If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Study hard enough, kids, and maybe one day you'll grow up to be a professional robot fighter. A few years ago, Boston Dynamics set the standard for the field by having people wielding hockey sticks try to keep Spot the quadrupedal robot from opening a door. Previously, in 2015, the far-out federal research agency Darpa hosted a challenge in which it forced clumsy humanoid robots to embarrass themselves on an obstacle course way outside the machines' league. And now, behold: The makers of the Jueying robot dog have taught it a fascinating way to fend off a human antagonizer who kicks it over or pushes it with a stick. A team of researchers from China's Zhejiang University--where the Jueying's hardware was also developed--and the University of Edinburgh didn't teach the Jueying how to recover after an assault, so much as they let the robot figure it out.
Artificial Intelligence (AI) is affecting the way we work, learn, shop, and now creating new opportunities for playing and watching our favorite sports. Using AI and machine learning to mimic the behavior of the likes of Cristiano Ronaldo and Lionel Messi, a team of researchers from the Institute for Big Data Analytics at Dalhousie were recently named as runners up in 2020's largest international AI soccer simulation competition, RoboCup Japan Open. This is the first time a Canadian team has made the finals for more than 10 years. International robotics competition RoboCup uses soccer simulation to promote robotics and AI research with the research findings used to advance many areas. By 2050, the competition aims to train a team of fully autonomous humanoid robots to win a soccer game against the winner of the most recent World Cup.
In a soccer game, fans get excited seeing a player sprint down the sideline during a counterattack or when a team is controlling the ball in the 18-yard box because those actions could lead to goals. However, it is difficult for human eyes to fully capture such fast movements, let alone predict goals. With machine learning (ML), we can incorporate more fine-grained information at the pixel level to develop a solution that predicts goals with high confidence before they happen. Sportradar, a leading real-time sports data provider that collects and analyzes sports data, and the Amazon ML Solutions Lab collaborated to develop a computer vision-based Soccer Goal Predictor to detect exciting moments that lead to goals, thereby increasing fan engagement and helping broadcasters provide viewers an enhanced experience. Most action recognition models are used to identify events when they occur, but Amazon ML Solutions Lab developed a novel computer vision-based Soccer Goal Predictor that can predict future soccer goals 2 seconds in advance of the event.
In this paper, we present a new application-focused benchmark dataset and results from a set of baseline Natural Language Processing and Machine Learning models for prediction of match outcomes for games of football (soccer). By doing so we give a baseline for the prediction accuracy that can be achieved exploiting both statistical match data and contextual articles from human sports journalists. Our dataset is focuses on a representative time-period over 6 seasons of the English Premier League, and includes newspaper match previews from The Guardian. The models presented in this paper achieve an accuracy of 63.18% showing a 6.9% boost on the traditional statistical methods.
Advances in NLP have yielded impressive results for the task of machine reading comprehension (MRC), with approaches having been reported to achieve performance comparable to that of humans. In this paper, we investigate whether state-of-the-art MRC models are able to correctly process Semantics Altering Modifications (SAM): linguistically-motivated phenomena that alter the semantics of a sentence while preserving most of its lexical surface form. We present a method to automatically generate and align challenge sets featuring original and altered examples. We further propose a novel evaluation methodology to correctly assess the capability of MRC systems to process these examples independent of the data they were optimised on, by discounting for effects introduced by domain shift. In a large-scale empirical study, we apply the methodology in order to evaluate extractive MRC models with regard to their capability to correctly process SAM-enriched data. We comprehensively cover 12 different state-of-the-art neural architecture configurations and four training datasets and find that -- despite their well-known remarkable performance -- optimised models consistently struggle to correctly process semantically altered data.
Kapanipathi, Pavan, Abdelaziz, Ibrahim, Ravishankar, Srinivas, Roukos, Salim, Gray, Alexander, Astudillo, Ramon, Chang, Maria, Cornelio, Cristina, Dana, Saswati, Fokoue, Achille, Garg, Dinesh, Gliozzo, Alfio, Gurajada, Sairam, Karanam, Hima, Khan, Naweed, Khandelwal, Dinesh, Lee, Young-Suk, Li, Yunyao, Luus, Francois, Makondo, Ndivhuwo, Mihindukulasooriya, Nandana, Naseem, Tahira, Neelam, Sumit, Popa, Lucian, Reddy, Revanth, Riegel, Ryan, Rossiello, Gaetano, Sharma, Udit, Bhargav, G P Shrivatsa, Yu, Mo
Knowledge base question answering (KBQA) is an important task in Natural Language Processing. Existing approaches face significant challenges including complex question understanding, necessity for reasoning, and lack of large training datasets. In this work, we propose a semantic parsing and reasoning-based Neuro-Symbolic Question Answering(NSQA) system, that leverages (1) Abstract Meaning Representation (AMR) parses for task-independent question under-standing; (2) a novel path-based approach to transform AMR parses into candidate logical queries that are aligned to the KB; (3) a neuro-symbolic reasoner called Logical Neural Net-work (LNN) that executes logical queries and reasons over KB facts to provide an answer; (4) system of systems approach,which integrates multiple, reusable modules that are trained specifically for their individual tasks (e.g. semantic parsing,entity linking, and relationship linking) and do not require end-to-end training data. NSQA achieves state-of-the-art performance on QALD-9 and LC-QuAD 1.0. NSQA's novelty lies in its modular neuro-symbolic architecture and its task-general approach to interpreting natural language questions.
Multiple object tracking(MOT) is the task of studying object appearance and movements to analyze their trajectories. For a given input video the algorithm is supposed to output which portions of the image represent the same object in different frames of the video. Algorithms like these can be used to solve some exciting problems like analyzing a particular soccer player's movements during the game, predicting whether a person is going to cross the street or not, or to track and analyze the movement of microscopic organisms in time-lapse microscopy images, etc. In this article, we will go through a state of the art Offline tracking framework for solving the problem of MOT. The approach that we are about to discuss was published in a paper by the researchers at the Dynamic Vision and Learning Group at TUM. Their proposed algorithm achieved SOTA on MOT15, MOT16, and MOT17 challenges.
In this paper, we present an active vision method using a deep reinforcement learning approach for a humanoid soccer-playing robot. The proposed method adaptively optimises the viewpoint of the robot to acquire the most useful landmarks for self-localisation while keeping the ball into its viewpoint. Active vision is critical for humanoid decision-maker robots with a limited field of view. To deal with an active vision problem, several probabilistic entropy-based approaches have previously been proposed which are highly dependent on the accuracy of the self-localisation model. However, in this research, we formulate the problem as an episodic reinforcement learning problem and employ a Deep Q-learning method to solve it. The proposed network only requires the raw images of the camera to move the robot's head toward the best viewpoint. The model shows a very competitive rate of 80% success rate in achieving the best viewpoint. We implemented the proposed method on a humanoid robot simulated in Webots simulator. Our evaluations and experimental results show that the proposed method outperforms the entropy-based methods in the RoboCup context, in cases with high self-localisation errors.
The IEEE Very Small Size Soccer (VSSS) is a robot soccer competition in which two teams of three small robots play against each other. Traditionally, a deterministic coach agent will choose the most suitable strategy and formation for each adversary's strategy. Therefore, the role of a coach is of great importance to the game. In this sense, this paper proposes an end-to-end approach for the coaching task based on Reinforcement Learning (RL). The proposed system processes the information during the simulated matches to learn an optimal policy that chooses the current formation, depending on the opponent and game conditions. We trained two RL policies against three different teams (balanced, offensive, and heavily offensive) in a simulated environment. Our results were assessed against one of the top teams of the VSSS league, showing promising results after achieving a win/loss ratio of approximately 2.0.