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UNDERSTANDING HUMAN AND ARTIFICIAL INTELLIGENCE with Tricia MacKenzie, 16 hour intensive workshop

Understanding human and artificial intelligence



The internet and computers have fundamentally changed what it means to be human. This class explores the intersection of human and artificial intelligence (AI). It covers the foundational concepts of both neuroscience and AI, starting from the origin of AI and building up to an in depth discussion of innovations at the bleeding edge. This course covers the concepts and math required to understand AI being implemented by tech companies and created in academia, from the perspective of how advances in AI relate to advances in neuroscience.

This course has an optional second section for programming that teaches the Julia language, a new language designed for AI. Julia allows AI innovations to be quickly translated into products that run at real time. It is accessible, dynamically-typed and interactive, yet fast and designed for high performance like C.

No previous programming experience in Julia is needed or expected. Feel free to come with a project or idea that you want to work on throughout the class.


Tricia MacKenzie, PhD, works at the intersection of neuroscience and artificial intelligence. She received her PhD from NYU, where she applied Systems Biology to biological neural networks, and her B.S. from University of Washington Neurobiology. She is currently CEO of an AI company, called Ailaire. She was a finalist for a Creative Capital Award for proposing startups are an emerging field of art. She was once almost expelled from her doctoral program for using AI to render labs in her field redundant as a possible solution to sexism in academia. 

Session one

Intro to AI + neuroscience 

Understanding how neurons communicate and how to predict these computations. Deriving the Nernst potential from the second law of thermodynamics. Using the second law of thermodynamics to conceptualize artificial and biological intelligence

(programming section)

How do we play with this? Intro to the Julia language. Julia is super fast, efficient and optimized for advancing AI 

Session two

Artificial and biological neural networks 

Cortical biological networks and how they can be organized into functional circuits. Artificial networks are based on early discoveries on how neurons learn (Hebbian plasticity). Intro to probability

(programming section)

How do we play with this? Linear algebra in Julia. Introduction to the bayesian brain. Julia's machine and deep learning library, which includes TensorFlow and a computer vision library

Session three

How biological and artificial environments make predictions 

How cortical networks in visual and auditory systems perceive our environment. How AI makes predictions 

(programming section)

How do we play with this?

In depth explanation of probability and uncertainty (Bayesian networks, hidden Markov models, Kalmann filters, game theory) in Julia

Session four

How our brains and AI learn 

Synaptic plasticity in biological neural networks. Learning in artificial neural networks (Convolutional, recurrent artificial neural networks, deep learning). Introduction to how to interface biological neural networks with AI 

(programming section)

How do we play with this?

Combining multiple techniques and integrating AI into real products in real time. Bleeding edge techniques available in Julia