The Bible of AI

S++: A Fast and Deployable Secure-Computation Framework for Privacy-Preserving Neural Network Training

S++: A Fast and Deployable Secure-Computation Framework for Privacy-Preserving Neural Network Training

We introduce S++, a simple, robust, and deployable framework for training a neural network (NN) using private data from multiple sources, using secret-shared secure function evaluation. In short, consider a virtual third party to whom every data-holder sends their inputs, and which computes the neural network: in our case, this virtual third party is actually a set of servers which individually learn nothing, even with a malicious (but non-colluding) adversary.

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Multi-Image Steganography Using Deep Neural Networks

Multi-Image Steganography Using Deep Neural Networks

Steganography is the science of hiding a secret message within an ordinary public message. Over the years, steganography has been used to encode a lower resolution image into a higher resolution image by simple methods like LSB manipulation. We aim to utilize deep neural networks for the encoding and decoding of multiple secret images inside a single cover image of the same resolution.

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R0:5ad410ba3fa0191312506cf94754bfd9-Addressing Ethical Dilemmas in AI: Listening to Engineers

Addressing Ethical Dilemmas in AI: Listening to Engineers

Documentation is key – design decisions in AI development must be documented in detail, potentially taking inspiration from the field of risk management. There is a need to develop a framework for large-scale testing of AI effects, beginning with public tests of AI systems, and moving towards real-time validation and monitoring. Governance frameworks for decisions in AI development need to be clarified, including the questions of post-market surveillance of product or system performance. Certification of AI ethics expertise would be helpful to support professionalism in AI development teams. Distributed responsibility should be a goal, resulting in a clear definition of roles and responsibilities as well as clear incentive structures for taking in to account broader ethical concerns in the development of AI systems.

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El arte de la Inteligencia Artificial desde una perspectiva léxica

El arte de la Inteligencia Artificial desde una perspectiva léxica

El principal objetivo de este documento es construir un glosario, a partir de las propuestas léxicas realizadas por los diferentes entes tecnológicos (ISO, IEEE, Wikipedia y Oxford University Press). Adicionalmente, el glosario estará estructurado según las ramas de conocimiento de esta área de trabajo, determinando exhaustiva y detalladamente las características de los términos que se incluirán en él para así facilitar una lectura amigable a la par que eficiente al usuario.

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R0:daa46e0033d013818bcc056d269d5737-Unsupervised deep clustering and reinforcement learning can accurately segment MRI brain tumors with very small training sets

Unsupervised deep clustering and reinforcement learning can accurately segment MRI brain tumors with very small training sets

«We have demonstrated a proof-of-principle application of unsupervised deep clustering and reinforcement learning to segment brain tumors. The approach represents human-allied AI that requires minimal input from the radiologist without the need for hand-traced annotation».

Unsupervised deep clustering and reinforcement learning can accurately segment MRI brain tumors with very small training sets Leer más »

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R0: dde004c79ac901067ab1189ea01b8ac7-Data Science: A First Introduction

Data Science: A First Introduction

The book is structured so that learners spend the first four chapters learning how to use the R programming language and Jupyter notebooks to load, wrangle/clean, and visualize data, while answering descriptive and exploratory data analysis questions. The remaining chapters illustrate how to solve four common problems in data science, which are useful for answering predictive and inferential data analysis questions[…]

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R0identifier_ 7029b309b114bdb1aa72b6fd0da905f3-Yann LeCun’s Deep Learning Course at CDS

Yann LeCun’s Deep Learning Course at CDS

This course concerns the latest techniques in deep learning and representation learning, focusing on supervised and unsupervised deep learning, embedding methods, metric learning, convolutional and recurrent nets, with applications to computer vision, natural language understanding, and speech recognition. The prerequisites include: DS-GA 1001 Intro to Data Science or a graduate-level machine learning course.

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R0_fe33488e78e8d3bac711f1ffb6ea5a48-Bayesian-Data-Analysis-course

Bayesian Data Analysis: book & course

This book is intended to have three roles and to serve three associated audiences: an introductory text on Bayesian inference starting from first principles, a graduate text on effective current approaches to Bayesian modeling and computation in statistics and related fields, and a handbook of Bayesian methods in applied statistics for general users of and researchers in applied statistics. Although introductory in its early sections, the book is definitely not elementary in the sense of a first text in statistics

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Side-Channel Sensing: Exploiting Side-Channels to Extract Information for Medical Diagnostics and Monitoring

Side-Channel Sensing: Exploiting Side-Channels to Extract Information for Medical Diagnostics and Monitoring

Information within systems can be extracted through side-channels; unintended communication channels that leak information. The concept of side-channel sensing is explored, in which sensor data is analysed in non-trivial ways to recover subtle, hidden or unexpected information.

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