Can AI tell you if you will need an umbrella?

SEBASTIEN BOZON/AFP via Getty Images

AI can predict the weather 10 days ahead more accurately than current state-of-the-art simulations, says AI firm Google DeepMind but meteorologists have warned against abandoning weather models based in real physical principles and just relying on patterns in data, while pointing out shortcomings in the AI approach.

Existing weather forecasts are based on mathematical models, which use physics and powerful supercomputers to deterministically predict what will happen in the future. These models have slowly become more accurate by adding finer detail, which in turn requires more computation and therefore ever more powerful computers and higher energy demands.

Rmi Lam at Google DeepMind and his colleagues have taken a different approach. Their GraphCast AI model is trained on four decades of historical weather data from satellites, radar and ground measurements, identifying patterns that not even Google DeepMind understands. Like many machine-learning AI models, its not very easy to interpret how the model works, says Lam.

To make a forecast, it uses real meteorological readings, taken from more than a million points around the planet at two given moments in time six hours apart, and predicts the weather six hours ahead. Those predictions can then be used as the inputs for another round, forecasting a further six hours into the future.

Researchers at DeepMind ran this process with data from the European Centre for Medium-Range Weather Forecasts (ECMWF) to create a 10-day forecast. They say it beat the ECMWFs gold-standard high-resolution forecast (HRES) by giving more accurate predictions on more than 90 per cent of tested data points. At some altitudes, this accuracy rose as high as 99.7 per cent.

Matthew Chantry at the ECMWF, who worked with Google DeepMind, says his organisation had previously seen AI as a tool to supplement existing mathematical models, but that in the past 18 months it has come to be regarded as something that could actually provide forecasts on its own.

We at the ECMWF view this as a hugely exciting technology to lower the energy costs of making forecasts, but also potentially improve them. Theres probably more work to be done to create reliable operational products, but this is likely the beginning of a revolution this is our assessment in how weather forecasts are created, he says. Google DeepMind says that making 10-day forecasts with GraphCast takes less than a minute on a high-end PC, while HRES can take hours of supercomputer time.

But some meteorologists have expressed caution about turning weather forecasting over to AI.Ian Renfrew at the University of East Anglia, UK, says GraphCast currently lacks the ability to marshal data for its own starting state, a process known as data assimilation. In traditional forecasts, this data is carefully placed into the simulation after thorough checks against physics and chemistry calculations to ensure accuracy and consistency. Currently, GraphCast needs to use starting states prepared in the same way by the ECMWFs own tools.

Google is not going to be running weather forecasts anytime soon, because they cannot do the data assimilation, says Renfrew. And the data assimilation is typically half to two-thirds of the computing time in these forecasting systems.

He says that he would also have concerns about ditching deterministic models based on chemistry and physics entirely and relying on AI output alone.

You can have the best forecast model in the world, but if the public dont trust you, and dont act, then whats the point? If you set out an order to evacuate 30 miles of coastline in Florida, and then nothing happens, then youve blown decades of trust that has been built up, he says. The advantage of a deterministic model is you can interrogate it and if you do get bad forecasts, you can interrogate why theyre bad forecasts and try to target those aspects for improvement.

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DeepMind AI can beat the best weather forecasts - but there is a catch - New Scientist

Note to readers: Ancient Wisdom is a series of guides that shines a light on age-old wisdom that has helped people for generations with time-honoured wellness solutions to everyday fitness problems, persistent health issues and stress management, among others. Through this series, we try to provide contemporary solutions to your health worries with traditional insights.

Some ancient practices, once lost but now rediscovered, have significantly contributed to transforming our modern wellness journey. Deep breathing, dating thousands of years back, was practiced by Yogis in India and came to be known as Pranayama. Prana means life force and yama means control. By controlling your breathing, one can not only master mind but also keep several diseases at bay. Deep breathing is more relevant in today's world than ever before in the light of increasing stress, deteriorating air, decreased immunity and growing threat of chronic diseases like diabetes, asthma, heart disease, blood pressure among a host of other health issues.

Not just India, deep breathing has been linked with health and vitality, and also as a way to connect to divine in different countries for centuries. China's Qigong practice involved moving meditation, deep rhythmic breathing, and a calm meditative state of mind. In ancient Greece, deep breathing was called pneuma. As per Egyptians, deep breathing helped form a connection with the divine.

Deep breathing also known as diaphragmatic breathing improves oxygen flow in the body significantly which could calm nerves reducing all the stressful thoughts and anxiety symptoms. Deep breathings also helps improve release of endorphins which can naturally elevate mood. Deep breathing is also known to work wonders for your lung health. According to British Lung Foundation, deep breathing can help remove mucus from the lungs after pneumonia and allows more air to circulate. The practice can also provide a workout to your heart muscles which strengthen as a result.

In today's edition of Ancient Wisdom, let's discuss how this age-old practice can transform your overall health.

Deep breathing, often associated with modern wellness and mindfulness practices, has a deep history in ancient cultures. Across civilizations, deep breathing has been recognized for its potential to enhance physical, mental, and spiritual well-being.

"In ancient India deep breathing was known as pranayama. In Sanskrit, 'prana' translates to life force, and 'yama' means control. Ancient Indian yogis believed that deep breathing influences the flow of vital energy throughout the body which improves physical health and mental clarity. Sutra 2.51 of Maharishi Patanjalis Yoga Sutras asserts that the fluctuations of the mind are intimately connected to the breath, and by breathing deeply, we can achieve mastery over the mind," says Dr Hansaji Yogendra, Director of The Yoga Institute.

"In ancient China, the Daoist tradition emphasized the cultivation and balance of Qi, the vital energy that flows through the body. Deep breathing was a fundamental aspect of Daoist practices, with exercises like 'Dao Yin' focusing on the regulation of breath to harmonize and enhance the flow of Qi. While in ancient Greece, deep breathing was known as 'pneuma'. Pneuma was the vital breath or life force and was believed to be the divine breath of the gods. Early Greek philosophers, like Empedocles, associated pneuma with the fundamental elements of air and fire, considering it the animating force that sustained life. In ancient Egypt, the hieroglyphic symbol for breath, 'ankh', represented life and immortality. The Egyptians believed that deep breathing, facilitates a connection with the Divine," adds Dr Hansaji.

"Various indigenous cultures too practiced deep breathing. In Native American ceremonial rituals rhythmic breathing was a means to connect with Nature and the spirit world. Similarly, in the Aboriginal cultures of Australia, the concept of 'Dadirri' uses deep and mindful breathing, as a means of connecting with the land and ancestral wisdom," says the Yoga expert.

"The stream of breath should be always like the stream of relaxed reverse, relax in and relax out. For therapeutic purposes, Kapalbhati and Bhastrika can be sustained at a pace, but otherwise for equalizing our polarities, ha-tha, Chandra-surya, ida-pingala. Slow breathing in, slow breathing out popularly known as Anulom-vilom is of the highest order when it comes to neutralizing diseases, increasing immune response, immunoglobulins, triggering our T cells and B cells and increasing oxygen in our lungs, thus in blood, activating your brain functions and most importantly all the simple breathing exercises also can do the same," says Dr. Mickey Mehta, Global Leading Holistic Coach.

Breathing techniques should not stress you out, it should not create any kind of mental stress, even the temporary pace of your kapalbhati and bhastrika can be focused on releasing your mental and your emotional disturbing thoughts, hurt, pain etc.

"Do not forget the relaxed rhythm of the reverse, that is the way your breath should be. When you do shavasana, youre in and out through the nose consciously while the stomach balloons and retreats as you breathe out is the best way of relaxing as your entire body releases all the stress into gravity," says Dr Mehta.

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Acharya Dr. Lokesh Muni, Founder President at Ahimsa Vishwa Bharti shares many benefits of deep breathing.

Delving into the ancient wisdom of various cultures and studies reveal that conscious and deep breathing is far more than a biological necessity. It is a gateway to physical, mental, and spiritual transformations. It creates harmony in the body and the mind in miraculous ways by:

Deep breathing taps into divine consciousness, creating a calming effect on the mind. It helps achieve inner peace by liberating the mind from chaos and fostering mindfulness. Conscious breathwork is believed to cleanse and boosts the energy channels, aligning the individual with higher states of consciousness.

Encouraging the acknowledgment and observation of feelings without judgment, deep breathing creates a space for emotional acceptance and understanding. It teaches us to respond thoughtfully rather than react impulsively.

Deep breathing significantly boosts the immune system, relaxes the nervous system, and improves oxygen flow to the lungs, positively impacting the heart. It also helps mitigate conditions like diabetes, asthma, blood pressure, and other respiratory difficulties.

Deep breathing aids in alleviating panic attacks, anxiety, and depression by restoring control over breath, promoting a steady heartbeat, and triggering a relaxation response that decreases the jitters and fosters a happy and calm mind.

A composed and alert mind, facilitated by mindful breathing, creates a physiological state conducive to quick reflexes. It improves cognitive function, enabling quicker decision-making it also aids in anger management, stress management and concentration of mind.

"Regularly practicing breathing exercises improves lung capacity and overall health. Focusing on the breath is a sort of meditation that helps us calm down. It forces us to focus all of our attention on one thing, serving as a mental break from everyday stress. Breathing helps us to instantly ease anxiety and reduce stress," says Shivani Bajwa, Founder, of YogaSutra Holistic Living, India's Leading Functional Medicine Health Clinic.

hile the benefits of deep breathing are numerous, regular practice is essential for noticeable changes in the body. As the mind and body align, a universal language emergesa language spoken by the body, mind, and spirit. This language whispers the profound truth that the key to a well-lived life lies in the ebb and flow of breath.

Shivani Bajwa shares deep breathing exercises to reduce stress.

Exhalation emphasis: Bring the shoulders away from the ears, relax the muscles in the face, and soften the eyelids. You can close your eyes if it's comfortable for you. If not, you can leave them open, relaxing your eyelids. Take a slow, deep inhale through the nose without forcing anything, and exhale slowly with control through the nose. Were trying to lengthen each exhale, making the exhales longer than the inhale.

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Ancient Wisdom Part 26: Mind-blowing benefits of deep breathing - Hindustan Times

In 2010, Mike Williams traveled from London to Amsterdam for a physics workshop. Everyone there was abuzz with the possibilitiesand possible drawbacksof machine learning, which Williams had recently proposed incorporating into the LHCb experiment. Williams, now a professor of physics and leader of an experimental group at the Massachusetts Institute of Technology, left the workshop motivated to make it work.

LHCb is one of the four main experiments at the Large Hadron Collider at CERN. Every second, inside the detectors for each of those experiments, proton beams cross 40 million times, generating hundreds of millions of proton collisions, each of which produces an array of particles flying off in different directions. Williams wanted to use machine learning to improve LHCbs trigger system, a set of decision-making algorithms programmed to recognize and save only collisions that display interesting signalsand discard the rest.

Of the 40 million crossings, or events, that happen each second in the ATLAS and CMS detectorsthe two largest particle detectors at the LHCdata from only a few thousand are saved, says Tae Min Hong, an associate professor of physics and astronomy at the University of Pittsburgh and a member of the ATLAS collaboration. Our job in the trigger system is to never throw away anything that could be important, he says.

So why not just save everything? The problem is that its much more data than physicists could everor would ever want tostore.

Williams work after the conference in Amsterdam changed the way the LHCb detector collected data, a shift that has occurred in all the experiments at the LHC. Scientists at the LHC will need to continue this evolution as the particle accelerator is upgraded to collect more data than even the improved trigger systems can possibly handle. When the LHC moves into its new high-luminosity phase, it will reach up to 8 billion collisions per second.

As the environment gets more difficult to deal with, having more powerful trigger algorithms will help us make sure we find things we really want to see, says Michael Kagan, lead staff scientist at the US Department of Energys SLAC National Accelerator Laboratory, and maybe help us look for things we didnt even know we were looking for.

Hong says that, at its simplest, a trigger works like a motion-sensitive light: It stays off until activated by a preprogrammed signal. For a light, that signal could be a person moving through a room or an animal approaching a garden. For triggers, the signal is often an energy threshold or a specific particle or set of particles. If a collision, also called an event, contains that signal, the trigger is activated to save it.

In 2010, Williams wanted to add machine learning to the LHCb trigger in the hopes of expanding the detectors definitions of interesting particle events. But machine-learning algorithms can be unpredictable. They are trained on limited datasets and dont have a humans ability to extrapolate beyond them. As a result, when faced with new information, they make unpredictable decisions.

That unpredictability made many trigger experts wary, Williams says.We dont want the algorithm to say, That looks like [an undiscovered particle like] a dark photon, but its lifetime is too long, so Im going to ignore it, Williams says. That would be a disaster.

Still, Williams was convinced it could work. On the hour-long plane ride home from that conference in Amsterdam, he wrote out a way to give an algorithm set rules to followfor example, that a long lifetime is always interesting. Without that particular fix, an algorithm might only follow that rule up to the longest lifetime it had previously seen. But with this tweak, it would know to keep any longer-lived particle, even if its lifetime exceeded any of those in its training set.

Williams spent the next few months developing software that could implement his algorithm. When he flew to the United States for Christmas, he used the software to train his new algorithm on simulated LHC data. It was a success. It was an absolute work of art, says Vava Gligorov, a research scientist at the National Centre for Scientific Research in France, who worked on the system with Williams.

Updated versions of the algorithm have been running LHCbs main trigger ever since.

Physicists use trigger systems to store data from the types of particle collisions that they know are likely to be interesting. For example, scientists store collisions that produce two Higgs bosons at the same time, called di-Higgs events. Studying such events could enable physicists to map out the potential energy of the associated Higgs field, which could provide hints about the eventual fate of our universe.

Higgses are most often signaled by the appearance of two b quarks. If a proton collision produces a di-Higgs, four b quarks should appear in the detector. A trigger algorithm, then, could be programmed to capture data only if it finds four b quarks at once.

But spotting those four quarks is not as simple as it sounds. The two Higgs are interacting as they move through space, like two water balloons thrown at one another through the air. Just as the droplets of water from colliding balloons continue to move after the balloons have popped, the b quarks continue to move as the particle decays.

If a trigger can see only one spatial area of the event, it may pick up only one or two of the four quarks, letting a di-Higgs go unrecorded. But if the trigger could see more than that, looking at all of them at the same time, that could be huge, says David Miller, an associate professor of physics at the University of Chicago and a member of the ATLAS experiment.

In 2013, Miller started developing a system that would allow triggers to do just that: analyze an entire image at once. He and his colleagues called it the global feature extractor, or gFEX. After nearly a decade of development, gFEX started being integrated into ATLAS this year.

Trigger systems have traditionally had two levels. The first, or level-1, trigger, might contain hundreds or even thousands of signal instructions, winnowing the saved data down to less than 1%. The second, high-level trigger contains more complex instructions, and saves only about 1% of what survived the level-1. Those events that make it through both levels are recorded for physicists to analyze.

For now, at the LHC, machine learning is mostly being used in the high-level triggers. Such triggers could over time get better at identifying common processesbackground events they can ignore in favor of a signal. They could also better identify specific combinations of particles, such as two electrons whose tracks are diverging at a certain angle.

You can feed the machine learning the energies of things and the angles of things and then say, Hey, can you do a better job distinguishing things we dont want from the things we want? Hong says.

Future trigger systems could use machine learning to more precisely identify particles, says Jennifer Ngadiuba, an associate scientist at Fermi National Accelerator Laboratory and a member of the CMS experiment.

Current triggers are programmed to look for individual features of a particle, such as its energy. A more intelligent algorithm could learn all the features of a particle and assign a score to each particle decayfor example, a di-Higgs decaying to four b quarks. A trigger could then simply be programmed to look for that score.

You can imagine having one machine-learning model that does only that, Ngadiuba says. You can maximize the acceptance of the signal and reduce a lot of the background.

Most high-level triggers run on computer processors called central processing units or graphics processing units. CPUs and GPUs can handle complex instructions, but for most experiments, they are not efficient enough to quickly make the millions of decisions needed in a high-level trigger.

At the ATLAS and CMS experiments, scientists use different computer chips called field-programmable gate arrays, or FPGAs. These chips are hard-wired with custom instructions and can make decisions much faster than a more complex processor. The trade-off, though, is that FPGAs have a limited amount of space, and some physicists are unsure whether they can handle more complex machine-learning algorithms.The concern is that the limits of the chips would mean reducing the number of instructions they can provide to a trigger system, potentially leaving interesting physics data unrecorded.

Its a new field of exploration to try to put these algorithms on these nastier architectures, where you have to really think about how much space your algorithm is using, says Melissa Quinnan, a postdoctoral researcher at the University of California, San Diego and a member of the CMS experiment. You have to reprogram it every time you want it to do a different calculation.

Many physicists dont have the skillset needed to program FPGAs. Usually, after a physicist writes code in a computer language like Python, an electrical engineer needs to convertthe code to a hardware description language, which directs switch-flipping on an FPGA. Its time-consuming and expensive, Quinnan says. Abstract hardware languages, such as High-Level Synthesis, or HLS, can facilitate this process, but many physicists dont know how to use them.

So in 2017, Javier Duarte, now an assistant professor of physics at UCSD and a member of the CMS collaboration, began collaborating with other researchers on a tool that directly translates computer language to FPGA code using HLS. The team first posted the tool, called hls4ml, to the software platform GitHub on October 25 that year. Hong is developing a similar platform for the ATLAS experiment. Our goal was really lowering the barrier to entry for a lot of physicists or machine-learning people who arent FPGA experts or electronics experts, Duarte says.

Quinnan, who works in Duartes lab, is using the tool to add to CMS a type of trigger that, rather than searching for known signals of interest, tries to identify any events that seem unusual, an approach known as anomaly detection.

Instead of trying to come up with a new theory and looking for it and not finding it, what if we just cast out a general net and see if we find anything we dont expect? Quinnan says. We can try to figure out what theories could describe what we observe, rather than trying to observe the theories.

The trigger uses a type of machine learning called an auto-encoder. Instead of examining an entire event, an auto-encoder compresses it into a smaller version and, over time, becomes more skilled at compressing typical events. If the auto-encoder comes across an event it has difficulty compressing, it will save it, hinting to physicists that there may be something unique in the data.

The algorithm may be deployed on CMS as early as 2024, Quinnan says, which would make it the experiments first machine learning-based anomaly-detection trigger.

A test run of the system on simulated data identified a potentially novel event that wouldnt have been detected otherwise due to its low energy levels, Duarte says. Some theoretical models of new physics predict such low-energy particle sprays.

Its possible that the trigger is just picking up on noise in the data, Duarte says. But its also possible the system is identifying hints of physics beyond what most triggers have been programmed to look for. Our fear is that were missing out on new physics because we designed the triggers with certain ideas in mind, Duarte says. Maybe that bias has made us miss some new physics.

Illustration by Sandbox Studio, Chicago with Thumy Phan

Physicists are thinking about what their detectors will need after the LHCs next upgrade in 2028. As the beam gets more powerful, the centers of the ATLAS and CMS detectors, right where collisions happen, will generate too much data to ever beam it onto powerful GPUs or CPUs for analyzing. Level-1 triggers, then, will largely still need to function on more efficient FPGAsand they need to probe how particles move at the chaotic heart of the detector.

To better reconstruct these particle tracks, physicist Mia Liu is developing neural networks that can analyze the relationships between points in an image of an event, similar to mapping relationships between people in a social network.She plans to implement this system in CMS in 2028. That impacts our physics program at large, says Liu, an assistant professor at Purdue University. Now we have tracks in the hardware trigger level, and you can do a lot of online reconstruction of the particles.

Even the most advanced trigger systems, though, are still not physicists. And without an understanding of physics, the algorithms can make decisions that conflict with realitysay, saving an event in which a particle seems to move faster than light.

The real worry is its getting it right for reasons you dont know, Miller says. Then when it starts getting it wrong, you dont have the rationale.

To address this, Miller took inspiration from a groundbreaking algorithm that predicts how proteins fold. The system, developed by Googles DeepMind, has a built-in understanding of symmetry that prevents it from predicting shapes that arent possible in nature.

Miller is trying to create trigger algorithms that have a similar understanding of physics, which he calls self-driving triggers. A person should, ideally, be able to understand why a self-driving car decided to turn left at a stop sign. Similarly, Miller says, a self-driving trigger should make physics-based decisions that are understandable to a physicist.

What if these algorithms could tell you what about the data made them think it was worth saving? Miller says. The hope is its not only more efficient but also more trustworthy.

The rest is here:
LHC physicists can't save them all - Symmetry magazine

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