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A New Capability Maturity Model for Deep Learning

A New Capability Maturity Model for Deep Learning

How can we understand progress in Deep Learning without a map? I created one such map a couple years ago, but this map needs a drastic…
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Carlos E. Perez
Introducing the Deep Learning AI Canvas

Introducing the Deep Learning AI Canvas

One of the big mysteries of Deep Learning is, how do we apply this disruptive new AI technology to improving our businesses? There are…
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Carlos E. Perez
How Human and Deep Learning Perception are Very Different

How Human and Deep Learning Perception are Very Different

How do we perceive the world? To understand this, let us explore how we incorrectly perceive the world. The ‘glitches in the matrix’ shall…
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Carlos E. Perez
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The Curious Case of AGI without a Body

The Curious Case of AGI without a Body

Previously, I explained the difference between biological evolutionary designs and human technological designs. Biological designs are…
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Carlos E. Perez
Is Massively Unsupervised Language Learning Dangerous?
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Is Massively Unsupervised Language Learning Dangerous?

Ilya Stuskever, who leads OpenAI research, has a simple theory about how to reach AGI. He argues that all that is needed is massively…
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Carlos E. Perez
Why Randomness should be Embraced and Not Feared

Why Randomness should be Embraced and Not Feared

Previously I wrote about how the design strategy of biology differs from the design strategy of human technology. The difference can be…
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Carlos E. Perez
Organic Programming: A New Kind of Programming
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Organic Programming: A New Kind of Programming

I want to discuss with you today four different kinds of programming. This framework should help you better understand the uniqueness of…
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Carlos E. Perez
How Biological Design Differs from Human Design

How Biological Design Differs from Human Design

Here is a very insightful question you should ask yourself, why hasn’t natural evolution invented the wheel? The reason why this is…
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Carlos E. Perez
10 Predictions for Deep Learning in 2019
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10 Predictions for Deep Learning in 2019

The year 2018 is ending and its now time for a new set of Deep Learning predictions for 2019. Here are my previous predictions and…
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Carlos E. Perez
12 Blind Spots in AI Research

12 Blind Spots in AI Research

Humans by their nature have many cognitive biases. This becomes detrimental toward real scientific progress when we are biased towards…
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Carlos E. Perez
What Deep Learning can Learn from Cybernetics

What Deep Learning can Learn from Cybernetics

“Progress, far from consisting in change, depends on retentiveness. When change is absolute there remains no being to improve and no…
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Carlos E. Perez
Deep Learning, Semiotics and Why Not Symbols

Deep Learning, Semiotics and Why Not Symbols

Semiotics is the study of signs and their use and interpretation. Any study of cognition, whether that be biological or artificial…
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Carlos E. Perez
Deep Learning: Perturbations and Diversity is All You Need

Deep Learning: Perturbations and Diversity is All You Need

Judea Pearl in a recent tweet expresses intractable nature of generative processes:
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Carlos E. Perez
Why Stylistic GANs are So Deceptive

Why Stylistic GANs are So Deceptive

NVidia shocked the world again by its release of A Style-Based Generator Architecture for Generative Adversarial Network (GAN). A year…
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Carlos E. Perez
Nano-Intentionality and Deep Learning

Nano-Intentionality and Deep Learning

It’s an established fact that biological cognition is fundamentally based on swarm intelligence. Michael Levin drives this fact home with…
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Carlos E. Perez
Bypassing the Agent Alignment Problem via Intrinsic Motivation

Bypassing the Agent Alignment Problem via Intrinsic Motivation

One curious coincidence is that the phrase “Deep Learning” is used in two apparently different fields of study. The one that I am familiar…
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Carlos E. Perez
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