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July 29, 2021

The UW will lead a new artificial intelligence research institute that will focus on fundamental AI and machine learning theory, algorithms and applications for real-time learning and control of complex dynamic systems, which describe chaotic situations where conditions are constantly shifting and hard to predict.Andy Freeberg/University of Washington

The U.S. National Science Foundation today announced 11 new artificial-intelligence research institutes, including one led by the University of Washington. These institutes are part of a $220 million investment spanning seven research areas in AI. Each institute will receive about $20 million over five years.

J. Nathan KutzUniversity of Washington

The UW-led AI Institute for Dynamic Systems will focus on fundamental AI and machine learning theory, algorithms and applications for real-time learning and control of complex dynamic systems, which describe chaotic situations where conditions are constantly shifting and hard to predict.

The engineering sciences are undergoing a revolution that is aided by machine learning and AI algorithms, said institute director J. Nathan Kutz, a UW professor of applied mathematics. This institute brings together a world-class team of engineers, scientists and mathematicians who aim to integrate fundamental developments in AI with applications in critical and emerging technological applications.

Researchers know the basic physics principles behind dynamic systems, which include situations such as turbulence or how the body recovers from an injury. But these scenarios are often happening on multiple timescales at once and can be a combination of many types of physics, making it hard for researchers to understand exactly whats going on.

My favorite dynamic system is turbulence, said institute associate director Steve Brunton, a UW associate professor of mechanical engineering. We literally live and breathe inside of a working fluid, and so do nearly all of our machines. But because of the multiscale complexity of the fluid, which involves a cascade of increasingly smaller eddies, we still have an incredibly hard time predicting what fluids will do outside of idealized and controlled settings.

The overall goal of this institute is to integrate physics-based models with AI and machine learning approaches to develop data-enabled efficient and explainable solutions for challenges across science and engineering.

Some of our specific questions include: Can we develop better machine-learning technologies by baking in and enforcing known physics, such as conservation laws, symmetries, etc.? Brunton said. Similarly, in complex systems where we only have partially known or unknown physics such as neuroscience or epidemiology can we use machine learning to learn the physics of these systems?

In addition to research, the institute will be focused on training future researchers in this field throughout the education pipeline. Some examples include: partnering with high school programs that focus on AI-related projects and creating a post-baccalaureate program that will actively recruit and support recent college graduates from underrepresented groups, United States veterans and first-generation college students with the goal of helping them attend graduate school.

The institute will provide massive open-source educational materials that include lectures, data and code packages for advancing and empowering AI, Kutz said. Importantly, we will provide AI ethics training for all involved in the institute. We will also make this training available to the community at large, thus enforcing a disciplined approach to thinking about AI and its implications for our emerging societal concerns around data, data privacy and the ethical application of AI algorithms.

As part of the educational component, the team will use a lightboard (Steve Brunton shown here) to create a range of high-quality educational and research videos focusing on key aspects of AI and machine learning for engineering dynamical systems and control. Educational content will be made freely available to the community on YouTube.Dennis Wise/University of Washington

For this institute, the UW is partnering with several regional institutions the University of Hawaii at Mnoa, Montana State University, the University of Nevada Reno, Boise State University, the University of Alaska Anchorage and Portland State University as well as with Harvard University and Columbia University.

We are so excited to bring together a critical mass of amazing and innovative researchers from across the U.S. to really move the needle in developing machine learning technology for physical and engineering dynamic systems, Brunton said. We also have a deep connection with industry partners, such as Boeing, which provides us with an incredible opportunity to make sure that we are focused on important and relevant problems and that our technology will actually be used.

Additional UW researchers who are part of this institute are lead researcher Krithika Manohar, assistant professor of mechanical engineering; Maryam Fazel, professor of electrical and computer engineering; Daniela Witten, professor of biostatistics; and David Beck, a research associate professor of chemical engineering.

Im glad to see this substantial investment going to one of our states premier research institutions, said U.S. Sen. Patty Murray, D-Wash. As the University of Washington and other research institutions in our state continue to lead on artificial intelligence, this investment will be critical to ensuring that the state of Washington remains a leader in innovation, research and scientific achievement. Ill keep fighting for important federal investments like this one to move this work forward.

The UW is also a partner institution on another newly announced NSF institute, the AI EDGE Institute, which is led by Ohio State University. The goal of this institute is to design future generations of wireless edge networks that are highly efficient, reliable, robust and secure.

These 11 new AI institutes are building on the first round of seven AI institutes funded in 2020, and expand the reach of these institutes to include a total of 40 states and Washington D.C.In addition to the UW-led institute, the state of Washington will also house the Institute for Agricultural AI for Transforming Workforce and Decision Support, or AgAID Institute, led by Washington State University. The institutes goal is to use AI to tackle some of agricultures biggest challenges related to labor, water, weather and climate change.

The state of Washington is already a leader in artificial intelligence, said U.S. Sen. Maria Cantwell, D-Wash. From the University of Washingtons Tech Policy Lab that studies the grand challenges around artificial intelligence to Washington State Universitys work in precision agriculture, we are more than ready for these two grants to help us understand more artificial intelligence applications.The UW will work in the area of complex systems to improve fields like manufacturing, and WSU will work on improvements in farming.

The AI Institute for Dynamic Systems is partially funded by the U.S. Department of Homeland Security.

For more information, contact Kutz at kutz@uw.edu and Brunton at sbrunton@uw.edu.

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UW to lead new NSF institute for using artificial intelligence to understand dynamic systems - UW News

While the country continues to struggle convincing people it is safe and smart to get vaccinated against Covid-19 despite emergency authorization and advocacy from the U.S. Federal Drug Administration (FDA), other growing numbers of people are willing to jump in with both feet when it comes to trusting their lives to Artificial Intelligence (AI), which has no standards or organizational oversight. At this time, only guidelines exist from the U.S. Federal Trade Commission (FTC) for AI.

I will take you through an example of how even impactful applications of AI still need a human assistant to ensure trustworthy, explainable, and unbiased decision making.

Internet law concept with 3d rendering ai robot with law scale

Using AI for automating manufacturing and improving our work streams, such as providing robust CMMS (computerized maintenance management systems) is an excellent application for AI. CMMS is a proactive methodology to keep systems running and to optimize maintenance operations. Alternatively, if businesses and institutions dont invest in smart maintenance approaches and choose only to take action after a system fails, it ultimately impacts operations, and customer service. Especially, if the components needed for repair are not readily available.

CMMS systems utilize sensors and other Internet of Things (IoT) devices and leverage on cloud services. They can track equipment assets, sense whether device performance is degrading, maintain the history of repair times and help us determine whether it is time to invest in replacing or upgrading systems versus investing in more repairs. AI can be used to look at this plethora of data and help make inferences that could save money, anticipate risk factors and build risk mitigation plans.

So far in this example, it appears that an individual human cant be adversely affected by the AI used in this CMMS application. However, lets consider a scenario when a relationship appears between the number of times a repair was done on a device that repeatedly failed and the person who performed that repair.

Does this indicate that the repair person needs more training or does it simply mean the device has too many faults and should be replaced? This is when the AI needs to ask a human for help and the human needs to be smart about interpreting what the data means.

Whenever AI makes a decision on a humans livelihood or is used to evaluate an individuals competency, we need to hit the big red STOP button and understand how the AI is making decisions and what it means. There have been many examples of companies using AI to evaluate employee job performance, which resulted in the subsequent dismissal or poor evaluations of employees. Lending institutions using AI have encountered algorithms that have unfairly eliminated underrepresented groups of individuals from qualifying for loans.

This kind of blind faith in computer generated decisions gives me a flashback to high school when they gave us career assessment tests to match us to a future vocation.

I was at the top of my class and my best friend, a male had scored slightly lower than I did in math and science. We were both expecting the outputs of our career assessments to be similar. How wrong I was!

His assessment said he could become an engineer, scientist, or politician. The results of my assessment, said I could be a cook, or sell cosmetics.

True fact:I stink at cooking. I never could cook and I still cant. Furthermore, it would be cruel and unjust for anyone to be subjected to eating my cooking.

When I complained to the guidance counselor, he reminded me that the results had to be correct, because this was a computer generated result and therefore it had to be accurate. Thankfully, I have always been a rebel and I ignored that assessment and went on to become an engineer.

AI has so much potential, but it has a long way to go before it can be considered a standalone replacement for human decision making that is trustworthy and unbiased.

In a recent IEEE/IEEE-HKN webinar, Dr.Manuela M. Veloso, Head of J.P. Morgan AI Research discussed human assisted AI as the bridge in the quest to help create more robust trustworthy AI. Including the human in the loop while the AI is exploring the data and asking the human for help is a new paradigm that many have leaped over to expedite the use of AI. Its time to embrace that Human Assisted AI is necessary if we are to move forward developing robust applications of AI. We need to demystify AI as a building block and have a way for the AI to declare it needs help from its human partner.

Finally, we need to be vigilant questioning the decisions from AI and help lawmakers develop standards to prevent blind trust in computer generated outputs that could have disastrous impacts of turning aspiring engineers into horrible cooks.

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Human Assisted Artificial Intelligence: A Pathway to Trustworthy And Unbiased AI - Forbes

Humans have been seeking the answer to a colossal challenge for half a century: identifying the basic building blocks of life, essential knowledge in the battle against terminal illnesses. Water is easy to imagine. It is simply two hydrogen atoms connected to another of oxygen: HO. But the protein that enriches blood, hemoglobin, carries the less memorable formula CHNOSFe. In 1969, US biologist Cyrus Levinthal calculated that it would require more time than has passed since the formation of the universe around 14 billion years to untangle all the potential configurations of a single protein from its amino acid sequence, which are the links of these macromolecules. However, an artificial intelligence system created by the Google conglomerate has achieved this feat in just a few minutes. Its predictions for practically every single human protein were published last week in a giant step for biology that has removed a blindfold from human knowledge.

At the vanguard of this revolution is British neuroscientist Demis Hassabis. The 44-year-old researcher was a chess child prodigy who in 1997 was left in awe of the match between Russian grandmaster Garry Kasparov and the super computer Deep Blue. The machine beat the man, but Hassabis was left with the impression that it was a cumbersome piece of junk, and useless in a game of tic-tac-toe. When the final game was over, the University of Cambridge undergraduate came up with the idea of designing a machine that could learn any game. In 2010, Hassabis founded a company called DeepMind to lead the investigative drive toward Artificial Intelligence (AI). In 2013, his first creation taught itself to play and win at various games on the legendary Atari console. In 2014, Google bought DeepMind for $650 million (around 500 million at the exchange rate of the time).

After limbering up on video games, the scientists at DeepMind then set themselves the task of solving one of the greatest challenges in biology. Proteins like hormones, enzymes and antibodies are tiny machines that carry out the basic functions of life. They are made up of chains of smaller molecules, amino acids, much like a pearl necklace. These necklaces are folded in convoluted configurations that determine their function. Antibodies, the human bodys defense mechanism against invaders like the coronavirus, have a Y shape.

The recipes of all the proteins required to function are written in the DNA of every cell. The DeepMind system, baptized AlphaFold, reads this information a sequence of amino acids and predicts the structure of each protein. Its precision is similar to that achieved in laboratory experiments, which require considerably more time and money. It is like guessing the structure of a quiche after seeing pie crust, eggs, pepper, salt, milk and cream for the first time.

DeepMind and the European Molecular Biology Laboratory (EMBL) published more than 350,000 structures on July 22, including some 20,000 human proteins and those of 20 other organisms, such as a lab mouse and the tuberculosis bacteria. Venki Ramakrishnan of the Medical Research Council Laboratory of Molecular Biology in Cambridge and winner of the 2009 Nobel Prize in Chemistry, says that is an astonishing advancement with unpredictable consequences. It has taken place long before many experts had predicted. It is going to be exciting to see the many ways in which it is going to radically change biological investigation.

Some organizations are already working with the new database. The Drugs for Neglected Diseases initiative, a global non-profit set up with the aid of Doctors Without Borders, uses the structure of proteins to seek new treatments. Practically all diseases, from cancer to Alzheimers, and including Covid-19, are related to the structure of one protein or another. Other institutions, such as the University of Portsmouth in the UK, are using the program to try and design proteins capable of recycling plastic.

Hassabis, executive director of DeepMind, has announced the companys plan is to publish 100 million structures over the next few months. The idea is to offer the predictions for the structure of practically every protein with a known sequence of amino acids free of charge. We believe that this is the most important contribution to date that artificial intelligence has contributed to scientific knowledge, he said following the publication of DeepMinds research in the medical journal Nature.

The AlphaFold system was not created from nothing, as Edith Heard, director general of the EMBL, has emphasized. AlphaFold has been trained using data from public resources developed by the scientific community, so it makes sense that its predictions are also made public. In Heards view, the system represents a genuine revolution in life sciences, like the genome was decades ago.

To determine the real structure of a protein, hugely expensive infrastructure is required, such as the European Synchrotron Radiation Facility, a particle accelerator covering almost a square kilometer in Grenoble, France. The radiation emitted by the electrons that circle the ring, which basically consist of X-rays, allow researchers to observe the secrets of matter. Spanish biologist Jos Antonio Mrquez explains that elucidating the shape of a protein with a synchrotron, or with the alternative method of cryogenic electron microscopy, could require months or even years. AlphaFold can achieve it in minutes, albeit with errors.

We are talking about computer-generated predictions, not the experimental determination of the structure. And the precision is 58%, says Mrquez, a 52-year-old researcher who heads the Crystallography Platform at the EMBL in Grenoble. As things stand, if a scientist wants to study a protein connected to cancer, it could take months or years to analyze its structure. There are only around 180,000 structures in available databases. The information published by DeepMind has doubled that number. And in a few months millions will be available. Today it is common to not find a protein in the databases. With AlphaFold you can obtain a prediction with a 58% reliability. You save an enormous amount of time, says Mrquez, who did not participate in the project. The systems imprecisions are concentrated in specific regions of the proteins, which are unstructured to adapt to the environment.

Mrquez points out other limitations. The DeepMind system can predict the structure of an isolated molecule, but proteins tend to interact with other proteins. AlphaFold is not yet capable of predicting the structure of these complexes but it is a system designed to learn on its own. Mrquez is optimistic: It will speed up discoveries in practically all areas of biology.

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Artificial intelligence uncovers the building blocks of life and paves the way for a new era in science - EL PAS in English

Artificial Intelligence is not a new technology, but widespread adoption and use of AI and machine learning in supply chain is still in its infancy. Nevertheless, there are indications that AI in the warehouse is becoming a reality a lot sooner than most people might have expected.

Preliminary results from a Lucas-commissioned survey of 350 companies in the US and UK found that the majority of the companies are already employing AI in one way or another within their warehouses and distribution/fulfillment centers. A separate survey of retail and CPG supply chain leaders found that AI usage is more prevalent in DCs and warehouses than elsewhere in supply chain 54% in distribution/fulfillment centers vs. 36% in other supply chain functions. On the surface, thats a bit of a surprise. But not when you dig a little deeper.

First, a word about the terms artificial intelligence and machine learning, as I am using them here. There are many forms of AI, but for purposes of this article Im referring to systems that use machine learning approaches to solve specific problems. Machine learning is a process by which learning algorithms are applied to large sets of data to create predictive models. This type of AI can be embedded within robotics (to identify objects), automation (to predict failures), and software systems, including business applications that provide recommendations to managers (suggested product moves) or that initiate actions themselves (such as creating a daily staffing plan).

Now, why is this form of advanced AI emerging so quickly in the DC?

It turns out that the distribution center is a target rich environment for using AI, with the potential to drive significant operational gains. First, DCs are a controlled environment for collecting and aggregating historical and real-time data and data is a key to effective AI. By contrast, other supply chain optimization problems often require data that resides in disparate systems, some of which may be controlled by other entities or may not be accessible in real-time.

Furthermore, AI is a natural fit for many of the foundational warehouse management questions that most operators solve today using spreadsheets, inherited best practices, or rules-based decision making. For example, only a minority of DCs today have installed systems for product slotting, workforce planning and other core warehouse functions. The reason is simple: previous expert systems to address these optimization challenges are engineering-heavy and costly to install and maintain.

AI and machine learning-based solutions can eliminate some of those drawbacks. As a result, AI has the potential to make advanced optimization practical for smaller operations, and more flexible and cost-effective for larger facilities.

One of the things that makes machine learning so compelling is that the predictive models are not developed or maintained by teams of engineers, so they are easier to implement. In addition, by their very nature, machine learning systems are designed to adapt to changes in the operating environment. And AI is particularly good at solving complex problems that are difficult to solve with traditional expert systems.

Here are two examples.

Warehouse slotting is both a combinatorial optimization problem (many input factors to consider) and a multiple objective optimization problem (with many goals, sometimes competing). Adding to the challenge, there are typically thousands of products and product locations (slots) involved, and those products and/or locations may change, sometimes frequently.

This is a complex problem with a very large set of possible answers that is very difficult to solve with a general-purpose model. Thats one reason why typical slotting solutions require tremendous amounts of engineering time for each facility. This is the kind of problem that AI is really good at.

AI-based slotting can provide better results and it can lower implementation costs by eliminating much of the engineering work and manual warehouse mapping and data inputs. The AI-based software can learn the spatial characteristics and travel time predictions required for the model based on activity-level data captured in the DC.

Another application of AI is for orchestrating and optimizing warehouse workers and autonomous mobile robots (AMRs). Today, robotic and manual processes can be optimized using various forms of AI, but they are typically optimized independently. Orchestrating and optimizing robot-human workflows is a wholly different challenge.

To take one example, consider an order-picking process using AMRs as a type of picking cart with multiple order totes per AMR, where human pickers are subservient to the AMRs. As the robotic system directs the robot to a location, a nearby user deliversone or more picksto the robot based on instructions on a tablet mounted to the machine. After completing those picks, the picker finds the next closest robot, and the first robot moves off to its next destination where it meets a second worker. This approach does not require any means to independently direct the human workers, but it also doesnt optimize their work.

Using AI and adding a means to direct workers independent of the AMRs (using mobile devices rather than AMR-mounted devices) the system can orchestrateand optimize for both the robots and the pickers time.This is accomplished in part using machine learning-based predictions about where the robots and pickers will be located at a given point in time (adjusted in real-time based on actual location data). Separate learning algorithms can organize and sequence the work among people and robots which orders to group together on each AMR, when to direct a person to a new aisle, etc. This is a more complex problem than independently optimizing the work of people or the AMRs.

As noted above, machine learning requires large amounts of data, but you need the right data for the questions you want to answer. For the DC applications we discussed here, some of the data would not be found in enterprise software systems that capture general transaction data, such as an ERP or WMS.

Instead, machine learning relies on streams of fine-grained data that is often associated with IoT (the Internet of Things) devices, such as mobile robots or the mobile devices used in RF or voice picking applications that collect time-stamped data about every user interaction. In the past, some of this data may have been used for short term purposes (debugging, training, etc.), but it was not usually collected or saved because it had no value beyond those immediate uses. But machine learning can discern patterns and find meaningful information buried in this wealth of IoT data.

Collecting the right data is just one of the challenges to wider AI adoption. In the Lucas survey mentioned earlier, almost 90 percent of respondents said their organizations needed more guidance and direction for implementing AI-based solutions, and 8 in 10 believe there is a general lack of understanding about how AI can be used.

Cost was seen as the biggest perceived impediment to AI adoption among the survey respondents. But the cost for implementing AI-based systems for slotting and other warehouse optimization problems may actually be lower than traditional engineering approaches. In that respect, AI removes barriers to advanced DC optimization.

Notwithstanding the challenges both real and perceived all indications are that warehouses are eager to get started with AI-based solutions. Many DCs are getting started a lot sooner than operators themselves might have thought possible.

Joe Blazick leads the data science team at Lucas Systems, with overall responsibility for the development of advanced data science technologies and AI applications within the Lucas Warehouse Optimization Suite. Prior to Lucas he held research and management positions within the Data Science group at Dicks Sporting Goods, responsible for developing applications of AI for supply chain. Prior to his civilian career, Joe served for ten years in the U.S. Navy. He holds MS degrees in Finance and Statistics from Rochester Institute of Technology.

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Artificial Intelligence In The Warehouse Is Coming Sooner Than You Might Think - Logistics Viewpoints - Logistics Viewpoints

CyVerse, National Science Foundation and Iowa State University

Today

The University of Arizona will take part in a $20 million institute that aims to transform agriculture through artificial intelligence.

The Artificial Intelligence Institute for Resilient Agriculture, led by Iowa State University and funded by the U.S. Department of Agriculture National Institute of Food and Agriculture, will focus on innovative AI-driven methods for agriculture, promote the study of cyber-agricultural systems, and support education, workforce development and community engagement.

With $1.3 million from USDA-NIFA, CyVerse headquartered at the University of Arizona BIO5 Institute will provide the institute with expertise in cyberinfrastructure, along with education and engagement opportunities for Native Nations, farmers and community stakeholders to address how technological advances in AI can answer agricultural needs.

The Artificial Intelligence Institute for Resilient Agriculture, or AIIRA, is one of 11 new National Science Foundation National Artificial Intelligence Research Institutes, expanding upon seven institutes funded in 2020.

"These institutes are hubs for academia, industry and government to accelerate discovery and innovation in AI," said NSF Director Sethuraman Panchanathan. "Inspiring talent and ideas everywhere in this important area will lead to new capabilities that improve our lives, from medicine to entertainment to transportation and cybersecurity, and position us in the vanguard of competitiveness and prosperity."

The institutes are part of a $200 million federal effort to develop hubs for AI research that address national needs such as predicting severe weather, educating students in science and manufacturing new materials.

"These are problems that can't be answered by any individual," said Baskar Ganapathysubramanian, the Joseph C. and Elizabeth A. Anderlik Professor in Engineering at Iowa State University, who will lead the institute. "We need engineers, data scientists, plant scientists, social scientists, farmers, educators and entrepreneurs. AIIRA will bring all this expertise together."

"The University of Arizona's participation in this institute is an expression of our land-grant mission, and it speaks to our commitment to tackling some of the world's most pressing challenges and improving people's lives through innovation and thoughtful collaboration," said University of Arizona President Robert C. Robbins. "The work of AIIRA also aligns perfectly with our continued focus on the Fourth Industrial Revolution, in which the digital world, including cutting-edge technologies such as artificial intelligence and robotics, converge with the physical and biological worlds."

Applying Precision Agriculture to a Changing Landscape

Modern technologies such as drones and rolling robots are already collecting detailed agricultural data, which can be used to create scientific modeling tools to help address farmers' most pressing questions, such as when to plant or how to allocate fertilizer and irrigation resources while minimizing environmental impact.

AIIRA, the project leaders say, brings together scientists, farmers, industry and government to adapt these technologies and encourage their adoption to help agriculture meet the needs of a growing population and increasingly climate-challenged planet.

"We want to make these methods accessible, affordable, and easily usable by farmers to make productive decisions," said AIIRA investigator Nirav Merchant, CyVerse co-principal investigator and director of UArizona's Data Science Institute. "Every farm is different in its own way, so a one-size-fits-all approach doesn't work. Regardless of the scale of the farm, we want to optimize these technologies for farmers' specific questions."

He added, "We have to prepare for our changing climate. There are limits on how much water and resources we will be able to use, but we can use AI to optimize the planting cycles and use of resources to reduce the stress in agriculture."

Providing Customized Training in Data Science

AIIRA will educate students, scientists, business people and farmers to understand and use new digital tools to make better decisions.

To help make the power of AI available to all, the CyVerse Training Team will work closely with The Carpentries a community initiative to teach software engineering and data science to develop customized workshops on using AI-powered tools and data to address specific research, community and stakeholder questions.

The training programs will be customized for various audiences, including AIIRA members, students, scientists, political leaders, agricultural stakeholders and Indigenous peoples.

Enhancing Native Nations' Data Sovereignty

Engagement with Native American communities which have been historically underrepresented and overlooked with regard to agricultural challenges, technological advancements and data rights and ownership is a key focus of CyVerse's work with AIIRA, said AIIRA investigator Stephanie Carroll, director of theCollaboratory for Indigenous Data Governance and associate director of the UArizona Native Nations Institute.

"We have a strong investment in encouraging Native student interest in data science and STEM careers," said Carroll, who is also an assistant professor of public health.

"Indigenous data sovereignty represents an effort to reassert authority over data and research so that Indigenous communities can govern and control the use, access and storage of their own data," she added.

Carroll co-created one of the nation's first classes on Indigenous data sovereignty, taught through the UArizona James E. Rogers College of Law and theNative Nations Institute'sIndigenous Governance Program. The class has inspired the creation of other such courses across the country and will be adapted to help the AIIRA initiative reach Native American communities and inform those working with Indigenous data.

Carroll and Merchant also plan to engage Indigenous farmers, community leaders and students in workshops designed to determine their agricultural questions and how Indigenous ways of knowing, AI technology and data can be leveraged to address their specific needs.

CyVerse's ultimate role in AIIRA is to integrate the project's many components, both through physical infrastructure and community engagement, Merchant said.

"The friction at the boundaries of these complex analyses, training communities, connecting resources that's where we'll be working," he said.

The University of Arizona is uniquely positioned to partipcate in AIIRA, said Elizabeth "Betsy" Cantwell, the university's senior vice president for research and innovation.

"The collaborationwith colleagues at Iowa State University allows us to integrate several of our strengths, both in terms of innovationand public outreach," Cantwellsaid."We are home to CyVerse and its cyberinfrastructure expertise. We are the land-grant university, giving us the agriculture perspective as well as partnerships with Arizona's 22 federally recognized tribes. With the accelerationof technology-based applications, the University of Arizona is uniquely positioned to meet real-world agricultural challenges with advanced solutions."

CyVerse is a federation of the University of Arizona, Texas Advanced Computing Center and Cold Spring Harbor Laboratory, funded by National Science Foundation award numbers DBI-0735191, DBI-1265383 and DBI-1743442.

AIIRA is led by Iowa State University with collaborators from Carnegie Melon University, New York University, the University of Arizona, the University of Nebraska-Lincoln, George Mason University, the University of Missouri and the Iowa Soybean Association.

The National AI Research Institutes are funded at a combined $220 million and led by the NSF, in partnership with the USDA-NIFA, U.S. Department of Homeland Security, Google, Amazon, Intel and Accenture.

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CyVerse Receives $1.3M to Provide Cyberinfrastructure and Training for New NSF Artificial Intelligence Institute - University of Arizona News

Image depicting Sun's solar cycles.

Researchers are using artificial intelligence (AI) techniques to calibrate some of NASA's images of the Sun. Launched in 2010, NASA's Solar Dynamics Observatory (SDO) has provided high-definition images of the Sun for over a decade.

The Atmospheric Imagery Assembly, or AIA, is one of two imaging instruments on SDO and looks constantly at the Sun, taking images across 10 wavelengths of ultraviolet light every 12 seconds.

This creates a wealth of information of the Sun like no other, but like all Sun-staring instrumentsAIA degrades over time, and the data needs to be frequently calibrated, NASA said in a statement.

To overcome this challenge, scientists decided to look at other options to calibrate the instrument, with an eye towards constant calibration.

Machine learning, a technique used in artificial intelligence, seemed like a perfect fit. To start, the team would teach the algorithm what a solar flare looked like by showing it solar flares across all of AIA's wavelengths until it recognised solar flares in all different types of light.

Once the programme can recognise a solar flare without any degradation, the algorithm can then determine how much degradation is affecting AIA's current images and how much calibration is needed for each.

"This was the big thing. Instead of just identifying it on the same wavelength, we're identifying structures across the wavelengths," said Dr Luiz Dos Santos, a solar physicist at NASA's Goddard Space Flight Center in Greenbelt, Maryland, and lead author on the paper published in the journal Astronomy & Astrophysics.

"It's also important for deep space missions, which won't have the option of sounding rocket calibration. We're tackling two problems at once."

Since AIA looks at the Sun in multiple wavelengths of light, researchers can also use the algorithm to compare specific structures across the wavelengths and strengthen its assessments.

As machine learning advances, its scientific applications will expand to more and more missions.

"For the future, this may mean that deep space missionswhich travel to places where calibration rocket flights aren't possiblecan still be calibrated and continue giving accurate data, even when getting out to greater and greater distances from Earth or any stars," said NASA.

**

The above article has been published from a wire agency with minimal modifications to the headline and text.

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Scientists Look Up To Artificial Intelligence Techniques to Improve Solar Data from the Sun | The Weather Channel - Articles from The Weather Channel...

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This desalination plant in Carlsbad, California - the largest in the Western Hemisphere - produces 50 million gallons of drinking water daily enough for 400 thousand homes in San Diego County.And now, as Western states face an epic drought, Poseidon Water - which operates the plant - could soon get approval to build another desalination plant this time, near a power plant in Huntington Beach.And environmentalists arent happy about it."It's great to be water independent, and we should be striving for that. But we should be doing it in a responsible way. And desalinated water is not the way to go.Andrea Leon-Grossmann is with the ocean conservation group Azul.This is the most expensive way to source water, it's the most energy intensive way to do it. And the way it decimates the ocean, both by the intake and by how we're dumping brine back into the ocean, is really, it should be the last resort, not the first way for sourcing water.Desalination - at its most basic - removes salt water from ocean water, making it fresh and drinkable.But the intake method is problematic, according to environmentalists, who say that tiny organisms such as larvae and plankton get killed in the process.Poseidon is now required to add finer intake screens to protect more fish. Poseidon - which has been trying to build the Huntington plant for 22 years and some $100 million has been spent navigating state regulations - insists the new project will actually help the environment. VP of Poseidon Water, Scott Maloni:In the case of Huntington Beach, the total quantity of impact would be no more than 0.02 percent of the plankton at risk of being entrained. There's no threatened or endangered species that are at risk, and the mitigation that's in place will ensure that the project will be a net environmental benefit, by producing more habitat that will be impacted by the operation of the facility.A regional water board has approved a permit for the project on condition that the company increase its commitment to rehabilitate a nearby wetlands reserve and build an artificial reef. There is one last major regulatory hurdle; the California Coastal Commission, which is expected to vote before the end of the year.Despite the opposition from conservationists, the company feels confident enough to talk of breaking ground by the end of 2022 on the $1.4 billion plant that would produce tens of millions of gallons of drinking water daily Much needed good news for communities struggling with the ravages of drought.For Poseidons Scott Maloni, its a no brainer telling Reuters: The Pacific Ocean is the largest reservoir in the world and it's always full.

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Artificial intelligence could be the latest tool in fighting wildfires - Yahoo News

Europe is lagging behind not only the US and Japan, but also Chinain terms of technological innovation. According to a2019 article on World Economic Forum(WEF), China overtook the EU with R&D expenditure equivalent to 2.1% of GDP.The worlds 15 largest digital firms are not European!

It is beyond question that Europe produces bright minds with amazing ideas and an entrepreneurial mindset. The problem is very simple:European companies do not make it beyond the start-up phase and if they do, their business is believed to be better off out of Europe. Skype is one famous example that was bought up by Microsoft. As a result, Europe is facing an annual contraction phase when it comes to market capitalisation of the Top 100 companies.

Source:https://www.economist.com/briefing/2021/06/05/once-a-corporate-heavyweight-europe-is-now-an-also-ran-can-it-recover-its-footing

The EU proposal to regulate AI will be a brake on innovation and a a challenge not to be underestimated for promising start-ups that are using artificial intelligence.According to a report of the Washington-based think tank Center for Data Innovation,a new law regulating artificial intelligence in Europe could cost the EU economy 3.1 billion over the next five years. This week, the European Commission published its proposal for a Regulation on Artificial Intelligence of the EU putting forth new rules on the use of artificial intelligence in the EU.The realization of AI projects will become significantly more difficult with the new law and leaving developing their business further outside the EU will almost certainly be likely for ambitious entrepreneurs. The US, China and Japanwould welcome them with open arms.

The regulation framework proposed in the White Paper is based on the idea that development and use of artificial intelligence entails high risks for fundamental rights, consumer rights and safety. The proposal aims to ban AI systems that harm people, manipulate their behaviour, opinions and decisions, or deliberately exploit their vulnerabilities for mass surveillance. Distributors, importers, users and other third parties would also be obliged to make significant changes to artificial intelligence, market it under their own name, change its purpose or discourage adaptive use.

Source:https://digital-strategy.ec.europa.eu/en/policies/regulatory-framework-ai

Key features include training, data and record keeping requirements, providing information, technology accuracy and robustness, human supervision and specific requirements for certain AI applications such as the use of biometric remote recognition. In addition to existing legislation, the European Commission is proposing a voluntary labelling scheme for low-risk AI applications not subject to mandatory requirements.

European officials also want to restrict the police use of facial recognition and to ban the use of certain types of AI systems - one of the broader efforts to regulate high-risk applications of artificial intelligence. The EU pushes forward with the first of its kind of rules on artificial intelligence (AI) amid fears that the technology is beyond the reach of regulators. Proponents of the rules say adequate human oversight is needed for artificial intelligence. Others warn that the world's first rules on how companies use artificial intelligence (AI) could hinder innovation with lasting economic consequences. The regulatory and policy developments in the first quarter of 2021 reflect a global turning point for serious regulation of artificial intelligence in the USA and Europe, with massive implications for technology companies and government agencies. The efforts to monitor the use of artificial intelligence are no surprise to anyone who has followed policy developments in recent years, but the EU is undoubtedly pushing for stricter oversight at this time.

To meet its global AI ambitions, the EU has joined forces with as-minded states to consolidate its global vision of how AI should be used. This includes the geopolitical dimension of the European Commission's forthcoming new legislative proposal on artificial intelligence. Meanwhile, domestic AI policy is continuing to take shape in the United States, but it is largely focused on ensuring international competitiveness and strengthening national security capabilities.

On 11 February 2021, the European Union (ENISA) and the Joint Research Centre (JRC) of the European Commission released a joint report on the cybersecurity risks associated with the use of artificial intelligence in autonomous vehicles. The report makes recommendations on how to mitigate such risks in a cybersecurity report. In June 2019, Chinas National New Generation Artificial Intelligence Governance Committee predicted harmony, fairness, justice, respect for privacy, security, transparency, accountability, cooperation and ethical principles for controlling AI development.

Europe is discovering AI, and the European Commission has recognised the need to take action to cope with the technological changes caused by AI technologies. The European Union surely wants to avoid the worst of artificial intelligence while at the same time trying to increase its potential for the economy in general. According to a draft of future EU rules obtained by Politico, the EU will ban certain applications of high-risk artificial intelligence systems and will prohibit others from entering the bloc if they do not meet EU standards.Companies that fail to comply could be fined up to 20 million euros, or 4 percent of their turnover. Proposals to require non-medical algorithms to conduct pre-market studies could also harm the development of artificial intelligence, as these studies are time-consuming and expensive. For example, fifty US states, such as New York, require autonomous vehicle manufacturers to conduct road tests under the paid supervision of the police, but testing such vehicles is expensive.

Respondents attach great importance to the EU's role in shaping a coherent strategic vision for technology policy, with 70% describing it as "very important" or "somewhat important.".This is not surprising given its prominent role in digital regulation and ambitious regulatory agenda.Digital Services Act, Digital Markets Act, Data Governance, Cloud Rules and Cybersecurity, GDPR, just to name some examples. In all these areas the role of members states has been rated worse than that of the EU, showing recognition of the desire and need for multi-level coordination between the EU and individual member states, as well as the role of each of them.

The EU's artificial intelligence act has caused high waves within a few hours after its becoming known. However, its advantages should not be neglected. Algorithmic accountability for example requires operators to use algorithms to make decisions that comply with laws that regulate people's actions, such as anti-discrimination laws and attitudes. In addition,the EU Commission is considering a temporary ban on use of facial recognition technology in public spaces for the next 3-5 years. In contrast, more than 600 law enforcement agencies in the US have started using the ClearView app. In the USA, states such as New York and Oregon, as well as a number of cities have responded to these developments by banning facial recognition technologies from police and government.

The idea of regulating AI is not a bad one.If technology organizations are not responsible for the way they use personal data, we are creating a predatory world. We tend to assume that the real world has one set of rules and the digital world has another set of rules. The truth is that we have only one world. Criticism towards the EU's aspirations mustbe voiced in the sense that many companies are still trying to adjust to the EU's General Data Protection Regulation (GDPR). The EU's highly anticipated comprehensive privacy regulations should have changed the Internet for the better, but so far it has mostly frustrated users, businesses, and regulators.So it stands to reason and we are well advised to prepare ourselves for an AI act full of challenges. At the same time, it is to be hoped that important lessons have been learned from the GDPR.

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The EU's Artificial Intelligence Act Could Become A Brake On Innovation - Finextra

Sean Lane, the CEO of health care artificial intelligence firmOlive AI, believes the future of the U.S.workforce is a combination of human labor and artificial intelligence people and technology working side-by-side. Lane wants to see Olive have a role in that force.

The Columbus-based software company, which grew exponentially during the pandemic, tripling to 600 employees, continues to expand, and it closed a $400 million funding round July 1 andwas valued at $4 billion.

In an interview with the Dispatch, Lane broke down the firm's somewhat complex business, talked about the meaning behind the name "Olive," and described the brightfuture he sees in Columbus tech.

Dispatch: What Olive does is pretty high-tech, and its a little complicated. In laymans terms, could you break down what Olive does, and what problems the company is working to solve?

Lane: First and foremost, health care doesnt have the internet. Thats the biggest problem. You see that every time you go to a doctors office you have to fill out the same form, every single time. Its like health care doesnt know who you are. Thats because the systems arent connected and they dont talk to each other and the software doesnt talk to each other. Olive is really automation that connects all of those things together. We use artificial intelligence to do it, to create this workforce of AI (artificial intelligence) workers, that provide automation to connect everything together, to take on a lot of the administrative burdens, to work on these workflows inside health care, so that ultimately, the experience of health care is much more like what you get in other areas that have the internet, from shopping to hotels or anything else.

AI in Columbus:: Path Robotics CEO wants Columbus to be 'next big mecca' for robots

Could you describe Olives customer base for me? Our customer base today is about 80% health systems around the country. We have about 900 hospitals as our customers, and then also insurance companies, health plans. That constitutes the other portion of our customer base.

Whats the meaning behind the name Olive? We decided that, to do this, we wanted to create an artificial intelligence, which means we wanted it to be difficult to distinguish from a human as it is working. Olive, herself, takes on the persona, so we picked a persons name. The cool thing about Olive is its a thing and its a persons name, but it also has the word live in it. You say it 'all of'the time without realizing it: Because all of is the same as Olive. The O is a pretty iconic symbol, and really, these workflows are like circuits, theyre these circles and loops. Youll see the circular kind of name in a lot of things we do.

Ive noticed it in just reading about the company: Olive is kind of her own person. Yeah, thats right. We wanted Olive to take on a persona, like part of your team. Olive is a part of your team. Its hospitals and at these insurance companies providing automation. We think that theres a new workforce, for the future, and that workforce contains humans and AI workers. Olive is one of those AI workers. The way we think about that is, health systems in the future are going to have AI workers, an AI workforce. Olive is just one of those employees.

The Dispatch has previously covered this, but could you tell me a little bit, in your own words, about The Grid and your workforce model at Olive as we sort of emerge from the pandemic? We had always believed that the greatest companies in the world were built in one building, and that that was kind of the way to do it. Once the pandemic happened, and we were out of the building, a lot of those assumptions basically didnt hold any water. They werent true, and we kind of invalidated assumption after assumption about being in one building. We decided that the best approach for us, moving forward, was to get rid of the word remote, get rid of the word work from home, and allow people to work from wherever theyd like. Wed only have two statuses: On the grid and off the grid. So youre either working or youre not working working from home is not less of a status than working in an office. We adopted this new model, we then started recruiting around the country. And you know, it worked. The great thing about it is, it allowed us to scale super, super fast. We needed to hire a ton of people, and really the only way we could have done it was with adopting The Grid.

The Grid at Olive AI:Olive is hiring big time, and most of its new employees don't live in Columbus

Big news came at the start of the month when Olives most recent valuation had it at $4 billion. Could you describe what this means for the company? Its another milestone in our growth. The reality is, our company is just getting started. Were close to 1,000 customers, close to 1,000 employees. Weve raised close to $1 billion dollars. But the reality is, its still the very, very early stages of this company. We have so much to do, so many products to build, so many new customers to expand to. Its a great milestone because it just proves that what were doing is important to the world, and specifically, to the health care industry.

Is there anything else that you wanted to talk about? I would just say that we are trying to build a technology company for health care that can invest significant resources into R&D (research and development) the same way that tech companies do for other industries. Health care is not going to be the laggard anymore, health care is not going to take the seconds of technology from other industries. This is the moment for health care to be the leader in technology, the same way the defense industry led the creation of Silicon Valley, the same way the space race led to a lot of the creation, again, of Silicon Valley. Health care innovation can lead to the creation of something really special. Columbus is one of the best places in the country to grow a startup, as weve shown. Its not that Silicon Valley is going away. Its just getting bigger, and the idea of Silicon Valley now exists in Columbus, Ohio.

sdonaldson@dispatch.com

@SarahEDon

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Columbus-based health care software startup Olive is valued at $4 billion. So what exactly does the company do? - The Columbus Dispatch

Recently, the pandemic has pushed digital transformation to the front of the line. While collaborative tools allowed us to work from home and maintain close contact with our co-workers, the next step is just around the corner, thanks to artificial intelligence and machine learning. In every element of the company, the pandemic is driving a move towards a hybrid work paradigm, changing peoples management and the way we work. Enterprises are on the verge of digital transformation and the use of artificial intelligence in HR departments will accelerate this process. Digital transformation improves the customer experience while also unlocking new value.

The adoption of artificial intelligence is a significant driver of digital transformation. While there are numerous definitions and explanations of artificial intelligence, Deloittes is the most straightforward and relatable. It says, Artificial intelligence is the theory and development of computer systems able to perform tasks, that normally require human intelligence. Visual perception, speech recognition, decision-making under ambiguity, learning, and language translation are just a few examples. This approach, which defines AI in terms of activities performed by people rather than how humans think, allows us to consider real-world applications.

Artificial intelligence has spawned a subset of cognitive technologies that are improving with time at doing certain activities that were previously solely performed by humans. The application of these cognitive technologies, either alone or in combination, is what gives Artificial Intelligence its strength. Cognitive technologies include, deep and machine learning, natural language processing (NLP), and robotics process automation, which are already at the forefront of a substantial revolution in the design and delivery of work processes in companies. Organizations are rapidly increasing their use of these technologies to completely reimagine their work architectures due to their ability to perform a wide range of tasks ranging from analyzing numbers, texts, and images to digital and physical tasks that lead to potential gains in efficiency and productivity.

From a practical standpoint, some of the applications of these technologies in Human resources and business are further highlighted.

It may be utilized in any sector where huge volumes of data must be analyzed quickly and predictive models must be developed. It might be utilized in Human resources for predictive talent management, as well as in business for sales forecasting and other purposes.

It may be applied to situations in which a significant amount of data must be analyzed and judgments made. If accompanied by adequate algorithmic tools, CV shortlisting in Human Resources might be a viable option. Other developing fields include gaining information from judicial procedures, customer feedback, and so forth. NLP and machine learning are used by a chatbot. For employee inquiry resolution, several Digital Human Resource systems use chatbots.

The technique is similar to that used in natural language processing (NLP), but with the added problems of various accents, ambient noise, and so on. Siri, Amazon Alexa, and other voice-based chatbots are just a few examples.

This technology allows you to distinguish between objects, pictures, and scenes. Face recognition for attendance is a fairly widespread usage in offices. Advanced applications might include better medical diagnosis and treatment of x-ray pictures.

It integrates technology such as computer vision, machine learning, high-tech sensors, actuators, and other well-engineered components into a device capable of duplicating human motor abilities while operating in high-fatigue and dangerous environments. In industries, repetitive physical operations such as lifting, loading, and unloading may simply be automated.

Artificial intelligence (AI) will revolutionize efficiency, enhance employee engagement, sharpen talent management and make processes more adaptable if it is used wisely. Accuracy in data capture is a crucial success element in AI deployment. The future rests in finding a balance between managing people and utilizing data to make employee-employer communication as smooth as possible. Maintaining the important cultural element in any organization requires a combination of technology enablement, empathy, and human touch.

The pandemics last fifteen months have taught us the value of empathy and compassion for one another in both our personal and professional lives. If applied correctly, Artificial intelligences capabilities can be a wonderful chance to speed up objective and fact-based decision-making while still allowing for solid human judgment. We observed an exploding use of AI in HR, with socially concerned but tech-savvy individuals using it to connect people to hospital beds, oxygen cylinders, and medications, among other things. The GOIs ArogyaSetu App is an excellent illustration of how AI may be used to safeguard the countrys inhabitants. Vaccination will be the only way to survive the pandemic. This opens up a big window of opportunity for artificial intelligence to be used to make the world a safer place.

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Artificial Intelligence in HR is Balancing Tech and Touch - Analytics Insight