Planning is an essential topic in the realm of automated driving. Besides planning algorithms that are widely covered in the literature, planning requires different software tools for its development, validation, and execution. This paper presents a survey of such tools including map representations, communication, traffic rules, open-source planning stacks and middleware, simulation, and visualization tools as well as benchmarks. We start by defining the planning task and different supporting tools. Next, we provide a comprehensive review of state-of-the-art developments and analysis of relations among them. Finally, we discuss the current gaps and suggest future research directions.

A few years ago, Amazon employed a new automated hiring tool to review the resumes of job applicants. Shortly after launch, the company realized that resumes for technical posts that included the word "women's" (such as "women's chess club captain"), or contained reference to women's colleges, were downgraded. The answer to why this was the case was down to the data used to teach Amazon's system. Based on 10 years of predominantly male resumes submitted to the company, the "new" automated system in fact perpetuated "old" situations, giving preferential scores to those applicants it was more "familiar" with. Defined by AI4ALL as the branch of computer science that allows computers to make predictions and decisions to solve problems, artificial intelligence (AI) has already made an impact on the world, from advances in medicine, to language translation apps.

The soccer bot lines up to take a shot at the goal. But instead of getting ready to block it, the goalkeeper drops to ground and wiggles its legs. Confused, the striker does a weird little sideways dance, stamping its feet and waving one arm, and then falls over. It's not a tactic you'll see used by the pros, but it shows that an artificial intelligence trained via deep reinforcement learning--the technique behind cutting-edge game-playing AIs like AlphaZero and the OpenAI Five--is more vulnerable to attack than previously thought. And that could have serious consequences.

Women in Big Data is spotlighting 8 amazing women on March 8th, International Women's Day. Naomi Molefe is WiBD South African Chapter Lead. I recently joined the group talent team at Discovery Holdings, an insurance business headquarted in Johannesburg with 7000 employees, operating in 19 countries with a revenue of just under $700 million (F18). My role is to assist the business to build, source and attract talent pipelines in support of business strategy and talent requirements. I work with the head of Talent Acquisition in executing strategic recruitment for senior and scarce skills.

Women in Big Data is spotlighting 8 amazing women on March 8th, International Women's Day. Naomi Molefe is WiBD South African Chapter Lead. I recently joined the group talent team at Discovery Holdings, an insurance business headquarted in Johannesburg with 7000 employees, operating in 19 countries with a revenue of just under $700 million (F18). My role is to assist the business to build, source and attract talent pipelines in support of business strategy and talent requirements. I work with the head of Talent Acquisition in executing strategic recruitment for senior and scarce skills.

DURBAN - Across the globe, trends in technology, economics and socioeconomics are culminating to disrupt the way entire industries operate and deliver products and services to consumers. When it comes to the impact of technology, there is no industry riper for disruption than the financial services sector, including insurance, which for the longest time remained trapped in outdated product development and delivery models. That has changed, and today we're seeing the pace of change and meaningful innovation in the insurance sector escalating. Not only is the existing insurance model from advice, underwriting, onboarding, risk management, servicing, and claims processing being turned on its head, but new product solutions are now possible for market sectors that have been entirely underserved and marginalised by the formal economy. Until now, most innovation in the insurance sector has been internally focused with little direct value to the customer.

Data Science Africa summer school is aimed at equipping participants with Machine Leaning, Data Science and Artificial Intelligence skills. This will be organised in sesions and after each session there will be exercises to assess the learning. To this end, we require that participants are well versed with the basics of the technologies and languages that will be used in the summer school. Particularly, we want to make sure participants have sufficient base skills in Python programming, Data science and Machine learning. You are required to download the notebook from the link below (by clicking it), complete the notebook and then fill out the registration form below that requires you to upload the completed notebook.

The U.S. Child Welfare System (CWS) is charged with improving outcomes for foster youth; yet, they are overburdened and underfunded. To overcome this limitation, several states have turned towards algorithmic decision-making systems to reduce costs and determine better processes for improving CWS outcomes. Using a human-centered algorithmic design approach, we synthesize 50 peer-reviewed publications on computational systems used in CWS to assess how they were being developed, common characteristics of predictors used, as well as the target outcomes. We found that most of the literature has focused on risk assessment models but does not consider theoretical approaches (e.g., child-foster parent matching) nor the perspectives of caseworkers (e.g., case notes). Therefore, future algorithms should strive to be context-aware and theoretically robust by incorporating salient factors identified by past research. We provide the HCI community with research avenues for developing human-centered algorithms that redirect attention towards more equitable outcomes for CWS.

With the advancements in computing technology and web-based applications, data is increasingly generated in multi-dimensional form. This data is usually sparse due to the presence of a large number of users and fewer user interactions. To deal with this, the Nonnegative Tensor Factorization (NTF) based methods have been widely used. However existing factorization algorithms are not suitable to process in all three conditions of size, density, and rank of the tensor. Consequently, their applicability becomes limited. In this paper, we propose a novel fast and efficient NTF algorithm using the element selection approach. We calculate the element importance using Lipschitz continuity and propose a saturation point based element selection method that chooses a set of elements column-wise for updating to solve the optimization problem. Empirical analysis reveals that the proposed algorithm is scalable in terms of tensor size, density, and rank in comparison to the relevant state-of-the-art algorithms.

Last week the daughter of the president of Russia, Vladimir Putin, Katerina Tikhonova, was appointed to head the Artificial Intelligence (AI) Institute located at Moscow State University. The university has produced 13 Nobel prizes, six Fields Medals and one Turing award, so in matters of science, putting the AI institute there is a big deal. In Russian, if a husband's last name is, for instance, Komlev, the wife's surname becomes Komleva. Thinking algorithmically, you add an "a" at the end of the husband's or the father's last name to get the wife's or the daughter's last name. So Katerina's surname is Tikhonova, which means that her husband's or one of her paternal ancestor's last name was Tikhonov.