In this paper we present the impact of alternative data that originates from an app-based marketplace, in contrast to traditional bureau data, upon credit scoring models. These alternative data sources have shown themselves to be immensely powerful in predicting borrower behavior in segments traditionally underserved by banks and financial institutions. Our results, validated across two countries, show that these new sources of data are particularly useful for predicting financial behavior in low-wealth and young individuals, who are also the most likely to engage with alternative lenders. Furthermore, using the TreeSHAP method for Stochastic Gradient Boosting interpretation, our results also revealed interesting non-linear trends in the variables originating from the app, which would not normally be available to traditional banks. Our results represent an opportunity for technology companies to disrupt traditional banking by correctly identifying alternative data sources and handling this new information properly. At the same time alternative data must be carefully validated to overcome regulatory hurdles across diverse jurisdictions.

Increasing number of sectors which affect human lives, are using Machine Learning (ML) tools. Hence the need for understanding their working mechanism and evaluating their fairness in decision-making, are becoming paramount, ushering in the era of Explainable AI (XAI). In this contribution we introduced a few intrinsically interpretable models which are also capable of dealing with missing values, in addition to extracting knowledge from the dataset and about the problem. These models are also capable of visualisation of the classifier and decision boundaries: they are the angle based variants of Learning Vector Quantization. We have demonstrated the algorithms on a synthetic dataset and a real-world one (heart disease dataset from the UCI repository). The newly developed classifiers helped in investigating the complexities of the UCI dataset as a multiclass problem. The performance of the developed classifiers were comparable to those reported in literature for this dataset, with additional value of interpretability, when the dataset was treated as a binary class problem.

Forgetting is removing variables from a logical formula while preserving the constraints on the other variables. In spite of being a form of reduction, it does not always decrease the size of the formula and may sometimes increase it. This article discusses the implications of such an increase and analyzes the computational properties of the phenomenon. Given a propositional Horn formula, a set of variables and a maximum allowed size, deciding whether forgetting the variables from the formula can be expressed in that size is $D^p$-hard in $\Sigma^p_2$. The same problem for unrestricted propositional formulae is $D^p_2$-hard in $\Sigma^p_3$. The hardness results employ superredundancy: a superirredundant clause is in all formulae of minimal size equivalent to a given one. This concept may be useful outside forgetting.

Hospitals are exploring the use of artificial intelligence and robotics technologies to assess patients remotely as they look to protect overworked emergency-room and intensive-care personnel from Covid-19, the respiratory disease caused by the new coronavirus. A shortage of personal protective equipment in the U.S. has likely contributed to medical staff exposure to the virus. A recent survey by the Centers for Disease Control and Prevention found more than 9,000 health-care workers had contracted Covid-19.

Benjamin Lee, a second-year Ph.D. student in the Allen School's Artificial Intelligence group working with professor Daniel Weld, has been named a 2020 Innovator in Residence by the Library of Congress. Now in its second year, the Innovator in Residence program aims to enlist artists, researchers, journalists, and others in developing new and creative ways of using the library's digital collections. During his residency, Lee will apply deep learning to enable the automatic extraction and tagging of photographs and illustrations contained in the more than 15 million newspaper scans comprising the library's Chronicling America collection. His goal is to produce interactive visualizations, searchable by topic, that will make the content more accessible to users and support cultural heritage research. "A primary motivation behind my project is to excite the American public by demonstrating the possibilities of applying machine learning to library collections," Lee explained in an interview posted on the library's blog.

Elon Musk has a vision of linking human brains to computers in order to avoid our species from being outpaced by artificial intelligence – and this dream is set to become a reality. Speaking on Joe Rogan's podcast, the billionaire said his company Neuralink will have a version of its brain implant ready'within a year.' Musk explained that the process involves removing a chunk of the skull, robots then insert electrodes into the brain and the device into the hole, with only a small scar left behind. Neuralink, which was founded in 2016, is designing tiny flexible'threads' that are ten times thinner than a human hair with the goal of treating brain injuries and trauma. The tech tycoon also revealed that the technology could develop into a full brain interface in just 25 years, which would enable'symbiosis' between humans and AI. Speaking on Joe Rogan's podcast, Elon Musk said his company Neuralink will have a version of its brain implant ready'within a year' 'We're not testing people yet, but I think it won't be too long,' Musk told Rogan.

After a day-long Photoshop teaser and a cinematic trailer, we finally got to see Ubisoft's new Assassin's Creed game in action. During Microsoft's recent Inside Xbox stream, the company showed off gameplay footage of Assassin's Creed Valhalla. The trailer had a bit of everything. We got to see Eivor, the game's protagonist, lead an attack on a castle as well as sail a longboat. The trailer also offered a variety of vistas to see, with England's historic Stonehenge making an appearance at one point.

Last year, the company released an Android app called Bolo to help kids in India learn to read. The app, which is in early access, taps into Google's speech recognition and text-to-speech tech to understand how well kids are reading aloud. A virtual reading buddy named Diya provides visual and verbal encouragement. If a kid is struggling to pronounce a word or sentence, they can tap on Diya for help. The app will also recommend difficulty levels for stories and games based on reading performance.

"Deepfakes" are digitally altered images that make incidents appear real when they are not. Such altered files could have broad implications for politics. "Deepfakes" are digitally altered images that make incidents appear real when they are not. Such altered files could have broad implications for politics. Sophisticated fake media hasn't emerged as a factor in the disinformation wars in the ways once feared -- and two specialists say it may have missed its moment.

NEW YORK (Reuters) - After a week or so sick in bed in their New York City apartment in March, members of the Johnson-Baruch family were convinced they had been stricken by the novel coronavirus. Subsequent test results left them with more questions than answers. Tests both for the virus itself and for the antibodies the immune system produces to fight the infection are becoming more widely available, but they are not perfect. For Maree Johnson-Baruch, her husband, Jason Baruch, and their two teenage daughters, their experience ran the gamut. They all became sick around the same time with the same symptoms.