Artificial intelligence (AI) is already embedded in a range of digital services. Voice assistants such as Alexa, car routing or content translation all involve machine learning - the most popular form of artificial intelligence technology. There are many warnings these days about AI, such as the ethics behind these machine driven decision systems or threats of automation and the loss of many jobs. Very little is reported about how artificial intelligence can improve public services and can have positive social impact. Smart algorithms combined with cloud computing power allow unprecedented forms of data analysis that would take much longer if humans were doing it.

At first blush, the idea of scanning images synced up to iCloud for child sexual abuse materials against the hash list of known CSAM images seems like a good idea. As a survivor of childhood sexual abuse myself, I want tech companies to takes some initiative to deal with this issue. They also want to scan images on kids' phones using AI to see if kids are getting into any trouble with sending or receiving sexual material. Again, that sounds like a good thing. But, as the EFF points out, this all requires a backdoor, and backdoors, once created, almost never remain used for just one purpose.

A new report from FICO and Corinium has found that many companies are deploying various forms of AI throughout their businesses with little consideration for the ethical implications of potential problems. The increasing scale of AI is raising the stakes for major ethical questions. There have been hundreds of examples over the last decade of the many disastrous ways AI has been used by companies, from facial recognition systems unable to discern darker skinned faces to healthcare apps that discriminate against African American patients to recidivism calculators used by courts that skew against certain races. Despite these examples, FICO's State of Responsible AI report shows business leaders are putting little effort into ensuring that the AI systems they use are both fair and safe for widespread use. The survey, conducted in February and March, features the insights of 100 AI-focused leaders from the financial services sector, with 20 executives hailing from the US, Latin America, Europe, the Middle East, Africa, and the Asia Pacific regions.

Many researchers have studied student academic performance in supervised and unsupervised learning using numerous data mining techniques. Neural networks often need a greater collection of observations to achieve enough predictive ability. Due to the increase in the rate of poor graduates, it is necessary to design a system that helps to reduce this menace as well as reduce the incidence of students having to repeat due to poor performance or having to drop out of school altogether in the middle of the pursuit of their career. It is therefore necessary to study each one as well as their advantages and disadvantages, so as to determine which is more efficient in and in what case one should be preferred over the other. The study aims to develop a system to predict student performance with Artificial Neutral Network using the student demographic traits so as to assist the university in selecting candidates (students) with a high prediction of success for admission using previous academic records of students granted admissions which will eventually lead to quality graduates of the institution. The model was developed based on certain selected variables as the input. It achieved an accuracy of over 92.3 percent, showing Artificial Neural Network potential effectiveness as a predictive tool and a selection criterion for candidates seeking admission to a university.

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Every day for over four years, Ramses woke up in his home in Barquisimeto, Venezuela, turned on his computer, and began labeling images that will help make self-driving cars ubiquitous one day. Through a microtasking platform called Remotasks, he would identify mundane objects that line the streets everywhere -- trees, lampposts, pedestrians, stop signs -- so that autonomous vehicles could learn to notice them, too. Like many Venezuelans, Ramses turned to microtasking when his country plunged into economic turmoil. The gig gave him the opportunity to earn American dollars instead of the local currency, which is subject to extraordinarily high inflation. "I would work Sunday to Sunday," Ramses, who asked to use only his first name for privacy reasons, told Rest of World over WhatsApp.

At first glance, a recently granted South African patent relating to a "food container based on fractal geometry" seems fairly mundane. The innovation in question involves interlocking food containers that are easy for robots to grasp and stack. On closer inspection, the patent is anything but mundane. That's because the inventor is not a human being – it is an artificial intelligence (AI) system called DABUS. DABUS (which stands for "device for the autonomous bootstrapping of unified sentience") is an AI system created by Stephen Thaler, a pioneer in the field of AI and programming.

Traumatic brain injury can be caused by various types of head impacts. However, due to different kinematic characteristics, many brain injury risk estimation models are not generalizable across the variety of impacts that humans may sustain. The current definitions of head impact subtypes are based on impact sources (e.g., football, traffic accident), which may not reflect the intrinsic kinematic similarities of impacts across the impact sources. To investigate the potential new definitions of impact subtypes based on kinematics, 3,161 head impacts from various sources including simulation, college football, mixed martial arts, and car racing were collected. We applied the K-means clustering to cluster the impacts on 16 standardized temporal features from head rotation kinematics. Then, we developed subtype-specific ridge regression models for cumulative strain damage (using the threshold of 15%), which significantly improved the estimation accuracy compared with the baseline method which mixed impacts from different sources and developed one model (R^2 from 0.7 to 0.9). To investigate the effect of kinematic features, we presented the top three critical features (maximum resultant angular acceleration, maximum angular acceleration along the z-axis, maximum linear acceleration along the y-axis) based on regression accuracy and used logistic regression to find the critical points for each feature that partitioned the subtypes. This study enables researchers to define head impact subtypes in a data-driven manner, which leads to more generalizable brain injury risk estimation.

The core network architecture of telecommunication systems has undergone a paradigm shift in the fifth-generation (5G)networks. 5G networks have transitioned to software-defined infrastructures, thereby reducing their dependence on hardware-based network functions. New technologies, like network function virtualization and software-defined networking, have been incorporated in the 5G core network (5GCN) architecture to enable this transition. This has resulted in significant improvements in efficiency, performance, and robustness of the networks. However, this has also made the core network more vulnerable, as software systems are generally easier to compromise than hardware systems. In this article, we present a comprehensive security analysis framework for the 5GCN. The novelty of this approach lies in the creation and analysis of attack graphs of the software-defined and virtualized 5GCN through machine learning. This analysis points to 119 novel possible exploits in the 5GCN. We demonstrate that these possible exploits of 5GCN vulnerabilities generate five novel attacks on the 5G Authentication and Key Agreement protocol. We combine the attacks at the network, protocol, and the application layers to generate complex attack vectors. In a case study, we use these attack vectors to find four novel security loopholes in WhatsApp running on a 5G network.

An efficient method for obtaining low-density hyperplane separators in the unsupervised context is proposed. Low density separators can be used to obtain a partition of a set of data based on their allocations to the different sides of the separators. The proposed method is based on applying stochastic gradient descent to the integrated density on the hyperplane with respect to a convolution of the underlying distribution and a smoothing kernel. In the case where the bandwidth of the smoothing kernel is decreased towards zero, the bias of these updates with respect to the true underlying density tends to zero, and convergence to a minimiser of the density on the hyperplane can be obtained. A post-processing of the partition induced by a collection of low-density hyperplanes yields an efficient and accurate clustering method which is capable of automatically selecting an appropriate number of clusters. Experiments with the proposed approach show that it is highly competitive in terms of both speed and accuracy when compared with relevant benchmarks. Code to implement the proposed approach is available in the form of an R package from https: //github.com/DavidHofmeyr/iMDH.