<p>Galit and I discuss the independent roles of prediction and explanation in scientific models, their history and eventual separation in the philosophy of science, how they can inform each other, and how statisticians like Galit view the current deep learning explosion.</p>





<ul class="wp-block-list"><li>Galit's <a href="https://www.galitshmueli.com/">website</a>. </li><li>Follow her on twitter: <a href="https://twitter.com/gshmueli">@gshmueli</a>. </li><li>The papers we discuss or mention: <ul><li><a href="https://www.galitshmueli.com/system/files/Stat%20Science%20published.pdf">To Explain or To Predict?</a></li><li><a href="https://www.galitshmueli.com/system/files/MISQ-Predictive-Analytics-in-IS-Shmueli-Koppius%20-2011.pdf">Predictive Analytics in Information Systems Research.</a></li></ul></li></ul>

Galit and I discuss the independent roles of prediction and explanation in scientific models, their history and eventual separation in the philosophy of science, how they can inform each other, and how statisticians like Galit view the current deep learning explosion. Galit's website. Follow her on twitter: @gshmueli. The papers we discuss or mention: To Explain or To Predict?Predictive Analytics in Information Systems Research.

<p>Galit and I discuss the independent roles of prediction and explanation in scientific models, their history and eventual separation in the philosophy of science, how they can inform each other, and how statisticians like Galit view the current deep learning explosion.</p>





<ul><li>Galit's&nbsp;<a href="https://www.galitshmueli.com/" rel="noopener noreferrer">website</a>. </li><li>Follow her on twitter: <a href="https://twitter.com/gshmueli" rel="noopener noreferrer">@gshmueli</a>. </li><li>The papers we discuss or mention: <ul><li><a href="https://www.galitshmueli.com/system/files/Stat%20Science%20published.pdf" rel="noopener noreferrer">To Explain or To Predict?</a></li><li><a href="https://www.galitshmueli.com/system/files/MISQ-Predictive-Analytics-in-IS-Shmueli-Koppius%20-2011.pdf" rel="noopener noreferrer">Predictive Analytics in Information Systems Research.</a></li></ul></li></ul>

BI 064 Galit Shmueli: Explanation vs. Prediction

Neuroscience and artificial intelligence work better together. Brain inspired is a celebration and exploration of the ideas driving our progress to understand intelligence. I interview experts about their work at the interface of neuroscience, artificial intelligence, cognitive science, philosophy, psychology, and more: the symbiosis of these overlapping fields, how they inform each other, where they differ, what the past brought us, and what the future brings. Topics include computational neuroscience, supervised machine learning, unsupervised learning, reinforcement learning, deep learning, convolutional and recurrent neural networks, decision-making science, AI agents, backpropagation, credit assignment, neuroengineering, neuromorphics, emergence, philosophy of mind, consciousness, general AI, spiking neural networks, data science, and a lot more. The podcast is not produced for a general audience. Instead, it aims to educate, challenge, inspire, and hopefully entertain those interested in learning more about neuroscience and AI.

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BI 064 Galit Shmueli: Explanation vs. Prediction

BI 064 Galit Shmueli: Explanation vs. Prediction

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