First, we explore the effect of additional directions for obtaining the best performance in the answerselection information by adopting a pretrained language model to task. First, we explore the effect of additional information compute the vector representation of the input text and by applying by adopting a pretrained language model (LM) to compute the vector transfer learning from a large-scale corpus. Second, we enhance representation of the input text. Recent studies have shown that the compare-aggregate model by proposing a novel latent clustering replacing the word-embedding layer with a pretrained language method to compute additional information within the target model helps the model capture the contextual meaning of words corpus and by changing the objective function from listwise in the sentence [2, 6]. Following this study, we select an ELMo [6] to pointwise. To evaluate the performance of the proposed approaches, language model for this study. Furthermore, we investigate the experiments are performed with the WikiQA and TREC-applicability of transfer learning (TL) by using a large-scale corpus QA datasets. The empirical results demonstrate the superiority of that is created for relevant-sentence-selection task (i.e., questionanswering our proposed approach, which achieve state-of-the-art performance

In many real world situations, collective decisions are made using voting. Moreover, scenarios such as committee or board elections require voting rules that return multiple winners. In multi-winner approval voting (AV), an agent may vote for as many candidates as they wish. Winners are chosen by tallying up the votes and choosing the top-$k$ candidates receiving the most votes. An agent may manipulate the vote to achieve a better outcome by voting in a way that does not reflect their true preferences. In complex and uncertain situations, agents may use heuristics to strategize, instead of incurring the additional effort required to compute the manipulation which most favors them. In this paper, we examine voting behavior in multi-winner approval voting scenarios with complete information. We show that people generally manipulate their vote to obtain a better outcome, but often do not identify the optimal manipulation. Instead, voters tend to prioritize the candidates with the highest utilities. Using simulations, we demonstrate the effectiveness of these heuristics in situations where agents only have access to partial information.

Although "black box" models such as Artificial Neural Networks, Support Vector Machines, and Ensemble Approaches continue to show superior performance in many disciplines, their adoption in the sensitive disciplines (e.g., finance, healthcare) is questionable due to the lack of interpretability and explainability of the model. In fact, future adoption of "black box" models is difficult because of the recent rule of "right of explanation" by the European Union where a user can ask for an explanation behind an algorithmic decision, and the newly proposed bill by the US government, the "Algorithmic Accountability Act", which would require companies to assess their machine learning systems for bias and discrimination and take corrective measures. Top Bankruptcy Prediction Models are A.I.-based and are in need of better explainability -the extent to which the internal working mechanisms of an AI system can be explained in human terms. Although explainable artificial intelligence is an emerging field of research, infusing domain knowledge for better explainability might be a possible solution. In this work, we demonstrate a way to collect and infuse domain knowledge into a "black box" model for bankruptcy prediction. Our understanding from the experiments reveals that infused domain knowledge makes the output from the black box model more interpretable and explainable.

Productive Edge partners with enterprise clients to develop transformative digital strategies and customer experiences, and we deliver these by applying Artificial Intelligence, Internet of Things (IoT), Intelligent Automation, and Cloud Native technologies. Our digital business consulting and technology solutions are focused on measurable business outcomes. Our culture is built upon our values of commitment, constant improvement, and pride without ego. Our team members are masters at their craft and down-to-earth people who believe in working hard, playing hard, and celebrating our successes.

NetApp which is a Hybrid cloud data provider has now revealed the opening of the new AI center of excellence in the Bangalore in collaboration with the Nvidia chipmaker. According to the report which has been revealed, this will be going to be an addition to the NetApp Data Vsionioanry Centre which was inaugurated a year ago at the NetApp campus in Bangalore. The two companies revealed that the center of excellence will be going to help create an environment, where they can help to showcase technologies to enterprises and the government. The center will also involve the NetApp making an investment into the Nvidia DGX workstations, the Nvidia built specifically to tackle the use cases around the AI, ML and deep learning. The center will also offer the enterprise an opportunity to experience solutions for the AI build by the Nvidia and NetApp while also working with their engineers and subject matter experts in the field.

Successful AI-powered customer service systems will depend on bots working with humans, not replacing them. Customer service is traditionally considered a cost center, so many organizations have focused their customer improvement efforts on reducing costs. This proves to be a critical mistake, as everyone is left unhappy. Even as customers are sick of pressing two for reservations and three for service, service reps are sick of answering the same questions over and over. The latest technology for service is virtual agents: Automated systems, trained on service transcripts, that can use AI to recognize and respond to customer requests whether by phone or chat.

Since then, any mention of the Tiananmen Square Massacre in Chinese media is forbidden, and in recent decades China has relied on whole teams of extra censors to attempt to scrub the internet and social media of any and all references to the event from online memory. But now, according to Reuters, wiping online memory of the Tiananmen Square Massacre is easier than ever, thanks to artificial intelligence. Reuters spoke with several employees at Chinese internet companies who revealed that censorship of forbidden content on Chinese media and social networks is now largely carried out by sophisticated machine learning tools instead of humans. "We sometimes say that the artificial intelligence is a scalpel, and a human is a machete," one content screening employee at Beijing Byte Dance, an app and digital content company, told Reuters. Another employee at the same company said, "When I first began this kind of work four years ago, there was opportunity to remove the images of Tiananmen, but now the artificial intelligence is very accurate."

The last few years have seen an uptick in pop culture stories featuring time travel, from the repetitions and revisions of "The Good Place" and "Russian Doll" to developments in "Game of Thrones," "Star Trek: Discovery" and "Avengers: Endgame." Sometimes the MacGuffin by which we get to play with anachronism, but often also rooted in questions of free will and determinism, time travel is a fascinating springboard for fiction: Are there many futures, or just one? Can you change the past without changing the future, or yourself? This column brings together books about time fractured and out of joint, time as an unbroken lineage resisting empire, and time travel glimpsed through the overlapping lenses of psychology, philosophy and physics. Kameron Hurley's THE LIGHT BRIGADE (Saga, $26.99) is based on her 2015 short story of the same name, fleshing out the high-concept skeleton of a story about soldiers who are literally broken into light in order to teleport them to different theaters of war.

The film world is full of intriguing what-ifs. Will Smith famously turned down the role of Neo in The Matrix. Nicolas Cage was cast as the lead in Tim Burton's Superman Lives, but he only had time to try on the costume before the film was canned. Actors and directors are forever glancing off projects that never get made or that get made by someone else, and fans are left wondering what might have been. For the people who make money from movies, that isn't good enough.

In February 2017, news broke that Goldman Sach's New York headquarters sacked 600 traders and replaced them with some 200 computer engineers who are overseeing automated trading programs. Marty Chavez, the Goldman Sach's deputy chief financial officer and former chief information officer, while speaking at a symposium on computer's impact on economic activity,notes that "Goldman Sachs has already begun to automate currency trading, and has found consistently that four traders can be replaced by one computer engineer, Chavez said at the Harvard conference. Some 9,000 people, about one-third of Goldman's staff, are computer engineers." Artificial Intelligence is causing massive paradigm shifts across many industries, but its biggest impacts is felt in financial services sector. Simply put, artificial intelligence provides unfair advantage in the financial markets.