Rescuers deployed sonar and camera equipment early on as officials scoured the rubble for survivors. Heavy machinery was brought in to remove some bits of the pancaked building materials. Yet, nearly 150 people remain unaccounted for. And officials still have a tedious mission ahead as teams try to avoid falling debris and other unforeseen obstacles.
Are you loving the Elevator? Not the ones in high-rise buildings, although I'm sure plenty more of those exist in your time. Condensed city living makes environmental sense, as do vertical farms; stronger and lighter building materials mean more towers; the international competition for tallest skyscraper is unlikely to end any decade soon. No, the Elevators I mean are the ones that deserve capitalization: Space Elevators. We're talking super-tall, super-thin tethers that deliver people, satellites and other goods to high Earth orbit in elevator cars the size of trains.
Like superheroes capable of seeing through obstacles, environmental regulators may soon wield the power of all-seeing eyes that can identify violators anywhere at any time, according to a new Stanford University-led study. The paper, published the week of April 19 in Proceedings of the National Academy of Sciences (PNAS), demonstrates how artificial intelligence combined with satellite imagery can provide a low-cost, scalable method for locating and monitoring otherwise hard-to-regulate industries. "Brick kilns have proliferated across Bangladesh to supply the growing economy with construction materials, which makes it really hard for regulators to keep up with new kilns that are constructed," said co-lead author Nina Brooks, a postdoctoral associate at the University of Minnesota's Institute for Social Research and Data Innovation who did the research while a Ph.D. student at Stanford. While previous research has shown the potential to use machine learning and satellite observations for environmental regulation, most studies have focused on wealthy countries with dependable data on industrial locations and activities. To explore the feasibility in developing countries, the Stanford-led research focused on Bangladesh, where government regulators struggle to locate highly pollutive informal brick kilns, let alone enforce rules.
It uses comparative empirical experiments (Charrette Test Method) to draw comparisons between the visibility of state-of-practice and blockchain-enabled payment systems in a commercial construction project. Comparisons were drawn across four levels of granularity. The findings are twofold: 1) blockchain improved information completeness and information accuracy respectively by an average 216% and 261% compared with the digital state-of-practice solution. The improvements were significantly more pronounced for inquiries that had higher product, trade, and temporal granularity; 2) blockchain-enabled solution was robust in the face of increased granularity, while the conventional solution experienced 50% and 66.7% decline respectively in completeness and accuracy of information. The paper concludes with a discussion of mechanisms contributing to visibility and technology adoption based on business objectives.
For e-commerce companies with large product selections, the organization and grouping of products in meaningful ways is important for creating great customer shopping experiences and cultivating an authoritative brand image. One important way of grouping products is to identify a family of product variants, where the variants are mostly the same with slight and yet distinct differences (e.g. color or pack size). In this paper, we introduce a novel approach to identifying product variants. It combines both constrained clustering and tailored NLP techniques (e.g. extraction of product family name from unstructured product title and identification of products with similar model numbers) to achieve superior performance compared with an existing baseline using a vanilla classification approach. In addition, we design the algorithm to meet certain business criteria, including meeting high accuracy requirements on a wide range of categories (e.g. appliances, decor, tools, and building materials, etc.) as well as prioritizing the interpretability of the model to make it accessible and understandable to all business partners.
Scientists created an intelligent material that acts as a brain by physically changing when it learns. This is an important step toward a new generation of computers that could dramatically increase computing power while using less energy. Currently, it is run on machine learning software. But the "smarter" computers get, the more computing power they require. This can lead to a sizable energy footprint, which could destabilize the computer.
You see lots of little bits of it happening. So, now is the time to get the scientific community together to say: 'This is where we're going, so now let's change our mode of working'. At the moment it is very disparate, with pockets of work all over the place, not talking to each other and without a common aim.
The products and services we use in our daily lives have to abide by safety and security standards, from car airbags to construction materials. But no such broad, internationally agreed-upon standards exist for artificial intelligence. And yet, AI tools and technologies are steadily being integrated into all aspects of our lives. AI's potential benefits to humanity, such as improving health-care delivery or tackling climate change, are immense. But potential harms caused by AI tools –from algorithmic bias and labour displacement to risks associated with autonomous vehicles and weapons – risk leading to a lack of trust in AI technologies. To tackle these problems, a new partnership between AI Global, a nonprofit organization focused on advancing responsible and ethical adoption of artificial intelligence, and the Schwartz Reisman Institute for Technology and Society (SRI) at the University of Toronto will create a globally recognized certification mark for the responsible and trusted use of AI systems.
Researchers at MIT's Center for Bits and Atoms have created tiny building blocks that exhibit a variety of unique mechanical properties, such as the ability to produce a twisting motion when squeezed. These subunits could potentially be assembled by tiny robots into a nearly limitless variety of objects with built-in functionality, including vehicles, large industrial parts, or specialized robots that can be repeatedly reassembled in different forms. The researchers created four different types of these subunits, called voxels (a 3D variation on the pixels of a 2D image). Each voxel type exhibits special properties not found in typical natural materials, and in combination they can be used to make devices that respond to environmental stimuli in predictable ways. Examples might include airplane wings or turbine blades that respond to changes in air pressure or wind speed by changing their overall shape. The findings, which detail the creation of a family of discrete "mechanical metamaterials," are described in a paper published in the journal Science Advances, authored by recent MIT doctoral graduate Benjamin Jenett PhD '20, Professor Neil Gershenfeld, and four others.
At Topos, we are fascinated by exactly this type of variation and believe it provides a powerful view into the culture of a location. While data sources like the United States Census are useful for understanding broad demographic trends over decades, they give little insight into what defines the moment-to-moment culture of a city, a neighborhood, a street corner. Inspired by thinkers like Walter Benjamin, who, in his unfinished Arcades Project examined subjects as varied as fashion, construction materials, poetry, lighting, and mirrors in order to understand Paris in the 19th century, we are fascinated by the way seemingly simple, ubiquitous subjects like the coffee we drink or the concerts we go to define a place. However, unlike Benjamin, we are interested in constructing this understanding in a way that can dynamically scale across the globe, allowing us to understand how different locations relate to one another, and how locations evolve in real time. To achieve this, we use data from dozens of different sources and techniques from a wide range of technologies and disciplines including computer vision, natural language processing, statistics, machine learning, network science, topology, architecture and urbanism.