kamal

Threads is offically getting ads

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Illustration: The Verge Your Threads feed will soon have ads. On Friday, Meta announced that it’s rolling out a “limited, early test of ads in Threads,” and the test will happen with a “handful of brands in the US and Japan,” according to Instagram boss Adam Mosseri. Image: Meta Ads on Threads will appear as … Read more

Kodiak has made its first driverless truck deliveries to customer Atlas Energy

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Kodiak Robotics has officially handed off two autonomous trucks to customer Atlas Energy Solutions, marking the startup’s first commercial launch.  Atlas, a provider of proppant (i.e., sand) and oilfield logistics, received its first Kodiak-equipped trucks in December and began driverless operations in an off-road environment in West Texas’s remote Permian Basin shortly after. The company … Read more

Threads is testing ads in the U.S. and Japan

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Meta’s X rival Threads said Friday that it is now testing ads with select brands in the U.S. and Japan. Instagram head Adam Mosseri said that the company will listen to feedback before expanding this experiment in other markets. “We know there will be plenty of feedback about how we should approach ads, and we … Read more

Security best practices to consider while fine-tuning models in Amazon Bedrock

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Amazon Bedrock has emerged as the preferred choice for tens of thousands of customers seeking to build their generative AI strategy. It offers a straightforward, fast, and secure way to develop advanced generative AI applications and experiences to drive innovation. With the comprehensive capabilities of Amazon Bedrock, you have access to a diverse range of … Read more

Understanding Bias-Driven Opponent Models in Competitive Gameplay

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:::info Authors: (1) Avrim Blum, Toyota Technological Institute at Chicago, IL, USA; (2) Melissa Dutz, Toyota Technological Institute at Chicago, IL, USA. ::: Table of Links Abstract and 1 Introduction 2 Setting and 2.1 Models of behaviorally-biased opponents 3 Preliminaries and Intuition 4.1 Myopic Best Responder and 4.2 Gambler’s Fallacy Opponent 4.3 Win-Stay, Lose-Shift Opponent … Read more

Ways to Counter Limited-History Opponents with Algorithmic Tools

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:::info Authors: (1) Avrim Blum, Toyota Technological Institute at Chicago, IL, USA; (2) Melissa Dutz, Toyota Technological Institute at Chicago, IL, USA. ::: Table of Links Abstract and 1 Introduction 2 Setting and 2.1 Models of behaviorally-biased opponents 3 Preliminaries and Intuition 4.1 Myopic Best Responder and 4.2 Gambler’s Fallacy Opponent 4.3 Win-Stay, Lose-Shift Opponent … Read more

Future Directions for Exploiting Behavioral Biases in Games

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:::info Authors: (1) Avrim Blum, Toyota Technological Institute at Chicago, IL, USA; (2) Melissa Dutz, Toyota Technological Institute at Chicago, IL, USA. ::: Table of Links Abstract and 1 Introduction 2 Setting and 2.1 Models of behaviorally-biased opponents 3 Preliminaries and Intuition 4.1 Myopic Best Responder and 4.2 Gambler’s Fallacy Opponent 4.3 Win-Stay, Lose-Shift Opponent … Read more

Broader Insights into Exploitable Strategies in Zero-Sum Games

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:::info Authors: (1) Avrim Blum, Toyota Technological Institute at Chicago, IL, USA; (2) Melissa Dutz, Toyota Technological Institute at Chicago, IL, USA. ::: Table of Links Abstract and 1 Introduction 2 Setting and 2.1 Models of behaviorally-biased opponents 3 Preliminaries and Intuition 4.1 Myopic Best Responder and 4.2 Gambler’s Fallacy Opponent 4.3 Win-Stay, Lose-Shift Opponent … Read more

How Behavioral Biases Shape Gameplay Without Payoff Visibility

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:::info Authors: (1) Avrim Blum, Toyota Technological Institute at Chicago, IL, USA; (2) Melissa Dutz, Toyota Technological Institute at Chicago, IL, USA. ::: Table of Links Abstract and 1 Introduction 2 Setting and 2.1 Models of behaviorally-biased opponents 3 Preliminaries and Intuition Strategies for Beating Behaviorally Biased Opponents 4.1 Myopic Best Responder and 4.2 Gambler’s … Read more

Methods for Decoding Opponent Actions and Optimizing Responses

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:::info Authors: (1) Avrim Blum, Toyota Technological Institute at Chicago, IL, USA; (2) Melissa Dutz, Toyota Technological Institute at Chicago, IL, USA. ::: Table of Links Abstract and 1 Introduction 2 Setting and 2.1 Models of behaviorally-biased opponents 3 Preliminaries and Intuition 4.1 Myopic Best Responder and 4.2 Gambler’s Fallacy Opponent 4.3 Win-Stay, Lose-Shift Opponent … Read more

The Key to Defeating Win-Stay, Lose-Shift Opponent Variants

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:::info Authors: (1) Avrim Blum, Toyota Technological Institute at Chicago, IL, USA; (2) Melissa Dutz, Toyota Technological Institute at Chicago, IL, USA. ::: Table of Links Abstract and 1 Introduction 2 Setting and 2.1 Models of behaviorally-biased opponents 3 Preliminaries and Intuition 4.1 Myopic Best Responder and 4.2 Gambler’s Fallacy Opponent 4.3 Win-Stay, Lose-Shift Opponent … Read more

Hydra Achieves 1 Million TPS, Validating Cardano as the Fastest Blockchain

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The Hydra Doom Tournament is a high-stakes gaming event, held both online and live, that highlights the classic game Doom while demonstrating the scalability and capabilities of the Hydra solution on the Cardano blockchain. The first stages of the tournament have delivered decisive results, with Cardano being crowned the fastest blockchain, achieving an unprecedented 1 … Read more

Strategies to Exploit Myopic and Gambler’s Fallacy Opponents

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:::info Authors: (1) Avrim Blum, Toyota Technological Institute at Chicago, IL, USA; (2) Melissa Dutz, Toyota Technological Institute at Chicago, IL, USA. ::: Table of Links Abstract and 1 Introduction 2 Setting and 2.1 Models of behaviorally-biased opponents 3 Preliminaries and Intuition 4.1 Myopic Best Responder and 4.2 Gambler’s Fallacy Opponent 4.3 Win-Stay, Lose-Shift Opponent … Read more

Algorithm 8’s Approach to Countering the Highest Average Payoff Opponent

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:::info Authors: (1) Avrim Blum, Toyota Technological Institute at Chicago, IL, USA; (2) Melissa Dutz, Toyota Technological Institute at Chicago, IL, USA. ::: Table of Links Abstract and 1 Introduction 2 Setting and 2.1 Models of behaviorally-biased opponents 3 Preliminaries and Intuition 4.1 Myopic Best Responder and 4.2 Gambler’s Fallacy Opponent 4.3 Win-Stay, Lose-Shift Opponent … Read more

A Guide to Exploiting Unknown Strategies From a Known Bias Set

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:::info Authors: (1) Avrim Blum, Toyota Technological Institute at Chicago, IL, USA; (2) Melissa Dutz, Toyota Technological Institute at Chicago, IL, USA. ::: Table of Links Abstract and 1 Introduction 2 Setting and 2.1 Models of behaviorally-biased opponents 3 Preliminaries and Intuition 4.1 Myopic Best Responder and 4.2 Gambler’s Fallacy Opponent 4.3 Win-Stay, Lose-Shift Opponent … Read more

Techniques to Beat the Tie-Stay Variant of Win-Stay, Lose-Shift

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:::info Authors: (1) Avrim Blum, Toyota Technological Institute at Chicago, IL, USA; (2) Melissa Dutz, Toyota Technological Institute at Chicago, IL, USA. ::: Table of Links Abstract and 1 Introduction 2 Setting and 2.1 Models of behaviorally-biased opponents 3 Preliminaries and Intuition 4.1 Myopic Best Responder and 4.2 Gambler’s Fallacy Opponent 4.3 Win-Stay, Lose-Shift Opponent … Read more

2025 looks like a great year for Xbox

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Image: Cath Virginia / The Verge There’s a long-running joke in the Xbox community that Microsoft will finally hit its stride with Game Pass and Xbox releases “next year.” The joke has been going around since 2018, when Microsoft made a series of big studio acquisitions to create more Xbox games and make Game Pass … Read more

What’s the Best Way to Control an Exoskeleton?

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:::info Authors: (1) Mohammad Shushtari, Department of Mechanical and Mechatronics Engineering, University of Waterloo (smshushtari@uwaterloo.ca); (2) Julia Foellmer, Mechanics and Ocean Engineering Department, Hamburg University of Technology (julia.foellmer@tuhh.de); (3) Sanjay Krishna Gouda, Department of Mechanical and Mechatronics Engineering, University of Waterloo and Toronto Rehabilitation Institute (KITE), University Health Network (arash.arami@uwaterloo.ca). ::: Table of Links Abstract … Read more

How User Weight Affects Exoskeleton Performance

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:::info Authors: (1) Mohammad Shushtari, Department of Mechanical and Mechatronics Engineering, University of Waterloo (smshushtari@uwaterloo.ca); (2) Julia Foellmer, Mechanics and Ocean Engineering Department, Hamburg University of Technology (julia.foellmer@tuhh.de); (3) Sanjay Krishna Gouda, Department of Mechanical and Mechatronics Engineering, University of Waterloo and Toronto Rehabilitation Institute (KITE), University Health Network (arash.arami@uwaterloo.ca). ::: Table of Links Abstract … Read more

Why Some Exoskeletons Make Walking Easier Than Others

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:::info Authors: (1) Mohammad Shushtari, Department of Mechanical and Mechatronics Engineering, University of Waterloo (smshushtari@uwaterloo.ca); (2) Julia Foellmer, Mechanics and Ocean Engineering Department, Hamburg University of Technology (julia.foellmer@tuhh.de); (3) Sanjay Krishna Gouda, Department of Mechanical and Mechatronics Engineering, University of Waterloo and Toronto Rehabilitation Institute (KITE), University Health Network (arash.arami@uwaterloo.ca). ::: Table of Links Abstract … Read more

Why Do Exoskeletons Work Better at Faster Walking Speeds?

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:::info Authors: (1) Mohammad Shushtari, Department of Mechanical and Mechatronics Engineering, University of Waterloo (smshushtari@uwaterloo.ca); (2) Julia Foellmer, Mechanics and Ocean Engineering Department, Hamburg University of Technology (julia.foellmer@tuhh.de); (3) Sanjay Krishna Gouda, Department of Mechanical and Mechatronics Engineering, University of Waterloo and Toronto Rehabilitation Institute (KITE), University Health Network (arash.arami@uwaterloo.ca). ::: Table of Links Abstract … Read more