True, that title’s been meant to let you know what this is about, and – if I’m lucky – to even make you curious. In reality, there is no such debate: not in a factual, concrete way, and not with regard to content/substance either. Rather, there are two books, Determined – The Science of Life without Free Will by Robert Sapolsky, author of Behave, and Free Agents by Kevin Mitchell, author of Innate. For me, the link has been there ever since I listened to Brain Science Podcast episode 213, featuring Kevin Mitchell and his new book. Somehow, there was mention of another book forthcoming on that topic, by Robert Sapolsky. I immediately pre-ordered the book.
Here, then, follow my impressions from reading both books… – although, and I may be totally wrong there, “free will”, for many people, is not, not really, a topic up for discussion. Instead, you already know the answer; it is there, deep inside you. (To be frank, that’s how it is to me.) The answer is Yes, or No, and whichever it is, it is as a matter of course. Why then this post? Because still, you might be curious whether “the other side” had any new and/or convincing arguments, and because still, you might want to learn how someone like-minded addresses open questions and implications.
This is a high-level, introductory article about Large Language Models (LLMs), the core technology that enables the much-en-vogue chatbots as well as other Natural Language Processing (NLP) applications. It is directed at a general audience, possibly with some technical and/or scientific background, but no knowledge is assumed of either deep learning or NLP. Having looked at major model ingredients, training workflow, and mechanics of output generation, we also talk about what these models are not.
Escnn, built on PyTorch, is a library that, in the spirit of Geometric Deep Learning, provides a high-level interface to designing and training group-equivariant neural networks. This post introduces important mathematical concepts, the library’s key actors, and essential library use.
Please allow us to introduce Deep Learning and Scientific Computing with R torch. Released in e-book format today, and available freely online, this book starts out by introducing torch basics. From there, it moves on to various deep-learning use cases. Finally, it shows how to use torch for more general topics, such as matrix computations and the Fourier Transform.
We code up a simple group-equivariant convolutional neural network (GCNN) that is equivariant to rotation. The world may be upside down, but the network will know.
In this first in a series of posts on group-equivariant convolutional neural networks (GCNNs), meet the main actors — groups — and concepts (equivariance). With GCNNs, we finally revisit the topic of Geometric Deep Learning, a principled, math-driven approach to neural networks that has consistently been rising in scope and impact.
El Niño-Southern Oscillation (ENSO), North Atlantic Oscillation (NAO), and Arctic Oscillation (AO) are atmospheric phenomena of global impact that strongly affect people’s lives. ENSO, first and foremost, brings with it floods, droughts, and ensuing poverty, in developing countries in the Southern Hemisphere. Here, we use the new torchwavelets package to comparatively inspect patterns in the three series.
