Deep Convolutional Neural Networks (DCNNs) is currently the method of choice both for generative, as well as for discriminative learning in computer vision and machine learning. The success of DCNNs can be attributed to the careful selection of their building blocks (e.g., residual blocks, rectifiers, sophisticated normalization schemes, to mention but a few). In this paper, we propose $\Pi$-Nets, a new class of DCNNs. $\Pi$-Nets are polynomial neural networks, i.e., the output is a high-order polynomial of the input. $\Pi$-Nets can be implemented using special kind of skip connections and their parameters can be represented via high-order tensors. We empirically demonstrate that $\Pi$-Nets have better representation power than standard DCNNs and they even produce good results without the use of non-linear activation functions in a large battery of tasks and signals, i.e., images, graphs, and audio. When used in conjunction with activation functions, $\Pi$-Nets produce state-of-the-art results in challenging tasks, such as image generation. Lastly, our framework elucidates why recent generative models, such as StyleGAN, improve upon their predecessors, e.g., ProGAN.

Edge intelligence refers to a set of connected systems and devices for data collection, caching, processing, and analysis in locations close to where data is captured based on artificial intelligence. The aim of edge intelligence is to enhance the quality and speed of data processing and protect the privacy and security of the data. Although recently emerged, spanning the period from 2011 to now, this field of research has shown explosive growth over the past five years. In this paper, we present a thorough and comprehensive survey on the literature surrounding edge intelligence. We first identify four fundamental components of edge intelligence, namely edge caching, edge training, edge inference, and edge offloading, based on theoretical and practical results pertaining to proposed and deployed systems. We then aim for a systematic classification of the state of the solutions by examining research results and observations for each of the four components and present a taxonomy that includes practical problems, adopted techniques, and application goals. For each category, we elaborate, compare and analyse the literature from the perspectives of adopted techniques, objectives, performance, advantages and drawbacks, etc. This survey article provides a comprehensive introduction to edge intelligence and its application areas. In addition, we summarise the development of the emerging research field and the current state-of-the-art and discuss the important open issues and possible theoretical and technical solutions.

Hisham Elamir is a data scientist with expertise in machine learning, deep learning, and statistics. He currently lives and works in Cairo, Egypt. In his work projects, he faces challenges ranging from natural language processing (NLP), behavioral analysis, and machine learning to distributed processing. He is very passionate about his job and always tries to stay updated about the latest developments in data science technologies, attending meet-ups, conferences, and other events.

As we have announced recently, Appsilon Data Science's AI for Good initiative is working together with biodiversity conservationists at the National Parks Agency in Gabon and in collaboration with experts from the University of Stirling. Part of our role in the project is to develop an image classification algorithm capable of classifying wildlife seen in images taken by camera traps located in the forests of Gabon. The project has received support from the Google for Education fund which allowed us to embark on this journey with the immense power of the latest computational resources at hand. Below are some interesting findings we made so far. Stay tuned for more news on the progress! We have recently participated (and taken the 5th place out of 811 participants) in the Hakuna Ma-data competition, in which we classified images of wildlife from the savannahs of Serengeti.

The companies already partner on charging system operations managed services. The new deal, which is already being implemented, includes network operations center services and field services in radio, core and transmission technology. AI-driven intelligent and data driven operations are part of the deal. AI-related efficiency benefits, automation and data analytics will enable MTN Benin and Ericsson to run predictive operations to enable a shift from reactive to proactive IT and network management – boosting customers' experiences, as well as network quality and performance. Stephen Blewett, Chief Executive Officer, MTN Benin, says: "Network managed operations play a significant role in improving MTN customers' satisfaction and enhancing customer experience as well as enabling revenue growth and cost efficiency. We expect advanced technologies like AI, automation and analytics to further accelerate operational transformation through this new managed services partnership."

Robotic Process Automation Market Restraint/Challenges analysis 3.3.1.3.

The global degradation of water resources is a matter of great concern, especially for the survival of humanity. The effective monitoring and management of existing water resources is necessary to achieve and maintain optimal water quality. The prediction of the quality of water resources will aid in the timely identification of possible problem areas and thus increase the efficiency of water management. The purpose of this research is to develop a water quality prediction model based on water quality parameters through the application of a specialised recurrent neural network (RNN), Long Short-Term Memory (LSTM) and the use of historical water quality data over several years. Both multivariate single and multiple step LSTM models were developed, using a Rectified Linear Unit (ReLU) activation function and a Root Mean Square Propagation (RMSprop) optimiser was developed. The single step model attained an error of 0.01 mg/L, whilst the multiple step model achieved a Root Mean Squared Error (RMSE) of 0.227 mg/L.

We're officially a month into 2020 and the new decade is well underway. So much so, it is worth reflecting back as it jolted our eyes open and set the stage for what's to come. To sum it up in a word, data. Data, data everywhere – how to get it, how to use it, how to see it. Everywhere you looked there were analytics dashboards.

According to him, there are various test kits that get individuals the results of whether or not they have the virus in just 20 seconds, noting that "it is a 96% accuracy rate too." Although Mr Appiah Mensah has faced various backlash from customers following the Menzgold saga, he has noted that the coronavirus menace is a national problem thus taking to his Instagram page to share some tips about how best he believes other countries in the world, especially, how Ghana can deal with the virus to get the needed healing. He said, "The procurement of the AI system developed by a Chinese tech Baidu, which uses cameras equipped computer vision and infrared sensors to do temperature predictions of masses in public places that are indispensable like, Government Ministries and Agencies, transport terminals, Courts market places and so. The AI system is capable of screening up to 200 people per minute and detects their temperature within a range of 0.5 degrees Celsius."

In recent times, organizations have been competing with one another to implement chatbots for various reasons, including enhancing customer experience, streamlining processes, and fueling the demand for digital and innovative technologies. Cognitive technologies such as chatbots have become an apt candidate for end-use application as they have high automation feasibility, high potential of accuracy, low complexity and low execution time. Raising the bar through intelligence, virtual assistants have been propelled by advancements of mobile technology. Technology giants are putting their weight on a platform designed to answer ad-hoc queries in real-time and fuel sales as chatbots can remember customer preference and use order history to learn from customer responses to the product advertisements, suggest products, and cross-sell aptly. For instance, if a customer asks for a pizza recommendation with a chatbot, it can remember which pizza the customer ordered and follow up with it when offering a recommendation for another pizza or a restaurant.