In this post, I interview my former UMBC lab mate, Dr. Huguens Jean, who was just hired to work at Google's Video AI Group as an artificial intelligence researcher. Huguens shares his inspirational story, starting from Port-au-Prince, Haiti where he was born and raised, to his schooling at UMBC, and now to his latest position at Google. He also shares details on his humanitarian efforts where he's successfully applied computer vision and deep learning to rural Rwanda to help count footfall traffic. The data him and his team gathered through footfall traffic analysis was used to help the non-profit organization, Bridges to Prosperity, to construct infrastructure such as bridges and roads, to better connect Rwanda villages. Let's give a warm welcome to Dr. Huguens Jean as he shares his story. Thank you for doing this interview. It's such a wonderful pleasure to have you here on the PyImageSearch blog.

My father, Richard Zobel, who has died aged 81, was a pioneering computer scientist at the University of Manchester, birthplace of "Baby", the world's first stored-program computer. He rode the wave of the information technology revolution, starting in the early 1960s on military flight simulators for the electronics and equipment company Sperry's – the valve analog computers they used ran so hot that he had to work in the cool of the night – and in later years recommending improvements to the distant early warning system (Dews) protecting Indian Ocean coastlines from tsunami, but it was his 40-year academic career that defined his professional life. Richard was born in Lewisham, south London, the son of Joan, a dressmaker, and Norman Zobel, a car mechanic, just before the outbreak of the second world war, and narrowly escaped early tragedy when a water tank came through the ceiling and landed on his bed during the blitz. He went to Colfe's school (then a grammar school) on a scholarship, and graduated in 1963 in electrical engineering from London University, sponsored on his sandwich course by Sperry Gyroscope, a UK arm of the US company, which had headquarters in Bracknell. He met Lesley Winks at Peggy Spencer's ballroom dancehall in Penge, and they married in 1964.

The AI solution for clamping systems in the automotive industry prevents expensive production line shutdowns due to clamp failure. Festo has been named an artificial intelligence (AI) champion for the company's project Intelligent Pneumatic Runtime Monitoring. The award was given during the inaugural Baden-Württemberg awards ceremony on August 11. Baden-Württemberg, Germany's third largest state, is one of the country's leading regions for AI development. Thousands of pneumatic clamping systems are used daily in the automotive industry for tasks such as holding individual parts in place during body-shop welding.

Many interactive data systems combine visual representations of data with embedded algorithmic support for automation and data exploration. To effectively support transparent and explainable data systems, it is important for researchers and designers to know how users understand the system. We discuss the evaluation of users' mental models of system logic. Mental models are challenging to capture and analyze. While common evaluation methods aim to approximate the user's final mental model after a period of system usage, user understanding continuously evolves as users interact with a system over time. In this paper, we review many common mental model measurement techniques, discuss tradeoffs, and recommend methods for deeper, more meaningful evaluation of mental models when using interactive data analysis and visualization systems. We present guidelines for evaluating mental models over time that reveal the evolution of specific model updates and how they may map to the particular use of interface features and data queries. By asking users to describe what they know and how they know it, researchers can collect structured, time-ordered insight into a user's conceptualization process while also helping guide users to their own discoveries.

Mia Dand is an instigator. She has created an important platform in AI Ethics that has proven crucial in the times we currently live. Her most recent event, Women in AI Ethics Annual Conference brought together important voices in the current state of diversity in ethics and AI. Founded in 2018, 100 Brilliant Women in AI Ethics (WAIE) list has cultivated an engaged community and has created an emergence of women in research, technology, culture, business – spanning across the globe. What has emanated are the stories and lessons from their important works that have spilled into the mainstream.

But the kind of power generation that Edison pioneered on that September day in 1882 put us on a trajectory that has had unfortunate outcomes. He kicked into overdrive our reliance on fossil fuels for energy, allowing it to permeate all aspects of our lives--from the electricity we need to power our homes, offices and factories, to the petroleum we need to run our cars, ships and planes. This forced us down a path of high-energy consumption that has resulted in the rapid depletion of naturally occurring carbon-based fuel sources and inflicted near-irreversible damage on our planet. Edison's choice of coal as the fuel source for his power plant should not be taken as indicative of his support for fossil fuels as a source of energy. At least in the context of transportation, he believed that automobiles should run on electricity--not petrol--and even built a vehicle powered by alkaline batteries of his own invention.

Neural networks are a prevalent and effective machine learning component, and their application is leading to significant scientific progress in many domains. As the field of neural network systems is fast growing, it is important to understand how advances are communicated. Diagrams are key to this, appearing in almost all papers describing novel systems. This paper reports on a study into the use of neural network system diagrams, through interviews, card sorting, and qualitative feedback structured around ecologically-derived examples. We find high diversity of usage, perception and preference in both creation and interpretation of diagrams, examining this in the context of existing design, information visualisation, and user experience guidelines. Considering the interview data alongside existing guidance, we propose guidelines aiming to improve the way in which neural network system diagrams are constructed.

Automatically detecting personality traits can aid several applications, such as mental health recognition and human resource management. Most datasets introduced for personality detection so far have analyzed these traits for each individual in isolation. However, personality is intimately linked to our social behavior. Furthermore, surprisingly little research has focused on personality analysis using low resource languages. To this end, we present a novel peer-to-peer Hindi conversation dataset- Vyaktitv. It consists of high-quality audio and video recordings of the participants, with Hinglish textual transcriptions for each conversation. The dataset also contains a rich set of socio-demographic features, like income, cultural orientation, amongst several others, for all the participants. We release the dataset for public use, as well as perform preliminary statistical analysis along the different dimensions. Finally, we also discuss various other applications and tasks for which the dataset can be employed.

Recently, the surge in popularity of Internet of Things (IoT), mobile devices, social media, etc. has opened up a large source for graph data. Graph embedding has been proved extremely useful to learn low-dimensional feature representations from graph structured data. These feature representations can be used for a variety of prediction tasks from node classification to link prediction. However, existing graph embedding methods do not consider users' privacy to prevent inference attacks. That is, adversaries can infer users' sensitive information by analyzing node representations learned from graph embedding algorithms. In this paper, we propose Adversarial Privacy Graph Embedding (APGE), a graph adversarial training framework that integrates the disentangling and purging mechanisms to remove users' private information from learned node representations. The proposed method preserves the structural information and utility attributes of a graph while concealing users' private attributes from inference attacks. Extensive experiments on real-world graph datasets demonstrate the superior performance of APGE compared to the state-of-the-arts. Our source code can be found at https://github.com/uJ62JHD/Privacy-Preserving-Social-Network-Embedding.

In a one-hour-long video interview with This Week in Startups, Clearview AI CEO Hoan Ton-That discusses the misconceptions around biometric facial recognition, arguing his technology is "a tool to help get a lead" and only "used when there is probable cause for a crime." In the middle of an international scandal, the New York startup had signed over 2,400 service contracts with law enforcement agencies to deploy its facial recognition software, at the time of the interview in May. Interviewer Jason Calacanis based his interview on a New York Times investigation discussing the company's business strategy to scrape images from social networks. Despite the controversy, Ton-That claims "it's an honor to be at the center of the debate now and talk about privacy," and confirms the paper's reporting is "extremely fair." The interview was recorded in May, but posted this week, as protests against police violence gripped America and changed the context of discussions on law enforcement tools.