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Philosophy of Deep Learning

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Empirical AI Research
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Philosophy of Deep Learning

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Deep Qualia: Philosophy of Statistics, Deep Learning, and Blockchain

Deep learning: What is it, why is it important, and what do I need to know?

The aim of this talk is to discuss deep learning as an advanced computational method and its philosophical implications. Computing is a fundamental model by which we are understanding more about ourselves and the world. We think that reality is composed of patterns, which can be detected by machine learning methods.

Deep learning is a complexity optimization technique in which algorithms learn from data by modeling high-level abstractions and assigning probabilities to nodes as they characterize the system and make predictions. An important challenge in deep learning is that these methods work in certain domains (image, speech, and text recognition), but we do not have a good explanation for why, which impedes a wider application of these solutions.

Another recent advance in computational methods is blockchain technology which allows the secure transfer of assets and information, and the automated coordination of operations via a trackable remunerative ledger and smart contracts (automatically-executing Internet-based programs).

This talk looks at how deep learning technology, particularly as coupled with blockchain systems, might be used to produce a new kind of global computing platform. The goal is for blockchain deep learning systems to address higher-dimensional computing challenges that require learning and dynamic response in domains such as economics and financial risk, epidemiology, social modeling, public health (cancer, aging), dark matter, atomic reactions, network-modeling (transportation, energy, smart cities), artificial intelligence, and consciousness.

Deep Qualia: Philosophy of Statistics, Deep Learning, and Blockchain

Deep learning: What is it, why is it important, and what do I need to know?

The aim of this talk is to discuss deep learning as an advanced computational method and its philosophical implications. Computing is a fundamental model by which we are understanding more about ourselves and the world. We think that reality is composed of patterns, which can be detected by machine learning methods.

Deep learning is a complexity optimization technique in which algorithms learn from data by modeling high-level abstractions and assigning probabilities to nodes as they characterize the system and make predictions. An important challenge in deep learning is that these methods work in certain domains (image, speech, and text recognition), but we do not have a good explanation for why, which impedes a wider application of these solutions.

Another recent advance in computational methods is blockchain technology which allows the secure transfer of assets and information, and the automated coordination of operations via a trackable remunerative ledger and smart contracts (automatically-executing Internet-based programs).

This talk looks at how deep learning technology, particularly as coupled with blockchain systems, might be used to produce a new kind of global computing platform. The goal is for blockchain deep learning systems to address higher-dimensional computing challenges that require learning and dynamic response in domains such as economics and financial risk, epidemiology, social modeling, public health (cancer, aging), dark matter, atomic reactions, network-modeling (transportation, energy, smart cities), artificial intelligence, and consciousness.

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