What we do have are cars where Artificial Intelligence (AI) can assist drivers.

It includes 80,000 questions along with their answers, with 25% of the questions being adversarially unanswerable. We examine various properties of the dataset to show the diversity and the level of its difficulty as a MRC benchmark. By releasing this dataset, we aim to ease research on Persian reading comprehension and development of persian question answering systems. Our experiments on different state-of-the-art pre-trained contextualized language models shows 74.8% Exact Match (EM) and 87.6% F1-score that can be used as the baseline results for further research on Persian QA.

Ontological knowledge modelling of epics, though being an established research arena backed by concrete multilingual and multicultural works, still suffer from two key shortcomings. Firstly, all epic ontological models developed till date have been designed following ad-hoc methodologies, most often, combining existing general purpose ontology development methodologies. Secondly, none of the ad-hoc methodologies consider the potential reuse of existing epic ontological models for enrichment, if available. The paper presents, as a unified solution to the above shortcomings, the design and development of GENOME - the first dedicated methodology for iterative ontological modelling of epics, potentially extensible to works in different research arenas of digital humanities in general. GENOME is grounded in transdisciplinary foundations of canonical norms for epics, knowledge modelling best practices, application satisfiability norms and cognitive generative questions. It is also the first methodology (in epic modelling but also in general) to be flexible enough to integrate, in practice, the options of knowledge modelling via reuse or from scratch. The feasibility of GENOME is validated via a first brief implementation of ontological modelling of the Indian epic - Mahabharata by reusing an existing ontology. The preliminary results are promising, with the GENOME-produced model being both ontologically thorough and performance-wise competent

The relevance of the video is that the browser identified the application being used by the IAI as Google Earth and, according to the OSC 2006 report, the Arabic-language caption reads Islamic Army in Iraq/The Military Engineering Unit – Preparations for Rocket Attack, the video was recorded in 5/1/2006, we provide, in Appendix A, a reproduction of the screenshot picture made available in the OSC report. Now, prior to the release of this video demonstration of the use of Google Earth to plan attacks, in accordance with the OSC 2006 report, in the OSC-monitored online forums, discussions took place on the use of Google Earth as a GEOINT tool for terrorist planning. On August 5, 2005 the user "Al-Illiktrony" posted a message to the Islamic Renewal Organization forum titled A Gift for the Mujahidin, a Program To Enable You to Watch Cities of the World Via Satellite, in this post the author dedicated Google Earth to the mujahidin brothers and to Shaykh Muhammad al-Mas'ari, the post was replied in the forum by "Al-Mushtaq al-Jannah" warning that Google programs retain complete information about their users. This is a relevant issue, however, there are two caveats, given the amount of Google Earth users, it may be difficult for Google to flag a jihadist using the functionality in time to prevent an attack plan, one possible solution would be for Google to flag computers based on searched websites and locations, for instance to flag computers that visit certain critical sites, but this is a problem when landmarks are used, furthermore, and this is the second caveat, one may not use one's own computer to produce the search or even mask the IP address. On October 3, 2005, as described in the OSC 2006 report, in a reply to a posting by Saddam Al-Arab on the Baghdad al-Rashid forum requesting the identification of a roughly sketched map, "Almuhannad" posted a link to a site that provided a free download of Google Earth, suggesting that the satellite imagery from Google's service could help identify the sketch.

Valentine's Day is approaching… Do want to sneak in a robot movie to watch on date night? Do you wonder about whether robots and love is possible? Here are five recommendations for sci-fi movies with a discussion of the related real-world robotics science. And remember to check out Learn AI and Human-Robot Interaction from Asimov's I, Robot Stories– it's a great primer on social interactions! Can roboticists make the perfect partner?

Machine learning generally focuses on data and algorithms to enable machines to learn a task with minimal or no human intervention.

We previously discussed which portions of an artificial intelligence/machine-learning ("AI/ML") platform can be patented.

Data is the fuel for software development, and developers use historical data from existing products to train algorithms and build AI and machine …

Deepdub, an AI-based entertainment localisation startup based out of Tel Aviv, Israel has raised USD 20 million in Series A funding led by New York-based global venture capital and private equity firm Insight Partners, with participation from existing investors Booster Ventures and Stardom Ventures and new investors Swift VC. Angel investors joining this round include Emiliano Calemzuk (former President of Fox Television Studios), Kevin Reilly (former CCO of HBO Max), Danny Grander (co-founder of Snyk), Roi Tiger (VP, Engineering at Meta), Gideon Marks and Daniel Chadash. The fresh funds will be used to expand the global reach of the company's sales and delivery teams. Deepdub plans on strengthening the R&D team with excellent researchers and developers and will improve its deep-learning based localisation platform. "This funding round is an acknowledgement of the revolutionary technology that we have built, taking generative AI to an industry where every pixel and every sound wave is rigorously examined. By creating a deep learning platform that can generate without fail, we have consistently left our clients in awe," said Nir Krakowski, co-founder and CTO of Deepdub.

Artificial intelligence (AI) has been invading our daily lives without us fully realizing it. When you wake up in the morning, you may ask Alexa to give you a run-down of your daily schedule. When you drive to work using Waze, the app is using a machine-learning algorithm to provide the best route for you. When you watch a movie or a show on Netflix or make a purchase on Amazon, the platforms use AI to make content or product recommendations for you. If Netflix can use AI to make content recommendations for us, businesses and enterprises can also use AI to make personalized content recommendations when prospects visit their websites.