This latest research report that completely centers "Global Artificial Intelligence Market" is an intensive analysis of propulsive forces, propulsive risks, business opportunities, Artificial Intelligence threats and challenges includes in Artificial Intelligence market. It provides conclusive flecks of the Artificial Intelligence market such as major prominent players, market size over the forecast period of 2017-2026, market share, segmentation study, present Artificial Intelligence market trends, progress and major geographical sectors involved in Artificial Intelligence market. For cosmopolitan understanding, the Artificial Intelligence market is split into segments and sub-segments. Artificial Intelligence report also provides high-advance data and certain information about manufacturing plants used in the survey of Artificial Intelligence industry. All the information points and assembles data about Artificial Intelligence market is pictured statistically in the form of bar graphs, pie diagrams, tables and product figure to give a generous understanding of the users.

The list of tech failures in development is long. Whether it be drone pilots getting in the way of emergency relief in Nepal, or computers gathering dust in Indian schools because teachers don't know how to use them, the outcome is rarely positive when techies fall in love with their preferred solutions rather than taking time to understand real problems faced by real people. The best example of a technology that has driven great development gains in recent years is of course the mobile phone. But mobile's extraordinary impact in Africa did not flow from some grand design. Instead, it was the result of African citizens innovating their way around information and money transfer problems in the absence of banking systems and other infrastructure.

The majority of Indian startups expect to have a difficult year on the fundraising front while mergers and acquisitions (M&A) are their preferred mode of exit, according to a survey conducted by venture debt firm InnoVen Capital. The India Startup Outlook Report 2018 presented insights from more than 100 startup founders and top executives across sectors and at various stages of growth. More than half of the respondents felt the fundraising environment would be more challenging in 2018 as compared to the previous year, for which around the same numbers found the fundraising experience to be favourable. Only 37% had found the climate to be favourable in 2016. Founders said they had pitched to six to ten investors on an average to close a funding round.

Overall, 2017 saw an upward trend in talent acquisition across Machine Learning. This will further increase in 2018. With technology such as Machine learning, AI and predictive analytics reshaping the business landscape, software product, aggregators, Fintech and E-commerce will drive the demand for technology professionals in India. Machine Learning is usually associated with Artificial Intelligence (AI) that provides computers with the ability to do certain tasks, such as recognition, diagnosis, planning, robot control, prediction, etc., without being explicitly programmed. It focuses on the development of algorithms that can teach themselves to grow and change when exposed to new data.

SHARJAH: The Minister of Tolerance, Sheikh Nahyan Bin Mubarak Al Nahyan, said that UAE's leaders strive to achieve a successful and prosperous future for the country, to keep abreast with the global developments in knowledge and technology, as well as to adopt the cutting-edge technology and practices in all spheres of life. This came during the opening speech of the 18th edition of Dar Al Khaleej's Annual Conference that was held on Saturday under the title "Artificial Intelligence Strategy in the UAE", in the presence of Khalid Abdullah Taryam, Chairman of Dar Al Khaleej for Press, Printing and Publishing, and Dr Yousef Al Hassan, an Emirati writer and thinker. "The conference's theme exactly reflects what the two late brothers (Taryam Omran Taryam and Dr. Abdullah Omran Taryam) were seeking to fulfill by enabling the community to be aware of the future developments and work hard to shape its features, so that the UAE will be able to contribute effectively to all the achievements global development," Sheikh Nahyan said. "The conference's discussion of Artificial Intelligence in UAE embodies what we learned from the founding leader late Sheikh Zayed Bin Sultan Al Nahyan and his wisdom to use the latest in the world in terms of advanced systems and techniques. This approach has been continued under the leadership of President His Highness Sheikh Khalifa Bin Zayed Al Nahyan, His Highness Sheikh Mohammed Bin Rashid Al Maktoum, Vice President and Prime Minister of the UAE and Ruler of Dubai, His Highness Sheikh Mohamed Bin Zayed Al Nahyan, Crown Prince of Abu Dhabi and Deputy Supreme Commander of the UAE Armed Forces and Their Highnesses Rulers of the Emirates," Sheikh Nahyan said.

Non-negative Matrix Factorization (NMF) is a popular clustering and dimension reduction method by decomposing a non-negative matrix into the product of two lower dimension matrices composed of basis vectors. In this paper, we propose a semi-orthogonal NMF method that enforces one of the matrices to be orthogonal with mixed signs, thereby guarantees the rank of the factorization. Our method preserves strict orthogonality by implementing the Cayley transformation to force the solution path to be exactly on the Stiefel manifold, as opposed to the approximated orthogonality solutions in existing literature. We apply a line search update scheme along with an SVD-based initialization which produces a rapid convergence of the algorithm compared to other existing approaches. In addition, we present formulations of our method to incorporate both continuous and binary design matrices. Through various simulation studies, we show that our model has an advantage over other NMF variations regarding the accuracy of the factorization, rate of convergence, and the degree of orthogonality while being computationally competitive. We also apply our method to a text-mining data on classifying triage notes, and show the effectiveness of our model in reducing classification error compared to the conventional bag-of-words model and other alternative matrix factorization approaches.

The system's ability to adapt and self-organize are two key factors when it comes to how well the system can survive the changes to the environment and the plant they work within. Intelligent control improves these two factors in controllers. Considering the increasing complexity of dynamic systems along with their need for feedback controls, using more complicated controls has become necessary and intelligent control can be a suitable response to this necessity. This paper briefly describes the structure of intelligent control and provides a review on fuzzy logic and neural networks which are some of the base methods for intelligent control. The different aspects of these two methods are then compared together and an example of a combined method is presented.

This paper proposes a new dimensionality reduction algorithm named branching embedding (BE). It converts a dendrogram to a two-dimensional scatter plot, and visualizes the inherent structures of the original high-dimensional data. Since the conversion part is not computationally demanding, the BE algorithm would be beneficial for the case where hierarchical clustering is already performed. Numerical experiments revealed that the outputs of the algorithm moderately preserve the original hierarchical structures.

This paper presents a novel Differential Evolution algorithm for protein folding optimization that is applied to a three-dimensional AB off-lattice model. The proposed algorithm includes two new mechanisms. A local search is used to improve convergence speed and to reduce the runtime complexity of the energy calculation. For this purpose, a local movement is introduced within the local search. The designed evolutionary algorithm has fast convergence speed and, therefore, when it is trapped into the local optimum or a relatively good solution is located, it is hard to locate a better similar solution. The similar solution is different from the good solution in only a few components. A component reinitialization method is designed to mitigate this problem. Both the new mechanisms and the proposed algorithm were analyzed on well-known amino acid sequences that are used frequently in the literature. Experimental results show that the employed new mechanisms improve the efficiency of our algorithm and that the proposed algorithm is superior to other state-of-the-art algorithms. It obtained a hit ratio of 100% for sequences up to 18 monomers, within a budget of $10^{11}$ solution evaluations. New best-known solutions were obtained for most of the sequences. The existence of the symmetric best-known solutions is also demonstrated in the paper.

User and item features of side information are crucial for accurate recommendation. However, the large number of feature dimensions, e.g., usually larger than 10^7, results in expensive storage and computational cost. This prohibits fast recommendation especially on mobile applications where the computational resource is very limited. In this paper, we develop a generic feature-based recommendation model, called Discrete Factorization Machine (DFM), for fast and accurate recommendation. DFM binarizes the real-valued model parameters (e.g., float32) of every feature embedding into binary codes (e.g., boolean), and thus supports efficient storage and fast user-item score computation. To avoid the severe quantization loss of the binarization, we propose a convergent updating rule that resolves the challenging discrete optimization of DFM. Through extensive experiments on two real-world datasets, we show that 1) DFM consistently outperforms state-of-the-art binarized recommendation models, and 2) DFM shows very competitive performance compared to its real-valued version (FM), demonstrating the minimized quantization loss.