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Amazon scraps secret AI recruiting tool that showed bias against women | Article [AMP] | Reuters
AI  Artificial_Intelligence 
6 days ago by chrisdymond
[1706.04317] Schema Networks: Zero-shot Transfer with a Generative Causal Model of Intuitive Physics
"The recent adaptation of deep neural network-based methods to reinforcement learning and planning domains has yielded remarkable progress on individual tasks. Nonetheless, progress on task-to-task transfer remains limited. In pursuit of efficient and robust generalization, we introduce the Schema Network, an object-oriented generative physics simulator capable of disentangling multiple causes of events and reasoning backward through causes to achieve goals. The richly structured architecture of the Schema Network can learn the dynamics of an environment directly from data. We compare Schema Networks with Asynchronous Advantage Actor-Critic and Progressive Networks on a suite of Breakout variations, reporting results on training efficiency and zero-shot generalization, consistently demonstrating faster, more robust learning and better transfer. We argue that generalizing from limited data and learning causal relationships are essential abilities on the path toward generally intelligent systems"
to:NB  artificial_intelligence  reinforcement_learning  graphical_models  heard_the_talk 
6 days ago by cshalizi
Artificial Intelligence: Foundations of Computational Agents, 2e
"Artificial Intelligence: Foundations of Computational Agents, second edition, Cambridge University Press 2017, is a book about the science of artificial intelligence (AI). It presents artificial intelligence as the study of the design of intelligent computational agents. The book is structured as a textbook, but it is accessible to a wide audience of professionals and researchers. In the last decades we have witnessed the emergence of artificial intelligence as a serious science and engineering discipline. This book provides an accessible synthesis of the field aimed at undergraduate and graduate students. It provides a coherent vision of the foundations of the field as it is today. It aims to provide that synthesis as an integrated science, in terms of a multi-dimensional design space that has been partially explored. As with any science worth its salt, artificial intelligence has a coherent, formal theory and a rambunctious experimental wing. The book balances theory and experiment, showing how to link them intimately together. It develops the science of AI together with its engineering applications."
in_NB  to_browse  artificial_intelligence 
7 days ago by cshalizi
live-training / pragmatic-ai · GitLab
safari training materials for pragmatic ai
These notebooks are ported from Google Colab version found here: For the most part all the examples should be compatible, although there may occasionally be some differences.
For additional content on these topics please view:

Read Pragmatic AI: An Introduction to Cloud-Based Machine Learning
Watch Essential Machine Learning and AI with Python and Jupyter Notebook
18 days ago by istemi
Anatomy of an AI System
When a human engages with an Echo, or another voice-enabled AI device, they are acting as much more than just an end-product consumer. It is difficult to place the human user of an AI system into a single category: rather, they deserve to be considered as a hybrid case. Just as the Greek chimera was a mythological animal that was part lion, goat, snake and monster, the Echo user is simultaneously a consumer, a resource, a worker, and a product. This multiple identity recurs for human users in many technological systems. In the specific case of the Amazon Echo, the user has purchased a consumer device for which they receive a set of convenient affordances. But they are also a resource, as their voice commands are collected, analyzed and retained for the purposes of building an ever-larger corpus of human voices and instructions. And they provide labor, as they continually perform the valuable service of contributing feedback mechanisms regarding the accuracy, usefulness, and overall quality of Alexa’s replies. They are, in essence, helping to train the neural networks within Amazon’s infrastructural stack....

At this moment in the 21st century, we see a new form of extractivism that is well underway: one that reaches into the furthest corners of the biosphere and the deepest layers of human cognitive and affective being. Many of the assumptions about human life made by machine learning systems are narrow, normative and laden with error. Yet they are inscribing and building those assumptions into a new world, and will increasingly play a role in how opportunities, wealth, and knowledge are distributed.

The stack that is required to interact with an Amazon Echo goes well beyond the multi-layered ‘technical stack’ of data modeling, hardware, servers and networks. The full stack reaches much further into capital, labor and nature, and demands an enormous amount of each. The true costs of these systems – social, environmental, economic, and political – remain hidden and may stay that way for some time.
artificial_intelligence  supply_chain  extraction  geology  labor  teaching 
28 days ago by shannon_mattern

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