Author: Inayet K

Artificial intelligence reduces a 100,000-equation quantum physics problem to only four equations.

The new approach makes it simpler to find “hidden patterns”.

Physicists must deal with all of the electrons at once rather than one at a time.

A new AI program created by Columbia University researchers has found its own strange version of physics.

The program’s output captured the Hubbard model’s physics even with just four equations.

It took weeks for the machine learning algorithm to train, which required a lot of computational power.

AI has now become advanced enough to handle real-world problems. And it is frequently more effective than humans at doing so, as demonstrated by Alexa, Tesla’s self-driving cars, OpenAI’s GPT3 defeating a human philosopher, and DeepMind’s AlphaZero defeating human chess grandmasters. Adding to this, scientists now claim that by using a method known as neural networks to reduce the mathematical representation used to describe a quantum system, they can learn a lot more about the system.

The new approach also makes it simpler to find hidden patterns, as opposed to just explaining physics and forecasting outcomes for other scientists to find, claim the scientists from Flatiron’s Center for Computational Quantum Physics (CCQ).

Physicists used artificial intelligence to simplify a difficult quantum problem that previously required 100,000 equations into a simple work that only requires four equations — all while maintaining accuracy.

The research may change how scientists examine systems with plenty of interacting electrons. The method may also help in the development of materials with desirable qualities like superconductivity or use in the production of renewable energy if it is transferrable to other issues. Additionally, it may inspire the development of novel technological applications that call for precise models of electrons in many-particle systems, such as quantum computing hardware and software.

“We start with this huge object of all these coupled-together differential equations; then we’re using machine learning to turn it into something so small you can count it on your fingers,” says study lead author Domenico Di Sante, a visiting research fellow at the Flatiron Institute’s Center for Computational Quantum Physics in New York City and an assistant professor at the University of Bologna in Italy.

The significant challenge relates to the electron movement on a lattice that resembles a grid. Interaction occurs when two electrons are present at the same lattice location. Scientists can study how electron behavior leads to desired phases of matter, such as superconductivity, in which electrons flow through a material without resistance, using this configuration, known as the Hubbard model, which idealizes several significant classes of materials. In addition, scientists test the new methods on the model before they use them on quantum systems with greater complexity.

The Hubbard model is quite simple, though. Modern computing techniques and even a small number of electrons are not enough to solve the problem. Because the fates of electrons can become quantum mechanically entangled as they interact, physicists must deal with all of the electrons at once rather than one at a time. This is true even when the electrons are widely separated on distinct lattice sites. The computational difficulty becomes exponentially more complex when there are more entanglements as a result of more electrons.

One method for studying a quantum system is a renormalization group. Renormalization groups (RGs) are formal tools, which scientists use in theoretical physics to systematically assess how a physical system changes when seen from different scales. As the energy scale at which physical processes occur changes, it reflects changes in the underlying force laws (codified in a quantum field theory), with energy/momentum and resolution distance scales essentially combining under the uncertainty principle.

Unfortunately, there may be tens of thousands, hundreds of thousands, or even millions of individual equations that we must solve for a renormalization group that analyzes all potential electron couplings and makes no concessions. The equations are also challenging to comprehend because they each show the interaction of two electrons.

Di Sante and his colleagues questioned whether they could utilize the neural network, a machine learning technology, to simplify the renormalization group. The neural network is like a cross between a frantic switchboard operator and survival-of-the-fittest evolution. First, the machine learning program creates connections within the full-size renormalization group. The neural network then tweaks the strengths of those connections until it finds a small set of equations that generates the same solution as the original, jumbo-sized renormalization group. The program’s output captured the Hubbard model’s physics even with just four equations.

“It’s essentially a machine that has the power to discover hidden patterns,” Di Sante says. “When we saw the result, we said, ‘Wow, this is more than what we expected.’” “We were really able to capture the relevant physics.”

It took weeks for the machine learning algorithm to train, which required a lot of computational power. The good news, according to Di Sante, is that they can modify their curriculum to address other issues without having to start from scratch now that it has been coached. To gain extra insights that could otherwise be challenging for physicists to comprehend, he and his partners are also looking into what machine learning is “learning” about the system.

The main unanswered question is how well the new technique applies to more complicated quantum systems, such as materials with long-range electron interactions. In addition, there are exciting possibilities for using the technique in other fields that deal with renormalization groups, Di Sante says, such as cosmology and neuroscience.

There seems to be a connection between physics and AI. AI recently changed how we perceive physics (see video link above). Yes, you read that right. A new AI program created by Columbia University researchers has found its own strange version of physics.

The AI developed different variables to explain what it saw rather than rediscovering the ones we presently use after being shown films of earthly physical processes.

The progress of our understanding of the universe, quantum physics, and everything else depends on AI experiments like the ones we are currently witnessing. This is so that AI can better understand the most recent statistics by copying and analyzing them. The ultimate goal is to help us understand ourselves better.

As AI advances, there will be plenty more to come. AI has already started, with ‘Physics’ and ‘Art’.

Key Points:

• A study suggests watching TV with children may benefit their brain development.

• Researchers analyzed 478 studies that were published in the last 20 years.

• Early exposure to television may be detrimental to play and language development.

• Screen content that is age-appropriate for children is more likely to have a positive impact.

• The study warns that watching TV shouldn’t take the place of other learning activities, like socializing, even though the right kind of information can be more beneficial than harmful.

• The authors advise reinforcing learning-promoting contexts, like kids watching age-appropriate content under adult supervision and avoiding having a background TV or device on.

A new research examined the impact of passive screen use on a young child’s cognitive development, revealing that, depending on the situation, exposure to screens – whether from a TV or a mobile device – might be beneficial.

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The researchers from the University of Portsmouth and Paris Nanterre University in France said that the team analyzed 478 studies that were published in the last 20 years. The research showed that, particularly for young infants, early exposure to television may be detrimental to play, language development, and executive functioning.

Dr. Eszter Somogyi of the University of Portsmouth’s Department of Psychology said, “We’re used to hearing that screen exposure is bad for a child and can do serious damage to their development if it’s not limited to, say, less than an hour a day. While it can be harmful, our study suggests the focus should be on the quality or context of what a child is watching, not the quantity.

A child may find it difficult to extract or generalize information because of a weak narrative, quick editing, and complex inputs. However, screen content that is age-appropriate for children is more likely to have a positive impact, especially if it’s made to promote interaction.

According to studies, a parent or other adult should be present for a child to watch TV so they may engage with them and ask them questions.

 “Families differ a lot in their attitudes toward and use of media,” explained Dr. Somogyi.

Dr. Somogyi also stated that the strength and character of TV’s influence on children’s cognitive development are strongly affected by these changes in viewing context. “Your child’s comprehension of the content can be strengthened by watching television with them and discussing what they see, adding to the skills they learn from educational programs,” Dr. Somogyi added.

According to the researchers, coviewing can also contribute to the development of their conversation skills and provide children with a role model for appropriate television viewing behavior.

Warning

The study warns that watching TV shouldn’t take the place of other learning activities, like socializing, even though the right kind of information can be more beneficial than harmful. Instead, it is crucial to educate parents and other adults who care for children under the age of 3 about the dangers of excessive screen use in unsuitable situations.

The authors advise reinforcing learning-promoting contexts, like kids watching age-appropriate content under adult supervision and avoiding having a background TV or device on.

“The important take home message here is that caregivers should keep in mind new technologies.” ” Television or smartphones should be used as potential tools to complement some social interactions with their young children but not to replace them,” said Dr. Bahia Guellai, from the Department of Psychology at Paris Nanterre University.

Future

As stated by the researchers, the most important challenge of our society for future generations is to make adults and young people aware of the risk of unconsidered or inappropriate use of screens. This will help in preventing situations in which screens are used as the new type of child-minding, as they have been during the pandemic lockdowns in different countries.

“I am optimistic with the concept of finding an equilibrium between the rapid spread of new technological tools and the preservation of the beautiful nature of human relationships,” concluded Dr. Guellai.

The study report has been published in the journal Frontiers in Psychology.

As Japanese researchers are programming them to assist in disaster relief efforts and research in catastrophe-hit areas, swarms of cyborg cockroaches will be the first to identify trapped survivors in any catastrophe, such as an earthquake.

Hope for immediate action to save and rescue victims has been aroused by the researchers’ recent presentation of their capability to mount “backpacks” of solar cells and electronics on the bugs and control their motion with a remote.

The flexible solar cell film was developed by Kenjiro Fukuda and his team at the Thin-Film Device Laboratory at the Japanese research giant Riken. It is 4 microns thick, or roughly 1/25 the width of a human hair, and can fit on the insect’s abdomen.

The film allows the roach to move around without limitation, and the solar cell generates enough power to process and convey directional information to the sensory organs on the bug’s hindquarters.

The study expanded on previous insect-control studies conducted at Nanyang Technological University in Singapore, and it may one day produce cyborg insects that can enter danger zones far more rapidly than robots.

“The batteries inside small robots run out quickly, so the time for exploration becomes shorter,” Fukuda said. “A key benefit (of a cyborg insect) is that when it comes to an insect’s movements, the insect is causing itself to move, so the electricity required is nowhere near as much.”

The research team chose Madagascar hissing cockroaches for the research because they are sufficiently large to carry the necessary equipment and lack wings that would hinder the experiment. The bugs can maneuver around minor obstacles or right themselves when they are flipped over, even with the backpack and film attached to their backs.

There is still much to learn about this topic. In a recent demonstration, Riken researcher Yujiro Kakei told a cyborg roach to turn left, causing it to scramble in that general direction using a specialized computer and wireless Bluetooth signal. But the bug turned in circles when it obtained the “right” signal.

The next task is to miniaturize the parts so that the insects can move more easily and so that sensors and even cameras may be mounted. Kakei stated that he invested 5,000 yen ($1 = 143.3100 yen) on parts from Tokyo’s famous Akihabara electronics district to construct the cyborg backpack.

The roaches can be removed from the lab’s terrarium by removing the backpack and film. The insects can live up to five years in captivity and reach maturity in just four months.

Fukuda sees a wide range of applications for the solar cell film, which is made up of small layers of plastic, silver, and gold, beyond just disaster rescue bugs. The film could be integrated into skin patches or clothing to track vital signs.

He said that on a sunny day, a parasol covered with the material might produce enough electricity to recharge a phone.

Researchers have used artificial intelligence techniques to examine structural and cellular features of human brain tissues to help determine the causes of Alzheimer’s disease and other related disorders.

Instead of using traditional markers like amyloid plaques, the Mount Sinai research team found that studying the causes of cognitive impairment using an unbiased AI-based technique revealed unexpected microscopic abnormalities that might predict the presence of cognitive impairment. On September 20, these findings were published in the journal Acta Neuropathologica Communications.

Stating that AI represents an entirely new paradigm for studying dementia and will have a transformative effect on research into complex brain diseases, especially Alzheimer’s disease, co-corresponding author John Crary, MD, PhD, Professor of Pathology, Molecular and Cell-Based Medicine, Neuroscience, and Artificial Intelligence and Human Health, at the Icahn School of Medicine at Mount Sinai, said, “The deep learning approach was applied to the prediction of cognitive impairment, a challenging problem for which no current human-performed histopathologic diagnostic tool exists.”

The medial temporal lobe and frontal cortex were the two brain regions whose underlying architecture and cellular features the research team identified and analyzed. The researchers used a weakly supervised deep learning algorithm to assess slide images of human brain autopsy tissues from a group of more than 700 elderly donors in order to predict the presence or absence of cognitive impairment in an attempt to improve the standard for postmortem brain assessment in order to identify signs of diseases.

Related Readings:

• Building a model of an Artificial brain
• Scientists Now Identify How the Brain Links Memories

In a supervised learning environment, the weakly supervised deep learning approach can handle noisy, limited, or imprecise sources to provide signals for labeling substantial amounts of training data. The amount of myelin, the protective layer around brain nerves, is measured using the Luxol rapid blue staining, and this deep learning model was used to identify a decrease in the quantity of myelin.

The white matter, which is involved in learning and brain functions, showed a concentration of myelin staining that was decreasing in amount, dispersed in a non-uniform manner across the tissue, and associated with cognitive impairment. The accuracy of the two sets of models that the researchers developed and used to predict the presence of cognitive impairment was better than that of guessing.

According to their findings, the diminished staining intensity in certain brain regions identified by AI may provide a scaleable platform to assess the presence of brain impairment in other related disorders.

The methodology establishes the framework for further research, which may involve using artificial intelligence models on a bigger scale and further dissecting the algorithms to improve their reliability and accuracy. The group said that the ultimate aim of this neuropathologic research program is to create better therapeutic and diagnostic methods for patients with Alzheimer’s disease and associated disorders.

“Leveraging AI allows us to look at exponentially more disease relevant features, a powerful approach when applied to a complex system like the human brain,” said co-corresponding author Kurt W. Farrell, PhD, Assistant Professor of Pathology, Molecular and Cell-Based Medicine, Neuroscience, and Artificial Intelligence and Human Health, at Icahn Mount Sinai.

Read: AI is earning trust as much as humans to flag hate speech

“It is critical to perform further interpretability research in the areas of neuropathology and artificial intelligence, so that advances in deep learning can be translated to improve diagnostic and treatment approaches for Alzheimer’s disease and related disorders in a safe and effective manner,” added W. Farrell.

“Interpretation analysis was able to identify some, but not all, of the signals that the artificial intelligence models used to make predictions about cognitive impairment. As a result, additional challenges remain for deploying and interpreting these powerful deep learning models in the neuropathology domain,” said the study’s lead author, Andrew McKenzie, MD, PhD, Co-Chief Resident for Research in the Department of Psychiatry at Icahn Mount Sinai. As a result, there are still more difficulties in applying and understanding these potent deep learning models in the field of neuropathology.

This new study provides an important step forward in the development of AI models that can be used to predict cognition clinically from tissue slides. Future work will involve continuing AI and deep learning work on more standard histopathologic features as well as other brain regions relevant to cognition.

Also involved in this study were researchers from the University of Texas Health Science Center in San Antonio, Texas; Newcastle University in Tyne, England; Boston University School of Medicine in Boston; and UT Southwestern Medical Center in Dallas.

One of the largest academic medical systems in the New York metropolitan area is Mount Sinai Health System, which employs over 43,000 people across eight hospitals, over 400 outpatient services, around 300 labs, a school of nursing, a leading medical school, and graduate education.

For more information, visit the cited source.

Researchers have developed Artificial Intelligence (AIs) to improve gamers’ overall experience by adjusting their dynamic difficulty.

Dynamic difficulty adjustment (DDA), a recent development by Korean researchers, uses in-game data to predict player emotions and adjusts the difficulty level in order to maximize a gamer’s satisfaction.

Although the difficulty is a challenging aspect to balance in video games, doing it properly is essential to giving gamers a satisfying experience.

The researchers’ work may help to balance game complexity and enhance the appeal of games to different sorts of gamers.

Gamers’ dynamic difficulty adjustment

Dynamic difficulty adjustment (DDA) is a method for automatically altering a game’s features, behaviors, and scenarios in real-time depending on the player’s skill so that the player does not get bored or annoyed whether the game is very easy or very difficult.

For instance, the game’s DDA agent may automatically raise the difficulty if player performance exceeds the developer’s expectations for a particular difficulty level, raising the challenge for the gamer. This method is helpful, but it has limitations because it just considers player performance, not how much pleasure they are truly having.

Generally, games with difficulty levels will run on a scale that includes some or all of the following:

• Easier Than Easy,
• Easy / Beginner / Novice,
• Normal / Medium / Standard / Average / Intermediate,
• Hard / Expert / Difficult.

To help make its races more exciting and entertaining, regardless of the skill level of its players, Mario Kart, for example, has included various levels of dynamic difficulty in nearly every iteration.

Will AIs improve gamers’ dynamic difficulty adjustment?

A research team from the Gwangju Institute of Science and Technology in Korea modified the DDA approach in the study published in Expert Systems With Applications.

They developed DDA agents that adjusted the game’s difficulty to optimize one of four different aspects related to a player’s satisfaction: challenge, competence, flow, and valence, as opposed to focusing on the player’s performance.

The DDA agents were trained using machine learning, utilizing data collected from real-world players who participated in a fighting game against different artificial bits of intelligence (AIs) and then provided feedback.

Each DDA agent used both real-world game data and simulated data to fine-tune the opponent AI’s fighting strategy to enhance a particular feeling, or ‘affective state’, using the Monte-Carlo tree search algorithm.

Associate Professor Kyung-Joong Kim, who led the study said that one advantage of the approach over other emotion-centered methods is that it does not rely on external sensors, such as electroencephalography. “Once trained, our model can estimate player states using in-game features only”, added Associate Prof. Kim.

Related: A way to “Detect Speech” from People’s brain

Through an experiment with 20 volunteers, the researchers verified that the proposed DDA agents could produce AIs that improved the players’ overall experience, no matter their preference. This marks the first time that affective states are incorporated directly into DDA agents, which could be useful for commercial games.

According to Associate Prof. Kim, commercial game companies already have huge amounts of player data, which they can exploit to model the players and solve various issues related to game balancing using our approach. As mentioned by the team, this technique also has potential for other fields that can be ‘gamified,’ such as healthcare, exercise, and education.

The researchers’ effort in developing AIs could contribute to balancing the difficulty of games and making them more appealing to all types of players.