The Bible of AI™

International scientific and technical publication on Artificial Intelligence | ISSN 2695-6411 | Officially founded in September, 2019

Papers

https://estadosia.files.wordpress.com/2020/09/probabilistic-machine-learning-for-healthcare.jpg

Probabilistic Machine Learning for Healthcare

Machine learning can be used to make sense of healthcare data. Probabilistic machine learning models help provide a complete picture of observed data in healthcare. In this review, we examine how probabilistic machine learning can advance healthcare. We consider challenges in the predictive model building pipeline where probabilistic models can be beneficial including calibration and missing data. Beyond predictive models, we also investigate the utility of probabilistic machine learning models in phenotyping, in generative models for clinical use cases, and in reinforcement learning.

https://estadosia.files.wordpress.com/2020/09/report-on-publications-norms-for-responsible-ai.jpg

Report on Publications Norms for Responsible AI

The history of science and technology shows that seemingly innocuous developments in scientific theories and research have enabled real-world applications with significant negative consequences for humanity.

https://estadosia.files.wordpress.com/2020/09/cocir-analyses-application-of-medical-device-legislation-to-artificial-intelligence.jpg

COCIR analyses application of medical device legislation to Artificial Intelligence

“The European Commission has shown its ambition in the area of artificial intelligence (AI) in its recent White Paper on Artificial Intelligence – a European approach to excellence and trust. This White Paper is at the same time a precursor of possible legislation of AI in products and services in the European Union. However, COCIR sees no need for novel regulatory frameworks for AI-based devices in Healthcare, because the requirements of EU MDR and EU IVDR in combination with GDPR are adequate to ensure that same excellence and trust.” (COCIR paper).

https://estadosia.files.wordpress.com/2020/08/ieee-use-casee28093criteria-for-addressing-ethical-challenges-in-transparency-accountability-and-privacy-of-cta_ctt.jpg

IEEE Use Case–Criteria for Addressing Ethical Challenges in Transparency, Accountability, and Privacy of CTA/CTT

There are substantial public health benefits gained through successfully alerting individuals and relevant public health institutions of a person’s exposure to a communicable disease. Contact tracing techniques have been applied to epidemiology for centuries, traditionally involving a manual process of interview and follow-up. This is time-consuming, difficult, and dangerous work. Manual processes are also open to incomplete information because they rely on individuals being willing and able to remember and report all contact possibilities.

https://estadosia.files.wordpress.com/2020/08/the-deep-learning-revolution-and-its-implications-for-computer-architecture-and-chip-design.jpg

The Deep Learning Revolution and Its Implications for Computer Architecture and Chip Design

The past decade has seen a remarkable series of advances in machine learning, and in particular deep learning approaches based on artificial neural networks, to improve our abilities to build more accurate systems across a broad range of areas, including computer vision, speech recognition, language translation, and natural language understanding tasks.

https://estadosia.files.wordpress.com/2020/07/textattack-a-framework-for-adversarial-attacks-data-augmentation-and-adversarial-training-in-nlp.jpg

TextAttack: A Framework for Adversarial Attacks, Data Augmentation, and Adversarial Training in NLP

This paper introduces TextAttack, a Python framework for adversarial attacks, data augmentation, and adversarial training in NLP. TextAttack builds attacks from four components: a goal function, a set of constraints, a transformation, and a search method. TextAttack’s modular design enables researchers to easily construct attacks from combinations of novel and existing components. TextAttack provides implementations of 16 adversarial attacks from the literature and supports a variety of models and datasets, including BERT and other transformers, and all GLUE tasks.

R0=5f28cd89d8b22d05e145c722a304e1c6

Diagnostic uncertainty calibration: towards reliable machine predictions in medical domain

We further formalize the metrics for higher-order statistics, including inter-rater disagreement, in a unified way, which enables us to assess the quality of distributional uncertainty. In addition, we propose a novel post-hoc calibration method that equips trained neural networks with calibrated distributions over class probability estimates. With a large-scale medical imaging application, we show that our approach significantly improves the quality of uncertainty estimates in multiple metrics.

https://estadosia.files.wordpress.com/2020/06/the-state-of-a-ethics-report-june-2020.jpg

The State of AI Ethics Report (June 2020)

It has never been more important that we keep a sharp eye out on the development of this field and how it is shaping our society and interactions with each other. With this inaugural edition of the State of AI Ethics we hope to bring forward the most important developments that caught our attention at the Montreal AI Ethics Institute this past quarter. Our goal is to help you navigate this ever-evolving field swiftly and allow you and your organization to make informed decisions.

https://estadosia.files.wordpress.com/2020/06/explaining-autonomous-driving-by-learning-end-to-end-visual-attention.jpg

Explaining Autonomous Driving by Learning End-to-End Visual Attention

In this work we propose to train an imitation learning based agent equipped with an attention model. The attention model allows us to understand what part of the image has been deemed most important. Interestingly, the use of attention also leads to superior performance in a standard benchmark using the CARLA driving simulator.

https://estadosia.files.wordpress.com/2020/05/techdispatch-1_2020_-contact-tracing-with-mobile-applications-2.jpg

TechDispatch #1/2020: Contact Tracing with Mobile Applications

In public health, contact tracing is the process to identify individuals who have been in contact with infected persons. Proximity tracing with smartphone applications and sensors could support contact tracing. It involves processing of sensitive personal data.

https://estadosia.files.wordpress.com/2020/05/a-human-centered-evaluation-of-a-deep-learning-system-deployed-in-clinics-for-the-detection-of-diabetic-retinopathy.jpg

A Human-Centered Evaluation of a Deep Learning System Deployed in Clinics for the Detection of Diabetic Retinopathy

This paper contributes the first human-centered observational study of a deep learning system deployed directly in clinical care with patients. Through field observations and interviews at eleven clinics across Thailand, we explored the expectations and realities that nurses encounter in bringing a deep learning model into their clinical practices. First, we outline typical eye-screening workflows and challenges that nurses experience when screening hundreds of patients. Then, we explore the expectations nurses have for an AI-assisted eye screening process. Next, we present a human-centered, observational study of the deep learning system used in clinical care, examining nurses’ experiences with the system, and the socio-environmental factors that impacted system performance. Finally, we conclude with a discussion around applications of HCI methods to the evaluation of deep learning algorithms in clinical environments.

https://estadosia.files.wordpress.com/2020/05/a-socio-technical-framework-for-digital-contact-tracing-2.jpg

A socio-technical framework for digital contact tracing

In their efforts to tackle the COVID-19 crisis, decision makers are considering the development and use of smartphone applications for contact tracing. Even though these applications differ in technology and methods, there is an increasing concern about their implications for privacy and human rights. Here we propose a framework to evaluate their suitability in terms of impact on the users, employed technology and governance methods.

https://estadosia.files.wordpress.com/2020/05/artificial-intelligence-and-machine-learning-in-software-as-a-medical-device_-discussion-paper-and-request-for-feedback-4.jpg

Artificial Intelligence and Machine Learning in Software as a Medical Device: discussion Paper and Request for Feedback

Artificial intelligence and machine learning technologies have the potential to transform health care by deriving new and important insights from the vast amount of data generated during the delivery of health care every day. Medical device manufacturers are using these technologies to innovate their products to better assist health care providers and improve patient care. The FDA is considering a total product lifecycle-based regulatory framework for these technologies.

https://estadosia.files.wordpress.com/2020/04/e74ee944-e7de-480c-9b60-665e97c78261.png

Encrypted Traffic Analysis

This report explores the current state of affairs in Encrypted Traffic Analysis and in particular discusses research and methods in 6 key use cases; viz. application identification, network analytics, user information identification, detection of encrypted malware, file/device/website/location fingerprinting and DNS tunnelling detection.

https://estadosia.files.wordpress.com/2020/04/cmglee_cambridge_science_festival_2015_da_vinci.jpg

The need for a system view to regulate artificial intelligence/machine learning-based software as medical device

FDA need to widen their scope from evaluating medical AI/ML-based products to assessing systems. This shift in perspective—from a product view to a system view—is central to maximizing the safety and efficacy of AI/ML in health care, but it also poses significant challenges for agencies like the FDA who are used to regulating products, not systems. We offer several suggestions for regulators to make this challenging but important transition

https://estadosia.files.wordpress.com/2020/04/defining-artificial-intelligence.jpg

Defining Artificial Intelligence |🇪🇸 Definiendo Inteligencia Artificial

The starting point to develop the operational definition is the definition of AI adopted by the High Level Expert Group on artificial intelligence. To derive this operational definition we have followed a mixed methodology. On one hand, we apply natural language processing methods to a large set of AI literature. On the other hand, we carry out a qualitative analysis on 55 key documents including artificial intelligence definitions from three complementary perspectives: policy, research and industry.

https://estadosia.files.wordpress.com/2020/03/mathematics-for-machine-learning.png

Mathematics for Machine Learning

Machine learning uses tools from a variety of mathematical fields. This document is an attempt to provide a summary of the mathematical background needed.

https://estadosia.files.wordpress.com/2020/02/image-7.png

Machine learning in Python: the top programming language

Phyton’s most notable points are:
-Is a great library ecosystem (Scikit-learn, Pandas, Matplotlib, NLTK, Scikit-image, PyBrain, Caffe, StatsModels, TensorFlow, Keras, etc).
-Growing popularity.
-Has a low entry barrier, has flexibility, is a platform independence, has readability, good visualization options, good community support and growing popularity.

WHITE PAPER On Artificial Intelligence – A European approach to excellence and trust

The purpose of this White Paper is to set out policy options on how to achieve these objectives. It does not address the development and use of AI for military purposes.The Commission invites Member States, other European institutions, and all stakeholders, including industry, social partners, civil society organisations, researchers, the public in general and any interested party, to react to the options and to contribute to the Commission’s future decision-making in this domain.

Cognitive Anthropomorphism of AI: How Humans and Computers Classify Images

Modern AI image classifiers have made impressive advances in recent years, but their performance often appears strange or violates expectations of users. This suggests humans engage in cognitive anthropomorphism: expecting AI to have the same nature as human intelligence. This mismatch presents an obstacle to appropriate human-AI interaction.

https://arxiv.org/pdf/2001.06309.pdf

Cyber Attack Detection thanks to Machine Learning Algorithms

Cybersecurity attacks are growing both in frequency and sophistication over the years. This increasing sophistication and complexity call for more advancement and continuous innovation in defensive strategies. Traditional methods of intrusion detection and deep packet inspection, while still largely used
and recommended, are no longer sufficient to meet the demands of growing security threats.

Sustainable Development Goals (SDGs)

The role of artificial intelligence in achieving the Sustainable Development Goals: an excellent work presented to the members of the European Alliance for AI

Our director (as a member of The European AI Alliance) uploaded this work and contributed to the documentation that the members of the European Alliance for AI and the high-level AI group are handling. He consider these points of view is important in terms of sustainable development. Link to the post at european AI alliance […]

Quickstart to ML with dabl. thi is an interesting new python package that makes supervised machinelearning easy

Machine Learning with dabl ( 0.1.8)

“dabl tries to reduce the turnaround time required for a quick baseline estimate of a supervised learning problem. It does so by automating the task of iterating through different techniques of data preprocessing, feature engineering, parameter tuning and model building to generate efficacious baseline models”.

Manifiesto de IBM con motivo del 50 encuentro en el foro de Davos. ‘Regulación de precisión para la inteligencia artificial’

El panel de Davos, organizado por el CEO de IBM, Ginni Rometty, explora la regulación de precisión de la inteligencia artificial y la tecnología emergente. El evento lanza formalmente IBM Policy Lab, un nuevo foro para promover recomendaciones de políticas audaces y viables para una sociedad digital y fomentar la confianza en la innovación.

People + AI Guidebook by Google

Escrita para profesionales de experiencia de usuario (UX) y gerentes de producto como una forma de ayudar a crear un enfoque centrado en el ser humano para la IA en sus equipos de producto.

A scalable pipeline for designing reconfigurable organisms

Most technologies are made from steel, concrete, chemicals, and plastics, which degrade over time and can produce harmful ecological and health side effects. It would thus be useful to build technologies using self-renewing and biocompatible materials, of which the ideal candidates are living systems themselves. Thus, we here present a method that designs completely biological machines from the ground up: computers automatically design new machines in simulation, and the best designs are then built by combining together different biological tissues. This suggests others may use this approach to design a variety of living machines to safely deliver drugs inside the human body, help with environmental remediation, or further broaden our understanding of the diverse forms and functions life may adopt.

Aprendizaje automático en análisis de datos sísmicos 4D

(Deep Neural Networks in Geophysics) Esta tesis investiga las propiedades fundamentales de las redes neuronales en aplicaciones geofísicas. Incluye la reutilización de redes neuronales entrenadas, que son excelentes para identificar imágenes y aplicarlas para identificar capas de rocas y eventos geológicos en imágenes geofísicas. Esta tesis profundiza para evaluar si la teoría de incluir información específica de […]

El Programa de Certificación de Ética para Sistemas Autónomos e Inteligentes (ECPAIS)

El objetivo del Programa de Certificación de Ética para Sistemas Autónomos e Inteligentes (ECPAIS) del IEEE SA es crear especificaciones para los procesos de certificación y marcado que promuevan la transparencia, la rendición de cuentas y la reducción del sesgo algorítmico en los Sistemas Autónomos e Inteligentes (A / IS). El objetivo de ECPAIS es […]

Ley: diseño de Inteligencia Artificial éticamente alineado (IEEE)

EAD, Primera edición, incluye comentarios sobre cómo la ley debe responder a una serie de desafíos éticos y legales específicos que plantea el desarrollo y despliegue de A/IS (Autonomous and Intelligent Systems) en la vida contemporánea. También se centra en el impacto de A/IS en la práctica del derecho mismo. Más específicamente, se estudia tanto […]

Loading…

Something went wrong. Please refresh the page and/or try again.

%d bloggers like this: