Deep tech is the newest catchphrase in the Indian startup ecosystem. A bunch of homegrown companies are using new-age technologies like artificial intelligence, machine learning, data analytics, cloud, and the internet-of-things (IoT) to solve real-world problems, and essentially, alter the way humans lead daily lives. On Day One of TechSparks 2019, YourStory's flagship annual conference, a panel of founders, investors, and technical heads gathered to take stock of the evolution of the local deep tech startups ecosystem. Swapan Rajdev, Co-Founder and CTO, Haptik (maker of AI chatbots, recently acquired by Reliance Jio) elaborated on how the growth of AI has spurred new jobs and roles. Gone are the days when Indian companies failed to make a mark in hardware.

--Subjective Logic (SL) is one of well-known belief models that can explicitly deal with uncertain opinions and infer unknown opinions based on a rich set of operators of fusing multiple opinions. Due to high simplicity and applicability, SL has been substantially applied in a variety of decision making in the area of cybersecurity, opinion models, trust models, and/or social network analysis. However, SL and its variants have exposed limitations in predicting uncertain opinions in real-world dynamic network data mainly in threefold: (1) a lack of scalability to deal with a large-scale network; (2) limited capability to handle heterogeneous topological and temporal dependencies among node-level opinions; and (3) a high sensitivity with conflicting evidence that may generate counterintuitive opinions derived from the evidence. In this work, we proposed a novel deep learning (DL)- based dynamic opinion inference model while node-level opinions are still formalized based on SL meaning that an opinion has a dimension of uncertainty in addition to belief and disbelief in a binomial opinion (i.e., agree or disagree). The proposed DLbased dynamic opinion inference model overcomes the above three limitations by integrating the following techniques: (1) state-of-the-art DL techniques, such as the Graph Convolutional Network (GCN) and the Gated Recurrent Units (GRU) for modeling the topological and temporal heterogeneous dependency information of a given dynamic network; (2) modeling conflicting opinions based on robust statistics; and (3) a highly scalable inference algorithm to predict dynamic, uncertain opinions in a linear computation time. We validated the outperformance of our proposed DLbased algorithm (i.e., GCN-GRU-opinion model) via extensive comparative performance analysis based on four real-world datasets. In the decision making domain, including the fields of evidence and belief theories, reasoning or managing uncertainty has been studied since 1960s. The examples include Fuzzy Logic, Dempster-Shafer Theory (DST), Transferable Belief Model, and Dezert-Smarandache Theory [6]. These theories deal with uncertainty implicitly. In 1990's, as another variant of DST, Subjective Logic (SL) [16] is proposed to deal with a dimension of uncertainty in subjective opinions more explicitely. SL defines a binomial opinion (e.g., agree vs. disagree) with three dimensions, including belief, disbelief, and uncertainty.

Spectrum management and resource allocation (RA) problems are challenging and critical in a vast number of research areas such as wireless communications and computer networks. The traditional approaches for solving such problems usually consume time and memory, especially for large size problems. Recently different machine learning approaches have been considered as potential promising techniques for combinatorial optimization problems, especially the generative model of the deep neural networks. In this work, we propose a resource allocation deep autoencoder network, as one of the promising generative models, for enabling spectrum sharing in underlay device-to-device (D2D) communication by solving linear sum assignment problems (LSAPs). Specifically, we investigate the performance of three different architectures for the conditional variational autoencoders (CVAE). The three proposed architecture are the convolutional neural network (CVAE-CNN) autoencoder, the feed-forward neural network (CVAE-FNN) autoencoder, and the hybrid (H-CVAE) autoencoder. The simulation results show that the proposed approach could be used as a replacement of the conventional RA techniques, such as the Hungarian algorithm, due to its ability to find solutions of LASPs of different sizes with high accuracy and very fast execution time. Moreover, the simulation results reveal that the accuracy of the proposed hybrid autoencoder architecture outperforms the other proposed architectures and the state-of-the-art DNN techniques.

Effective network congestion control strategies are key to keeping the Internet (or any large computer network) operational. Network congestion control has been dominated by hand-crafted heuristics for decades. Recently, ReinforcementLearning (RL) has emerged as an alternative to automatically optimize such control strategies. Research so far has primarily considered RL interfaces which block the sender while an agent considers its next action. This is largely an artifact of building on top of frameworks designed for RL in games (e.g. OpenAI Gym). However, this does not translate to real-world networking environments, where a network sender waiting on a policy without sending data is costly for throughput. We instead propose to formulate congestion control with an asynchronous RL agent that handles delayed actions. We present MVFST-RL, a scalable framework for congestion control in the QUIC transport protocol that leverages state-of-the-art in asynchronous RL training with off-policy correction. We analyze modeling improvements to mitigate the deviation from Markovian dynamics, and evaluate our method on emulated networks from the Pantheon benchmark platform. The source code is publicly available at https://github.com/facebookresearch/mvfst-rl.

Is Kenya ready for five 5G network? With Asia and other continents of the world taking steps to become a global leader in 5G and the Advanced Intelligence (AI) technology, Kenya will be ranked one of the first East African countries to tap into this, with the completion of the Data Centre by Huawei in Konza Smart City. This would help to revolutionize several industries, including manufacturing, agriculture, transport, health sector, making factory automation, as well as communication between self-driving vehicles to regulate traffic. According to Ms Pamela Tutui, a director at Konza Technopolis Development Authority (KoTDA), Kenya is set to borrow ideas from a campus in China's Shenzhen City to lift Konza Smart City to a technology hub in Africa. "Smart cities is bringing solutions to a city that is not smart. Like in Nairobi what is stopping us from having street lights, from looking at our road networks and to go beyond just taxis and looking into our busing lanes," says Ann Theresse Jatta Ndong – Director UNESCO regional Eastern Africa.

"Please think forward to the year 2030. Analysts expect that people will become even more dependent on networked artificial intelligence (AI) in complex digital systems. Some say we will continue on the historic arc of augmenting our lives with mostly positive results as we widely implement these networked tools. Some say our increasing dependence on these AI and related systems is likely to lead to widespread difficulties. Our question: By 2030, do you think it is most likely that advancing AI and related technology systems will enhance human capacities and empower them? That is, most of the time, will most people be better off than they are today? Or is it most likely that advancing AI and related technology systems will lessen human autonomy and agency to such an extent that most people will not be better off than the way things are today? Please explain why you chose the answer you did and sketch out a vision of how the human-machine/AI collaboration will function in 2030.

Artificial intelligence (AI) and machine learning (ML) are here to stay. Besting humans in complex games such as Go and Poker was just the beginning. Today, companies in fields as diverse as life and medical sciences, telecommunications, energy management, security, and manufacturing are seeing the benefits that artificial intelligence and machine learning can bring. This impact is reflected both in the number of scientific publications in the field (over 1.6 million and counting) and also in the number of patent filings (nearly 340'000 worldwide to date). The pace shows no signs of slowing down: patent filings in deep learning (an area of AI) experienced an average annual growth rate of 175% between 2013 and 2016.

Security officers keep watch in front of an AI (Artificial Intelligence) sign at the annual Huawei Connect event in Shanghai, China September 18, 2019. Rapid advances in digital technologies amplify the potential for data acquisition from and influence over other states. One state aggressively pursuing digital advantage globally is China, especially in its leveraging of artificial intelligence (AI). This memo presents recent data from multiple sources and initial analysis to set the stage for discussion about the profound implications of imminent digital power by China. A leading concern for those states not presently engaged in AI and related technologies is that they will fall behind those that are already heavily innovating and investing.

Data science is aiming to be the biggest tool nowadays for multiple industries to diversify their business and stimulate it at a very high rate. Data science is responsible for the exponential growth of the business and increasing its effectiveness at compound rates. It is a multidisciplinary field which includes detailed study of ample data in various structured, semi-structured and unstructured form. Data science helps in enriching the data and execute it better for further use by the company. Data science is working in the same way as the fuel works for the vehicle. Therefore, every industry is striving to incorporate this technology as quickly as possible.

Singapore/Bengaluru, 12-07-2019: Avanseus announces the release of 4.0 version of their Artificial Intelligence based Cognitive Assistant for Networks (CAN). The release is available from 12 th of July 2019 to all customers across the globe and for new upcoming requirements. This version of CAN is more suited for hassle-free operationalization and equipped with more intelligence and learning capability in comparison with its previous versions. The existing version CAN 3.0 is already powering the majority of telecom operators across the world with prediction success rate of more than 75% setting a benchmark in the industry. The new release will enable the customer in easy decision making and management of their network incidents as CAN 4.0 provides meaningful and relevant inputs on the important and critical fault predictions.