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Manufacturing breakthrough will lead to quantum chips with the precision required to build the worlds first useful quantum computer

PALO ALTO, Calif., March 15, 2022--(BUSINESS WIRE)--PsiQuantum's partnership with GlobalFoundries (GF) has been included in Fast Companys prestigious annual list of the Worlds Most Innovative Companies. PsiQuantum is using GFs advanced semiconductor manufacturing facilities to build the worlds first useful quantum computer, and the Fast Company award recognizes this unprecedented collaboration.

This years list honors businesses that are making the biggest impact on their industries and culture as a whole. These companies are creating the future today with some of the most inspiring accomplishments of the 21st century. In addition to the World's 50 Most Innovative Companies, 528 organizations are recognized across 52 categories.

Quantum computing is anticipated to unlock the solutions to otherwise impossible problems and enable extraordinary advances across a broad range of applications including climate, healthcare, life sciences, energy and beyond. Whether its improving carbon capture catalysts, optimizing the energy grid, or modelling the chemistries of lifesaving drugs or new battery materials, quantum computers are key to solving many of the worlds most demanding challenges that will forever be beyond the capabilities of any conventional computer.

World-changing applications require a large-scale, fault-tolerant quantum computer built in a scalable and proven manufacturing environment. Silicon photonics and semiconductor chip manufacturing offer the scalability and manufacturability needed to deliver a commercially useful quantum computer on any sensible time or money scale.

PsiQuantum is building the worlds first commercially useful, fault-tolerant quantum computer based on breakthroughs in silicon photonics and quantum architecture. Its team of world-renowned quantum computing experts has developed unique technology in which single photons (particles of light) are manipulated using complex photonic circuits, patterned onto a silicon chip using standard semiconductor manufacturing techniques.

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PsiQuantum and GF demonstrated a world-first ability to manufacture core quantum components, such as single-photon sources and single-photon detectors, with precision and in volume, representing a significant milestone in PsiQuantums roadmap to deliver a large-scale quantum computer. Fast Company recognized the collaboration between PsiQuantum and GF as one of the 10 most innovative joint ventures of 2022, an award category defined by Fast Company as "the best business pairings, whether one-off collaborations or new companies".

"A commercially useful quantum computer has to be large, fault-tolerant, manufacturable, and scalable," said Fariba Danesh, chief operating officer at PsiQuantum. "We have identified a clear path for building a large-scale quantum computer, leveraging our unique technology in silicon photonics and quantum system architecture, and the scalable and proven manufacturing processes of our semiconductor partner GF."

"We are proud that our partnership with PsiQuantum has been recognized as one of the most innovative business pairings of 2022," said Amir Faintuch, senior vice president and general manager of Computing and Wired Infrastructure at GF. "Our partnership is a powerful combination of PsiQuantums photonic quantum computing expertise and GFs silicon photonics manufacturing capability that will transform industries and technology applications across climate, energy, healthcare, materials science, and government."

Fast Companys editors and writers sought out the most groundbreaking businesses across the globe and industries. They also judged nominations received through their application process. The Worlds Most Innovative Companies is Fast Companys signature franchise and one of its most highly anticipated editorial efforts of the year. It provides both a snapshot and a road map for the future of innovation across the most dynamic sectors of the economy.

"The worlds most innovative companies play an essential role in addressing the most pressing issues facing society, whether theyre fighting climate change by spurring decarbonization efforts, ameliorating the strain on supply chains, or helping us reconnect with one another over shared passions," said Fast Company Deputy Editor David Lidsky.

For the second year in a row, coinciding with the issue launch, Fast Company will host its Most Innovative Companies Summit on April 26 27. The virtual, multi-day summit celebrates the Most Innovative Companies in business and provides an early look at major business trends and an inside look at what it takes to innovate in 2022. Fast Companys Most Innovative Companies issue (March/April 2022) is available online here, as well as in app form via iTunes and on newsstands beginning March 15. The hashtag is #FCMostInnovative.

About PsiQuantum

Powered by breakthroughs in silicon photonics and quantum architecture, PsiQuantum is building the first commercially useful quantum computer to solve some of the worlds most important challenges. PsiQuantum believes silicon photonics is the only way to achieve the necessary scale required to deliver a fault-tolerant, general-purpose quantum computer. With quantum chips now being manufactured in a world-leading semiconductor fab, PsiQuantum is uniquely positioned to deliver quantum capabilities that will drive advances in climate, healthcare, finance, energy, agriculture, transportation, communications, and beyond. To learn more, visit http://www.psiquantum.com.

Follow PsiQuantum: LinkedIn

About Fast Company

Fast Company is the only media brand fully dedicated to the vital intersection of business, innovation, and design, engaging the most influential leaders, companies, and thinkers on the future of business. Headquartered in New York City, Fast Company is published by Mansueto Ventures LLC, along with our sister publication Inc., and can be found online at http://www.fastcompany.com.

2022 PsiQuantum. PsiQuantum and our logo are trademarks of PsiQuantum, Corp. in the U.S. and other countries. All other trademarks are the property of their respective holders.

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PsiQuantums Partnership with GlobalFoundries Named to Fast Companys Worlds Most Innovative Companies List - Yahoo Finance

Newswise ALBUQUERQUE, N.M. Postdoctoral researchers who are designated Truman and Hruby fellows experience Sandia National Laboratories differently from their peers.

Appointees to the prestigious fellowships are given the latitude to pursue their own ideas, rather than being trained by fitting into the research plans of more experienced researchers. To give wings to this process, the four annual winners two for each category are 100 percent pre-funded for three years. This enables them, like bishops or knights in chess, to cut across financial barriers, walk into any group and participate in work by others that might help illuminate the research each has chosen to pursue.

The extraordinary appointments are named for former President Harry Truman and former Sandia President Jill Hruby, now the U.S. Department of Energy undersecretary for nuclear security and administrator of the National Nuclear Security Administration.

Truman wrote to the president of Bell Labs that he had an opportunity, in managing Sandia in its very earliest days, to perform exceptional service in the national interest. ThePresident Harry S. Truman Fellowship in National Security Science and Engineeringcould be said to assert Sandias intention to continue to fulfill Trumans hope.

TheJill Hruby Fellowship in National Security Science and Engineeringoffers the same pay, benefits and privileges as the Truman. It honors former Sandia President Jill Hruby, the first woman to direct a national laboratory. While all qualified applicants will be considered for this fellowship, and its purpose is to pursue independent research to develop advanced technologies to ensure global peace, another aim is to develop a cadre of women in the engineering and science fields who are interested in technical leadership careers in national security.

The selectees are:

Alicia Magann: The quantum information science toolkit

To help speed the emergence of quantum computers as important research tools, Alicia Magann is working to create a quantum information science toolkit. These modeling and simulation algorithms should enable quantum researchers to hit the ground running with meaningful science as quantum computing hardware improves, she says.

Her focus will extend aspects of her doctoral research at Princeton University to help explore the possibilities of quantum control in the era of quantum computing.

At Sandia, she will be working with Sandias quantum computer science department to develop algorithms for quantum computers that can be used to study the control of molecular systems.

Im most interested in probing how interactions between light and matter can be harnessed towards new science and technology, Magann said. How well can we control the behavior of complicated quantum systems by shining laser light on them? What kinds of interesting dynamics can we create, and what laser resources do we need?

A big problem, she says, is that its so difficult to explore these questions in much detail on conventional computers. But quantum computers would give us a much more natural setting for doing this computational exploration.

Her mentor, Mohan Sarovar, is an ideal mentor because hes knowledgeable about quantum control and quantum computing the two fields Im connecting with my project.

During her doctoral research, Magann was a DOE Computational Science Graduate Fellow and also served as a graduate intern in Sandias extreme-scale data science and analytics department, where she heard by word of mouth about the Truman and Hruby fellowships. She applied for both and was thrilled to be interviewed and thrilled to be awarded the Truman.

Technical journals in which her work has been published include Quantum, Physical Review A, Physical Review Research, PRX Quantum, and IEEE Transactions on Control Systems Technology. One of her most recent 2021 publications is Digital Quantum Simulation of Molecular Dynamics & Control in Physical Review Research.

Gabriel Shipley: Mitigating instabilities at Sandias Z machine

When people mentioned the idea to Gabe Shipley about applying for a Truman fellowship, he scoffed. He hadnt gone to an Ivy League school. He hadnt studied with Nobel laureates. What he had done, by the time he received his doctorate in electrical engineering from the University of New Mexico in 2021, was work at Sandia for eight years as an undergraduate student intern from 2013 and a graduate student intern since 2015. He wasnt sure that counted.

The candidates for the Truman are rock stars, Shipley told colleague Paul Schmit. When they graduate, theyre offered tenure track positions at universities.

Schmit, himself a former Truman selectee and in this case a walking embodiment of positive reinforcement, advised, Dont sell yourself short.

That was good advice. Shipley needed to keep in mind that as a student, he led 75 shots on Mykonos, a relatively small Sandia pulsed power machine, significantly broadening its use. I was the first person to execute targeted physics experiments on Mykonos, he said. He measured magnetic field production using miniature magnetic field probes and optically diagnosed dielectric breakdown in the target.

He used the results to convince management to let him lead seven shots on Sandias premier Z machine, an expression of confidence rarely bestowed upon a student. I got amazing support from colleagues, he said. These are the best people in the world.

Among them is theoretical physicist Steve Slutz, who theorized that a magnetized target, preheated by a laser beam, would intensify the effect of Zs electrical pulse to produce record numbers of fusion reactions. Shipley has worked to come up with physical solutions that would best embody that theory.

With Sandia physicist Thomas Awe, he developed methods that may allow researchers to scrap external structures called Helmholtz coils to provide magnetic fields and instead create them using only an invented architecture that takes advantage of Zs own electrical current.

His Truman focus investigating the origins and evolution of 3D instabilities in pulsed-power-driven implosions would ameliorate a major problem with Z pinches if what he finds proves useful. Instabilities have been recognized since at least the 1950s as weakening pinch effectiveness. They currently limit the extent of compression and confinement achievable in the fusion fuel. Mitigating their effect would be a major achievement for everyone at Z and a major improvement for every researcher using those facilities.

Shipley has authored articles in the journal Physics of Plasmas and provided invited talks at the Annual Meeting of the APS Division of Plasma Physics and the 9thFundamental Science with Pulsed Power: Research Opportunities and User Meeting. His most recent publication in Physics of Plasmas, Design of Dynamic Screw Pinch Experiments for Magnetized Liner Inertial Fusion, represents another attempt to increase Z machine output.

Sommer Johansen: Wheres the nitrogen?

Sommer Johansen received her doctorate in physical chemistry from the University of California, Davis, where her thesis involved going backward in time to explore the evolution of prebiotic molecules in the form of cyclic nitrogen compounds; her time machine consisted of combining laboratory spectroscopy and computational chemistry to learn how these molecules formed during the earliest stages of our solar system.

Cyclic nitrogen-containing organic molecules are found on meteorites, but we have not directly detected them in space. So how were they formed and why havent we found where that happens? she asked.

That work, funded by a NASA Earth and Space Science Fellowship, formed the basis of publications in The Journal of Physical Chemistry and resulted in the inaugural Lewis E. Snyder Astrochemistry Award at the International Symposium on Molecular Spectroscopy. The work also was the subject of an invited talk she gave at the Harvard-Smithsonian Center for Astrophysics Stars & Planets Seminar in 2020.

At Sandia, she intends to come down to Earth, both literally and metaphorically, by experimenting at Sandias Combustion Research Facility in Livermore on projects of her own design.

She hopes to help improve comprehensive chemical kinetics models of the after-effects on Earths planetary ecology of burning bio-derived fuels and the increasingly severe forest fires caused by climate change.

Every time you burn something that was alive, nitrogen-containing species are released, she says. However, the chemical pathways of organic nitrogen-containing species are vastly under-represented in models of combustion and atmospheric chemistry, she says. We need highly accurate models to make accurate predictions. For example, right now it isnt clear how varying concentrations of different nitrogenated compounds within biofuels could affect efficiency and the emission of pollutants, she said.

Johansen will be working with the gas-phase chemical physics department, studying gas-phase nitrogen chemistry at Sandias Livermore site under the mentorship of Lenny Sheps and Judit Zdor. UC Davis is close to Livermore, and the Combustion Research Facility there was always in the back of my mind. I wanted to go there, use the best equipment in the world and work with some our fields smartest people.

She found particularly attractive that the Hruby fellowship not only encouraged winners to work on their own projects but also had a leadership and professional development component to help scientists become well-rounded. Johansen had already budgeted time outside lab work at UC Davis, where for five years she taught or helped assistants teach a workshop for incoming graduate students on the computer program Python. We had 30 people a year participating, until last year (when we went virtual) and had 150.

The program she initiated, she says, became a permanent fixture in my university.

Alex Downs: Long-lived wearable biosensors

As Alex Downs completed her doctorate at the University of California, Santa Barbara, in August 2021, she liked Sandia on LinkedIn. The Hruby postdoc listing happened to show up, she said, and it interested her. She wanted to create wearable biosensors for long duration, real-time molecular measurements of health markers that would be an ongoing measurement of a persons well-being. This would lessen the need to visit doctors offices and labs for evaluations that were not only expensive but might not register the full range of a persons illness.

Her thesis title was Electrochemical Methods for Improving Spatial Resolution, Temporal Resolution, and Signal Accuracy of Aptamer Biosensors.

She thought, Theres a huge opportunity here for freedom to explore my research interests. I can bring my expertise in electrochemistry and device fabrication and develop new skills working with microneedles and possibly other sensing platforms. That expertise is needed because a key problem with wearable biosensors is that in the body, they degrade. To address this, Downs wants to study the stability of different parts of the sensor interface when its exposed to bodily fluids, like blood.

I plan not only to make the sensors longer lasting by improved understanding of how the sensors are impacted by biofouling in media, I will also investigate replacing the monolayers used in the present sensor design with new, more fouling resistant monolayers, she said.

The recognition element for this type of biosensor are aptamers strands of DNA that bind specifically to a given target, such as a small molecule or protein. When you add a reporter to an aptamer sequence and put it down on a conductive surface, you can measure target binding to the sensor as a change in electrochemical signal, she said.

The work fits well with Sandias biological and chemical sensors team, and when Downs came to Sandia in October, she was welcomed with coffee and donuts from her mentor Ronen Polsky, an internationally recognized expert in wearable microneedle sensors. Polsky introduced her to other scientists, told her of related projects and discussed research ideas.

Right now, meeting with people all across the Labs has been helpful, she said. Later, I look forward to learning more about the Laboratory Directed Research and Development review process, going to Washington, D.C. and learning more about how science policy works. But right now, Im mainly focused on setting up a lab to do the initial experiments for developing microneedle aptamer-based sensors, Downs said.

Sandia National Laboratories is a multimission laboratory operated by National Technology and Engineering Solutions of Sandia LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energys National Nuclear Security Administration. Sandia Labs has major research and development responsibilities in nuclear deterrence, global security, defense, energy technologies and economic competitiveness, with main facilities in Albuquerque, New Mexico, and Livermore, California.

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Truman and Hruby 2022 fellows explore their positions - Newswise

Nothing can escape a black hole. General relativity is very clear on this point. Cross a black holes event horizon, and you are forever lost to the universe. Except thats not entirely true. Its true according to Einsteins theory, but general relativity is a classical model. It doesnt take into account the quantum aspects of nature. For that, youd need a quantum theory of gravity, which we dont have. But we do have some ideas about some of the effects of quantum gravity, and one of the most interesting is Hawking radiation.

One way to study quantum gravity is to look at how quantum objects might behave in curved space. Typically in quantum theory, we assume space is a fixed and flat background. Special relativity still applies, but general relativity doesnt. Basically, we just ignore gravity since its effects are so teeny. This works great for things like atoms in Earths gravity. But quantum mechanics around the event horizon of a black hole is very different.

Hawking wasnt the first to study the quantum effects of black holes, but he did show that event horizons arent immutable. If a quantum object was forever bound by a black hole, we would know with absolute certainty where the object is. But quantum systems are fuzzy, and there is always an uncertainty to their location. We could say the quantum object is probably within the black hole, there is a small chance it isnt. This means that over time objects can quantum tunnel past the event horizon and escape. This causes the black hole to lose a bit of mass, and the less mass a black hole has, the more easily quantum objects can escape.

So black holes can emit faint energy thanks to Hawking radiation. Whats interesting about this is that the effects connect black holes to thermodynamics. Since black holes emit some light, they, therefore, have a temperature. From this simple fact, physicists have developed the theory of black hole thermodynamics, which helps us understand what happens when black holes merge, among other things.

Its brilliant stuff, but the problem is we have never observed Hawking radiation. Most physicists think it does occur, but we cant prove it. And given (theoretically) how faint Hawking radiation is, and how far away even the closest black holes are, we arent likely to detect Hawking radiation in the foreseeable future. So instead, scientists look at analog systems such as water vortices or optical systems that have horizon-like properties.

A recent study in Physical Review Letters looks at optical black hole analogs, and found an interesting effect of Hawking radiation. One way to simulate black holes is to create a constrained packet of light in a non-linear optical material. The material acts as a kind of one-way gate, so photons can enter the packet in only one direction (like the one-way nature of a black hole event horizon). At the other side of the packet, photons can only leave, which is similar to a hypothetical white hole. So the optical system models a black-hole/white-hole pair.

The team used computer simulations to study what would happen when a quantum system passes through the simulated pair. They found that the pair could be used to create a quantum effect known as entanglement. When two particles are created as a quantum pair, they are entangled, which means an interaction with one particle affects the other as well. We think that when particles escape a black hole via Hawking radiation, they do so as entangled pairs. According to this latest work, the simulated black-hole/white-hole pair can be used to change the entanglement of a system passing through it. The system can even be tuned so that the entanglement is strengthened or weakened.

This work supports the idea that Hawking radiation occurs in entangled pairs, but it also shows how entanglement could be tweaked experimentally, which would be very useful to other research, such as information theory and quantum computing. The next step is to actually perform this kind of experiment in the lab. If it works as predicted, we could have a powerful new way to study quantum systems.

Reference: Agullo, Ivan, Anthony J. Brady, and Dimitrios Kranas. Quantum Aspects of Stimulated Hawking Radiation in an Optical Analog White-Black Hole Pair. Physical Review Letters 128.9 (2022): 091301.

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A new way to Confirm Hawking's Idea That Black Holes Give off Radiation - Universe Today

Algorithms the chunks of code that allow programs to sort, filter and combine data, among other things are the standard tools of modern computing. Like tiny gears inside a watch, algorithms execute well-defined tasks within more complicated programs.

Theyre ubiquitous, and in part because of this, theyve been painstakingly optimized over time. When a programmer needs to sort a list, for example, theyll reach for a standard sort algorithm thats been used for decades.

Now researchers are taking a fresh look at traditional algorithms, using the branch of artificial intelligence known as machine learning. Their approach, called algorithms with predictions, takes advantage of the insights machine learning tools can provide into the data that traditional algorithms handle. These tools have, in a real way, rejuvenated research into basic algorithms.

Machine learning and traditional algorithms are two substantially different ways of computing, and algorithms with predictions is a way to bridge the two, said Piotr Indyk, a computer scientist at the Massachusetts Institute of Technology. Its a way to combine these two quite different threads.

The recent explosion of interest in this approach began in 2018 with a paper by Tim Kraska, a computer scientist at MIT, and a team of Google researchers. In it, the authors suggested that machine learning could improve a well-studied traditional algorithm called a Bloom filter, which solves a straightforward but daunting problem.

Imagine you run your companys IT department and you need to check if your employees are going to websites that pose a security risk. Naively, you might think youll need to check every site they visit against a blacklist of known sites. If the list is huge (as is likely the case for undesirable sites on the internet), the problem becomes unwieldly you cant check every site against a huge list in the tiny amount of time before a webpage loads.

The Bloom filter provides a solution, allowing you to quickly and accurately check whether any particular sites address, or URL, is on the blacklist. It does this by essentially compressing the huge list into a smaller list that offers some specific guarantees.

Bloom filters never produce false negatives if they say the site is bad, its bad. However, they can produce false positives, so perhaps your employees wont be able to visit some sites they should have access to. Thats because they trade some accuracy for an enormous amount of data compression a trick called lossy compression. The more that Bloom filters compress the original data, the less accurate they are, but the more space they save.

To a simple Bloom filter, every website is equally suspicious until its confirmed to not be on the list. But not all websites are created equal: Some are more likely than others to wind up on a blacklist, simply because of details like their domain or the words in their URL. People understand this intuitively, which is why you likely read URLs to make sure theyre safe before you click on them.

Kraskas team developed an algorithm that can also apply this kind of logic. They called it a learned Bloom filter, and it combines a small Bloom filter with a recurrent neural network (RNN) a machine learning model that learns what malicious URLs look like after being exposed to hundreds of thousands of safe and unsafe websites.

When the learned Bloom filter checks a website, the RNN acts first and uses its training to determine if the site is on the blacklist. If the RNN says its on the list, the learned Bloom filter rejects it. But if the RNN says the site isnt on the list, then the small Bloom filter gets a turn, accurately but unthinkingly searching its compressed websites.

By putting the Bloom filter at the end of the process and giving it the final say, the researchers made sure that learned Bloom filters can still guarantee no false negatives. But because the RNN pre-filters true positives using what its learned, the small Bloom filter acts more as a backup, keeping its false positives to a minimum as well. A benign website that could have been blocked by a larger Bloom filter can now get past the more accurate learned Bloom filter. Effectively, Kraska and his team found a way to take advantage of two proven but traditionally separate ways of approaching the same problem to achieve faster, more accurate results.

Kraskas team showed that the new approach worked, but they didnt formalize why. That task fell to Michael Mitzenmacher, an expert on Bloom filters at Harvard University, who found Kraskas paper innovative and exciting, but also fundamentally unsatisfying. They run experiments saying their algorithms work better. But what exactly does that mean? he asked. How do we know?

In 2019, Mitzenmacher put forward a formal definition of a learned Bloom filter and analyzed its mathematical properties, providing a theory that explained exactly how it worked. And whereas Kraska and his team showed that it could work in one case, Mitzenmacher proved it could always work.

Mitzenmacher also improved the learned Bloom filters. He showed that adding another standard Bloom filter to the process, this time before the RNN, can pre-filter negative cases and make the classifiers job easier. He then proved it was an improvement using the theory he developed.

The early days of algorithms with predictions have proceeded along this cyclical track innovative ideas, like the learned Bloom filters, inspire rigorous mathematical results and understanding, which in turn lead to more new ideas. In the past few years, researchers have shown how to incorporate algorithms with predictions into scheduling algorithms, chip design and DNA-sequence searches.

In addition to performance gains, the field also advances an approach to computer science thats growing in popularity: making algorithms more efficient by designing them for typical uses.

Currently, computer scientists often design their algorithms to succeed under the most difficult scenario one designed by an adversary trying to stump them. For example, imagine trying to check the safety of a website about computer viruses. The website may be benign, but it includes computer virus in the URL and page title. Its confusing enough to trip up even sophisticated algorithms.

Indyk calls this a paranoid approach. In real life, he said, inputs are not generally generated by adversaries. Most of the websites employees visit, for example, arent as tricky as our hypothetical virus page, so theyll be easier for an algorithm to classify. By ignoring the worst-case scenarios, researchers can design algorithms tailored to the situations theyll likely encounter. For example, while databases currently treat all data equally, algorithms with predictions could lead to databases that structure their data storage based on their contents and uses.

And this is still only the beginning, as programs that use machine learning to augment their algorithms typically only do so in a limited way. Like the learned Bloom filter, most of these new structures only incorporate a single machine learning element. Kraska imagines an entire system built up from several separate pieces, each of which relies on algorithms with predictions and whose interactions are regulated by prediction-enhanced components.

Taking advantage of that will impact a lot of different areas, Kraska said.

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Machine Learning Reimagines the Building Blocks of Computing - Quanta Magazine

ARLINGTON, Va. U.S. military researchers are asking industry to develop artificial intelligence (AI) and machine learning technologies for difficult jobs like combat medical triage, which refers to sorting wounded warfighters according to their need for medical attention.

Officials of the U.S. Defense Advanced Research Projects Agency (DARPA) in Arlington, Va., issued a broad agency announcement (HR001122S0031) this week for the In the Moment (ITM) project.

DARPA researchers are asking industry to develop algorithmic decision-makers that can help humans with decision-making in difficult domains like combat medical triage.

Difficult domains are where trusted decision-makers disagree; no right answer exists; and uncertainty, time-pressure, resource limitations, and conflicting values create significant decision-making challenges. Other examples include first response and disaster relief.

Related: Top technology challenges this decade for the warfighter

The DARPA ITM project focuses on two areas: small unit triage in austere environments, and mass casualty triage. ITM seeks to develop techniques that enable building, evaluating, and fielding trusted algorithmic decision-makers for mission-critical operations where there is no right answer and, consequently, ground truth does not exist.

Researchers are looking for capabilities that:

-- quantify algorithmic decision-makers with key decision-making attributes of trusted humans;

-- incorporate key human decision-maker attributes into more human-aligned, trusted algorithms;

-- enable the evaluation of human-aligned algorithms in difficult domains where humans disagree and there is no right outcome; and

Difficult decisions occur when the decision-maker is confronted with challenges that include too many or too few options, too much or too little information, uncertainty about the consequences of decisions, and uncertainty about the value of foreseeable outcomes.

ITM seeks to develop AI and machine learning algorithms based on key human attributes as the basis for trust in algorithmic decision-makers, as well as a computational framework for key human attributes and an alignment score match the algorithmic decision-maker to key human decision-makers.

Related: Simulation and mission rehearsal relies on state-of-the-art computing

ITM is interested in the notion of trust, or the willingness of a human to delegate difficult decision-making to AI computers. The project also will focus on human-off-the-loop, algorithmic decision-making in difficult domains to understand the limits of such a computational framework.

ITM is 3.5-year, two-phase program that focuses on four technical areas: decision-maker characterization; human-aligned algorithms; evaluation; and policy and practice.

Decision-maker characterization seeks to develop technologies that identify and model key decision-making attributes of trusted humans to produce a quantitative decision-maker alignment score.

Human-aligned algorithms should be able to balance situational information with a preference for key decision-maker attributes. Evaluation will assess the willingness of humans to delegate difficult decisions to AI computers.

Related: The next 'new frontier' of artificial intelligence

Policy and practice will develop recommendations for how military leaders can update policies to take advantage of AI and machine learning in combat medical triage.

Companies interested should upload abstracts by 30 March 2022, and proposals by 17 May 2022 to the DARPA BAA website at https://baa.darpa.mil/.

Email questions or concerns to Matt Turek, the DARPA ITM program manager, at ITM@darpa.mil. More information is online at https://sam.gov/opp/baae2217401748dbaeb89a08044d6998/view.

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Military researchers to apply artificial intelligence (AI) and machine learning to combat medical triage - Military & Aerospace Electronics