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Spurred by the demands of the COVID-19 pandemic, researchers at Princeton and Google are applying mechanical engineering and artificial intelligence to increase the availability and effectiveness of ventilation treatments worldwide.

Ventilators and their support equipment are expensive and complex devices that require expert attention from doctors and other highly trained medical workers. The devices must be carefully calibrated and monitored to ensure they are meeting a range of parameters pressure, volume, breath rate tuned to each individual patient. Often, these measures change during treatment, requiring further tuning.

If that monitoring and adjustment is handled by artificial intelligence, it could ease the burden on medical workers and allow ventilators to be deployed in areas with staffing shortages. That was the logic that led Elad Hazan, a professor of computer science and director of Google AI Princeton, and Daniel Cohen, an assistant professor of mechanical and aerospace engineering, to launch the project.

Graduate student Daniel Suo and senior Paula Gradu are part of a team of researchers using AI to improve the way ventilators assist patients.

Photo by

Aaron Nathans, Office of Engineering Communications

Modern ventilators seek to maximize clinical outcomes while at the same time protecting patients from excessive levels of pressure and volume, said Daniel Notterman, a board certified pediatric intensive care physician with experience managing patients with respiratory failure, who is also a lecturer with the rank of professor in molecular biology. Although conceptually simple, the regulation of ventilator performance is extremely complex. This effort provided the opportunity for experts in programming, engineering and clinical medicine to rethink many of the usual solutions, under the leadership of Professor Cohen.

Since the initial COVID-19 outbreak last spring, Cohens team had been working to design low-cost ventilators using readily available parts. Initially, Cohen met with Hazan to discuss a control system for the new design. But the researchers realized that artificial intelligence could improve controls for all ventilators, not just the type designed at Princeton.

The hypothesis is that applying AI tools can make systems more robust and safer, Hazan said.

Access to Cohens ventilator has been critical, Hazan said. The physics underlying breathing is complex, and breaking the fluid dynamics down into working equations is generally impractical and inaccurate. So instead of approaching the control problem through the physics of the lungs, the researchers ran experiments on the Cohen teams ventilators and applied machine learning to uncover patterns in the data that would guide the safe and effective operation of the ventilator.

Tom Zajdel, a post doctoral researcher, was part of the team that designed and built a new ventilator at Princeton. The open-source design uses readily available parts.

The development of the ventilator began as part of an effort by Cohen and Notterman to design a new system that was inexpensive and could be assembled from off-the-shelf parts.

It basically goes together like Legos, said Julienne LaChance, a graduate student in Cohens lab who led the prototype construction efforts from her garage. I picture my high school robotics team putting this together.

The ventilator is now fully built and meets key FDA performance standards, while costing less than $1,500 a tenth or twentieth the price of commercial ventilators, Cohen said. The team is now actively seeking manufacturing partners to help push for regulatory approval, especially in less affluent countries in need of ventilators.

We have been using robust, simple parts that we put together with a lot of very well done software and coding, said Cohen. We are trying to develop a generalized platform that anyone can work with, or improve upon, anywhere in the world, even after the pandemic.

Researchers from Hazans lab include senior Paula Gradu; graduate studentsXinyi Chen, Udaya Ghai, Edgar Minasyan,Karan SinghandDaniel Suo; and recent Ph.D. graduatesNaman AgarwalandCyril Zhang. In addition to LaChance, Notterman and Cohen, the local Princeton ventilator team includes postdoctoral researchersTom ZajdelandManuel Schottdorf, senior research software engineer Grant Wallace, and graduate studentsSophie DvaliandZhenyu Song, as well as a number of external collaborators.

Editors note: For the full version of this story, visitthe Engineering website.

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Engineering and artificial intelligence combine to safeguard COVID-19 patients - Princeton University

Artificial intelligence has been making waves in recent years, enabling us to solve problems faster than traditional computing could ever allow. Recently, for example, Googles artificial intelligence subsidiary DeepMind developed AlphaFold2, a program which solved the protein-folding problem. This is a problem which has had baffled scientists for 50 years.

Advances in AI have allowed us to make progress in all kinds of disciplines and these are not limited to applications on this planet. From designing missions to clearing Earths orbit of junk, here are a few ways artificial intelligence can help us venture further in space.

Do you remember Tars and Case, the assistant robots from the film Interstellar? While these robots dont exist yet for real space missions, researchers are working towards something similar, creating intelligent assistants to help astronauts. These AI-based assistants, even though they may not look as fancy as those in the movies, could be incredibly useful to space exploration.

A recently developed virtual assistant can potentially detect any dangers in lengthy space missions such as changes in the spacecraft atmosphere for example increased carbon dioxide or a sensor malfunction that could be potentially harmful. It would then alert the crew with suggestions for inspection.

An AI assistant called Cimon was flown to the international space station (ISS) in December 2019, where it is being tested for three years. Eventually, Cimon will be used to reduce astronauts stress by performing tasks they ask it to do. NASA is also developing a companion for astronauts aboard the ISS, called Robonaut, which will work alongside the astronauts or take on tasks that are too risky for them.

Read more: Astronauts are experts in isolation, here's whatthey can teach us

Planning a mission to Mars is not an easy task, but artificial intelligence can make it easier. New space missions traditionally rely on knowledge gathered by previous studies. However, this information can often be limited or not fully accessible.

This means the technical information flow is constrained by who can access and share it among other mission design engineers. But what if all the information from practically all previous space missions were available to anyone with authority in just a few clicks. One day there may be a smarter system similar to Wikipedia, but with artificial intelligence that can answer complex queries with reliable and relevant information to help with early design and planning of new space missions.

Researchers are working on the idea of a design engineering assistant to reduce the time required for initial mission design which otherwise takes many human work hours. Daphne is another example of an intelligent assistant for designing Earth observation satellite systems. Daphne is used by systems engineers in satellite design teams. It makes their job easier by providing access to relevant information including feedback as well as answers to specific queries.

Earth observation satellites generate tremendous amounts of data. This is received by ground stations in chunks over a large period of time, and has to be pieced together before it can be analysed. While there have been some crowdsourcing projects to do basic satellite imagery analysis on a very small scale, artificial intelligence can come to our rescue for detailed satellite data analysis.

For the sheer volume of data received, AI has been very effective in processing it smartly. Its been used to estimate heat storage in urban areas and to combine meteorological data with satellite imagery for wind speed estimation. AI has also helped with solar radiation estimation using geostationary satellite data, among many other applications.

AI for data processing can also be used for the satellites themselves. In recent research, scientists tested various AI techniques for a remote satellite health monitoring system. This is capable of analysing data received from satellites to detect any problems, predict satellite health performance and present a visualisation for informed decision making.

One of the biggest space challenges of the 21st century is how to tackle space debris. According to ESA, there are nearly 34,000 objects bigger than 10cm which pose serious threats to existing space infrastructure. There are some innovative approaches to deal with the menace, such as designing satellites to re-enter Earths atmosphere if they are deployed within the low Earth orbit region making them disintegrate completely in a controlled way.

Another approach is to avoid any possible collisions in space, preventing the creation of any debris. In a recent study, researchers developed a method to design collision avoidance manoeuvres using machine-learning (ML) techniques.

Another novel approach is to use the enormous computing power available on Earth to train ML models, transmit those models to the spacecraft already in orbit or on their way, and use them on board for various decisions. One way to ensure safety of space flights has recently been proposed using already trained networks on board the spacecraft. This allows more flexibility in satellite design while keeping the danger of in orbit collision at a minimum.

On Earth, we are used to tools such as Google Maps which use GPS or other navigation systems. But there is no such a system for other extraterrestrial bodies, for now.

We do not have any navigation satellites around the Moon or Mars but we could use the millions of images we have from observation satellites such as the Lunar Reconnaissance Orbiter (LRO). In 2018, a team of researchers from NASA in collaboration with Intel developed an intelligent navigation system using AI to explore the planets. They trained the model on the millions of photographs available from various missions and created a virtual Moon map.

As we carry on to explore the universe, we will continue to plan ambitious missions to satisfy our inherent curiosity as well as to improve the human lives on Earth. In our endeavours, artificial intelligence will help us both on Earth and in space make this exploration possible.

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Five ways artificial intelligence can help space exploration - The Conversation UK

Through artificial intelligence and machine learning, Hitch helps companies find the talent most compatible with their organizational culture.

Let the business resources in our guide inspire you and help you achieve your goals in 2021.

January26, 20214 min read

Hitch is the talent discovery platform that offers the information based on data and on the development of applied neuroscience with Artificial Intelligence that companies need to select the best professionals, develop leaders and discover talents, that is, find that needle in the haystack for their key positions.

Among many things that are changing, for example, the traditional job interview has changed forever. Now, video interviews are studied by algorithms and in this way it is possible to know with much greater precision and depth the qualities of the candidates. That, in addition to many other resources, are part of what Hitch offers, the tech people created by Mexican entrepreneurs.

With Hitch, recruiting tasks can be carried out remotely, having access to a number of CV's that it is impossible to manually review for any company. We also facilitate talent inclusion decision-making based on the candidate's capabilities, qualities and compatibility with the company. All of this substantially raises the level of success in hiring and long-term retention of employees.

We free up the time of Human Capital personnel in companies so that they can focus on tasks that need greater human action, such as strengthening the organizational culture and the development and training of talent.

"At Hitch , we help companies discover the talent they need to be successful," said Gabriela Ceballos, CEO of Hitch during the press conference. "This launch makes finding talent an agile, intelligent and humane experience, injecting the right amount of technology to drive data-driven insights for better decision making. The fact that everything can be done virtually makes launching this product after a year marked by the COVID19 pandemic, is good news for companies and candidates. In addition, by finding the right candidate for the right position we generate long-term happy relationships where companies and talent develop their full potential. "

This SaaS offers:

" Hitch combines the best of neuroscience and organizational psychology with technology, creating a solution that generates great results by analyzing many more candidates and screening the most suitable ones step by step to ensure that companies find who they need, in addition to generating an experience of humane, fair user and with the least possible bias, comments Dr. Ral Arrabales, PhD in Computer Science and Artificial Intelligence and VP of Product at Hitch.

Gabriela Ceballos CEO Hitch. Photo: Courtesy

Because of Hitch's potential, we were able to raise $ 400,000 in pre-seed capital. For our first year in operations we plan to have more than 100 companies in our portfolio, in addition to that we will be processing more than 50,000 jobs for our clients, assured Ceballos.

As Hitch expands its Artificial Intelligence capabilities, the company is committed to a transparency approach, providing a clear path to how algorithms are built and how success predictions are made.

Hitch has experts in technology and psychology who monitor artificial intelligence and ensure the accuracy and fairness of algorithms. For the same reason, still in the pilot phase, it has been selected as part of the program for the Prototype of Public Policy on Transparency and Explicability of AI systems led by

C Minds, Facebook, the Inter-American Development Bank Group and the National Institute for Transparency, Access to Information and Protection of Personal Data (INAI).

Hitch enables companies to make the best decisions about their talent selection from hiring to targeting the type of leadership and culture they want to create for their human capital. Our talent and culture analytics, using AI and machine learning, provide companies with a competitive advantage when recruiting through a deep understanding of their candidates and the qualities that drive success. The result is outstanding employee performance, transforming the average workforce into high-performance, exponentially growing companies.

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This Startup Uses Artificial Intelligence to Help Companies Find Employees Who Fit Their Culture - Entrepreneur

Ever wish Cher Horowitzs virtual closet existed beyond the fictional realm ofClueless? Now, it does. FINESSE is the newly launched brand using artificial intelligence to predict trends, reduce fabric waste and deliver customers perfectly coordinated outfits at just-right prices.

Before actually producing anything, FINESSE uses proprietary algorithms and machine learning technology to analyze fashion trends across the internet. We look at fashion in the same way an economist or hedge fund trader would [look at stocks], CEO Ramin Ahmari says. What are the signals that predict this stock or fashion item will be going up to record highs, and what are the chances it will underperform? From there, the brand ideates three potential drops coordinated outfits that incorporate whats trending across the industry that shoppers then vote on. (Examples of fan-favorite drops include the Maddy, an ensemble inspired by Maddy Perez of Euphoria, and the Bella, an all-white fit taking cues from Bella Hadids street style.) The pick with the most votes goes into production while the other two are scrapped, cutting down on fabric waste. The entire process takes less than 25 days, a shockingly speedy turnaround time that puts Zara and H&M to shame.

Finesse

Ahmari, who is queer and non-binary, launched FINESSE in a quest to reclaim his identity. My narrative [was] consistently taken out of my hands by labels that were forced upon me, the founder, who studied computer science and art history at Stanford, explains. Fashion was my way of regaining control over that narrative. I would wear baggy jeans and oversized hoodies to fit in with the straight guys when I wasnt ready to come out yet. Eventually, I got older and would browse the female section, allowing me to explore my femininity when I was ready to, he reflects, adding the fashion is a powerful tool for self-determination. Seeking to remedy the rigid binaries that clothing often promotes, all FINESSE drops are unisex (though the brands website mostly features female models). In addition, the companys board is comprised entirely by people of color, and over 75 percent of FINESSE employees belong to minority communities. Most of fashion today has been told from a specifically white male gaze. True equality and diversity has to start from the very root of an organization, Ahmari advocates, expounding on values that are unapologetically expressed on the labels Instagram. After the Capitol riots, the brand quickly denounced white supremacy. On the day of Joe Bidens presidential inauguration, it celebrated the end of Donald Trumps term. The companys outspokenness is refreshing, especially considering it is backed by major investors including Hoxton Ventures and Mango Capital.

Mainstream fashion has absolutely no idea about what will sell, so they play it safe and produce everything under the sun. Our focus at FINESSE is to eliminate this outrageous inefficiency.

Though FINESSE may seem like a fast fashion brand, it aims to revise the wasteful and often unethical practices the industry at large operates on. By only producing what its customers want i.e. the most voted-on drop and using 3D-modeled samples during the early stages of garment development, it reduces fabric waste and streamlines the production pipeline. Mainstream fashion has absolutely no idea about what will sell, so they play it safe and produce everything under the sun, Ahmari states. Our focus at FINESSE is to eliminate this outrageous inefficiency. We produce only what we know will sell, and we pre-estimate carefully how much demand there is based on data. In turn, the company saves money by producing such a curated range of items, allowing it to sell drops at accessible price points. Most drops, which include about three items, are sold in sets that retail at approximately $100 USD. Individual pieces average at about $30 USD.

Finesse

Getting into the nitty gritty of its production methods, Ahmari explains that the brand works with just three factories in China. We have vetted these factories thoroughly to make sure they are both ethical and invested in innovation. Our main manufacturer is particularly invested in the use of 3D renderings to improve the production process. They have seen first-hand how archaic the industry is, and are fed up by large fashion houses commandeering them to output at all costs, the CEO says. Considering FINESSEs incredibly affordable prices, its worth noting that the brand likely isnt using the highest-quality materials. The founder acknowledges the need to explore more sustainable alternatives, as well as the possibility of working with fabric mills once the company hits scale (after all, it just launched today). In addition, FINESSE has plans to push out a recycling and up-cycling initiative in the next few months. There is so much you can do with garments if youre given the right tools, Ahmari hints.

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FINESSE Is the Innovative New Brand Using Artificial Intelligence To Disrupt Fashion - HYPEBAE

New York-based Moodys Analytics has enjoyed considerable success across a number of WatersTechnologys awards programs over the yearsfor example, in 2020 it won the best credit risk solution provider category in the Waters Rankingsalthough a win in the AFTAs has always eluded the financial intelligence and analytical tools specialist. Until the 2020 AFTAs that is: This year, Moodys Analytics walks away with a pair of wins, the first of them coming in the best artificial intelligence (AI) technology initiative category, thanks to its QUIQspread offering, an AI-based financial spreading tool unveiled in 2020, designed to help institutions automate the spreading of financial statements.

Financial spreading is the manually intensive process through which lenders extract key data from unstructured financial statements from the purposes of conducting credit risk analysis on borrowers. According to Eric Grandeo, senior director, product manager at Moodys Analytics, QUIQspread uses machine learning technology to automate the financial spreading process, resulting in normalized datasets and allowing lenders to make faster and more judicious lending and credit decisions. Its a process that can be cumbersome and inconsistent, potentially resulting in costly mistakes, Grandeo explains. Lenders want to empower their relationship managers and analysts to focus more on high-value credit risk analysis tasks and increase their throughput in the most efficient way possible, and QUIQspread helps them do that.

Given the unstructured nature of financial statements, incumbent rules-based applications tend to struggle when it comes to accounting for the variety of information/data formats presented in statements. Machine learning, Grandeo explains, is the ideal technology to automate that process. Machine learning technology learns from previous practices and behaviors and can adapt to change over time without any development work, he says. Spreading is an evolving practice and needs a technology that evolves with it. Today, QUIQspread is processing thousands of spreads for customers in production who are now benefiting from significant time savings and efficiencies.

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AFTAs 2020: Best Artificial Intelligence (AI) Technology InitiativeMoody's Analytics - http://www.waterstechnology.com