AI & Architecture
An Experimental Perspective
By Stanislas Chaillou, Harvard Graduate School of Design:
https://towardsdatascience.com/ai-architecture-f9d78c6958e0
#artificialintelligence #architecture #design #deeplearning #technology
🔗 AI & Architecture
An Experimental Perspective
An Experimental Perspective
By Stanislas Chaillou, Harvard Graduate School of Design:
https://towardsdatascience.com/ai-architecture-f9d78c6958e0
#artificialintelligence #architecture #design #deeplearning #technology
🔗 AI & Architecture
An Experimental Perspective
Medium
AI & Architecture
An Experimental Perspective
An Algorithmic Barrier to Neural Circuit Understanding
Venkatakrishnan Ramaswamy: https://www.biorxiv.org/content/10.1101/639724v1
#Algorithme #Neuroscience #innovation #technology
🔗 An Algorithmic Barrier to Neural Circuit Understanding
Neuroscience is witnessing extraordinary progress in experimental techniques, especially at the neural circuit level. These advances are largely aimed at enabling us to understand how neural circuit computations mechanistically cause behavior. Here, using techniques from Theoretical Computer Science, we examine how many experiments are needed to obtain such an empirical understanding. It is proved, mathematically, that establishing the most extensive notions of understanding need exponentially-many experiments in the number of neurons, in general, unless a widely-posited hypothesis about computation is false. Worse still, the feasible experimental regime is one where the number of experiments scales sub-linearly in the number of neurons, suggesting a fundamental impediment to such an understanding. Determining which notions of understanding are algorithmically tractable, thus, becomes an important new endeavor in Neuroscience.
Venkatakrishnan Ramaswamy: https://www.biorxiv.org/content/10.1101/639724v1
#Algorithme #Neuroscience #innovation #technology
🔗 An Algorithmic Barrier to Neural Circuit Understanding
Neuroscience is witnessing extraordinary progress in experimental techniques, especially at the neural circuit level. These advances are largely aimed at enabling us to understand how neural circuit computations mechanistically cause behavior. Here, using techniques from Theoretical Computer Science, we examine how many experiments are needed to obtain such an empirical understanding. It is proved, mathematically, that establishing the most extensive notions of understanding need exponentially-many experiments in the number of neurons, in general, unless a widely-posited hypothesis about computation is false. Worse still, the feasible experimental regime is one where the number of experiments scales sub-linearly in the number of neurons, suggesting a fundamental impediment to such an understanding. Determining which notions of understanding are algorithmically tractable, thus, becomes an important new endeavor in Neuroscience.
bioRxiv
An Algorithmic Barrier to Neural Circuit Understanding
Neuroscience is witnessing extraordinary progress in experimental techniques, especially at the neural circuit level. These advances are largely aimed at enabling us to understand how neural circuit computations mechanistically cause behavior. Here, using…
Should data scientists learn JavaScript?
https://www.google.com/amp/s/www.freecodecamp.org/news/should-data-scientists-learn-javascript-e611d45804b8/amp/
🔗 Should data scientists learn JavaScript?
The pros and cons of using the web’s #1 language for data science If you have been following the tech landscape in recent years, you have probably noticed at least two things. For one, you may have noticed that JavaScript is a very popular language [https://insights.stackoverflow.com/survey/2017#technology-programming-languages] these days. It has been growing in popularity ever since Node.js [https://nodejs.org/en/] allowed JavaScript developers to write server-side code. More recently, fram
https://www.google.com/amp/s/www.freecodecamp.org/news/should-data-scientists-learn-javascript-e611d45804b8/amp/
🔗 Should data scientists learn JavaScript?
The pros and cons of using the web’s #1 language for data science If you have been following the tech landscape in recent years, you have probably noticed at least two things. For one, you may have noticed that JavaScript is a very popular language [https://insights.stackoverflow.com/survey/2017#technology-programming-languages] these days. It has been growing in popularity ever since Node.js [https://nodejs.org/en/] allowed JavaScript developers to write server-side code. More recently, fram
freeCodeCamp.org
Should data scientists learn JavaScript?
The pros and cons of using the web’s #1 language for data scienceIf you have been following the tech landscape in recent years, you have probably noticed at least two things. For one, you may have noticed that JavaScript is a very popular language these days.…