Working with documents is a key part of almost any knowledge work, from contextualizing research in a literature review to reviewing legal precedent. Recently, as their capabilities have expanded, primarily text-based NLP systems have often been billed as able to assist or even automate this kind of work. But to what extent are these systems able to model these tasks as experts conceptualize and perform them now? In this study, we interview sixteen domain experts across two domains to understand their processes of document research, and compare it to the current state of NLP systems. We find that our participants processes are idiosyncratic, iterative, and rely extensively on the social context of a document in addition its content; existing approaches in NLP and adjacent fields that explicitly center the document as an object, rather than as merely a container for text, tend to better reflect our participants' priorities, though they are often less accessible outside their research communities. We call on the NLP community to more carefully consider the role of the document in building useful tools that are accessible, personalizable, iterative, and socially aware.

I remember the first time I saw the floaty rock. It was the middle of night, and I was facing the insomniac's dilemma: to reach for the phone or not. I reached and opened Twitter--this was two weeks ago; the new name hadn't yet sunk in--on the theory that a scroll through my feed might achieve some hypnotic effect, creating an opening for sleep to take hold. That's when I saw the blurry video. In it, a scrap of material, small and misshapen like a pencil's broken lead tip, hovers mystically above a thick wafer of polished metal.

Despite the remarkable technological advances that fill our lives today, the ways we work with the metals that underlie these developments haven't changed significantly in thousands of years. This is true of everything from the metal rods, tubes, and cubes that provide cars and trucks with their shape, strength, and fuel economy, to wires that move electrical energy in everything from motors to undersea cables. But things are changing rapidly: The materials manufacturing industry is using new and innovative technologies, processes, and methods to improve existing products and create new ones. Pacific Northwest National Laboratory (PNNL) is a leader in this space, known as advanced manufacturing. For example, scientists working in PNNL's Mathematics for Artificial Reasoning in Science initiative are pioneering approaches in the branch of artificial intelligence known as machine learning to design and train computer software programs that guide the development of new manufacturing processes.

Despite the remarkable technological advances that fill our lives today, the ways we work with the metals that underlie these developments haven't changed significantly in thousands of years. This is true of everything from the metal rods, tubes, and cubes that provide cars and trucks with their shape, strength, and fuel economy, to wires that move electrical energy in everything from motors to undersea cables. But things are changing rapidly: The materials manufacturing industry is using new and innovative technologies, processes, and methods to improve existing products and create new ones. Pacific Northwest National Laboratory (PNNL) is a leader in this space, known as advanced manufacturing. For example, scientists working in PNNL's Mathematics for Artificial Reasoning in Science initiative are pioneering approaches in the branch of artificial intelligence known as machine learning to design and train computer software programs that guide the development of new manufacturing processes.

By automating scientific processes and introducing artificial intelligence for decision-making, TRI's new closed-loop research platforms free up scientists' time for more creative tasks. When I first started graduate school almost 10 years ago, I was mixing ingredients by hand, writing down reaction conditions on a piece of paper, and grabbing a quick lunch in between lab sessions. At that time, the idea of a robot doing my experiments -- or using machine learning to predict the outcomes of my reactions -- would have never occurred to me. I accepted a future as a scientist where I would only be able to explore a tiny fraction of the billions of possible materials in the universe by hand. If lucky, a scientific discovery might arrive serendipitously as I became better at making "educated guesses."

From the stone age, to the bronze, iron age, and modern silicon age, the discovery and characterization of new materials has always been instrumental to humanity's progress and development. With the current pressing need to address sustainability challenges and find alternatives to fossil fuels, we look for solutions in the development of new materials that will allow for renewable energy. To discover materials with the required properties, materials scientists can perform high-throughput materials discovery, which includes rapid synthesis and characterization via X-ray diffraction (XRD) of thousands of materials. A central problem in materials discovery, the phase map identification problem, involves the determination of the crystal structure of materials from materials composition and structural characterization data. This analysis is traditionally performed mainly by hand, which can take days for a single material system.

Machines can learn a lot of things--probably more than you can imagine. But can they learn common sense? At his company, Elemental Cognition, Ferrucci described how his AI team gave an advanced language program the sentence, "Zoey moves her plant to a sunny window. The AI program was tasked to complete the second sentence. SEE: An IT pro's guide to robotic process automation (free PDF) (TechRepublic) A human would likely complete the sentence by saying, "the sun will help the plant to grow and stay healthy." In the real world, it's common knowledge that plants need light. Unfortunately, the AI program couldn't deliver this common observation. Instead, the AI completed the sentence by analyzing statistical patterns. It came up with these possible answers: "she finds something, not pleasant," "fertilizer is visible in the window," and "another plant is missing from the bedroom." This story is an entry point to myriad "common sense" issues that face today's AI. It begins to explain why a self-driving vehicle may not be able to decipher the varying degrees of danger between striking a traffic cone or striking a pedestrian. "The great irony of common sense--and indeed AI itself--is that it is stuff that pretty much everybody knows, yet nobody seems to know what exactly it is or how to build machines that possess it," said Gary Marcus, CEO and founder of Robust.AI. "Solving this problem is, we would argue, the single most important step towards taking AI to the next level.

BOSTON--In July 2018, Curtis Berlinguette, a materials scientist at the University of British Columbia in Vancouver, Canada, realized he was wasting his graduate student's time and talent. He had asked her to refine a key material in solar cells to boost its electrical conductivity. But the number of potential tweaks was overwhelming, from spiking the recipe with traces of metals and other additives to varying the heating and drying times. "There are so many things you can go change, you can quickly go through 10 million [designs] you can test," Berlinguette says. So he and colleagues outsourced the effort to a single-armed robot overseen by an artificial intelligence (AI) algorithm.

File photo - M1A1 Abrams main battle tanks assigned to 3rd Battalion, 67th Armored Regiment, 2nd Armored Brigade Combat Team, 3rd Infantry Division stage prior to a tactical movement during Spartan Focus, at Fort Stewart, Ga. When dismounted U.S. Army infantry are attacking fortified enemy positions, taking hostile fire and moving quickly to find the best points for continued assault -- "battery life" can determine mission success or failure and even -- life or death. Units of forward positioned Army soldiers may not have quick access to battery recharging and may, therefore, depend entirely upon the functionality of their batteries - needed to power night vision, radios, small soldier-worn sensors, portable laptops for drone control and other combat-essential items. Without the requisite battery power to advance, soldiers might be forced to retreat or, of even greater consequence, become far more vulnerable to enemy fire. It goes without saying that attacking soldiers, especially those on the move on foot, need lightweight, electrically powered equipment for communications, data sharing, enemy tracking, targeting and some weaponry.

From the stone age, to the bronze, iron age, and modern silicon age, the discovery and characterization of new materials has always been instrumental to humanity's progress and development. With the current pressing need to address sustainability challenges and find alternatives to fossil fuels, we look for solutions in the development of new materials that will allow for renewable energy. To discover materials with the required properties, materials scientists can perform high-throughput materials discovery, which includes rapid synthesis and characterization via X-ray diffraction (XRD) of thousands of materials. A central problem in materials discovery, the phase map identification problem, involves the determination of the crystal structure of materials from materials composition and structural characterization data. This analysis is traditionally performed mainly by hand, which can take days for a single material system. In this work we present Phase-Mapper, a solution platform that tightly integrates XRD experimentation, AI problem solving, and human intelligence for interpreting XRD patterns and inferring the crystal structures of the underlying materials. Phase-Mapper is compatible with any spectral demixing algorithm, including our novel solver, AgileFD, which is based on convolutive non-negative matrix factorization. AgileFD allows materials scientists to rapidly interpret XRD patterns, and incorporates constraints to capture prior knowledge about the physics of the materials as well as human feedback. With our system, materials scientists have been able to interpret previously unsolvable systems of XRD data at the Department of Energy’s Joint Center for Artificial Photosynthesis, including the Nb-Mn-V oxide system, which led to the discovery of new solar light absorbers and is provided as an illustrative example of AI-enabled high throughput materials discovery