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Unlocking Solutions: How Dream Hacking Can Help You Solve Complex Problems While You Sleep

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One of the study participants fell asleep during the experiment.

Mia Lux

Your brain can be gently nudged to tackle complex problems in your sleep, enhancing your ability to solve them upon waking.

Neuroscientists and psychologists are increasingly employing techniques involving sound, touch, movement, and particularly olfactory stimuli to influence dreams. This innovative approach demonstrates potential for applications like helping smokers quit, treating chronic nightmares, and even enhancing creativity.

Now, Karen Koncoly and her team at Northwestern University in Illinois have revealed that this technique may also aid in problem-solving. The researchers enlisted 20 self-identified lucid dreamers—individuals aware that they are dreaming and able to control their narratives—and tasked them with solving puzzles in two sessions within a sleep lab. Each puzzle was associated with unique soundtracks, featuring soothing elements like birdsong and steel drums.

The researchers meticulously monitored participants’ brain and eye movements to pinpoint when they transitioned into the rapid eye movement (REM) phase of sleep, which is known for its vivid and imaginative dreams. Upon entering this phase, a selection of unresolved puzzles was paired with the corresponding soundtracks. Participants were prompted to demonstrate lucidity by executing at least two rapid eye movements from left to right, indicating they were aware of the sound cues while striving to solve the puzzles in their dreams.

The following morning, participants reported that those who listened to the soundtracks during sleep found the puzzle features prominently featured in their dreams, significantly boosting their chances of solving them. Approximately 40% of participants who dreamed about puzzles managed to solve them, while only 17% who didn’t dream of the puzzles could achieve the same.

While the exact reasons behind these findings remain unclear, it’s suggested that pairing sound stimuli with learning tasks while awake may activate the memory of the puzzle when hearing the same sound during sleep, through a process known as targeted memory reactivation. This appears to activate the hippocampus—an essential brain region for memory—prompting what may resemble a spontaneous reactivation of memories that facilitates learning.

Although dreams can manifest at any stage of sleep, Konkoly indicates that targeting REM sleep may enhance problem-solving capabilities. “REM dreams are highly associative and atypical, blending new and prior memories with imaginative thought,” she states. “During this stage, your brain is quite active, potentially allowing for unrestricted access to various sections of your mind.”

Researcher Karen Concoly prepares a participant for the study by fitting a cap to their head that records brain activity.

Karen Konkoly

Tony Cunningham and researchers at Harvard University affirm that this study indicates “individuals may consciously focus on unresolved issues while dreaming.”

However, some experts caution that dream engineering could interfere with the critical functions of sleep, such as clearing toxins from the brain. There are concerns about the potential for companies to exploit these findings by placing ads within personal devices, which Cunningham particularly highlights. “Our senses are already bombarded during waking hours by advertisements, emails, and work stress; sleep remains one of the few times of respite,” he notes.

Koncoly plans to explore why certain individuals exhibit varying responses to sound stimuli on different days. “During this study, I stayed up all night monitoring brainwaves and providing cues during REM sleep. Sometimes participants would signal a response, and other times, they wouldn’t. Occasionally, they would wake and incorporate relevant puzzles into their dreams, while at other times, they simply processed the sound without any further reaction. Why do identical stimuli manifest differently in the same state of consciousness?”

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Source: www.newscientist.com

How Amateur Mathematicians Use AI to Tackle Age-Old Math Problems

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New Scientist: Explore groundbreaking science news and in-depth articles by expert journalists covering the latest advancements in science, technology, health, and the environment.

AI Tools Revolutionize Solutions for Old Math Problems

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Amateur mathematicians are leveraging artificial intelligence chatbots to tackle historic mathematical challenges, much to the astonishment of experts. Although the questions may not represent the pinnacle of mathematical complexity, their successful resolution suggests a significant breakthrough in AI’s capabilities in mathematics, potentially altering future methodologies, according to researchers.

The challenges addressed by AI are linked to Paul Erdős, a renowned Hungarian mathematician celebrated for posing intriguing yet complex questions throughout his prolific 60-year career. “The inquiries were often straightforward but exceedingly complex,” says Thomas Bloom from the University of Manchester, UK.

At the time of Erdős’ death in 1996, over 1,000 unsolved problems existed, spanning various mathematical disciplines, from combinatorics to number theory. Today, these challenges represent critical milestones for advancements in mathematics, Bloom explains. He maintains a website dedicated to cataloging these problems and tracking mathematicians’ progress in solving them.

Given the clarity of Erdős’ problems, mathematicians began experimenting with feeding them into AI tools like ChatGPT. Last October, Bloom noted an increase in users employing AI models to uncover pertinent references in mathematical literature to aid their solutions.

Shortly thereafter, AI tools began uncovering partial improvements in results—some were previously documented while others seemed to be novel.

“I was taken aback,” Bloom recalls. “Previously, when I tested ChatGPT, it provided mere conjectures, leading me to abandon it. However, since October, I discovered genuine papers, as ChatGPT effectively analyzed existing literature, uncovering substantial insights.”

Inspired by these advancements, Kevin Barrett, an undergraduate mathematics student at Cambridge, along with amateur mathematician Liam Price, set out to identify simpler and less-explored Erdős problems amenable to AI solutions. After discovering the number 728—a conjecture in number theory—they successfully solved it using ChatGPT-5.2 Pro.

“Upon seeing the statement, I thought, ‘Perhaps ChatGPT can solve this. Let’s give it a shot,’” Barrett remarks. “Indeed, numerous experts concur that the argument is elegant and quite sophisticated.”

After ChatGPT generated the proof, Barrett and Price employed another AI tool named Aristotle, developed by Harmonic, to validate their findings. Aristotle translates traditional proofs into the Lean mathematical programming language, which is swiftly verified for accuracy by a computer. Bloom highlights this process as vital, as it conserves researchers’ limited time when confirming their results’ validity.

As of mid-January, AI tools have completely solved six Erdős problems, but professional mathematicians later identified that five of these had existing solutions in the literature. Only problem number 205 was entirely resolved by Barrett and Price without prior solutions. Additionally, AI facilitated minor improvements and partial resolutions to seven other problems that were absent in existing literature.

This predicament has sparked debate regarding whether these AI tools unveil true innovations or simply resurrect old, overlooked solutions. Bloom notes that AI models frequently need to reconceptualize problems, discovering papers that make no mention of Erdős whatsoever. “Many papers I encountered would likely have remained undiscovered without this kind of AI documentation,” he remarks.

Another point of discussion is the potential limits of this approach. While the addressed problems aren’t the most formidable in mathematics, they could typically be resolved by first-year doctoral students; nonetheless, Bloom considers the achievement significant, noting the substantial effort required for such tasks.

Barrett further emphasizes that the problems currently being solved are relatively easier compared to more challenging Erdős problems, which contemporary AI models struggle to tackle. “Ultimately, AI will need more advanced models to address complex problems,” he forecasts. Some of these challenging issues even come with cash prizes for solutions, although Barrett believes that resolutions are unlikely in the near future, stating, “I don’t think we have a model for that yet.”

Utilizing AI to tackle Erdős’ problems offers promising potential for progress, according to Kevin Buzzard. Since most of the addressed challenges are straightforward or have received scant attention, it’s difficult to gauge whether these results signify substantial breakthroughs or if they warrant professional concern. “This is progress, but mathematicians aren’t quite ready to embrace it fully,” Buzzard observes. “It’s merely a budding advancement.”

Even with the models’ current limitations, their capability to work with moderately complex mathematics could fundamentally transform how researchers craft and analyze proofs. This advancement allows mathematicians with specialized knowledge to access insights from diverse mathematical fields.

“Few individuals possess expertise across all mathematical domains, limiting their toolkit,” Bloom explains. “Being able to obtain answers rapidly, without the hassle of consulting others or investing months in potentially irrelevant knowledge, creates numerous new connections. This is a groundbreaking shift that is likely to widen the scope of ongoing research.”

It may enable mathematicians to adopt entirely novel methodologies. Terence Tao at the University of California, Los Angeles, has been instrumental in validating AI-assisted methods for solving Erdős problems.

Given their limited schedules, mathematicians often prioritize a select few difficult problems, leaving many easier yet essential questions overlooked. If AI tools can be employed instantaneously across a multitude of problems, Tao believes it could facilitate a more empirical approach to mathematics, enabling extensive testing of various solutions.

“Currently, we neglect 99% of solvable problems due to our finite resources for expert analysis,” Tao asserts. “Therefore, we often bypass hundreds of significant issues, seeking just one or two that capture our interest. We also lack the capacity for comparative studies like, ‘Which of these two methods is superior?'”

“Such large-scale mathematics has yet to be undertaken,” he concludes. “However, AI demonstrates the feasibility of this approach.”

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  • Artificial Intelligence/
  • ChatGPT

Source: www.newscientist.com

Challenging Calculations: Quantum Computers May Struggle with ‘Nightmare’ Problems

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Certain problems remain insurmountable for quantum computers.

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Researchers have uncovered a “nightmare scenario” computation tied to a rare form of quantum material that remains unsolvable, even with the most advanced quantum computers.

In contrast to the simpler task of determining the phase of standard matter, such as identifying whether water is in a solid or liquid state, the quantum equivalent can prove exceedingly challenging. Thomas Schuster and his team at the California Institute of Technology have demonstrated that identifying the quantum phase of matter can be notably difficult, even for quantum machines.

They mathematically examined a scenario in which a quantum computer receives a set of measurements regarding the quantum state of an object and must determine its phase. Schuster mentioned that this is not necessarily an impossible task, but his team has shown that a considerable number of quantum phases of matter—such as the complex interactions between liquid water and ice, including unusual “topological” phases that exhibit strange electrical currents—might necessitate quantum computers to perform computations over extremely protracted periods. This situation mirrors a worst-case scenario in laboratory settings, where instruments may need to operate for billions or even trillions of years to discern the characteristics of a sample.

This doesn’t imply that quantum computers are rendered obsolete for this analysis. As Schuster noted, these phases are unlikely to manifest in actual experiments involving materials or quantum systems, serving more as an indicator of our current limitations in understanding quantum computers than posing an immediate practical concern. “They’re like nightmare scenarios. It would be quite unfortunate if such a case arose. It probably won’t happen, but we need to improve our comprehension,” he stated.

Bill Fefferman from the University of Chicago raised intriguing questions regarding the overall capabilities of computers. “This might illuminate the broader limits of computation: while substantial speed improvements have been realized for specific tasks, there will inevitably be challenges that remain too daunting, even for efficient quantum computers,” he asserted.

Mathematically, he explained, this new research merges concepts from quantum information science employed in quantum cryptography with foundational principles from materials physics, potentially aiding progress in both domains.

Looking ahead, the researchers aspire to broaden their analysis to encompass more energetic or excited quantum phases of matter, which are recognized as challenging for wider calculations.

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Source: www.newscientist.com

US to Launch Billions of Flies to Tackle Pest Problems

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Topeka, Kansas – The US government is gearing up to breed billions of flies, which will be released from planes in Mexico and southern Texas to combat meat-eating maggots.

This may sound like a horror movie plot, part of the government’s strategy to safeguard the US from pests that threaten the beef industry, wildlife, and even household pets. This innovative method has proven effective in the past.

“It’s an excellent technique,” remarks Edwin Burgess, an assistant professor at the University of Florida, who studies animal parasites, particularly in livestock. “It’s the best method we have for translating science into solving significant problems.”

The targeted pests are the meat-consuming larvae of the New World Screwworm Fly. The USDA is set to ramp up the breeding and distribution of adult male flies that will mate with wild females, resulting in eggs that will not hatch. Consequently, the larval population will decline over time.

Workers drop New World screwworm fly larvae into trays at a facility that breeds sterile flies in Pacola, Panama last year.
Copeg via AP file

This method is more effective and environmentally friendly than conventional pest control, which was used by the US and other countries north of Panama to eradicate these pests decades ago. Sterilized flies from Panama were effective for years, yet infestations resurfaced in southern Mexico late last year.

The USDA anticipates that a new Screwworm Fly Factory will begin operations in southern Mexico by July 2026. Additionally, a fly distribution center will be established in southern Texas by the end of this year, facilitating the import and distribution of flies from Panama as required.

Fried Live Meat

Most fly larvae consume dead flesh, feeding on decomposing matter from the New World screwworm as well as its counterparts from Asia and Africa, posing a significant threat to the American beef industry. Females lay eggs in wounds, which can sometimes expose the underlying tissue.

“A 1,000-pound cow could perish within two weeks,” stated Michael Bailey, the elected president of the American Veterinary Association.

Veterinarians have effective treatments for infested animals; however, an invasion can still cause significant discomfort and pain for affected animals.

Don Hineman, a retired rancher from Western Kansas, recalls an infected cow from his youth on the family farm.

“It had a terrible smell,” he recounted. “Like rotten meat.”

Utilizing Fly Biology Against Them

The New World Screwworm Fly is a tropical species that historically could not survive winters in the Midwest and Great Plains. However, from 1962 to 1975, the US and Mexico raised and released over 94 billion sterile flies, according to the USDA.

Workers hold two small containers of New World screwworm flies.
Copeg via AP file

The numbers must be large enough so that wild females have no option but to mate with sterile males.

A unique biological characteristic gives fly fighters an edge: females mate only once during their adult life over a short period.

Reasons for Increased Fly Breeding

Concerns have been raised about the potential northward movement of flies. The southern border has been closed to imports of live cattle, horses, and bison, which won’t fully reopen until at least mid-September.

However, female flies can inflict wounds on warm-blooded animals, including humans.

Decades ago, the US operated fly factories in Florida and Texas, which were shut down after the pests were eradicated.

Panama’s fly factory can produce up to 117 million flies per week, but the USDA aims to boost production to at least 400 million per week. It plans to invest $8.5 million in a Texas facility and $21 million to transform it into a breeding site for screwworm flies and fruit flies in southern Mexico.

Methods for Cultivating Millions of Flies

Growing large populations of flies is relatively simple, according to Cassandra Olds, an assistant professor of entomology at Kansas State University.

She notes, however, that “you need to provide females with the necessary cues to lay their eggs, and the larvae must have sufficient nutrients.”

Previous USDA studies indicate that larvae were once fed horse meat and honey before transitioning to a blend of dried eggs and honey or molasses. The Panama facility eventually utilized a mixture of egg powder, red blood cells, and cow plasma.

Workers use machines to mix food for the sterile fly breeding program in Pacola.
Copeg via AP file

In nature, larvae, akin to the pupal stage of butterflies, fall from their hosts to the ground, burrowing just below the surface to grow inside a protective casing resembling a dark brown tic-tac mint. In the Panama factory, workers place them into sawdust trays.

Security measures are crucial. According to Sonja Swiger, an entomologist at Texas A&M University’s Extension Services, breeding facilities need to prevent fertile adults from the breeding stock.

Aerial Fly Release

Dropping flies from aircraft presents certain risks. Recently, a plane releasing sterile flies crashed near the Mexican border, resulting in three fatalities.

Historically, during test runs in the 1950s, scientists placed flies in paper cups, which were then dropped from the planes using a specialized chute. These cups were loaded into boxes on a machine called the “whiz packer.”

The current method closely resembles this. Small aircraft equipped with wooden trays release the flies.

Burgess is recognized for developing the breeding and distribution of sterile flies in the 1950s and 60s, labeling it one of the USDA’s “greatest accomplishments.”

Some farmers now contend that new factories shouldn’t be closed after another successful eradication.

“What we perceive as full control — and declare victory — can always reemerge,” cautioned Burgess.

Source: www.nbcnews.com

DeepMind’s AI successfully tackles challenging geometry problems for Math Olympiad

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Geometric problems involve proving facts about angles and lines in complex shapes

Google Deep Mind

Google DeepMind's AI can solve some International Mathematics Olympiad (IMO) problems in geometry almost as well as the best human contestants.

“AlphaGeometry's results are surprising and breathtaking,” says IMO Chairman Gregor Driner. “It looks like AI will be winning his IMO gold medal much sooner than was thought a few months ago.”

IMO is one of the most difficult math competitions in the world for middle school students. Answering questions correctly requires mathematical creativity, something AI systems have long struggled with. For example, GPT-4, who has shown remarkable reasoning ability in other areas, gets his 0% score on IMO geometry problems, and even a specialized AI can answer them just as well as an average contestant. I'm having a hard time.

This is partly due to the difficulty of the problem, but also due to the lack of training data. This contest has been held annually since 1959, and each round consists of only six questions. However, some of the most successful AI systems require millions or even billions of data points. In particular, geometry problems, which account for one or two out of six questions and require proving facts about angles or lines in complex shapes, are particularly difficult to convert into a computer-friendly format.

Thanh Luong Google's DeepMind and his colleagues got around this problem by creating a tool that can generate hundreds of millions of machine-readable geometric proofs. Using this data he trained an AI called AlphaGeometry and when he tested it on 30 of his IMO geometry questions, the IMO gold medalist's estimated score based on his score in the contest was 25.9, whereas the AI answered 25 of them correctly.

“our [current] AI systems still struggle with capabilities such as deep reasoning. There you have to plan many steps in advance and understand the big picture. That's why mathematics is such an important benchmark and test set in our explorations. to artificial general intelligence,” Luong said at a press conference.

AlphaGeometry is made up of two parts, which Luong likens to different thinking systems in the brain. One system is fast and intuitive, the other is slower and more analytical. The first intuitive part is a language model called GPT-f, similar to the technology behind ChatGPT. It is trained on millions of generated proofs and suggests which theorems and arguments to try next for your problem. Once the next step is proposed, a slower but more careful “symbolic reasoning” engine uses logical and mathematical rules to fully construct the argument proposed by GPT-f. The two systems then work together and switch between each other until the problem is resolved.

While this method has been very successful in solving IMO geometry problems, Luong says the answers it constructs tend to be longer and less “pretty” than human proofs. However, it can also find things that humans overlook. For example, a better and more general solution was discovered for the question from his IMO in 2004 than the one listed in the official answer.

I think it's great that you can solve IMO geometry problems in this way. Yang Hui He However, IMO problems must be solvable using theorems taught at undergraduate level and below, so this system inherently limits the mathematics that can be used. Expanding the amount of mathematical knowledge that AlphaGeometry can access could improve the system and even help make new mathematical discoveries, he says.

It's also interesting to see how AlphaGeometry deals with situations where you don't know what you need to prove, since mathematical insight often comes from exploring theorems that have no fixed proof. Yes, he says. “If I don't know what an endpoint is, can I find it in all sets?” [mathematical] Are there any new and interesting theorems? ”

Last year, algorithmic trading firm XTX Markets Total prize money: $10 million For AI math models, the first publicly shared AI model to earn an IMO gold medal will receive a $5 million grand prize, with small progress awards for major milestones.

“Solving the IMO geometry problem is one of the planned advancement awards supported by the $10 million AIMO Challenge Fund,” said Alex Gerko of XTX Markets. “Even before we announce all the details of this Progress Award, we are excited to see the progress we are making towards this goal, including making our models and data openly available and , which involves solving real geometry problems during a live IMO contest.”

DeepMind declined to say whether it plans to use AlphaGeometry in live IMO contests or extend the system to solve other IMO problems that are not based on geometry. However, DeepMind previously entered a public protein folding prediction competition to test the AlphaFold system.

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Source: www.newscientist.com

Excessive Cannabis Use Associated with Numerous Health Problems According to New Study

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A Yale University study that analyzed the genomes of more than 1 million people revealed genetic factors associated with cannabis use disorder and potential links to psychiatric problems, substance abuse, and lung cancer risk. The importance of understanding the health effects is emphasized.Credit: Illustration by Michael S. Helfenbein

A comprehensive study conducted by researchers at Yale University and involving the analysis of the genomes of more than 1 million people has provided new insights into the biology of cannabis use disorder. The study also investigated links to various mental illnesses, the propensity to abuse other substances such as tobacco, and the potential increased risk of lung cancer associated with cannabis use.

For this study, researchers analyzed genome-wide genetic variation in individuals from multiple ancestry groups enrolled in the U.S. Department of Veterans Affairs’ Million Veterans Program, one of the world’s largest genetic databases. set and incorporated additional information from several other genomic databases. . They were able to identify dozens of genetic variants associated with cannabis use disorder, as well as a variety of behavioral and health problems associated with cannabis use disorder.

Understand the risks of marijuana use

The study was led by Daniel Levey, assistant professor of psychiatry, and Joel Gelernter, Foundation Professor of Psychiatry and Professor of Genetics and Neuroscience, and was published Nov. 20 in the journal Psychiatry. natural genetics.

“Understanding the biology of cannabis use disorder can help us better understand associated disorders and inform the public about the risks associated with cannabis use,” said Levy, lead author of the study. .

According to the U.S. Centers for Disease Control and Prevention, marijuana is the most commonly used federally illegal drug in the United States, with more than 48 million people (18% of Americans) using marijuana at least once in 2019. There is. Previous research has shown that approximately one-third of marijuana users develop cannabis use disorder, a pattern of problematic cannabis use that results in clinically significant impairment and distress. Defined.

Genetic factors and health risks associated with cannabis use

The new findings provide insight into the genetic factors underlying this phenomenon and other health risks that may be associated.

For example, researchers found that variants in genes encoding three different types of receptors on neurons are associated with an increased risk of developing cannabis use disorder.

They found that these mutations associated with cannabis use disorder were also associated with the development of lung cancer. However, the authors added that more research is needed to distinguish the effects of marijuana use from the effects of tobacco use and other environmental factors on cancer diagnosis.

“This is the largest genome-wide study of cannabis use disorder ever conducted, and as more states legalize or decriminalize marijuana use, studies like this one will “This could help us understand the public health risks associated with this increase,” said Gelernter.

Reference: “Multi-ancestral genome-wide association study of cannabis use disorder provides insight into disease biology and public health implications” Daniel F. Levey, Marco Galimberti, Joseph D. Dieck, Frank R. Wendt, Arjun Bhattacharya, Dora Koller, Kelly M. Harrington, Rachel Quaden, Emma C. Johnson, Priya Gupta, Mahantesh Birader, Max Lamb, Megan Cook, Veera M. Rajagopal, Stephanie LL Empke, Han Zhou, Yaira Z. Nunez, Henry R. Kranzler, Howard J. Edenberg, Alpana Agrawal, Jordan W. Smaller, Todd Lentz, David M. Hougaard, Anders D. Borglum, Ditte Demotis, Veterans Affairs Million – Veterans Program, J. Michael Gaziano, Michael J. Gandal, Renato Polimanti, Murray B. Stein, Joel Gelernter, November 20, 2023, natural genetics.
DOI: 10.1038/s41588-023-01563-z

Source: scitechdaily.com