doing proposal and get in touch: https://machinelearningxdoing.com/insights/video-game/soccer-video-game-economics/

Soccer Video Game Economics – Machine Learning X Doing Soccer Video Game Economics Proposal for Enhancing Soccer Sports Game Design Through Randomized Controlled Trials and Economic Theory Introduction The gaming industry is rapidly evolving, and with it, the expectations of players are also rising. In the realm of soccer sports games, major franchises,...

12/25/2023

Merry Christmas to you and yours!

10/13/2023

‪Game theory for winning video games: read our paper for rigorous economics for video game design and economic impact: ‬

https://machinelearningxdoing.com/simplicity-in-video-games-theory-and-applications/‬

10/12/2023

Don’t know what to do when facing data that almost looks like a waterfall? Not sure how to make sense of it all when the data is constantly coming in? The good news is, linear contextual bandits can be used to design randomized controlled trials. Unfortunately, the existing methods have limitations when analyzing live-streamed data. At Machine Learning X Doing, we have created a more robust approach for linear contextual bandits in real-time data applications, using regression discontinuity design for estimation & exploration.

Read our paper:
https://machinelearningxdoing.com/livestream-contextual-bandits-meet-regression-discontinuity-designs/

09/08/2023

The economics of reducing costs on virtual reality and mobile platforms: Check out our press release: https://machinelearningxdoing.com/machine-learning-x-doing-introduces-a-novel-theory-of-economies-of-score-for-virtual-reality-platforms-and-app-marketplaces/

Chatbot Auctions: How to Use Deep Reinforcement Learning and Transformer-Based Language Models to Create and Improve Advertising Markets and Institutions – Machine Learning X Doing 09/03/2023

How can technology companies run auctions on chatbots? Learn how by reading our paper:

Chatbot Auctions: How to Use Deep Reinforcement Learning and Transformer-Based Language Models to Create and Improve Advertising Markets and Institutions – Machine Learning X Doing Chatbot Auctions: How to Use Deep Reinforcement Learning and Transformer-Based Language Models to Create and Improve Advertising Markets and Institutions Kweku A. Opoku-Agyemang Working Paper Class 26 This paper develops a framework for auction design for AI chatbots, which are conversational agents...

09/03/2023

Learn how subspace designs can improve directed acyclic graphs in two ways: by providing efficient methods for encoding and decoding information in high-dimensional spaces, and by discovering new patterns and structures in data.

https://machinelearningxdoing.com/subspace-designs-and-directed-acyclic-graphs-an-approach-to-high-dimensional-causality/

08/12/2023

Important research by Kweku Opoku-Agyemang: thousands of patients are in need of kidney transplants, and thousands of individuals are willing to donate kidneys (sometimes on the condition that kidneys are allocated a certain way). However, kidneys can only be allocated to compatible patients, and there are always more people in need of kidneys than willing donors. How should kidneys be allocated with algorithms?

He proposes a framework – computational ethics – that specifies how the ethical challenges of AI can be addressed better by incorporating the study of how humans make moral decisions.

The driver of this framework is a computational version of reflective equilibrium.

The goal of this framework is twofold: (i) to inform the engineering of ethical AI systems, and (ii) to characterize human moral judgment and decision-making in computational terms.

Working jointly towards these two goals may prove to be beneficial in making progress on both fronts. Read the paper:

https://www.sciencedirect.com/science/article/pii/S1364661322000456 #:~:text=The%20proposed%20framework%20of%20computational,computational%20models%20to%20represent%20moral

08/11/2023

Real estate microeconomics: Let supply meet demand.
https://twitter.com/mlxdoing/status/1570839289956814850

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