In a narrow, windowless room at the University of Southern California, a group of graduate students is workshopping a short story. Its author is silent as her classmates deliver gentle feedback. Some suggest minor improvements of pacing, setting, and tone. One student would appreciate a more robust description of the protagonist's emotions, but enjoys the sparseness, too. "I like this version," another adds.

Capsule Networks, an extension to Neural Networks utilizing vector or matrix representations instead of scalars, were initially developed to create a dynamic parse tree where visual concepts evolve from parts to complete objects. Early implementations of Capsule Networks achieved and maintain state-of-the-art results on various datasets. However, recent studies have revealed shortcomings in the original Capsule Network architecture, notably its failure to construct a parse tree and its susceptibility to vanishing gradients when deployed in deeper networks. This paper extends the investigation to a range of leading Capsule Network architectures, demonstrating that these issues are not confined to the original design. We argue that the majority of Capsule Network research has produced architectures that, while modestly divergent from the original Capsule Network, still retain a fundamentally similar structure. We posit that this inherent design similarity might be impeding the scalability of Capsule Networks. Our study contributes to the broader discussion on improving the robustness and scalability of Capsule Networks.

In a perfect world, what you see is what you get. If this were the case, the job of Artificial Intelligence systems would be refreshingly straightforward. Take collision avoidance systems in self-driving cars. If visual input to on-board cameras could be trusted entirely, an AI system could directly map that input to an appropriate action--steer right, steer left, or continue straight--to avoid hitting a pedestrian that its cameras see in the road. But what if there's a glitch in the cameras that slightly shifts an image by a few pixels? If the car blindly trusted so-called'adversarial inputs,' it might take unnecessary and potentially dangerous action.

In a perfect world, what you see is what you get. If this were the case, the job of artificial intelligence systems would be refreshingly straightforward. Take collision avoidance systems in self-driving cars. If visual input to on-board cameras could be trusted entirely, an AI system could directly map that input to an appropriate action -- steer right, steer left, or continue straight -- to avoid hitting a pedestrian that its cameras see in the road. But what if there's a glitch in the cameras that slightly shifts an image by a few pixels?

You're probably familiar with deepfakes, the digitally altered "synthetic media" that's capable of fooling people into seeing or hearing things that never actually happened. Adversarial examples are like deepfakes for image-recognition A.I. systems -- and while they don't look even slightly strange to us, they're capable of befuddling the heck out of machines. Several years ago, researchers at the Massachusetts Institute of Technology's Computer Science and Artificial Intelligence Laboratory (CSAIL) found that they could fool even sophisticated image recognition algorithms into confusing objects simply by slightly altering their surface texture. In the researchers' demonstration, they showed that it was possible to get a cutting-edge neural network to look at a 3D-printed turtle and see a rifle instead. Or to gaze upon a baseball and come away with the conclusion that it is an espresso.

In a perfect world, what you see is what you get. If this were the case, the job of artificial intelligence systems would be refreshingly straightforward. Take collision avoidance systems in self-driving cars. If visual input to on-board cameras could be trusted entirely, an AI system could directly map that input to an appropriate action--steer right, steer left, or continue straight--to avoid hitting a pedestrian that its cameras see in the road. But what if there's a glitch in the cameras that slightly shifts an image by a few pixels? If the car blindly trusted so-called'adversarial inputs,' it might take unnecessary and potentially dangerous action.

In a perfect world, what you see is what you get. If this were the case, the job of artificial intelligence systems would be refreshingly straightforward. Take collision avoidance systems in self-driving cars. If visual input to on-board cameras could be trusted entirely, an AI system could directly map that input to an appropriate action -- steer right, steer left, or continue straight -- to avoid hitting a pedestrian that its cameras see in the road. But what if there's a glitch in the cameras that slightly shifts an image by a few pixels?

Both races are setting the stage for the next dominant world power. While research into AI and quantum technologies is being developed on a worldwide scale, with advances coming from different countries, China and the United States (US) are at the forefront of both races, with these technologies forming important stepping stones for geopolitical power accumulation. Indeed, China is currently playing the game for supremacy on both quantum technologies and AI, trying to surpass the US and become the leading world power (Smith-Goodson, 2019). If China wins the race for quantum supremacy then it will be in a leading geostrategic position, since it will become the major dominant power in the next technological infrastructure, if, along with quantum supremacy, China achieves AI supremacy (both classical and quantum), then it may topple the US, Russia, Europe and Asian geopolitical competition vectors. On the other hand, this race is not restricted to countries, it is a global geostrategic and geoeconomic race that includes cooperative networks involving the academia and the private sectors as well, indeed, the US geostrategic position depends strongly upon the private sector's US-based large technology companies' investment in quantum technologies. Regarding the issue of quantum supremacy, it is relevant to consider Kirkland (2020)'s reflection, quoting: "(…) One thing remains unchanged (…) and that is the glaring reality that those who manage to successfully harness the power of quantum mechanics will have supremacy over the rest of the world. How do you think they will use it?"

Orson Welles, who knew a thing or two about making movies, reportedly remarked after touring the RKO lot that it was "the biggest electric train set any boy ever had." And yet it is rare to see a feature film that communicates any of that delight, any of the sheer fun of playing around with the possibilities the medium offers. The Vast of Night, the debut feature from director Andrew Patterson and screenwriters James Montague and Craig W. Sanger, arriving on Amazon Prime on May 29, is one of the exceptions: Every scene has been staged and shot with intelligence, intent, inventiveness, and a sense of play. To watch it is to get excited about the billions of different ways you can combine sound and moving images to tell a story. That is not to say that you'll necessarily be astounded by the story The Vast of Night is telling.

A savvy approach to artificial intelligence can radically enhance productivity and slash costs. You're not imagining it: the pace of technological change is indeed quickening and it's placing tremendous competitive pressures on every corner of the global economy Manufacturers are acutely feeling the squeeze. Eighty-five percent of industrial equipment execs surveyed by Accenture say they need to innovate ever faster just to keep up. That puts them in a perilous catch-22: it's prohibitively expensive to upgrade equipment to meet customer demands, yet they risk losing customers altogether if they don't. Enter artificial intelligence, the great equalizer for manufacturers.