Facebook announces AI system to detect revenge porn, says accounts posting it will be deleted


Facebook first started trialling a system to combat revenge porn late last year, but it had one rather scary aspect: you had to upload your own nudes so the platform knew which images it should block. The earlier system, which is still in use and set for expanded rollout, relied on users uploading photos they were afraid might be shared, allowing Facebook to create a digital fingerprint to block uploads of matching images. You send the nude to yourself in Messenger, and Facebook creates a hashed digital fingerprint of the photo – an encrypted version of the raw data in the image file. Anytime someone tries to upload a photo, it is checked against that fingerprint and rejected if it matches. Facebook says its new AI-based system is designed to automatically detect nude or near-nude images, before passing them for a human moderator to decide whether the photo or video should be blocked.

Applying Active Diagnosis to Space Systems by On-Board Control Procedures

arXiv.org Artificial Intelligence

The instrumentation of real systems is often designed for control purposes and control inputs are designed to achieve nominal control objectives. Hence, the available measurements may not be sufficient to isolate faults with certainty and diagnoses are ambiguous. Active diagnosis formulates a planning problem to generate a sequence of actions that, applied to the system, enforce diagnosability and allow to iteratively refine ambiguous diagnoses. This paper analyses the requirements for applying active diagnosis to space systems and proposes ActHyDiag as an effective framework to solve this problem. It presents the results of applying ActHyDiag to a real space case study and of implementing the generated plans in the form of On-Board Control Procedures. The case study is a redundant Spacewire Network where up to 6 instruments, monitored and controlled by the on-board software hosted in the Satellite Management Unit, are transferring science data to a mass memory unit through Spacewire routers. Experiments have been conducted on a real physical benchmark developed by Thales Alenia Space and demonstrate the effectiveness of the plans proposed by ActHyDiag.

Algorithm for Identifying Ocular Conditions in Electronic Health Record Data


Results This study included 122 339 patients, with a mean (SD) age of 52.4 (25.1) years. Of these patients, 69 002 (56.4%) were female and 99 579 (81.4%) were white. The algorithm assigned a less than 10% probability of XFS for 121 085 patients (99.0%) as well as an XFS probability score of more than 75% for 543 patients (0.4%), more than 90% for 353 patients (0.3%), and more than 99% for 83 patients (0.07%). When there was ICD-9 or ICD-10 billing code documentation of XFS, in 86% or 96% of the records, respectively, evidence of XFS was also recorded elsewhere in the EHR. Conversely, when there was clinical examination or free-text evidence of XFS, it was documented with ICD-9 codes only approximately 40% of the time and even less often with ICD-10 codes.

Artificial Intelligence Decision Tree


In this article we will discuss decision points for selecting right components for Artificial Intelligence (AI) solutions. This is also an update to Machine Learning Decision Tree (v1). Keep in mind here that AI is a broader term compared to Machine Learning.

China has produced another study showing the potential of AI in medical diagnosis


A new study from China has found that an AI system can best some doctors when it comes to diagnosing common childhood diseases. The study, published in Nature Medicine yesterday (Feb. The study trained a deep-learning system on 101 million data points generated from the electronic records of 1.3 million patient visits to a medical center in Guangzhou. Researchers found that the AI system was able to meet or outperform two groups of junior physicians in accurately diagnosing a range of ailments, from asthma and pneumonia, to sinusitis and mouth-related diseases. The AI was also able to meet or exceed diagnostic performance with some groups of senior physicians, for instance, in the category of upper respiratory issues.

Adaptive Exact Learning of Decision Trees from Membership Queries

arXiv.org Machine Learning

In this paper we study the adaptive learnability of decision trees of depth at most $d$ from membership queries. This has many applications in automated scientific discovery such as drugs development and software update problem. Feldman solves the problem in a randomized polynomial time algorithm that asks $\tilde O(2^{2d})\log n$ queries and Kushilevitz-Mansour in a deterministic polynomial time algorithm that asks $ 2^{18d+o(d)}\log n$ queries. We improve the query complexity of both algorithms. We give a randomized polynomial time algorithm that asks $\tilde O(2^{2d}) + 2^{d}\log n$ queries and a deterministic polynomial time algorithm that asks $2^{5.83d}+2^{2d+o(d)}\log n$ queries.

Entropy: How Decision Trees Make Decisions – Towards Data Science


You've come a long way from writing your first line of Python or R code. You know your way around Scikit-Learn like the back of your hand. You spend more time on Kaggle than Facebook now. You're no stranger to building awesome random forests and other tree based ensemble models that get the job done. You want to dig deeper and understand some of the intricacies and concepts behind popular machine learning models.

Your Future Doctor Might Not Be Human – but Then Who Do You Blame for Medical Errors?


If your doctor makes a mistake, the question is usually when, not who, to sue. But what happens when the doctor is a robot? From the wrong diagnosis to wrong-site surgery, medical errors are made outrageously often by humans. As the third-leading cause of death in the U.S., they kill an estimated 440,000 people a year. But the involvement of artificial intelligence (AI) in these errors is brand new territory.

A Guide to Decision Trees for Machine Learning and Data Science


Decision Trees are a class of very powerful Machine Learning model cable of achieving high accuracy in many tasks while being highly interpretable. What makes decision trees special in the realm of ML models is really their clarity of information representation. The "knowledge" learned by a decision tree through training is directly formulated into a hierarchical structure. This structure holds and displays the knowledge in such a way that it can easily be understood, even by non-experts. You've probably used a decision tree before to make a decision in your own life.