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November Edition: On Neuroscience and Its Connections to AI and Machine Learning

Understanding how the human brain functions can help us advance our knowledge of neural networks

We’ve always been fascinated by human intelligence; it allows us to learn, create, and shape the modern world we live in today. Neuroscience has been useful in stimulating algorithmic-level questions about facets of learning and intelligence.

Many of our day-to-day behaviours, for instance, are guided by anticipating whether a given action will result in a positive outcome. That pattern inspired us to design the concept of reinforcement learning, where intelligent agents take actions in an environment to maximize cumulative rewards.

There are many similarities between how our brains are wired and how a neural network works, and we should continue to look at the human brain for inspiration and to borrow valuable concepts. If AI researchers dedicate more of their time to mimicking the internal processes of the human brain, the understanding they gain along the way can play a vital role in building intelligent machines.

If you’re curious to learn more about the connections between neuroscience research, AI, and machine learning, we invite you to browse the collection of articles we’ve selected below.

Jingles (Hong Jing), Volunteer Editorial Associate at TDS


Deep Lifelong Learning – Drawing Inspiration from the Human Brain

Replicating learning mechanisms from the human brain to prevent catastrophic forgetting in deep neural networks.

By Aliaksei Mikhailiuk (8 minutes)


Conceptualization as a Basis for Cognition – Human and Machine

A missing link to machine understanding and Cognitive AI.

By Gadi Singer (9 minutes)


Mindful Machines: Neuroscience & Critical Theory for Ethical AI

Creating ethical AI with a neuroscientific understanding of mindfulness combined with a critical theory-informed view of causality and neurosymbolic AI.

By Haaya Naushan (19 minutes)


Why We Might Be Looking at the Brain in the Wrong Way

…and why the implications are crucial for AI.

By Manuel Brenner (10 minutes)


The Cognitive Science of AGI

How a better understanding human cognition would be a crucial step towards artificial general intelligence.

By Mike Ferguson (10 minutes)


A Mathematical Approach to Constraining Neural Abstraction and the Mechanisms Needed to Scale to Higher-Order Cognition

To build Artificial General Intelligence, we must first understand how intelligence arises.

By Ananta Nair (29 minutes)


Fascinating Relationship between AI and Neuroscience

How they inspire and advance together to benefit each other.

By Jingles (Hong Jing) (6 minutes)


Our Reality And Why Consciousness Is Important

An interview with Professor Donald Hoffman from UCI about our reality, consciousness in the world of AI.

By Jun Wu (8 minutes)


One of the best parts of working on TDS is the chance we get to welcome new authors who share excellent work with our community. In the past month, we had the privilege of publishing debut articles from Tarek Amr, Agnes C, Mordechai Worch, Tino T. Herden, David Suarez, Olga Braginskaya, Paul Bendevis, Xinyu Chen, Srijith Rajamohan, Ph.D., khushnandan rai, Garret Nourse, Roman Zykov, Rizqi Eka Maulana, Yashaswi Nayak, Fetze Pijlman, Maria, Tal Rosenwein, Pranay Shah, Sebastian Reinhard, Chloe Lubin, Muhammad Monjurul Karim, Pau Labarta Bajo, Karel D’Oosterlinck, Jonny Hofmeister, Christophe Brown, Matt Dawidowicz, Lucas Kitzmüller, Liz Maag-Capriotti, David Borrelli, Jake Grigsby, Georgia Deaconu, Diogo A.P. Nunes, Celia Sagastume, Rick Shih, Klas Leino, Minseok Song, Laura O’Mahony, Federico Garza Ramírez, Bennett Meares, John Curry, Maximilian Faschan, Guangyuan(Frank) Li, Tyler Blume, Victor Seifert, Shahar Siegman, and Tiffany Meshkat, to name just a few. We invite you to take a look at their profiles and check out their work; if you’d like to join this illustrious group in the future, here’s a good place to start that journey.


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