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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

Dr. Steven Waldren, vice president and chief informatics officer at the American Academy of Family Physicians, right, and Dr. Kamel Sadek, director of informatics at Village Medical, speak at the HIMSS22 conference in Orlando.

Photo: Jeff Lagasse/Healthcare Finance News

ORLANDO, Fla. Even before COVID-19 made the business of healthcare a nightmare for countless physicians and clinicians, burnout was a prevalent issue. And even the slow, still-ongoing emergence into normalcy hasn't been enough to ease this trend: Clerical burdens, including clinical documentation, are a major contributor.

But for primary care physicians in particular, a new class of technology, including AI-powered digital assistants, is improving their capacity and capability, while reducing their administrative and cognitive burden.

Dr. Steven Waldren, vice president and chief informatics officer at the American Academy of Family Physicians, cited data showing that the average patient visit to a PCP takes about 18 minutes, and of that time, 27% is dedicated to face-to-face time with a patient. Forty-nine percent is consumed by EHR and desk work.

"A lot of the technology is not really designed to connect with patients," said Waldren. "Physicians and nurses don't necessarily give EHRs a passing grade."

The three main challenges to primary care, he said, are clerical burden, value-based payments and AI and machine learning, which fundamentally change what it means to be a physician. But innovations in the marketplace can be applied to the clinical setting, said Waldren, and if they focus on initial contact, comprehensiveness, and coordinated and continuous care, practices can improve both costs and quality while tackling the burnout issue.

In 2018, AAFP sought to do just that, developing processes to assess the value of AI-based solutions for primary care. The group called them "innovation labs," with the goal of gauging the value of emerging technology solutions.

"We have a lot of innovators in family medicine and primary care who are doing great things," said Waldren. "We try to help our doctors find a doctor like them and a practice like theirs. They have to find peers who have been there, done that and been successful. If you don't do that you're going to have a hard time doing this."

The innovation labs identify products that address the challenges of EHRs and clerical burden, and they're geared to be adaptable and usable by family physicians.

To reduce EHR burden, the labs pinpointed a voice-assisted AI assistant for documentation. Instead of the typical voice recognition technology that simply transcribes a doctor's dictations onto a screen, the program understands commands and can respond to them, removing dictation from the equation entirely.

In testing this particular piece of technology, the labs gathered 132 members from small practices across 40 states, with all reporting much higher satisfaction. That's largely because of a 50% reduction in documentation time.

"It's geared toward those with documentation burden," said Waldren. "Its key features arte integration into the EHR, mobility and it's affordable and adaptable at about $200 per month."

For the problem of clerical burden, the labs identified technology that resulted in a 70% decrease in physician prep time, and a 38% increase in RAF scores. Physicians ranked it 9.6 out of 10.

One of those physicians, Dr. Kamel Sadek, director of informatics at Village Medical, joined Waldren on the stage and testified to the solution's efficacy.

"It's been a year since we tested the product," said Sadek. "We just adopted it, and within 10 days, we had 230 physicians who wanted to start using the product. We're on the onboarding process as we speak. Many physicians want to use the dictation. So far it's been a very, very successful pilot study and adoption.

"You're not connected to the computers," he said. "You can use your phone, iPad, laptop, youcan use it from your car, or while your kid is playing soccer. It's bidirectional. That gives you the brief information of what you've done, and based on that you can finish the note."

What convinced Sadek to utilize this approach was the efficacy and the practical aspect of being able to dictate from anywhere, to say nothing of reducing wasted time.

"With all of these things we have to right now in the notes, with value-based care, you have to monitor a lot of things that come from different departments," said Sadek. "In order to perform well, you have to look at all these things."

According to Waldren, health IT hasn't been very consumer-friendly to date. But newer apps are being built by companies that have experience in the more user-friendly consumer space, so the situation is beginning to improve. The best part is that they don't require a large up-front investment or complicated integration.

"One of the biggest challenges of getting a solution in is our doctors are so burned out," said Waldren, "and they don't want to look at something and decide if this is something that's going to work."

Twitter:@JELagasseEmail the writer:jeff.lagasse@himssmedia.com

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Machine learning, AI can help ease the trend of physician burnout - Healthcare Finance News

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Co-evolution of machine learning and digital technologies to improve monitoring of Parkinson's disease motor symptoms | npj Digital Medicine -...