Over the past year, business leaders worldwide have been sensing that confidence is low – but where's the data to support that? The 2019 Worldcom Confidence Index fills that void with hard data gathered using a revolutionary methodology powered by artificial intelligence (AI). Last year, we predicted that AI would be the big issue of 2019, and we were right. The 2019 Worldcom Confidence Index is just one example of how AI has fundamentally altered our operating concept across a range of industries – from healthcare to manufacturing and now to survey methodology. This forward-thinking approach to gathering and analyzing data is emblematic of the work Worldcom Public Relations Group does and is one of the reasons we're proud to be a member of this global PR network.

The global artificial intelligence (AI) market size was valued at USD 20.67 Billion in 2018 is projected to reach USD 202.57 Artificial intelligence is the result of a perfect blend of several technologies leading to the creation of intelligent hardware or software, capable of replicating human behaviors, namely learning and problem-solving. The version of artificial intelligence technology available at present allows machines to complete various human tasks, such as driving automobiles and reacting to their environment, providing virtual assistance, and even playing games. Forms of AI in use today include digital assistants, chatbots and machine learning, among others. AI will facilitate more seamless integration of supply chain data, enabling anticipatory production and efficient delivery of products to customers.

A New Research on the Global Artificial Intelligence(AI) in Retail Market was conducted across a variety of industries in various regions to produce more than 150 page reports. This study is a perfect blend of qualitative and quantifiable information highlighting key market developments, industry and competitors' challenges in gap analysis and new opportunities and may be trending in the Artificial Intelligence(AI) in Retail market. Some are part of the coverage and are the core and emerging players being profiled IBM, Microsoft, Nvidia, Amazon Web Services, Oracle, SAP, Intel, Google, Sentient Technologies, Salesforce, Visenze. Import and export policies that can have an immediate impact on the global Artificial Intelligence(AI) in Retail market. This study includes EXIM * related chapters for all relevant companies dealing with the Artificial Intelligence(AI) in Retail market and related profiles and provides valuable data in terms of finances, product portfolio, investment planning and marketing and business strategy. The study is a collection of primary and secondary data that contains valuable information from the major suppliers of the market.

It is an open directory of AI ecosystem activities, stakeholder information and more that shall foster collaboration between AI practitioners worldwide, provide access to those interested in becoming active in the field of AI, and drive local and global AI agenda development via the necessary input from the grassroots AI community. As of now, we're having 26 cities from already 6 continents unlocked, roughly 500 upcoming events to join, over 1,000 active community groups to engage with, tens of local AI influencers to follow and almost 1,000 startups to discover. The past year, we've seen various ambassadors using the information to bring together local AI stakeholders such as Meetup organizers and other community actors, AI startup founders, data scientists and machine learning engineers from various organziations, AI-related initiative founders and governmental/municipality representatives discussing to develop (a) their local AI ecosystem further and (b) an aligned AI agenda. Our new director Valentina Colombo joined us to facilitate ambassadors and the AI community leveraging the AI Ecosystem Wiki in even more ways. Expect regular newsletters on the global AI ecosystem, progress benchmarks and reports, local AI ecosystem regulars, AI Council support and much more.

From Greta Thunberg's student-led climate strikes to the youth-driven protests in Hong Kong and Chile, the next generation is increasingly demanding a voice on pressing issues. Youth movements are reenergizing paralyzed debates among adults with fresh perspectives, inconvenient questions, and the rhetorical power of having to live with the long-term fallout of our short-term thinking. With another monumental societal transformation on the horizon--the rise of artificial intelligence--we have an opportunity to engage the power and imagination of youth to shape the world they will inherit. Many of us were caught off-guard by the unintended consequences of the first wave of digital technologies, from mass surveillance to election hacking. But the disruptive power of the internet to date only sets the stage for the even more radical changes AI will produce in the coming decades.

HIGHLIGHTS FROM DAY 1 WHERE IS IQ'WHALO? What will our generation be remembered for? This year marks the second IGF attended by UN Secretary-General António Guterres. His opening speech last year – together with French President Macron's speech – carried substantive reflections on the state of global digital policy, and an encouraging vision for the digital developments ahead of us. This year's opening speech couldn't be more different. Characterised by examples of how the Internet is being misused and exploited, Guterres gave a stark account of the profound issues which are affecting today's technology and tomorrow's developments. 'It is for me an enormous frustration to be that today, not only we are still building physical walls to separate people, but that there is also the tendency to create some virtual walls in the Internet also to separate people.' The three main divides – the digital divide, the social divide, and the political divide – are still profound.

Banana, a nutritionally-rich, delicious fruit, is a widely-cultivated crop across the world and is a staple diet of people living in parts of Africa, Asia and Latin America. Due to pests and diseases, only 13% of the global production is traded, and often, farmers in India experience severe loss due to fusarium wilt or Panama disease. A novel innovation now aims to change the fortunes of banana growers by helping them detect diseases and pests with their smartphone. In a recent study, researchers from the USA, Democratic Republic of Congo, Uganda, Ethiopia and India have developed a banana pest detection app powered by artificial intelligence (AI). Artificial Intelligence is an emerging arena in computer science where machines are programmed to simulate human intelligence and perform tasks like speech recognition, visual perception, language translation and decision-making.

On Nov. 13, Facebook announced with great fanfare that it was taking down substantially more posts containing hate speech from its platform than ever before. Facebook removed more than seven million instances of hate speech in the third quarter of 2019, the company claimed, an increase of 59% against the previous quarter. More and more of that hate speech (80%) is now being detected not by humans, they added, but automatically, by artificial intelligence. The new statistics, however, conceal a structural problem Facebook is yet to overcome: not all hate speech is treated equally. The algorithms Facebook currently uses to remove hate speech only work in certain languages. That means it has become easier for Facebook to contain the spread of racial or religious hatred online in the primarily developed countries and communities where global languages like English, Spanish and Mandarin dominate.

Federated machine learning systems have been widely used to facilitate the joint data analytics across the distributed datasets owned by the different parties that do not trust each others. In this paper, we proposed a novel Gradient Boosting Machines (GBM) framework SecureGBM built-up with a multi-party computation model based on semi-homomorphic encryption, where every involved party can jointly obtain a shared Gradient Boosting machines model while protecting their own data from the potential privacy leakage and inferential identification. More specific, our work focused on a specific "dual--party" secure learning scenario based on two parties -- both party own an unique view (i.e., attributes or features) to the sample group of samples while only one party owns the labels. In such scenario, feature and label data are not allowed to share with others. To achieve the above goal, we firstly extent -- LightGBM -- a well known implementation of tree-based GBM through covering its key operations for training and inference with SEAL homomorphic encryption schemes. However, the performance of such re-implementation is significantly bottle-necked by the explosive inflation of the communication payloads, based on ciphertexts subject to the increasing length of plaintexts. In this way, we then proposed to use stochastic approximation techniques to reduced the communication payloads while accelerating the overall training procedure in a statistical manner. Our experiments using the real-world data showed that SecureGBM can well secure the communication and computation of LightGBM training and inference procedures for the both parties while only losing less than 3% AUC, using the same number of iterations for gradient boosting, on a wide range of benchmark datasets.

Automated reasoning and theorem proving have recently become major challenges for machine learning. In other domains, representations that are able to abstract over unimportant transformations, such as abstraction over translations and rotations in vision, are becoming more common. Standard methods of embedding mathematical formulas for learning theorem proving are however yet unable to handle many important transformations. In particular, embedding previously unseen labels, that often arise in definitional encodings and in Skolemization, has been very weak so far. Similar problems appear when transferring knowledge between known symbols. We propose a novel encoding of formulas that extends existing graph neural network models. This encoding represents symbols only by nodes in the graph, without giving the network any knowledge of the original labels. We provide additional links between such nodes that allow the network to recover the meaning and therefore correctly embed such nodes irrespective of the given labels. We test the proposed encoding in an automated theorem prover based on the tableaux connection calculus, and show that it improves on the best characterizations used so far. The encoding is further evaluated on the premise selection task and a newly introduced symbol guessing task, and shown to correctly predict 65% of the symbol names.