In the years after it was founded in 1999, the Swedish video game company Paradox Interactive quietly built a reputation for developing some of the best, and most hardcore, strategy games on the market. "Deep, endless, complex, unyielding games," is how Shams Jorjani, the company's chief business development officer, describes Paradox's offerings. Most of its biggest hits, such as the middle ages-themed Crusader Kings, or Sengoku, in which you play as a 16th-century Japanese noble, were loosely based on history. But in 2016, Paradox decided to try something a little different. Its new game, Stellaris, was a work of sprawling science fiction, set 200 years in the future. In this virtual universe, players could explore richly detailed galaxies, command their own fusion-powered starship fleets and fight with extraterrestrials to expand their space empires. Gamers could choose to play as the human race, or one of many alien species. Another type of alien is a sentient crystal that eats rocks.) The game was an instant hit, selling more than 200,000 copies in its first 24 hours. Later that year, Paradox decided to take Stellaris to China. This would mean navigating the country's notoriously tricky censorship rules, but given that China was, at the time, home to an estimated 560 million gamers, the commercial appeal was irresistible. Paradox had been burned in China before.

We propose a novel method to capture data points near decision boundary in neural network that are often referred to a specific type of uncertainty. In our approach, we sought to perform uncertainty estimation based on the idea of adversarial attack method. In this paper, uncertainty estimates are derived from the input perturbations, unlike previous studies that provide perturbations on the model's parameters as in Bayesian approach. We are able to produce uncertainty with couple of perturbations on the inputs. Interestingly, we apply the proposed method to datasets derived from blockchain. We compare the performance of model uncertainty with the most recent uncertainty methods. We show that the proposed method has revealed a significant outperformance over other methods and provided less risk to capture model uncertainty in machine learning.

Learning multimodal representations involves integrating information from multiple heterogeneous sources of data. It is a challenging yet crucial area with numerous real-world applications in multimedia, affective computing, robotics, finance, human-computer interaction, and healthcare. Unfortunately, multimodal research has seen limited resources to study (1) generalization across domains and modalities, (2) complexity during training and inference, and (3) robustness to noisy and missing modalities. In order to accelerate progress towards understudied modalities and tasks while ensuring real-world robustness, we release MultiBench, a systematic and unified large-scale benchmark spanning 15 datasets, 10 modalities, 20 prediction tasks, and 6 research areas. MultiBench provides an automated end-to-end machine learning pipeline that simplifies and standardizes data loading, experimental setup, and model evaluation. To enable holistic evaluation, MultiBench offers a comprehensive methodology to assess (1) generalization, (2) time and space complexity, and (3) modality robustness. MultiBench introduces impactful challenges for future research, including scalability to large-scale multimodal datasets and robustness to realistic imperfections. To accompany this benchmark, we also provide a standardized implementation of 20 core approaches in multimodal learning. Simply applying methods proposed in different research areas can improve the state-of-the-art performance on 9/15 datasets. Therefore, MultiBench presents a milestone in unifying disjoint efforts in multimodal research and paves the way towards a better understanding of the capabilities and limitations of multimodal models, all the while ensuring ease of use, accessibility, and reproducibility. MultiBench, our standardized code, and leaderboards are publicly available, will be regularly updated, and welcomes inputs from the community.

The study of fairness in multiwinner elections focuses on settings where candidates have attributes. However, voters may also be divided into predefined populations under one or more attributes (e.g., "California" and "Illinois" populations under the "state" attribute), which may be same or different from candidate attributes. The models that focus on candidate attributes alone may systematically under-represent smaller voter populations. Hence, we develop a model, DiRe Committee Winner Determination (DRCWD), which delineates candidate and voter attributes to select a committee by specifying diversity and representation constraints and a voting rule. We show the generalizability of our model, and analyze its computational complexity, inapproximability, and parameterized complexity. We develop a heuristic-based algorithm, which finds the winning DiRe committee in under two minutes on 63% of the instances of synthetic datasets and on 100% of instances of real-world datasets. We present an empirical analysis of the running time, feasibility, and utility traded-off. Overall, DRCWD motivates that a study of multiwinner elections should consider both its actors, namely candidates and voters, as candidate-specific "fair" models can unknowingly harm voter populations, and vice versa. Additionally, even when the attributes of candidates and voters coincide, it is important to treat them separately as having a female candidate on the committee, for example, is different from having a candidate on the committee who is preferred by the female voters, and who themselves may or may not be female.

The Software Report is pleased to announce The Top 100 Software Companies of 2021. This year's awardee list is comprised of a wide range of companies from the most well-known such as Microsoft, Adobe, and Salesforce to the relatively newer but rapidly growing - Qualtrics, Atlassian, and Asana. A good number of awardees may be new names to some but that should be no surprise given software has always been an industry of startups that seemingly came out of nowhere to create and dominate a new space. Software has become the backbone of our economy. From large enterprises to small businesses, most all rely on software whether for accounting, marketing, sales, supply chain, or a myriad of other functions. Software has become the dominant industry of our time and as such, we place a significance on highlighting the best companies leading the industry forward. The following awardees were nominated and selected based on a thorough evaluation process. Among the key criteria considered were ...

The artificial intelligence revolution is poised to be more "profound" than the invention of electricity, the internet, and even fire, according to Google CEO Sundar Pichai, who made the comments to BBC media editor Amol Rajan in a podcast interview that first went live on Sunday. "The progress in artificial intelligence, we are still in very early stages, but I viewed it as the most profound technology that humanity will ever develop and work on, and we have to make sure we do it in a way that we can harness it to society's benefit," Pichai said. "But I expect it to play a foundational role pretty much across every aspect of our lives. You know, be it health care, be it education, be it how we manufacture things and how we consume information. And so I view it as a very profound enabling technology. You know, if you think about fire or electricity or the internet, it's like that, but I think even more profound," Pichai continued.

The main goal of this post is to detail my development of a model for doing predictive maintenance on commercial turbofan engines. The predictive maintenance method utilized here is a data-driven method, which means that data from the operating jet engine is used to simulate predictive maintenance. The project's goal is to develop a prediction model for estimating a jet engine's Remaining Useful Life ( RUL) based on run-to-failure data from a fleet of comparable jet engines. The Prognostics and Health Management PHM08 Challenge Data Set was developed by NASA and is now available to the public. The data collection is used to forecast jet engine problems over time.

The Covid 19 Pandemic has been detrimental for different sectoral developments. However, one sector that has benefitted from remote work practices is Information Technology (IT). Overbearing dependence on the use of Artificial Intelligence (AI) has made remote working fun and interactive. Artificial Intelligence has become an inseparable entity in our professional sphere. With increased data usage comes the risk of data breaches.

California gubernatorial candidate lays out his agenda on'Hannity' and Leo Terrell endorses him This is a rush transcript from "Hannity," July 13, 2021. This copy may not be in its final form and may be updated. I do get a kick out of it. Tonight, a massive record-setting inflation, spiking violent crime, unprecedented waves of illegal immigration, China, Russia, Iran rolling over this great country, and sadly, whoever is in charge at the Biden White House -- well, is just getting started. Now, state-mandated vaccine programs, that may also be headed your way. We'll tell you the details. Government doctor, wannabe celebrity, Anthony Fauci demanding that all your young children wear masks indefinitely. We have that news tonight. Larry Elder is now officially running to unseat Newsom as governor of the great state of California. He will join us for his very first TV interview since his big announcement, and it comes with an endorsement. But, first, an important update from the U.S. Olympic and Paralympic Committees' bizarre proposal to redesign the American flag. We're going to explain that in detail. We've had a back-and-forth with this organization all day. If you are one of millions of Americans who disagree with the radical policies put forth by the Democratic Party, watch out because Joe Biden -- well, he referred his political opponents today -- referred to them as domestic enemies. Where's the media that got so upset when Donald Trump said the media is an enemy of the people because they lie and tell fake news? Anyway, he's saying they're working to subvert American democracy. In a set of prepared remarks, this wasn't off the cuff, better suited for a despotic socialist dictator frankly, Joe Biden said that our country is facing its most significant test since the civil war echoing Jen Psaki because all state legislatures, why, they're requiring voter ID like his state?

American innovation, from smartphones to search engines to gene sequencing, is built on a foundation of impossibly intricate, perfectly etched silicon. But few of those semiconductors are actually made in the US. Only 12 percent of chips sold worldwide were made in the US in 2019, down from 37 percent in 1990. For decades, that wasn't seen as a problem. US companies were world leaders in designing cutting-edge chips, the most valuable and important part of the process.