Posts

We read a lot of papers. This is my standarized procedure for reading a paper such that the insight from it can be kept and spread.

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This paper presents an online autoencoder that produces non-negative sparse embedding.

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This paper defines intelligence as an agent’s ability to achieve goals in a wide range of environments. And authors present elegant mathematical formulation based on the concept of rewards and Kolmogorov complexity.

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This lecture summarizies the current challenge of Deep Learning and a few approaches by Yoshua Bengio.

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This paper presents HouseFly, which is an immersive video analytics platform where 3 years of videos from multiple camera can be shown as immersive video.

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This paper presents Sparsey model which uses sparse distributed coding or representation (SDR) to build a hierarchical classifier. The main idea is using familarity to control the randomness of representation. But the simplication poses limited applicability.

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The main idea is using an environment that resembles a preschool. I think it is an improvement over other tests such as Turing test, College Student test, or 8th grader test. But it still does not answer how the agent acquire communication skill using human language.

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This paper shows an overview of AGI capabilities from developmental psychology, mathematical, physiological, and information processing perspectives. Also it discusses how to evaluate AGI using environments, tasks, and scenarios.

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Below I summarize my plan for building artificial general intelligence (AGI). I start with the test for AGI, and then goes into environments and models for it.

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Intelligence is the rules to actuate actuators according to sensory input to improve one’s well-being. Learning is acquiring new rules after birth. Learning requires sensors, actuators, and memory. Olfactory learning is the first learning that can give insight to the minimum parts for learning.

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