Algorithmic Justice League is a collective started that aims to remove human bias from AI algorithms that can result in exclusionary experiences and discriminatory practices. It focuses on 3 key areas 1) Highlight Algorithmic Bias through Media, Art, and Science 2) Provide Space for People to Voice Concerns and Experiences with Coded Bias, 3) Develop Practices for Accountability During the Design, Development, and Deployment of Coded Systems. AI Now Institute at New York University is an interdisciplinary research center dedicated to understanding the social implications of artificial intelligence. Their work focuses on four core domains: Rights & Liberties, Labor & Automation, Bias & Inclusion, Safety & Critical Infrastructure. AI Ethics Lab brings together researchers and practitioners from various disciplines to detect and solve issues related to ethical design in AI. Based in US and Turkey, the Lab offers a comprehensive approach to ethical design of AI-related technology.

This week Athena Security announces the successful launch and implementation of the world's first artificial intelligence security camera system capable of instantly and accurately recognizing an active shooter before they shoot, alerting law enforcement and verbally alerting the assailant that Police are en route. Historically, security cameras are only as useful as the people actively monitoring them. Without real time oversight, unattended security camera feeds only help to piece together crimes after the fact. Past AI and computer vision technologies provided far too many false positives and therefore could not be deployed because they would erroneously alert law enforcement every couple of minutes rendering them useless. Athena Security's AI-powered system provides authorities with real time video footage helping speed police and medical aid to any type of crime scene decreasing fatalities with faster response times.

Here's how technology can help companies adopt a four-day workweekPexels.com The idea of a four-day workweek has been floating around for decades, spurring employees everywhere to picture what they could do with a three-day weekend every week. Employers might be a harder sell, but that hasn't stopped several companies from running the experiment. And, thanks to advances in technology, that corporate policy may become more common in the not-too-distant future. Re-opening this conversation is a Trades Union Congress (TUC) report, "A Future That Works For Working People." The report by the British labor group explains that as technology makes work more efficient, the time savings should be shared with the labor force.

Human action recognition refers to automatic recognizing human actions from a video clip, which is one of the most challenging tasks in computer vision. In reality, a video stream is often weakly-annotated with a set of relevant human action labels at a global level rather than assigning each label to a specific video episode corresponding to a single action, which leads to a multi-label learning problem. Furthermore, there are a great number of meaningful human actions in reality but it would be extremely difficult, if not impossible, to collect/annotate video clips regarding all of various human actions, which leads to a zero-shot learning scenario. To the best of our knowledge, there is no work that has addressed all the above issues together in human action recognition. In this paper, we formulate a real-world human action recognition task as a multi-label zero-shot learning problem and propose a framework to tackle this problem. Our framework simultaneously tackles the issue of unknown temporal boundaries between different actions for multi-label learning and exploits the side information regarding the semantic relationship between different human actions for zero-shot learning. As a result, our framework leads to a joint latent embedding representation for multi-label zero-shot human action recognition. The joint latent embedding is learned with two component models by exploring temporal coherence underlying video data and the intrinsic relationship between visual and semantic domain. We evaluate our framework with different settings, including a novel data split scheme designed especially for evaluating multi-label zero-shot learning, on two weakly annotated multi-label human action datasets: Breakfast and Charades. The experimental results demonstrate the effectiveness of our framework in multi-label zero-shot human action recognition.

Active learning aims to reduce annotation cost by predicting which samples are useful for a human teacher to label. However it has become clear there is no best active learning algorithm. Inspired by various philosophies about what constitutes a good criteria, different algorithms perform well on different datasets. This has motivated research into ensembles of active learners that learn what constitutes a good criteria in a given scenario, typically via multi-armed bandit algorithms. Though algorithm ensembles can lead to better results, they overlook the fact that not only does algorithm efficacy vary across datasets, but also during a single active learning session. That is, the best criteria is non-stationary. This breaks existing algorithms' guarantees and hampers their performance in practice. In this paper, we propose dynamic ensemble active learning as a more general and promising research direction. We develop a dynamic ensemble active learner based on a non-stationary multi-armed bandit with expert advice algorithm. Our dynamic ensemble selects the right criteria at each step of active learning. It has theoretical guarantees, and shows encouraging results on $13$ popular datasets.

Image source: Glenfinnan Viaduct – aka "The Harry Potter Bridge" source Wikipedia – an apt analogy bridging the known to the unknown! In April this year, I posted about the seven books to grasp the mathematical foundations of data science which was one of my most popular posts ever. It demonstrated to me that there is a real need to understand the maths foundations behind Data Science. As part of my teaching at the University of Oxford(Data Science for Internet of Things), I have often encountered the same issue in working with participants. I am also personally interested in democratising AI knowledge, especially for the younger generation.

BEIJING--(BUSINESS WIRE)--Sep 28, 2018--Schwarzman Scholars yesterday hosted a high level international dialogue on artificial intelligence (AI), aimed at encouraging greater discourse and cooperation on complicated topics associated with the development of AI. The event was held in conjunction with the release of the English language version of Tsinghua University's Chinese Institute for Science and Technology Policy's Artificial Intelligence Development Report. The report outlines China's AI development from four different perspectives: technological development, market applications, policy environment and social impact. It offers a multi-dimensional comparison between China and developed countries regarding AI development, and analyzes China's strengths, weaknesses and its position in the international AI competitive landscape. "Schwarzman Scholars is pleased to have served as the convening organization for this important international dialogue. I commend Dean Xue Lan on the release of his important report and thank the participants for their insightful comments and engagement," said Stephen A. Schwarzman, Founding Trustee of Schwarzman Scholars and Chairman, CEO and Co-Founder of Blackstone.

In A Beginner's Guide to Data Engineering series, I argued that academic institutions typically do not teach students the proper mental models when it comes to analytics workflows in real-life. Far too many classes only focus on the mechanics of data analysis without teaching concepts such as ETL or the importance of building robust data pipelines. Unfortunately, I see a similar pattern in machine learning education as well. Surely, studying the math behind ML and learning different algorithms are valuable. Yet, there exist crucial steps beyond model.fit(X,

How can you empower yourself? Because the skills required for jobs in the AI economy are changing so rapidly, we need to ensure that our systems for preparing, educating, training, and retraining the current and future workforce also evolve. Not only will the new AI economy require new technical skills, but there is a growing recognition that most workers will need to learn new skills throughout their working lives. At Microsoft, we believe everyone should have the resources to learn more and become an expert in AI, and this is why we are collaborating with organisations such as AI Singapore to empower as many persons as we can. In addition to that, we are making a lot of the AI learning assets that Microsoft uses free to use for individuals ranging from students to developers.

Using artificial intelligence to translate sign language in real time - see how we used Python to train a neural network with 86% accuracy in less than a day. Imagine a world where anyone can communicate using sign language over video. Inspired by this vision, some of our engineering team decided to bring this idea to HealthHack 2018. In less than 48 hours and using the power of artificial intelligence, their team was able to produce a working prototype which translated signs from the Auslan alphabet to English text in real time. People who are hearing impaired are left behind in video consultations.