Editor’s note: This story has been updated with information regarding sales and acquisitions.

CLEARLAKE, Calif. — The Clearlake Planning Commission this week will consider the city’s plans to sell or acquire several properties.

The famous first image of a black hole just got two times sharper. A research team used artificial intelligence to dramatically improve upon its first image from 2019, which now shows the black hole at the center of the M87 galaxy as darker and bigger than the first image depicted.

I’m an astronomer who studies and has written about cosmology, black holes and exoplanets. Astronomers have been using AI for decades. In fact, in 1990, astronomers from the University of Arizona, where I am a professor, were among the first to use a type of AI called a neural network to study the shapes of galaxies.

Since then, AI has spread into every field of astronomy. As the technology has become more powerful, AI algorithms have begun helping astronomers tame massive data sets and discover new knowledge about the universe.

Better telescopes, more data

As long as astronomy has been a science, it has involved trying to make sense of the multitude of objects in the night sky. That was relatively simple when the only tools were the naked eye or a simple telescope, and all that could be seen were a few thousand stars and a handful of planets.

A hundred years ago, Edwin Hubble used newly built telescopes to show that the universe is filled with not just stars and clouds of gas, but countless galaxies. As telescopes have continued to improve, the sheer number of celestial objects humans can see and the amount of data astronomers need to sort through have both grown exponentially, too.

For example, the soon-to-be-completed Vera Rubin Observatory in Chile will make images so large that it would take 1,500 high-definition TV screens to view each one in its entirety. Over 10 years it is expected to generate 0.5 exabytes of data – about 50,000 times the amount of information held in all of the books contained within the Library of Congress.

There are 20 telescopes with mirrors larger than 20 feet (6 meters) in diameter. AI algorithms are the only way astronomers could ever hope to work through all of the data available to them today. There are a number of ways AI is proving useful in processing this data.

Picking out patterns

Astronomy often involves looking for needles in a haystack. About 99% of the pixels in an astronomical image contain background radiation, light from other sources or the blackness of space – only 1% have the subtle shapes of faint galaxies.

AI algorithms – in particular, neural networks that use many interconnected nodes and are able to learn to recognize patterns – are perfectly suited for picking out the patterns of galaxies. Astronomers began using neural networks to classify galaxies in the early 2010s. Now the algorithms are so effective that they can classify galaxies with an accuracy of 98%.

This story has been repeated in other areas of astronomy. Astronomers working on SETI, the Search for Extraterrestrial Intelligence, use radio telescopes to look for signals from distant civilizations. Early on, radio astronomers scanned charts by eye to look for anomalies that couldn’t be explained. More recently, researchers harnessed 150,000 personal computers and 1.8 million citizen scientists to look for artificial radio signals. Now, researchers are using AI to sift through reams of data much more quickly and thoroughly than people can. This has allowed SETI efforts to cover more ground while also greatly reducing the number of false positive signals.

Another example is the search for exoplanets. Astronomers discovered most of the 5,300 known exoplanets by measuring a dip in the amount of light coming from a star when a planet passes in front of it. AI tools can now pick out the signs of an exoplanet with 96% accuracy.

Making new discoveries

AI has proved itself to be excellent at identifying known objects – like galaxies or exoplanets – that astronomers tell it to look for. But it is also quite powerful at finding objects or phenomena that are theorized but have not yet been discovered in the real world.

Teams have used this approach to detect new exoplanets, learn about the ancestral stars that led to the formation and growth of the Milky Way, and predict the signatures of new types of gravitational waves.

To do this, astronomers first use AI to convert theoretical models into observational signatures – including realistic levels of noise. They then use machine learning to sharpen the ability of AI to detect the predicted phenomena.

Finally, radio astronomers have also been using AI algorithms to sift through signals that don’t correspond to known phenomena. Recently a team from South Africa found a unique object that may be a remnant of the explosive merging of two supermassive black holes. If this proves to be true, the data will allow a new test of general relativity – Albert Einstein’s description of space-time.

Making predictions and plugging holes

As in many areas of life recently, generative AI and large language models like ChatGPT are also making waves in the astronomy world.

The team that created the first image of a black hole in 2019 used a generative AI to produce its new image. To do so, it first taught an AI how to recognize black holes by feeding it simulations of many kinds of black holes. Then, the team used the AI model it had built to fill in gaps in the massive amount of data collected by the radio telescopes on the black hole M87.

Using this simulated data, the team was able to create a new image that is two times sharper than the original and is fully consistent with the predictions of general relativity.

Astronomers are also turning to AI to help tame the complexity of modern research. A team from the Harvard-Smithsonian Center for Astrophysics created a language model called astroBERT to read and organize 15 million scientific papers on astronomy. Another team, based at NASA, has even proposed using AI to prioritize astronomy projects, a process that astronomers engage in every 10 years.

As AI has progressed, it has become an essential tool for astronomers. As telescopes get better, as data sets get larger and as AIs continue to improve, it is likely that this technology will play a central role in future discoveries about the universe.

Chris Impey, University Distinguished Professor of Astronomy, University of Arizona

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Mindfulness and self-compassion are now buzzwords for self-improvement. But in fact, a growing body of research shows these practices can lead to real mental health benefits. This research – ongoing, voluminous and worldwide – clearly shows how and why these two practices work.

One effective way to cultivate mindfulness and self-compassion is through meditation.

For more than 20 years, as a clinical psychologist, research scientist and educator, I taught meditation to students and clinical patients and took a deep dive into the research literature. My recent book, “The Self-Talk Workout: Six Science-Backed Strategies to Dissolve Self-Criticism and Transform the Voice in Your Head,” highlights much of that research.

I learned even more when I evaluated mental health programs and psychology classes that train participants in mindfulness and compassion-based techniques.

Defining mindfulness and self-compassion

Mindfulness means purposefully paying attention to the present moment with an attitude of interest or curiosity rather than judgment.

Self-compassion involves being kind and understanding toward yourself, even during moments of suffering or failure.

Both are associated with greater well-being.

But don’t confuse self-compassion with self-esteem or self-centeredness, or assume that it somehow lowers your standards, motivation or productivity. Instead, research shows that self-compassion is linked with greater motivation, less procrastination and better relationships.

Be patient when starting a meditation practice

I didn’t like meditation – the specific practice sessions that train mindfulness and self-compassion – the first time I tried it as a college student in the late ‘90s. I felt like a failure when my mind wandered, and I interpreted that as a sign that I couldn’t do it.

In both my own and others’ meditation practices, I’ve noticed that the beginning is often rocky and full of doubt, resistance and distraction.

But what seem like impediments can actually enhance meditation practice, because the mental work of handling them builds strength.

For the first six months I meditated, my body and mind were restless. I wanted to get up and do other tasks. But I didn’t. Eventually it became easier to notice my urges and thoughts without acting upon them. I didn’t get as upset with myself.

After about a year of consistent meditation, my mind seemed more organized and controllable; it no longer got stuck in self-critical loops. I felt a sense of kindness or friendliness toward myself in everyday moments, as well as during joyful or difficult experiences. I enjoyed ordinary activities more, such as walking or cleaning.

It took a while to understand that anytime you sit down and try to meditate, that’s meditation. It is a mental process, rather than a destination.

How meditation works on the mind

Just having a general intention to be more mindful or self-compassionate is unlikely to work.

Most programs shown to make meaningful differences involve at least seven sessions. Studies show these repeated workouts improve attention skills and decrease rumination, or repeated negative thinking.

They also lessen self-criticism, which is linked to numerous mental health difficulties, including depression, anxiety, eating disorders, self-harm and post-traumatic stress disorder.

Meditation is not just about sustaining your attention – it’s also about shifting and returning your focus after the distraction. The act of shifting and refocusing cultivates attention skills and decreases rumination.

Trying repeatedly to refrain from self-judgment during the session will train your mind to be less self-critical.

An interconnected group of brain regions called the default mode network is strikingly affected by meditation. Much of this network’s activity reflects repetitive thinking, such as a rehash of a decadeslong tension with your sister. It’s most prominent when you’re not doing much of anything. Activity of the default mode network is related to rumination, unhappiness and depression.

Research shows that just one month of meditation reduces the noise of the default mode network. The type of meditation practice doesn’t seem to matter.

Establishing the formal practice

A common misconception about mindfulness is that it’s simply a way to relax or clear the mind. Rather, it means intentionally paying attention to your experiences in a nonjudgmental way.

Consider meditation the formal part of your practice – that is, setting aside a time to work on specific mindfulness and self-compassion techniques.

Cultivating mindfulness with meditation often involves focusing on paying attention to the breath. A common way to start practice is to sit in a comfortable place and bring attention to your breathing, wherever you feel it most strongly.

At some point, probably after a breath or two, your mind will wander to another thought or feeling. As soon as you notice that, you can bring your attention back to the breath and try not to judge yourself for losing focus for five to 10 minutes.

When I was just getting started meditating, I would have to redirect my attention dozens or hundreds of times in a 20-to-30-minute session. Counting 10 breaths, and then another 10, and so on, helped me link my mind to the task of paying attention to my breathing.

The most well-established technique for cultivating self-compassion is called loving-kindness meditation. To practice, you can find a comfortable position, and for at least five minutes, internally repeat phrases such as, “May I be safe. May I be happy. May I be healthy. May I live with ease.”

When your attention wanders, you can bring it back with as little self-judgment as possible and continue repeating the phrases. Then, if you like, offer the same well wishes to other people or to all beings.

Every time you return your focus to your practice without judging, you’re flexing your mental awareness, because you noticed your mind wandered. You also improve your capacity to shift attention, a valuable anti-rumination skill, and your nonjudgment, an antidote to self-criticism.

These practices work. Studies show that brain activity during meditation results in less self-judgment, depression and anxiety and results in less rumination.

Mindfulness also occurs when you tune into present-moment sensations, such as tasting your food or washing the dishes.

An ongoing routine of formal and informal practice can transform your thinking. And again, doing it once in a while won’t help as much. It’s like situps: A single situp isn’t likely to strengthen your abdominal muscles, but doing several sets each day will.

Meditation reduces self-criticism

Studies show that mindfulness meditation and loving-kindness meditation reduce self-criticism, which leads to better mental health, including lower levels of depression, anxiety and PTSD. After an eight-week mindfulness program, participants experienced less self-judgment. These changes were linked with decreases in depression and anxiety.

One final point: Beginning meditators may find that self-criticism gets worse before it gets better.

After years or decades of habitual self-judgment, people often judge themselves harshly about losing focus during meditation. But once students get through the first few weeks of practice, the self-judgment begins to abate, both about meditation and about oneself in general.

As one of my students recently said after several weeks of mindfulness meditation: “I am more stable, more able to detach from unhelpful thoughts and can do all of this while being a little more compassionate and loving toward myself.”

Rachel Goldsmith Turow, Adjunct Assistant Professor in Population Health Science and Policy, Seattle University

This article is republished from The Conversation under a Creative Commons license. Read the original article.