What are Large Language Models? What are they not?
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.
Group-equivariant neural networks with escnn
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.
Deep Learning and Scientific Computing with R torch: the book
Much as we humans like to believe, consciousness is not a neocortex thing, a matter of analysis and meta-analysis. Instead – says Mark Solms, in his 2021 The Hidden Spring: A Journey to the Source of Consciousness – instead, consciousness is all about feeling. A claim that, if we take it seriously (and I don’t see why we shouldn’t) has far-ranging consequences.
Upside down, a cat’s still a cat: Evolving image recognition with Geometric Deep Learning
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.
AO, NAO, ENSO: A wavelet analysis example
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.
Getting started with deep learning on graphs
This post introduces deep learning on graphs by mapping its central concept - message passing - to minimal usage patterns of PyTorch Geometric’s foundation-laying MessagePassing class. Exploring those patterns, we gain some basic, very concrete insights into how graph DL works.
A book I’d say everyone should read if such were a kind of thing I’d say
Much as we humans like to believe, consciousness is not a neocortex thing, a matter of analysis and meta-analysis. Instead – says Mark Solms, in his 2021 The Hidden Spring: A Journey to the Source of Consciousness – instead, consciousness is all about feeling. A claim that, if we take it seriously (and I don’t see why we shouldn’t) has far-ranging consequences.
Forecasting El Niño-Southern Oscillation (ENSO)
El Niño-Southern Oscillation (ENSO) is an atmospheric phenomenon, located in the tropical Pacific, that greatly affects ecosystems as well as human well-being on a large portion of the globe. We use the convLSTM introduced in a prior post to predict the Niño 3.4 Index from spatially-ordered sequences of sea surface temperatures.
Beyond alchemy: A first look at geometric deep learning
Geometric deep learning is a “program” that aspires to situate deep learning architectures and techniques in a framework of mathematical priors. The priors, such as various types of invariance, first arise in some physical domain. A neural network that well matches the domain will preserve as many invariances as possible. In this post, we present a very conceptual, high-level overview, and highlight a few applications.
Starting to think about AI Fairness
The topic of AI fairness metrics is as important to society as it is confusing. Confusing it is due to a number of reasons: terminological proliferation, abundance of formulae, and last not least the impression that everyone else seems to know what they’re talking about. This text hopes to counteract some of that confusion by starting from a common-sense approach of contrasting two basic positions: On the one hand, the assumption that dataset features may be taken as reflecting the underlying concepts ML practitioners are interested in; on the other, that there inevitably is a gap between concept and measurement, a gap that may be bigger or smaller depending on what is being measured. In contrasting these fundamental views, we bring together concepts from ML, legal science, and political philosophy.
Convolutional LSTM for spatial forecasting
In forecasting spatially-determined phenomena (the weather, say, or the next frame in a movie), we want to model temporal evolution, ideally using recurrence relations. At the same time, we’d like to efficiently extract spatial features, something that is normally done with convolutional filters. Ideally then, we’d have at our disposal an architecture that is both recurrent and convolutional. In this post, we build a convolutional LSTM with torch.
AI ethics is not an optimization problem
Often, AI researchers and engineers think of themselves as neutral and “objective”, operating in a framework of strict formalization. Fairness and absence of bias, however, are social constructs; there is no objectivity, no LaTeX-typesettable remedies, no algorithmic way out. AI models are developed based on a history and deployed in a context. In AI as in data science, the very absence of action can be of political significance.
The hard problem of privacy
We live in a world of ever-diminishing privacy and ever-increasing surveillance - and this is a statement not just about openly-authoritarian regimes. Yet, we seem not to care that much, at least not until, for whatever reasons, we are personally affected by some negative consequence. This post wants to help increase awareness, casting a spotlight on recent history and also, letting words speak for themselves: Because nothing, to me, is less revealing than the “visions” that underly the actions.