Fox News Flash top entertainment and celebrity headlines are here. Check out what's clicking today in entertainment. In a Final Jeopardy question with the category "colleges and universities," guest host Sanjay Gupta asked contestants which school had recently trademarked the word "The." "In 2019 this public university attempted to trademark the word "The" for use on clothing and hats," the question read. 'JEOPARDY!' EP MIKE RICHARDS SAYS A'ROBUST TEAM' IS SEARCHING FOR A NEW HOST All three contestants got the right answer -- "The" Ohio State University -- prompting viewers to bash it as a question so no-duh, it wasn't even fun.

Grubhub is teaming up with Russian tech giant Yandex to deliver food to students and others on US college campuses with the help of autonomous robots. The companies have agreed a multi-year partnership, and the robots will start dropping off orders on select campuses this fall. Grubhub works with more than 250 colleges across the country. Yandex says its robots can access areas and navigate obstacles that cars cannot. It will be able to deliver food in mainly pedestrian areas and the robot delivery service will be integrated into Grubhub's app.

Are you also a Computer Science Student looking forward to exploring new fields and dimensions in this stream? If this is the case then you might have come across the term'Machine Learning. There is a lot of unclarity related to this topic. Well, here is our take on the topic with a detailed explanation, valuable insights, and myth-busting opinions. Machine Learning is the branch of Artificial Intelligence that deals with Data science, analytics, and algorithms to predict or classify future behavior without being specifically programmed to do so.

First of all, I want to state for the record that I have never played a video game that involved violence or war. I think the last time I played a "video game" was Flight Simulator. As a result, I suspect some readers are much more familiar with intensive and fanciful warfare than I am. Still, recently, I've been part of discussions with the Department of Defense and organizations that advise, consult and criticize the DoD on the topic AI in warfare. It is a complicated issue to introduce AI ethics with the violence and killing of war.

Artificial intelligence (AI) has been around for decades, with the first research coming into fruition in the 1950s. That was when the term was formally coined, and the technology continues to be studied and utilized by academics and researchers alike. But in recent years, we've seen mass adoption and usage of AI applications across all industries. As new developments arise in AI, more organizations are investing their resources into honing the business applications of the technology. In fact, global spending on AI tech and robotic process automation is predicted to reach almost $35 billion by 2023 -- further proving the surge in demand for this technology.

I started my career in 2010 after a master's in computer science. During my studies, I got hooked on software engineering. It can be really fascinating! You get to build stuff out of nothing. You start from a virtual blank sheet, and you can end up with an empire.

One of the most important barriers toward a widespread use of mobile robots in unstructured and human populated work environments is the ability to plan a safe path. In this paper, we propose to delegate this activity to a human operator that walks in front of the robot marking with her/his footsteps the path to be followed. The implementation of this approach requires a high degree of robustness in locating the specific person to be followed (the leader). We propose a three phase approach to fulfil this goal: 1. identification and tracking of the person in the image space, 2. sensor fusion between camera data and laser sensors, 3. point interpolation with continuous curvature curves. The approach is described in the paper and extensively validated with experimental results.

Due to their increasing spread, confidence in neural network predictions became more and more important. However, basic neural networks do not deliver certainty estimates or suffer from over or under confidence. Many researchers have been working on understanding and quantifying uncertainty in a neural network's prediction. As a result, different types and sources of uncertainty have been identified and a variety of approaches to measure and quantify uncertainty in neural networks have been proposed. This work gives a comprehensive overview of uncertainty estimation in neural networks, reviews recent advances in the field, highlights current challenges, and identifies potential research opportunities. It is intended to give anyone interested in uncertainty estimation in neural networks a broad overview and introduction, without presupposing prior knowledge in this field. A comprehensive introduction to the most crucial sources of uncertainty is given and their separation into reducible model uncertainty and not reducible data uncertainty is presented. The modeling of these uncertainties based on deterministic neural networks, Bayesian neural networks, ensemble of neural networks, and test-time data augmentation approaches is introduced and different branches of these fields as well as the latest developments are discussed. For a practical application, we discuss different measures of uncertainty, approaches for the calibration of neural networks and give an overview of existing baselines and implementations. Different examples from the wide spectrum of challenges in different fields give an idea of the needs and challenges regarding uncertainties in practical applications. Additionally, the practical limitations of current methods for mission- and safety-critical real world applications are discussed and an outlook on the next steps towards a broader usage of such methods is given.

Inverse reinforcement learning is a paradigm motivated by the goal of learning general reward functions from demonstrated behaviours. Yet the notion of generality for learnt costs is often evaluated in terms of robustness to various spatial perturbations only, assuming deployment at fixed speeds of execution. However, this is impractical in the context of robotics and building time-invariant solutions is of crucial importance. In this work, we propose a formulation that allows us to 1) vary the length of execution by learning time-invariant costs, and 2) relax the temporal alignment requirements for learning from demonstration. We apply our method to two different types of cost formulations and evaluate their performance in the context of learning reward functions for simulated placement and peg in hole tasks. Our results show that our approach enables learning temporally invariant rewards from misaligned demonstration that can also generalise spatially to out of distribution tasks.

Two-sided matching markets, such as Uber, Airbnb, stock markets, and dating apps, play a significant role in today's world. As a result, there is a tremendous and rising interest to design better mechanisms for two-sided matching. The seminal work of Gale and Shapley [14] introduced a simple mechanism for stable matching in two-sided markets--Deferred-acceptance (DA)--which has since has been applied in doctor-hospital matching [25], school choice [3, 22, 2], and the matching of cadets to their branches of military service [30, 29]. DA is stable, i.e., no pair of agents mutually prefer each other to their DA partners. On the other hand, DA is not strategy-proof (SP); that is, under fully general preferences, it is always possible that some agent can mis-report her preferences to obtain a better matching than she would receive under the DA mechanism.