There is an open debate about the impact of artificial intelligence (AI). Sceptics of AI question how much AI is a "threat" to our privacy, way of life and how much it can ultimately deliver. "It's healthy to approach with a certain sense of humility. People have been talking about the rise of artificial intelligence since Stanford Professor John McCarthy coined the term in 1956," Sam Blatteis, CEO of The MENA Catalysts, a public policy consulting firm for government innovation arms and high tech multinationals, told TechRadar Middle East. However, Blatteis said that, "there are those who believe that the disruptive potential of AI will have nothing less than the social impact of the industrial revolution, Henry Ford's assembly line, the invention of flight, and the Internet."

Global Intelligent Process Automation (IPA) Market Size (Sales) Market Share by Type (Product Category) [Natural Language Processing, Machine and Deep Learning, Neural Networks, Virtual Agents, Mini Bots and RPA] in 2018 Intelligent Process Automation (IPA) Market by Application/End Users [IT Operations, Business Process Automation, Application Management, Content Management, Security] Global Intelligent Process Automation (IPA) Sales (Volume) and Market Share Comparison by Applications Global Global Intelligent Process Automation (IPA) Sales and Growth Rate (2014-2025) Intelligent Process Automation (IPA) Competition by Players/Suppliers, Region, Type and Application Intelligent Process Automation (IPA) (Volume, Value and Sales Price) table defined for each geographic region defined. Global Intelligent Process Automation (IPA) Players/Suppliers Profiles and Sales Data Additionally Company Basic Information, Manufacturing Base and Competitors list is being provided for each listed manufacturers Market Sales, Revenue, Price and Gross Margin (2014-2018) table for each product type which include Cost Structure Analysis Key Raw Materials Analysis & Price Trends Supply Chain, Sourcing Strategy and Downstream Buyers, Industrial Chain Analysis ……..and view more in complete table of Contents

With the rise of self-driving technologies, there is great speculation as to how this new world of autonomous transportation will impact the economy and society. Critics claim that industries will suffer, millions of people will lose their jobs, and society will overall be worse off. Optimists predict that along with new technologies will come new industries and new jobs, and with the benefits of self-driving cars, there will be a ripple of benefits into other aspects of life. Before exploring these different possibilities of what the future may be, let's take a journey to the past. Here we find civilizations at war, and one of the earliest forms of transportation: the chariot.

Global demand for food is anticipated to increase by 60% by 2050. Today, a great percentage of the world's population is fed by small-scale farmers, primarily from developing countries, using traditional methods and rudimentary farming practices. The complex value chain and the lack of resources and connectivity add to the agricultural inefficiency, so, there is a strong need for a wider package of yield optimizing and risk decreasing services for these small-scale farmers. Project FARM, created at Capgemini's Applied Innovation Exchange (AIE) Collaboration Zone (CoZone) in the Netherlands, aims to address these issues. The Project FARM platform uses Artificial Intelligence to determine farming patterns through big data, generating insights from the data to make recommendations.

In 2017 the World Economic Forum's Centre for the Fourth Industrial Revolution teamed up with the Government of Rwanda to draft the world's first framework for governing drones at scale. Using a performance-based approach that set minimum safety requirements instead of equipment specifications, this innovative regulatory framework gave drone manufacturers the flexibility to design and test different types of drones. These drones have delivered life-saving vaccines, conducted agricultural land surveys, inspected infrastructure and had many other socially beneficial uses in Rwanda.

On fields across the world, phones, tablets, drones, and other technologies are changing how food is grown. Through these devices, artificial intelligence (AI)--technology able to perform tasks that require human intelligence--may help farmers use the techniques they already know and trust on a bigger scale. And Big Data--data sets that reveal telling patterns about growth, yield, weather, and more--may help farmers make better decisions before crises strike. According to the report Refresh: Food Tech, From Soil to Supper released in 2018, AI and Big Data may help produce more food, use less water, limit resource consumption, redirect food waste, and lower food prices--all while improving the lives and incomes of farmers and food producers. "Recent advances have the potential for big breakthroughs in the ways we grow, store, transport, distribute, and consume food," says the Refresh Report.

By the end of this decade, a milestone is reached in artificial intelligence, with computers now routinely passing the Turing Test.** This test is conducted by a human judge who is made to engage in a natural language conversation with one human and one machine, each of which tries to appear human. Participants are placed in isolated locations. For several decades, information technology had seen exponential growth – leading to vast improvements in computer processing power, memory, bandwidth, voice recognition, image recognition, deep learning and other software algorithms. By the end of the 2020s, it has reached the stage where an independent judge is literally unable to tell which is the real human and which is not.* Answers to certain "obscure" questions posed by the judge may appear childlike from the AI – but they are humanlike nonetheless.*

On the other hand, Tropical race 4 (TR4), the virulent strain of fungus Fusarium oxysporum cubense that is threatening banana crop globally with the fusarium wilt disease has killed off millions of bananas in Africa and Asia (from the 1980s onwards). It had surfaced in the Cavendish group of bananas in parts of Bihar and is now spreading to Uttar Pradesh, Madhya Pradesh and even Gujarat, which could spell havoc for the country's banana industry. Even though India is the largest banana producing country in the world and third-largest orange-producing country, our exports for these crops are mainly to the Middle East and some neighbouring countries. Very little, if not any, is exported to the US or EU. In spite of such high production, India's banana exports generate a meager$49.8

--Electromagnetic stimulation of the human brain is a key tool for the neurophysiological characterization and diagnosis of several neurological disorders. Transcranial magnetic stimulation (TMS) is one procedure that is commonly used clinically. However, personalized TMS requires a pipeline for accurate head model generation to provide target-specific stimulation. This process includes intensive segmentation of several head tissues based on magnetic resonance imaging (MRI), which has significant potential for segmentation error, especially for low-contrast tissues. Additionally, a uniform electrical conductivity is assigned to each tissue in the model, which is an unrealistic assumption based on conventional volume conductor modeling. This paper proposes a novel approach to the automatic estimation of electric conductivity in the human head for volume conductor models without anatomical segmentation. A convolutional neural network is designed to estimate personalized electrical conductivity values based on anatomical information obtained from T1-and T2-weighted MRI scans. This approach can avoid the time-consuming process of tissue segmentation and maximize the advantages of position-dependent conductivity assignment based on water content values estimated from MRI intensity values. The computational results of the proposed approach provide similar but smoother electric field results for the brain when compared to conventional approaches. In electromagnetic dosimetry applications, the use of computational models that imitate human anatomy is an essential process [1].

Propaganda aims at influencing people's mindset with the purpose of advancing a specific agenda. Previous work has addressed propaganda detection at the document level, typically labelling all articles from a propagandistic news outlet as propaganda. Such noisy gold labels inevitably affect the quality of any learning system trained on them. A further issue with most existing systems is the lack of explainability. To overcome these limitations, we propose a novel task: performing fine-grained analysis of texts by detecting all fragments that contain propaganda techniques as well as their type. In particular, we create a corpus of news articles manually annotated at the fragment level with eighteen propaganda techniques and we propose a suitable evaluation measure. We further design a novel multi-granularity neural network, and we show that it outperforms several strong BERT-based baselines.