Though artificial intelligence has evolved recently and appears to be a new phenomenon in modern society, it is much older than you would imagine. Being actively involved in the global AI community, I've noticed that many people still associate AI with sci-fi Hollywood movies displaying the distant future powered by intelligent robots and machines. However, this perception is waning as AI becomes more commonplace in our daily lives. The early instances of intelligent machines were found in ancient Greek mythology with conceptions of mechanical robots made to help the Greek god Hephaestus. Following were some milestones in the history of AI, which started as a field of research in the late 1950s with the development of the first algorithms to solve complex mathematical problems.

Recently, there has been an increasing interest in neural speech synthesis. While the deep neural network achieves the state-of-the-art result in text-to-speech (TTS) tasks, how to generate a more emotional and more expressive speech is becoming a new challenge to researchers due to the scarcity of high-quality emotion speech dataset and the lack of advanced emotional TTS model. In this paper, we first briefly introduce and publicly release a Mandarin emotion speech dataset including 9,724 samples with audio files and its emotion human-labeled annotation. After that, we propose a simple but efficient architecture for emotional speech synthesis called EMSpeech. Unlike those models which need additional reference audio as input, our model could predict emotion labels just from the input text and generate more expressive speech conditioned on the emotion embedding. In the experiment phase, we first validate the effectiveness of our dataset by an emotion classification task. Then we train our model on the proposed dataset and conduct a series of subjective evaluations. Finally, by showing a comparable performance in the emotional speech synthesis task, we successfully demonstrate the ability of the proposed model.

Attention mechanism enables the Graph Neural Networks(GNNs) to learn the attention weights between the target node and its one-hop neighbors, the performance is further improved. However, the most existing GNNs are oriented to homogeneous graphs and each layer can only aggregate the information of one-hop neighbors. Stacking multi-layer networks will introduce a lot of noise and easily lead to over smoothing. We propose a Multi-hop Heterogeneous Neighborhood information Fusion graph representation learning method (MHNF). Specifically, we first propose a hybrid metapath autonomous extraction model to efficiently extract multi-hop hybrid neighbors. Then, we propose a hop-level heterogeneous Information aggregation model, which selectively aggregates different-hop neighborhood information within the same hybrid metapath. Finally, a hierarchical semantic attention fusion model (HSAF) is proposed, which can efficiently integrate different-hop and different-path neighborhood information respectively. This paper can solve the problem of aggregating the multi-hop neighborhood information and can learn hybrid metapaths for target task, reducing the limitation of manually specifying metapaths. In addition, HSAF can extract the internal node information of the metapaths and better integrate the semantic information of different levels. Experimental results on real datasets show that MHNF is superior to state-of-the-art methods in node classification and clustering tasks (10.94% - 69.09% and 11.58% - 394.93% relative improvement on average, respectively).

This work has been submitted to the IEEE for possible publication. Abstract With the development of the 5G and Internet of Things, amounts of wireless devices need to share the limited spectrum resources. Dynamic spectrum access (DSA) is a promising paradigm to remedy the problem of inef!cient spectrum utilization brought upon by the historical command-and-control approach to spectrum allocation. In this paper, we investigate the distributed DSA problem for multiuser in a typical multi-channel cognitive radio network. The problem is formulated as a decentralized partially observable Markov decision process (Dec-POMDP), and we proposed a centralized off-line training and distributed on-line execution framework based on cooperative multi-agent reinforcement learning (MARL). We employ the deep recurrent Q-network (DRQN) to address the partial observability of the state for each cognitive user. The ultimate goal is to learn a cooperative strategy which maximizes the sum throughput of cognitive radio network in distributed fashion without coordination information exchange between cognitive users. This work was supported in part by the National Natural Science Foundation of China under Grant 6193000305. X. Tan, L. Zhou, Y. Sun, H. Wang, H. Zhao and J. Wei are all with College of Electronic Science and Technology, National University of Defense Technology, Changsha, 410073, China (E-mail: {tanxiang, zhouli2035, haijunwang14, sunyuli19, haitaozhao, wjbhw}@nudt.edu.cn). Boon-Chong Seet is with the Department of Electrical and Electronic Engineering, Auckland University of Technology, Auckland 1142, New Zealand (E-mail: boon-chong.seet@aut.ac.nz). Victor C. M. Leung is with Shenzhen University, Shenzhen, China and the University of British Columbia, Vancouver, Canada (E-mail: vleung@ieee.org). 2 From the simulation results, we can observe that the proposed algorithm can converge fast and achieve almost the optimal performance. The future network is involving into the Internet of Everything.

Dr Steven Thomas Smith is a senior staff member in the Artificial Intelligence Software Architectures and Algorithms Group at the Massachusetts Institute of Technology (MIT) Lincoln Laboratory. He told create that the project was built using open source machine learning (ML), publicly available application programming interfaces (APIs), the lab's own algorithms for ML features, as well as network causal inference to compute influence.

Berkeley Heights, NJ, June 16, 2021 (GLOBE NEWSWIRE) — With the elevated importance of data analytics and artificial intelligence (AI)-driven …

Thankfully, artificial intelligence is helping, not just with early detection but better accuracy to reduce false positives.

A research paper from the Swedish Media Council outlines a possible new approach to the automatic identification of'harmful content', by considering audio and video content separately, and using human-annotated data as a guiding index for material that may disturb viewers. Learning to Predict Harmfulness Ratings from Video, the paper illustrates the need for machine learning systems to take account of the entire context of a scene, and illustrates the many ways that innocuous content (such as humorous or satirical content) could be misinterpreted as harmful in a less sophisticated and multimodal approach to video analysis – not least because a film's musical soundtrack is often used in unexpected ways, either to unsettle or reassure the viewer, and as a counterpoint rather than a complement to the visual component. They also observe that to date, similar experiments have suffered from a sparsity of labels for full-length movies, which has led to prior work oversimplifying the contributing data, or keying in on only one aspect of the data, such as dominant colors or dialogue analysis. To address this, the researchers have compiled a video dataset of 4000 video clips, trailers cut down into chunks of around ten seconds in length, which were then labeled by professional film classifiers that oversee the application of ratings for new movies in Sweden, many with professional qualifications in child psychology. Under the Swedish system of film classification, 'harmful' content is defined based on its possible propensity to produce feelings of anxiety, fear, and other negative effects in children.

Looking for a shortcut, he found that neural networks, a type of artificial intelligence (AI) that uncovers patterns in huge data sets, can accurately predict …

According to recent Talkdesk Research™, only 31% of customer experience (CX) professionals currently use AI to meet customer expectations for fast, …