Automatic fact verification has received significant attention recently. Contemporary automatic fact-checking systems focus on estimating truthfulness using numerical scores which are not human-interpretable. A human fact-checker generally follows several logical steps to verify a verisimilitude claim and conclude whether its truthful or a mere masquerade. Popular fact-checking websites follow a common structure for fact categorization such as half true, half false, false, pants on fire, etc. Therefore, it is necessary to have an aspect-based (delineating which part(s) are true and which are false) explainable system that can assist human fact-checkers in asking relevant questions related to a fact, which can then be validated separately to reach a final verdict. In this paper, we propose a 5W framework (who, what, when, where, and why) for question-answer-based fact explainability. To that end, we present a semi-automatically generated dataset called FACTIFY-5WQA, which consists of 391, 041 facts along with relevant 5W QAs - underscoring our major contribution to this paper. A semantic role labeling system has been utilized to locate 5Ws, which generates QA pairs for claims using a masked language model. Finally, we report a baseline QA system to automatically locate those answers from evidence documents, which can serve as a baseline for future research in the field. Lastly, we propose a robust fact verification system that takes paraphrased claims and automatically validates them. The dataset and the baseline model are available at https: //github.com/ankuranii/acl-5W-QA

There's little question artificial intelligence and machine learning are playing an increased role in making business decisions. A 2022 survey of senior data and technology executives by NewVantage Partners found that 92% of large companies reported achieving returns on their data and AI investments -- an increase from 48% in 2017. But as these technologies enter the mainstream, new issues arise: How will they change the nature of workflow and workplace connection? Will they be ethically harnessed? Here's what to consider as AI and machine learning become omnipresent, according to MIT Sloan researchers, visiting scholars, and industry experts.

I'm Jennifer Strong, and this is I Was There When--an oral history project featuring the stories of breakthroughs and watershed moments in AI and computing… as told by those who witnessed them. This episode, we meet Dave Johnson, the chief data and artificial intelligence officer at Moderna. Dave Johnson: Moderna is a biotech company that was founded on the promise of mRNA technology. My name is Dave Johnson. I'm chief data and AI officer at Moderna. mRNA is essentially an information molecule.

This COVID-19 pandemic is so dreadful that it leads to severe anxiety, phobias, and complicated feelings or emotions. Even after vaccination against Coronavirus has been initiated, people feelings have become more diverse and complex, and our goal is to understand and unravel their sentiments in this research using some Deep Learning techniques. Social media is currently the best way to express feelings and emotions, and with the help of it, specifically Twitter, one can have a better idea of what is trending and what is going on in people minds. Our motivation for this research is to understand the sentiment of people regarding the vaccination process, and their diverse thoughts regarding this. In this research, the timeline of the collected tweets was from December 21 to July 21, and contained tweets about the most common vaccines available recently from all across the world. The sentiments of people regarding vaccines of all sorts were assessed by using a Natural Language Processing (NLP) tool named Valence Aware Dictionary for sEntiment Reasoner (VADER). By initializing the sentiment polarities into 3 groups (positive, negative and neutral), the overall scenario was visualized here and our findings came out as 33.96% positive, 17.55% negative and 48.49% neutral responses. Recurrent Neural Network (RNN) oriented architecture such as Long Short-Term Memory (LSTM and Bi-LSTM) is used to assess the performance of the predictive models, with LSTM achieving an accuracy of 90.59% and Bi-LSTM achieving an accuracy of 90.83%. Other performance metrics such as Precision, Recall, F-1 score, and Confusion matrix were also shown to validate our models and findings more effectively. This study will help everyone understand public opinion on the COVID-19 vaccines and impact the aim of eradicating the Coronavirus from our beautiful world.

Seven years have passed since world leaders met in New York and agreed on 17 Sustainable Development Goals (SDGs) to resolve major challenges including poverty, hunger, inequality, climate change and health. The pandemic undoubtedly diverted attention from some of these issues in the past couple of years. But even before COVID-19, the United Nations was warning that progress to meet the SDGs was not advancing at the speed or on the scale needed. Meeting them by 2030 will be tough. The pandemic demonstrated like nothing else the power of working collaboratively, across borders, for the benefit of society.

AI and Machine Learning, Cloud Computing, and 5G Will Dominate in 2022. Corporate Learning. • Moderna Will Train Its Employees in AI, …

Many people believe that the process for achieving breakthrough innovations is chaotic, random, and unmanageable. Breakthroughs can be systematically generated using a process modeled on the principles that drive evolution in nature: variance generation, which creates a variety of life-forms; and selection pressure to select those that can best survive in a given environment. Flagship Pioneering, the venture-creation firm behind Moderna Therapeutics, uses such an approach, which it calls emergent discovery. It involves prospecting for ideas in novel spaces; developing speculative conjectures; and relentlessly questioning hypotheses. On November 30, 2020, Moderna Therapeutics announced that Phase III clinical trials for its messenger RNA vaccine demonstrated 95% protective efficacy against the SARS-CoV-2 virus that had killed almost 1.5 million people worldwide in the previous 10 months. A relative upstart in the Covid-19 vaccine race and a company that few people had heard of before the pandemic, Moderna looked to be an overnight success. But as its CEO, Stéphane Bancel, has noted, that success was 10 years in the making. Far from a one-and-done stroke of luck, the vaccine was the product of a repeatable process that has been used countless times by the company from which Moderna emerged: Flagship Pioneering, a venture-creation firm based in Cambridge, Massachusetts, whose mission is to conceive, make, and commercialize breakthrough innovations in previously unexplored domains of the life sciences. The misconception about the Moderna case, as with many other breakthrough innovations, is understandable. Breakthrough innovations are typically seen as the result of chaotic, random, and unmanageable efforts--the product of pure serendipity or the inspiration of a rare visionary. That view, we believe, is deeply flawed. From our different vantage points (Afeyan has spent the past three decades starting ventures based on breakthrough science and technology, and Pisano has studied innovation processes during the same period), we have come to realize that breakthroughs tend to emerge from a relatively well-defined process modeled on the basic principles that drive evolution in nature: variance generation, which creates a variety of life-forms, and selection pressure to select those that can best survive and reproduce in a given environment. The approach, called emergent discovery, is a structured and disciplined process of intellectual leaps, iterative search and experimentation, and selection.

When pharmaceutical company Moderna announced the first clinical trial of a COVID-19 vaccine, it was a proud moment but not a surprising one for Dave Johnson, the company's chief data and artificial intelligence officer. Listen to or read a transcript of "AI and the COVID-19 Vaccine: Moderna's Dave Johnson." When Johnson joined the company in 2014, he helped put in place automated processes and AI algorithms to increase the number of small-scale messenger RNA (mRNA) needed to run clinical experiments. This groundwork contributed to Moderna releasing one of the first COVID-19 vaccines (using mRNA) even as the world had only started to understand the virus' threat. "The whole COVID vaccine development, we're immensely proud of the work that we've done there, and we're immensely proud of the superhuman effort that our people went through to bring it to market so quickly," Johnson said during a bonus episode of the MIT Sloan Management Review podcast "Me, Myself, and AI." "But a lot of it was built on … this infrastructure that we had put in place where we didn't build algorithms specifically for COVID; we just put them through the same pipeline of activity that we've been doing," Johnson said.

Every day, Q.ai brings you a list of trending stocks that have caught the fancy of hedge funds, retail investors, and the occasional Robinhood-er alike. And to celebrate the start of the new month, today's batch is a rather motley assortment spanning the sector spectrum, from vaccines and bath towels to spirits and cloud computing. Without further ado, let's see what stocks are trending as we celebrate the first day of July with an independence-sized bang. Q.ai runs daily factor models to get the most up-to-date reading on stocks and ETFs. Our deep-learning algorithms use Artificial Intelligence (AI) technology to provide an in-depth, intelligence-based look at a company – so you don't have to do the digging yourself.

Moderna's (NASDAQ:MRNA) vaccine is successfully helping people avoid the coronavirus. The billion-dollar product has demonstrated more than 90% efficacy in adults. And new data show it's 100% effective in teens. But Moderna faces one big challenge that could wreak havoc on how well its vaccine protects the population. The vaccine has handled them so far.