The Simulation Argument and the AI control problem

ere, I propose a modified version of the simulation argument that connects the simulation argument to the AI control problem. In short, if we succeed in controlling AI, then we will highly regulate ancestor simulations, whereas if we fail to control AI, we are likely living in a simulation. This article contains preliminary research and more will be published in the future. I call this argument The Simulated Argument.

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Reinforcement Learning vs Genetic Algorithm — AI for Simulations

I had to decide, on an optimization approach that would better suit the use case. I had Reinforcement Learning and Genetic Algorithm (the two roads among many) in mind, but then an epiphany… “Both are nature inspired AI approaches, how are the two different? And more importantly, in which scenarios, is one favoured over the other?” And thus today we will be dissecting parts of the thought process behind coming to a decision.

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SEER: Simulative Emotional Expression Robot

SEER Robot

“SEER” is a humanoid robotic head developed as an artistic work by Takayuki Todo. It explores the significance of gaze and facial expression in the sphere of human-machine research. Takayuki Todo is interested in how people establish an emotional relationship with humanoid robots. As the discipline of robotics has shown for years, a realistic similarity to the human form alone is not able to break down the distance between a human and a machine.

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Deep Science: Using machine learning to study anatomy, weather and earthquakes

Research papers come out far too rapidly for anyone to read them all, especially in the field of machine learning, which now affects (and produces papers in) practically every industry and company. This column aims to collect the most relevant recent discoveries and papers — particularly in but not limited to artificial intelligence — and explain why they matter.

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Parallel Domain raises $11 million to generate synthetic data for AI model training

Parallel Domain, a startup developing a synthetic data generation platform for AI and machine learning applications, today emerged from stealth with $11 million in funding. The company, which has over 25 employees and customers in auto manufacturing and drone delivery, plans to use the capital to accelerate its go-to-market efforts as it expands its product footprint.

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