Almost all the knowledge empowered applications rely upon accurate knowledge, which has to be either collected manually with high cost, or extracted automatically with unignorable errors. In this paper, we study 20 Questions, an online interactive game where each question-response pair corresponds to a fact of the target entity, to acquire highly accurate knowledge effectively with nearly zero labor cost. Knowledge acquisition via 20 Questions predominantly presents two challenges to the intelligent agent playing games with human players. The first one is to seek enough information and identify the target entity with as few questions as possible, while the second one is to leverage the remaining questioning opportunities to acquire valuable knowledge effectively, both of which count on good questioning strategies. To address these challenges, we propose the Learning-to-Ask (LA) framework, within which the agent learns smart questioning strategies for information seeking and knowledge acquisition by means of deep reinforcement learning and generalized matrix factorization respectively. In addition, a Bayesian approach to represent knowledge is adopted to ensure robustness to noisy user responses. Simulating experiments on real data show that LA is able to equip the agent with effective questioning strategies, which result in high winning rates and rapid knowledge acquisition. Moreover, the questioning strategies for information seeking and knowledge acquisition boost the performance of each other, allowing the agent to start with a relatively small knowledge set and quickly improve its knowledge base in the absence of constant human supervision.

Like any field of empirical science, AI may be approached axiomatically. We formulate requirements for a general-purpose, human-level AI system in terms of postulates. We review the methodology of deep learning, examining the explicit and tacit assumptions in deep learning research. Deep Learning methodology seeks to overcome limitations in traditional machine learning research as it combines facets of model richness, generality, and practical applicability. The methodology so far has produced outstanding results due to a productive synergy of function approximation, under plausible assumptions of irreducibility and the efficiency of back-propagation family of algorithms. We examine these winning traits of deep learning, and also observe the various known failure modes of deep learning. We conclude by giving recommendations on how to extend deep learning methodology to cover the postulates of general-purpose AI including modularity, and cognitive architecture. We also relate deep learning to advances in theoretical neuroscience research.

We report on the usefulness of using neural networks as a variational state ansatz for many-body quantum systems in the context of quantum information-processing tasks. In the neural network state ansatz, the complex amplitude function of a quantum state is computed by a neural network. The resulting multipartite entanglement structure captured by this ansatz has proven rich enough to describe the ground states and unitary dynamics of various physical systems of interest. In the present paper, we supply further evidence for the usefulness of neural network states to describe multipartite entanglement. We demonstrate that neural network states are capable of efficiently representing quantum codes for quantum information transmission and quantum error correction. In particular, we show that a) neural network states yield quantum codes with a high coherent information for two important quantum channels, the depolarizing channel and the dephrasure channel; b) neural network states can be used to represent absolutely maximally entangled states, a special type of quantum error correction codes. In both cases, the neural network state ansatz provides an efficient and versatile means as variational parametrization of these states.

Connectionist sequence models (e.g., RNNs) applied to musical sequences suffer from two known problems: First, they have strictly "absolute pitch perception". Therefore, they fail to generalize over musical concepts which are commonly perceived in terms of relative distances between pitches (e.g., melodies, scale types, modes, cadences, or chord types). Second, they fall short of capturing the concepts of repetition and musical form. In this paper we introduce the recurrent gated autoencoder (RGAE), a recurrent neural network which learns and operates on interval representations of musical sequences. The relative pitch modeling increases generalization and reduces sparsity in the input data. Furthermore, it can learn sequences of copy-and-shift operations (i.e. chromatically transposed copies of musical fragments)---a promising capability for learning musical repetition structure. We show that the RGAE improves the state of the art for general connectionist sequence models in learning to predict monophonic melodies, and that ensembles of relative and absolute music processing models improve the results appreciably. Furthermore, we show that the relative pitch processing of the RGAE naturally facilitates the learning and the generation of sequences of copy-and-shift operations, wherefore the RGAE greatly outperforms a common absolute pitch recurrent neural network on this task.

In this study, we predict the outcome of the football matches in the FIFA World Cup 2018 to be held in Russia this summer. We do this using classification models over a dataset of historic football results that includes attributes from the playing teams by rating them in attack, midfield, defence, aggression, pressure, chance creation and building ability. This last training data was a result of merging international matches results with AE games ratings of the teams considering the timeline of the matches with their respective statistics. Final predictions show the four countries with the most chances of getting to the semifinals as France, Brazil, Spain and Germany while giving Spain as the winner. The objective of this study is to build a predictive model that will allow us to make good predictions for the coming World Cup 2018 so we looked for dataset with historic data for match results, for this purpose we chose a dataset from Kaggle with data of almost 40,000 international matches played between 1872 and 2018.

Organisations are now resorting to artificial intelligence to enhance their search engine ranking. Search engine optimisation (SEO), which is an important criterion for gaining traffic, has a tremendous scope to be improved by AI and machine learning -- and not just for keywords and phrases. AI algorithms can help make better sense of parameters like search history, browsing history, activities within a website, and others to deliver a better experience. Since the time when SEO meant a simple optimisation of landing pages on desktop, to the present day where it indulges in more complex processes of enhancing content and engaging audience on various platforms, SEO has been a game changer in the online world. SEO gained importance when Google revealed that they used RankBrain, an ML algorithm, to process their search results in a unique way.

On 18 June, UAE's Minister of State for Artificial Intelligence (AI) Mr Omar Sultan Al Olama signed a Memorandum of Understanding (MoU) with the Dr Ali Sebaa Al Marri, Executive President of Mohammed Bin Rashid School of Government (MBRSG), to enhance cooperation and empower young Emiratis. The collaboration seeks to help prepare a future generation of leaders by honing their leadership skills, developing their practical experience and enhancing their knowledge in employing AI technology in government work through training, educational courses and workshops. At the MoU signing ceremony, Minister Al Olama highlighted the potentials of using AI for innovation solutions and the importance of empower future leaders to use the technology. According to him, the creation of government services, programmes and initiatives that enhance the quality of life in the community and support having a competitive knowledgeable economy requires tools, skills and future potentials. "Preparing young Emiratis in AI and the use of its techniques to create innovative solutions to future challenges, supports the vision and directives of our (the UAE's) leadership, to strengthen the UAE's position as a global hub in the use of AI to shape the future," said Minister Al Olama.

China's top voice recognition firm iFlytek has penned a deal with China International Publishing Group to build a national artificial intelligence translator and keep up with rising demand. AI translations can lift the burden off human translators, who can barely keep up with requirements at government departments and companies looking to operate overseas, state-owned news agency Xinhua cited CIPG Deputy Director Fang Zhenghui as saying. The machine can translate Chinese into 33 languages, added Liu Qingfeng, president of Anhui-based iFlytek, saying it uses cutting-edge technology to improve the accuracy of machine translations. "When translation machines fail to recognize some special nouns or specific terms, human translators can monitor the process and help to polish the text," he said. "The machine [can] learn from these mistakes and improve its work next time."

"I never forget a face", "She's got an honest face", "You could see it in his face", and "She looks young for her age" are just a few of the often-used phrases suggesting that faces are important for our interactions with other people and what we think we know about them. But can people really remember faces as well as they think they do, and can we really tell someone's age from their face? Or can artificial intelligence (AI) do it better? And can we really tell if someone is trustworthy just by looking at their face? Research shows that humans exhibit a wide range of facial recognition abilities.

Bloodhounds are famous for their ability to track scents over great distances. Now researchers have developed a modern-day bloodhound--a robot that can rapidly detect odors from sources on the ground, such as footprints. The robot, reported in ACS Sensors, could even read a message written on the ground using odors as a barcode. Over the past two decades, researchers have tried to develop robots that rival the olfactory system of bloodhounds. However, most robots can only detect airborne odors, or they are painstakingly slow at performing analyses.