Membership in ACM includes a subscription to Communications of the ACM (CACM), the computing industry's most trusted source for staying connected to the world of advanced computing. Studies conducted with families in the Arab GCC region found that digital addiction is highly prevalent among both parents and children. Digital addiction (DA) refers to a problematic relationship with technology characterized by symptoms of behavioral addiction, including mood modification, salience, tolerance, conflict, withdrawal symptoms, and relapse. While addictive use of technology is not yet officially recognized as a clinical diagnosis, certain forms, such as Internet gaming disorder (IGD), have been classified as clinical conditions. Notably, IGD was included in the ICD-11 (International Classification of Diseases) by the World Health Organization in 2018.

Machine-learning models are increasingly driving decisions in high-stakes settings, such as finance, law, and hiring, thus, highlighting the need for transparency. However, the key challenge is to balance transparency -- clarifying `why' a decision was made -- with recourse: providing actionable steps on `how' to achieve a favourable outcome from an unfavourable outcome. Counterfactual explanations reveal `why' an undesired outcome occurred and `how' to reverse it through targeted feature changes (interventions). Current counterfactual approaches have limitations: 1) they often ignore causal dependencies between features, and 2) they typically assume all interventions can happen simultaneously, an unrealistic assumption in practical scenarios where actions are typically taken in a sequence. As a result, these counterfactuals are often not achievable in the real world. We present P2C (Path-to-Counterfactuals), a model-agnostic framework that produces a plan (ordered sequence of actions) converting an unfavourable outcome to a causally consistent favourable outcome. P2C addresses both limitations by 1) Explicitly modelling causal relationships between features and 2) Ensuring that each intermediate state in the plan is feasible and causally valid. P2C uses the goal-directed Answer Set Programming system s(CASP) to generate the plan accounting for feature changes that happen automatically due to causal dependencies. Furthermore, P2C refines cost (effort) computation by only counting changes actively made by the user, resulting in realistic cost estimates. Finally, P2C highlights how its causal planner outperforms standard planners, which lack causal knowledge and thus can generate illegal actions.

The rumors were (mostly) true. Sony did indeed have a follow-up to its stellar WH-1000XM4 ready for a proper debut. Today the company announced the WH-1000XM5 ($400), its latest flagship noise-canceling headphones equipped with all of the things we've come to expect from Sony's 1000X line. This time around the company gave its premium cans a big exterior redesign. In the process, it massively increased comfort while also expanding the incredible performance in terms of noise cancelation and overall sound quality.

DOD is working to develop AI capabilities--computer systems capable of tasks that normally require human intelligence. We found that DOD's efforts …

The Department of Defense (DOD) is actively pursuing artificial intelligence (AI) capabilities. AI refers to computer systems designed to replicate a range of human functions and continually get better at their assigned tasks. GAO previously identified three waves or types of AI, shown below. DOD recognizes that developing and using AI differs from traditional software. Traditional software is programmed to perform tasks based on static instructions, whereas AI is programmed to learn to improve at its given tasks.

Deployment of a machine learning model means making your model predictions available to the users through API or through a web application. In this post let us see how we could leverage CherryPy and Docker to deploy a Machine Learning model. In this post, we are going to look at Model Deployment using CherryPy and Docker. CherryPy is a python, object-oriented web framework. A web framework is a software framework that assists us in developing web applications.

Loans are the core business of banks. The main profit comes directly from the loan's interest. The loan companies grant a loan after an intensive process of verification and validation. However, they still don't have assurance if the applicant is able to repay the loan with no difficulties. In this tutorial, we'll build a predictive model to predict if an applicant is able to repay the lending company or not.

Loans are the core business of banks. The main profit comes directly from the loan's interest. The loan companies grant a loan after an intensive process of verification and validation. However, they still don't have assurance if the applicant is able to repay the loan with no difficulties. In this tutorial, we'll build a predictive model to predict if an applicant is able to repay the lending company or not.

Deep learning, also known as deep structured learning or hierarchical learning, is a type of machine learning focused on learning data representations and feature learning rather than individual or specific tasks. Feature learning, also known as representation learning, can be supervised, semi-supervised or unsupervised. Deep learning architectures include deep neural networks, deep belief networks and recurrent neural networks. Real-world applications using deep learning include computer vision, speech recognition, machine translation, natural language processing, and image recognition. The following recipe introduces how to implement a deep neural network using TensorFlow, which is an open source software library, originally developed at Google, for complex computation by constructing network graphs of mathematical operations and data (Abadi et al. 2016; Cheng et al. 2017).

AI. Specifically, we address three Just how little can be illustrated by the reaction to Winograd and Flores's (1986) recent book Understanding Computers and Cognition. In personal comments, the book and its authors have been savaged. Published comments are, of course, more temperate (Vellino et al. 1987) but still reveal the hypersensitivity of the Penrose's (1989) even more recent book The Emperor's New Mind have been observed. Like Suchman (1987) and Clancey (1987), we feel that insights of significant value are to be gained from an objective consideration of traditional and alternative perspectives. Some efforts in this direction are evident (Haugeland [1985], Hill [1989], and Born [1987], for example), but the issue requires additional and ongoing attention.