Unfortunately, to over 40 million people across West African countries date, there isn't any usable machine translation including Mali, Guinea, Ivory Coast, Gambia, (MT) system for Nko, in part due to the unavailability Burkina Faso, Sierra Leone, Senegal, Liberia, and of large text corpora required by state-of-the-art Guinea-Bissau. Nko, which means'I say' in all neural machine translation (NMT) algorithms. Manding languages, was developed as both the Nko is a representative case study of the broader Manding literary standard language and a writing issues that interfere with the goal of universal machine system by Soulemana Kanté in 1949 for the translation. Thousands of languages still purpose of sustaining the strong oral tradition of don't have available or usable MT systems, mainly Manding languages (Niane, 1974; Conde, 2017; due to the unavailability of high-quality parallel Eberhard et al., 2023).

Machine learning models based on temporal point processes are the state of the art in a wide variety of applications involving discrete events in continuous time. However, these models lack the ability to answer counterfactual questions, which are increasingly relevant as these models are being used to inform targeted interventions. In this work, our goal is to fill this gap. To this end, we first develop a causal model of thinning for temporal point processes that builds upon the Gumbel-Max structural causal model. This model satisfies a desirable counterfactual monotonicity condition, which is sufficient to identify counterfactual dynamics in the process of thinning. Then, given an observed realization of a temporal point process with a given intensity function, we develop a sampling algorithm that uses the above causal model of thinning and the superposition theorem to simulate counterfactual realizations of the temporal point process under a given alternative intensity function. Simulation experiments using synthetic and real epidemiological data show that the counterfactual realizations provided by our algorithm may give valuable insights to enhance targeted interventions.

Remark Holdings, Inc. (MARK), a global technology company focused on artificial intelligence, and CP ALL Public Company Limited (CPALL.BK) today announced that they are partnering to deploy Remark's KanKan data intelligence and AI technologies in approximately 11,000 7-Eleven stores that CP ALL operates throughout Thailand. Remark and CP ALL will hold a press conference (the details of which appear below) on March 6, 2018, in Bangkok to launch the partnership.

What started of as a small Chinese big data startup, soon grew into a full data-intelligence service company listed among the top 10 in China. Data intelligence features Alibaba and other market leaders are offering, are often to expensive and complex for small to medium size businesses. This is exactly what KanKan, a US NASDAQ – listed Remark Holdings ("MARK") – subsidiary, sees as their opportunity. Their offerings are especially targeted towards SMEs, making their AI technology easy to use and very customisable.

Remark Media's image and video recognition technologies are based on Deep Learning, a type of algorithm-based machine learning that is used to model high-level abstractions in data. In developing its technologies, Remark Media leveraged its extensive KanKan data platform to train its AI with tens of millions of supervised and unsupervised samples, developing models that extract facial features and recognize objects in images or videos such as branded logos, animals, airplanes, license plates, etc., with high precision. The KanKan Data Intelligence Platform is a growing global platform of over 1.3 billion socially active user profiles, over 15 billion posts, more than 10 billion images, and over 50 billion comments and reviews. In addition to providing its technology to Alibaba Cloud, Remark Media will provide its turnkey AI platform as a service to companies or individuals requiring assistance with integrating the technology into their own models. Additional information on this partnership can be found on Alibaba Cloud's website.