#Data Science Explained

[takeoffupskill253]

Data Science Course Online & Offline

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[polysyndetonaddictsupportgroup]

pez dispenser update, yay!

I am Very Interested in the direction you're taking izuku here. He seems to have come out the other side of this breakdown going, "no look! I trust you guys! Here, I trust you guys so much! You can know about the severe injuries I had as a child that never got a police report!"

It's funny to read izuku's pov vs aizawa. Izuku is just like, wow this all needs to end so I can get back to being the Normal And Awesome Deku I have turned myself into, and aizawa is like thirty seconds from having his own panic attack at having a few months to turn this kid into a functional human being.

You can truly tell that with how izuku keeps insisting on that he's got this by himself, with no understanding how crazy it is to expect his friends and teachers to back out and let him take over, that he, still, still, STILL has simply 0 faith or expectation that his teacher is driven to help the little kid in izuku that he's buried so deep down there. That an authority figure who isn't all might wants to save him. I want to eat his unthinking, warped by trauma thought patterns, they are delicious.

Kinda touching that midoriya foresaw and tried to avert the all might conversation issue. Rip, dude really tried, but baby izuku is like one of those puddles in flooded old buildings you can find videos of people dropping a rock in -- it doesn't look that deep, but if you tried to put your foot in, you would be getting a whole lot more than your shoe wet.

Yeahhhhhh Izuku’s really not handling it the best.

Izuku genuinely didn’t keep everything a secret all these years because he didn’t trust his friends. It wasn’t that he thought they’d react poorly or hurt him with the information or spread it around or anything like that. This was purely due to his own internal issues around it.

But they’re three years deep into being in the fucking trenches together. And Izuku very much is considered a bedrock of the class. You can see it in their internal monologues—everyone trusts him implicitly. It’s Izuku. If one of them was going through something sensitive or painful, he’d be at the top of the list of people to turn to. For like, the entire class.

And while Izuku isn’t per se aware of the fact that the entire class views him as the best of them, he is painfully aware of the fact that they’ve opened up to him over the years. And that this is making it look like he didn’t tell them a single detail about his life before he came to the school. Which is fair, because he sort of didn’t.

So he’s overcompensating. He doesn’t need privacy because he trusts them so so much and this proves it, right?? They can totally know the sordid details of the past he’s in active crisis over.

He’s scared that he’s going to lose the people who have trusted him over the years because he seemingly didn’t trust them back. But they all trust him so much that they’re more beating themselves up than blaming him.

Todoroki and Mirio were in that scene like “uuuuhhhh you look like you’re a second from a panic attack we can totally give you space if it makes you more comfortable” and Izuku’s in a spiral like Why Would I Need Space I Trust You Both Implicitly Please Ignore The Obvious Distress.

Fundamentally, Izuku has never processed what happened to him as a kid. He didn’t tell them because he wasn’t ready to confront how bad it was back then. It wasn’t about trust. Telling them meant saying aloud what happened. He just wasn’t ready for that.

And from the path canon took, I don’t really see Izuku trusting adults. His childhood did absolutely nothing to make him think teachers would protect him. And for all Aizawa did right, I think this is one bag in canon he legitimately dropped.

I want to be clear—Aizawa was working at a severe disadvantage. He didn’t even have a lot to tell him the problem existed, let alone how to address it. But it’s specifically the Hero Killer Stain Arc that makes me think that Izuku only would trust Aizawa to a certain point.

After the Hero Killer Stain Arc, Aizawa canonically calls out Iida, Todoroki, and Izuku in front of the entire class. He doesn’t mention what it's about, but he makes it very clear that he knows what happened and that he disapproves. And his criticism is specific: In instances where you are out matched, it is better to run and get help. Iida, Midoriya, and Todoroki need to understand that

The thing is that Izuku and Todoroki both considered that as their first option and then correctly deduced that they'd be burying Iida if they did that.

I will actually die on the hill that is that Izuku and Todoroki did everything right when it came to the Hero Killer Stain. Iida caused the problem, but the fact that he made mistakes was the point of that arc for him. But Izuku and Todoroki?

They both reacted perfectly. And if they had done a single thing differently, they'd have two dead bodies.

When Izuku realizes that Iida's in danger, the city is on fire, Nomu are attacking the train, and his supervisor has fucked off to fight monsters attacking the city. He does not have an adult hero who is free to bring with him, and we know for a fact that he did not have time to hesitate or try to find other options, because he arrives the second before Iida dies as-is. When he's on scene, his absolute first instinct is to run. Izuku canonically clocked the fact that he was out matched, evaluated whether he could safely retreat, and realized he’d never be able to get out of there with Iida and Native. He’d have to leave one or both of them to die.

So he asked for help the safest way he could: sending out the mass text and stalling for time. And canonically, he wasn’t hoping a classmate would show up to the fight. He was hoping they’d report it to their supervisors and get him help, which is exactly what multiple of his classmates did.

Todoroki, for his part, correctly clocked that something was wrong with Izuku when he got the message. And he didn’t just fuck off without telling anyone where he was going. He evaluated the situation, realized the city was on fucking fire and there wasn’t a single hero free to go with them, and told the heroes with him that they needed to go to this exact location the first second they could. And he didn’t have a moment to hesitate or figure something else out, because he also showed up at the very last second before Iida took a sword to his spine.

Frankly, Todoroki and Izuku couldn’t have possibly handled the situation better, but they got absolutely shit on in the aftermath. I don’t recall a single adult who told them they did the right thing, except maybe Native. They had the fucking chief of police telling them they were no better than the guy who tried to kill their teenage friend with a sword and their teacher publicly calling them out in front of the class without the benefit of context.

If I was Izuku, I would have walked out of that entire thing having my preexisting distrust of adults affirmed. Like. There isn’t a world where Izuku realistically looks back on his actions and thinks “damn I really should have left Iida die.” He’s not going to change a fucking thing in what he did. Every single time, he’s going to go save his friend. The only realistic take away Izuku could have from Aizawa’s call out was “wow, that guy is not going to have my back if I have to make a tough call. So if I have to make one, then I’m just not going to him for help.”

Which is kind of where we're at in pez right now, and Aizawa's starting to realize it. Don't get me wrong, Izuku trusts Aizawa more than any teacher he ever had growing up. He doesn't think Aizawa is going to be actively malicious to him. But he also doesn't necessarily think Aizawa's going to have his back.

The crux of it is in chapter 4. Tiny Izuku says that Mr. Aizawa is already on Izuku's side, and Izuku's immediate reply is, "I promise you that Mr. Aizawa has never once been on my side." He back pedals fast, clarifies that he thinks Mr. Aizawa is fair and not on anyone's side, but his knee-jerk reaction is undeniable.

And to me? It's because Aizawa genuinely has not been on Izuku's side since he came to UA. And I don't mean Aizawa has been malicious to Izuku. Fundamentally, the issue is that he misdiagnosed the problem.

Aizawa has spent his entire time with Izuku mistakenly believing that the source of Izuku's issues was the same as Bakugou's. He is only now realizing that his issues were more like Shinsou's.

Fundamentally, Aizawa correctly recognized that Izuku's problems came from the fact that he was raised in an unjust system. But he misunderstood what Izuku's position in it was.

Here's what Aizawa knows, from the jump: Izuku and Bakugou came from the same school. Both have very powerful Quirks. Both have obvious issues with the other. Izuku specifically moves and looks like he had a professional trainer, meaning someone invested in his training as a hero. Bakugou talks like someone who's been told his entire life that the sun shines out his ass and never got punished for being a little shit. Izuku's more muted, but he came from the same school. Two kids with powerful quirks? Likely were getting away with the exact same shit.

When you have an unjust system, you have the people running it, the people benefitting from it, and the people being victimized by it. If the teachers at Aldera were letting kids with powerful quirks get away with murder, both Izuku and Bakugou were likely benefitting from that. And it is absolutely vital that Aizawa undoes that damage before they debut.

He doesn't even need to think Izuku, specifically, was abusing his position in this power imbalance. The damage is done from how the teachers at aldera were likely treating him. Teachers that produce kids like Bakugou tell talented, powerful kids that they're special, that they're above the rules, that they've got something so fundamentally important about them that they can get away with more. Even if you don't chose to abuse that narrative in the moment, that's a hell of a formative experience.

They're about to have a ridiculous amount of power. They are about to be in charge of enforcing the rules. And people who are in charge of enforcing the rules and think they're above them turn into Endeavor.

Aizawa's approached Izuku from a sort of tough love perspective from the jump. He didn't cut him an ounce of slack, and it's because he genuinely was trying to do right by Izuku. No, he's not going to get to smash up his body and make himself a hazard. Figure it out, or go home.

He's had plenty of time to learn how to manage his quirk, after all.

With Stain? I don't think Aizawa, if he knew the full circumstances, would genuinely say the right call is to have Iida's fucking funeral. I think he'd agree with the decisions Izuku and todoroki made. But he didn't have all the information, and, fatally, he didn't ask. He assumed.

He's got three powerful, bullheaded students who end up in a back alley in the middle of the night, having all separately ditched the heroes they were supposed to be joined at the fucking hip with. He absolutely thinks that they either planned it together or that, when they realized what Iida did, Todoroki and Iida went after him in secret to try to keep Iida from getting in trouble--and almost got them all killed in the process. There is absolutely no way Aizawa knows that they actually tried to run and get help at every turn.

Aizawa made assumptions. And a big reason why he felt comfortable making those assumptions was because he thought he knew what Izuku's problem was. He thought Izuku, like Bakugou, had been benefitting from teachers turning a blind eye to his misbehavior for years. But the problem was the exact opposite. Teachers had been turning a blind eye to his victimization for years.

He shouldn't have been treating him like Bakugou. He should have been treating him like Shinsou.

Aizawa's trying to correct the damage of past teachers. If they've spent years telling Izuku he's god's gift to mankind and it doesn't matter what he does because he's a hero and that makes up for it, Aizawa needs to hold him to the fucking rules. He needs him to understand that he's not special, he's not the main character, he's not intrinsically better or more important or above the rules in some magically important way. He doesn't want to hear excuses. He doesn't want to know why this time it was different. Izuku needs to understand that he has to live by the rules too, because he's going to be in charge of enforcing them soon.

But if they've spent years telling him he's worthless, that people can hurt him and it's okay, that he can never, ever expect help from them because he's not worth it? Then fuck, Aizawa needed to do the opposite. He needed the same end result, don't get me wrong--an understanding that the system equally applies to everyone--but he needs to make Izuku believe that the system will protect him again. That Aizawa will protect him. And Aizawa's combing over every fucking interaction they've ever had, and realizing that he hasn't done that, because he spent all his time trying to correct a problem that didn't exist.

I think Aizawa's been beating his head against the problem that is Midoriya Izuku for the past three years. Because Izuku's a hard-worker. He is brilliant. He is a natural leader. He is the fucking cornerstone of the class. He is shining so bright that it's going to kill him, because Aizawa knows how to recognize a star that's burning out.

For three years, Aizawa has tried and failed to get Izuku to realize he can and should ask for help. And he has failed because he thought the problem was that Izuku didn't think he needed help, when the problem was actually that he thought no one would give it to him.

In this last chapter, Izuku finally said aloud the reason behind the core issue Aizawa’s had with him his entire time at UA: Growing up, he thought that there was literally one man on the planet who would care enough to save him. He was the most hero-obsessed boy Aizawa’s ever met, and he thought All Might was the only hero alive he could count on to care if he lived or died.

There it is. The exact answer about every scrap of self destructive behavior that Aizawa’s been trying and failing to remedy for years. Why the fuck would he ask for help when he needs it? He’s spent his entire life living in a world where people wouldn’t piss on him if he were on fire. Aizawa needed every day of those three years to reverse that kind of damage, and he’s out of fucking time.

Aizawa is legitimately terrified that he fucked up and that it's going to kill Izuku.

Izuku’s Quirklessness is the missing piece of the puzzle that makes everything fall into place—which is why he’s so pissed at All Might for not telling him. Aizawa’s actually kicking himself for not noticing the obvious discrepancies in Izuku’s past. The fact that he grew up with a powerful Quirk was the factor that made him return to the same incorrect conclusion again and again. There were enough hints that he feels guilty for not figuring it out anyway, but if he had known about Izuku’s Quirklessness from the start? He would have figured it out in seconds.

Now that he knows, Aizawa’s changed how he handles Izuku. He doesn’t let there be a single doubt about what he’s doing or why. He makes Izuku explain himself, so that way there’s no more miscommunications around what he means. He makes sure to compliment him whenever he does something right—he’s trying to change courses, but he’s panicking that it’s too little, too late.

And now he’s got this goddamn criminal investigation that Izuku wants to bury, and it’s killing him. Because that’s his student, and he was hurt horribly. And his student just cannot comprehend why Aizawa cannot let it go.

And then there’s All Might.

All Might’s conversation with baby Izuku, for me, forecloses the possibility that explaining OfA is a solution here.

All Might really went in and knocked it out of the park with the best possible attempt at convincing Tiny Izuku that he’s himself. He immediately failed, albeit, but he honestly couldn’t have done better.

There he is, Izuku’s lifelong hero. And he’s there to say the things Izuku’s spent his whole life wanting to hear. All Might met him, and Izuku inspired him. He reminded him of himself when he was young. He thought he could be a hero. He was so impressed he offered to personally mentor Izuku.

And he loved him. Believe you are him, because I loved you too much to ever let anyone take you from me. There is a fundamental flaw in your theory that simply no one cared enough to notice or stop him, because I love you with all of me. I would have noticed. I would have saved you.

If there is absolutely anything that could have convinced Tiny Izuku, it would be that. This isn’t about quality of the explanation. There’s an internal issue that needs to be fixed before Tiny Izuku will believe any of this.

And I think Izuku recognizes this, on a level. As much as he and Tiny Izuku clash, Izuku gets him. He can typically predict Tiny Izuku’s exact responses to things.

But he’s never approached Tiny Izuku like someone he can explain this to. He’s spent this entire time trying to cheat code his way out of this situation. He wants Mr. Aizawa to erase him or to go find the Quirk user and find away to negate the Quirk. He’s never actually even considered explaining this all to himself as a solution.

Because he knows that there’s some kind of fundamental impossibility about it. Even if he can’t say exactly what it is, he knows that there’s an internal issue that means he’s not going to be able to just tell Tiny Izuku the truth.

Voice of God, he is dead fucking right about Tiny Izuku not buying OfA and being liable to tell everyone out of spite. Tiny Izuku would have that shit on the news.

Fundamentally, Izuku is aware that there is a deeper problem driving Tiny Izuku. He knows that it’s not about the quality of the explanation. There is something deeply, profoundly wrong because of what happened to him that makes him absolutely unable to accept that Izuku is him.

But Izuku has never known how to solve the mental wounds his childhood left him with. He still has them himself. He’s been burying them for years, and he can’t anymore.

When action opens in pez, Izuku himself is not okay. He’s just… bleeding internally. He knows how to hurt in ways people can’t see. But you can see how much his childhood is still bothering him in his defense of Mirio. He has never been able to let go of what happened to him. The wounds never healed.

And he doesn’t know how to go to these people he loves and tell them that what they’re trying fundamentally will fail, because he knows he’s been hiding this fucking shipwreck of his own mental health for the past three years but they don’t have a fucking clue at the scale of the problem.

At the end of the day, All Might went in there because he wanted to save Izuku. And Izuku told him not to because he cannot imagine himself being saved.

[unnonexistence]

i started doing climate data transcription on Zooniverse today & it's nice. i feel a certain kinship with these 1950s weather observatory scientists who were trying to use up their stack of preprinted-for-the-1940s observation sheets & had to keep crossing out the "4" in the year field. they were doing it until at least 1952

[omg-snakes]

Hello! Does the survey you and Talesfromtreatment are running also desire information from snakes with spinal kinks, or should we avoid submitting those? I don't think my boy's kinking affects his length much, and I can't see how it would affect his weight, but I figured I should ask :)

Yes, please! Give us that data!

We do have a little notes section on the survey, so if you could please just let us know that the snake is kinked that would be super extra helpful. Thank you soooooo much!

I don't know how much data we'll get or how it'll stack up, but I think it might be useful to see how a kinked snake's growth compares to a non-kinked snake.

We'll be making this information public once we get enough of it. I'm already getting our infographics set up and I can't wait to see numbers start trickling in!

[techit-rp]

Exploring Explainable AI: Making Sense of Black-Box Models

Artificial intelligence (AI) and machine learning (ML) have become essential components of contemporary data science, driving innovations from personalized recommendations to self-driving cars.

However, this increasing dependence on these technologies presents a significant challenge: comprehending the decisions made by AI models. This challenge is especially evident in complex, black-box models, where the internal decision-making processes remain unclear. This is where Explainable AI (XAI) comes into play — a vital area of research and application within AI that aims to address this issue.

What Is a Black-Box Model?

Black-box models refer to machine learning algorithms whose internal mechanisms are not easily understood by humans. These models, like deep neural networks, are highly effective and often surpass simpler, more interpretable models in performance. However, their complexity makes it challenging to grasp how they reach specific predictions or decisions. This lack of clarity can be particularly concerning in critical fields such as healthcare, finance, and criminal justice, where trust and accountability are crucial.

The Importance of Explainable AI in Data Science

Explainable AI aims to enhance the transparency and comprehensibility of AI systems, ensuring they can be trusted and scrutinized. Here’s why XAI is vital in the fields of data science and artificial intelligence:

Accountability: Organizations utilizing AI models must ensure their systems function fairly and without bias. Explainability enables stakeholders to review models and pinpoint potential problems.

Regulatory Compliance: Numerous industries face regulations that mandate transparency in decision-making, such as GDPR’s “right to explanation.” XAI assists organizations in adhering to these legal requirements.

Trust and Adoption: Users are more inclined to embrace AI solutions when they understand their functioning. Transparent models build trust among users and stakeholders.

Debugging and Optimization: Explainability helps data scientists diagnose and enhance model performance by identifying areas for improvement.

Approaches to Explainable AI

Various methods and tools have been created to enhance the interpretability of black-box models. Here are some key approaches commonly taught in data science and artificial intelligence courses focused on XAI:

Feature Importance: Techniques such as SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-Agnostic Explanations) evaluate how individual features contribute to model predictions.

Visualization Tools: Tools like TensorBoard and the What-If Tool offer visual insights into model behavior, aiding data scientists in understanding the relationships within the data.

Surrogate Models: These are simpler models designed to mimic the behavior of a complex black-box model, providing a clearer view of its decision-making process.

Rule-Based Explanations: Some techniques extract human-readable rules from complex models, giving insights into how they operate.

The Future of Explainable AI

With the increasing demand for transparency in AI, explainable AI (XAI) is set to advance further, fueled by progress in data science and artificial intelligence courses that highlight its significance. Future innovations may encompass:

Improved tools and frameworks for real-time explanations.

Deeper integration of XAI within AI development processes.

Establishment of industry-specific standards for explainability and fairness.

Conclusion

Explainable AI is essential for responsible AI development, ensuring that complex models can be comprehended, trusted, and utilized ethically. For data scientists and AI professionals, mastering XAI techniques has become crucial. Whether you are a student in a data science course or a seasoned expert, grasping and implementing XAI principles will empower you to navigate the intricacies of contemporary AI systems while promoting transparency and trust.

[freakystinky]

the way tumblr talks about medicine makes me wonder how many of us here actually have critical thinking skills

[ko-eko-ev-go-ms]

It continues to trip me up how much human brains are just weird organic computers

[moon-bell5]

the initial report, "Liquid water in Martian mid-crust" by Vashan Wright, Matthias Morzfeld, and Michael Manga; published by the Proceedings of the National Academy of Sciences (PNAS)

an article that does a decent job of summarizing that report, "Reservoir of liquid water found deep in Martian rocks" by Victoria Gill for BBC

[rsayoub]

🚨 Stop Believing the AI Hype, that’s the title of my latest conversation on the Localization Fireside Chat with none other than @Dr. Sidney Shapiro, Assistant Professor at the @Dillon School of Business, University of Lethbridge. We dive deep into what AI can actually do, and more importantly, what it can’t. From vibe coders and synthetic data to the real-world consequences of over-trusting black-box models, this episode is packed with insights for anyone navigating the fast-moving AI space. 🧠 Dr. Shapiro brings an academic lens and real-world practicality to an often-hyped conversation. If you're building, deploying, or just curious about AI, this is a must-read. 🎥 catch the full interview on YouTube: 👉 https://youtu.be/wsqN0964neM Would love your thoughts, are we putting too much faith in AI? #LocalizationFiresideChat #AIethics #DataScience #AIstrategy #GenerativeAI #MachineLearning #CanadianTech #HigherEd #Localization #TranslationTechnology #Podcast

[techinfotrends]

Drive greater progression with Black Box and Explainable AI in Data Science; facilitating data-driven decision-making for business worldwide. Enhance with popular machine learning models today. bit.ly/4e3g6Sv

[techtoio]

How Artificial Intelligence is Transforming Scientific Research

Introduction No one ever imagined how artificial intelligence would revolutionize scientific research. At TechtoIO, we look into how AI is not just a tool but the driver behind the rapid advancements in many scientific disciplines. That includes how science is being transformed—from better data analysis to catalyzing discovery, such as areas in health, climate science, physics, particle experimentation, and more. Read to continue link...

[jcmarchi]

Study: Heavy snowfall and rain may contribute to some earthquakes

New Post has been published on https://thedigitalinsider.com/study-heavy-snowfall-and-rain-may-contribute-to-some-earthquakes/

Study: Heavy snowfall and rain may contribute to some earthquakes

When scientists look for an earthquake’s cause, their search often starts underground. As centuries of seismic studies have made clear, it’s the collision of tectonic plates and the movement of subsurface faults and fissures that primarily trigger a temblor.

But MIT scientists have now found that certain weather events may also play a role in setting off some quakes.

In a study appearing today in Science Advances, the researchers report that episodes of heavy snowfall and rain likely contributed to a swarm of earthquakes over the past several years in northern Japan. The study is the first to show that climate conditions could initiate some quakes.

“We see that snowfall and other environmental loading at the surface impacts the stress state underground, and the timing of intense precipitation events is well-correlated with the start of this earthquake swarm,” says study author William Frank, an assistant professor in MIT’s Department of Earth, Atmospheric and Planetary Sciences (EAPS). “So, climate obviously has an impact on the response of the solid earth, and part of that response is earthquakes.”

The new study focuses on a series of ongoing earthquakes in Japan’s Noto Peninsula. The team discovered that seismic activity in the region is surprisingly synchronized with certain changes in underground pressure, and that those changes are influenced by seasonal patterns of snowfall and precipitation. The scientists suspect that this new connection between quakes and climate may not be unique to Japan and could play a role in shaking up other parts of the world.

Looking to the future, they predict that the climate’s influence on earthquakes could be more pronounced with global warming.

“If we’re going into a climate that’s changing, with more extreme precipitation events, and we expect a redistribution of water in the atmosphere, oceans, and continents, that will change how the Earth’s crust is loaded,” Frank adds. “That will have an impact for sure, and it’s a link we could further explore.”

The study’s lead author is former MIT research associate Qing-Yu Wang (now at Grenoble Alpes University), and also includes EAPS postdoc Xin Cui, Yang Lu of the University of Vienna, Takashi Hirose of Tohoku University, and Kazushige Obara of the University of Tokyo.

Seismic speed

Since late 2020, hundreds of small earthquakes have shaken up Japan’s Noto Peninsula — a finger of land that curves north from the country’s main island into the Sea of Japan. Unlike a typical earthquake sequence, which begins as a main shock that gives way to a series of aftershocks before dying out, Noto’s seismic activity is an “earthquake swarm” — a pattern of multiple, ongoing quakes with no obvious main shock, or seismic trigger.

The MIT team, along with their colleagues in Japan, aimed to spot any patterns in the swarm that would explain the persistent quakes. They started by looking through the Japanese Meteorological Agency’s catalog of earthquakes that provides data on seismic activity throughout the country over time. They focused on quakes in the Noto Peninsula over the last 11 years, during which the region has experienced episodic earthquake activity, including the most recent swarm.

With seismic data from the catalog, the team counted the number of seismic events that occurred in the region over time, and found that the timing of quakes prior to 2020 appeared sporadic and unrelated, compared to late 2020, when earthquakes grew more intense and clustered in time, signaling the start of the swarm, with quakes that are correlated in some way.

The scientists then looked to a second dataset of seismic measurements taken by monitoring stations over the same 11-year period. Each station continuously records any displacement, or local shaking that occurs. The shaking from one station to another can give scientists an idea of how fast a seismic wave travels between stations. This “seismic velocity” is related to the structure of the Earth through which the seismic wave is traveling. Wang used the station measurements to calculate the seismic velocity between every station in and around Noto over the last 11 years.

The researchers generated an evolving picture of seismic velocity beneath the Noto Peninsula and observed a surprising pattern: In 2020, around when the earthquake swarm is thought to have begun, changes in seismic velocity appeared to be synchronized with the seasons.

“We then had to explain why we were observing this seasonal variation,” Frank says.

Snow pressure

The team wondered whether environmental changes from season to season could influence the underlying structure of the Earth in a way that would set off an earthquake swarm. Specifically, they looked at how seasonal precipitation would affect the underground “pore fluid pressure” — the amount of pressure that fluids in the Earth’s cracks and fissures exert within the bedrock.

“When it rains or snows, that adds weight, which increases pore pressure, which allows seismic waves to travel through slower,” Frank explains. “When all that weight is removed, through evaporation or runoff, all of a sudden, that pore pressure decreases and seismic waves are faster.”

Wang and Cui developed a hydromechanical model of the Noto Peninsula to simulate the underlying pore pressure over the last 11 years in response to seasonal changes in precipitation. They fed into the model meteorological data from this same period, including measurements of daily snow, rainfall, and sea-level changes. From their model, they were able to track changes in excess pore pressure beneath the Noto Peninsula, before and during the earthquake swarm. They then compared this timeline of evolving pore pressure with their evolving picture of seismic velocity.

“We had seismic velocity observations, and we had the model of excess pore pressure, and when we overlapped them, we saw they just fit extremely well,” Frank says.

In particular, they found that when they included snowfall data, and especially, extreme snowfall events, the fit between the model and observations was stronger than if they only considered rainfall and other events. In other words, the ongoing earthquake swarm that Noto residents have been experiencing can be explained in part by seasonal precipitation, and particularly, heavy snowfall events.

“We can see that the timing of these earthquakes lines up extremely well with multiple times where we see intense snowfall,” Frank says. “It’s well-correlated with earthquake activity. And we think there’s a physical link between the two.”

The researchers suspect that heavy snowfall and similar extreme precipitation could play a role in earthquakes elsewhere, though they emphasize that the primary trigger will always originate underground.

“When we first want to understand how earthquakes work, we look to plate tectonics, because that is and will always be the number one reason why an earthquake happens,” Frank says. “But, what are the other things that could affect when and how an earthquake happens? That’s when you start to go to second-order controlling factors, and the climate is obviously one of those.”

This research was supported, in part, by the National Science Foundation.

[sanjanabia]

Can AI Explain Itself? Unveiling the Mystery Behind Machine Decisions with a Data Science Course 

Artificial intelligence has become ubiquitous in our lives, from influencing our social media feeds to powering self-driving cars. However, the inner workings of many AI models remain shrouded in mystery. This lack of transparency, often referred to as the "black box" problem, raises critical questions: How are these decisions made? Can we trust AI to make fair and unbiased choices? 

This is where Explainable AI (XAI) comes in. XAI aims to shed light on the decision-making processes of AI models, allowing us to understand why a particular prediction was made or a specific recommendation was offered. A well-designed data science course can equip you with the knowledge and skills to navigate the world of XAI and contribute to the development of more transparent and trustworthy AI systems. 

Unveiling the Black Box: Why Explainability Matters in AI 

The lack of explainability in AI raises several concerns: 

Bias and Fairness: AI models can perpetuate societal biases present in the data they are trained on. Without understanding how these models arrive at their decisions, it's difficult to identify and mitigate potential bias. 

Accountability and Trust: When an AI system makes a critical decision, such as denying a loan application or flagging someone for security reasons, it's crucial to explain the rationale behind the decision. This fosters trust and accountability in AI systems. 

Debugging and Improvement: If an AI model consistently makes inaccurate predictions, being able to explain its reasoning is essential for debugging and improving its performance. 

XAI offers various techniques to make AI models more interpretable. Here are a few examples: 

Feature Importance: This technique identifies the input features that have the most significant influence on the model's output. By understanding which features matter most, we gain insights into the model's decision-making process. 

Decision Trees: Decision trees represent the model's logic in a tree-like structure, where each branch represents a decision point based on specific features. This allows for a clear visualization of the steps leading to the final prediction. 

LIME (Local Interpretable Model-Agnostic Explanations): LIME generates local explanations for individual predictions, providing insights into why a specific instance received a particular outcome. 

Unlocking the Power of XAI: What a Data Science Course Offers 

A comprehensive data science course plays a crucial role in understanding and applying XAI techniques. Here's what you can expect to gain: 

Foundational Knowledge: The program will provide a solid foundation in machine learning algorithms, the very building blocks of AI models. Understanding these algorithms forms the basis for understanding how they make predictions. 

Introduction to XAI Techniques: The course will delve into various XAI methodologies, equipping you with the ability to choose the most appropriate technique for a specific AI model and application. 

Hands-on Learning: Through practical projects, you'll gain experience applying XAI techniques to real-world datasets. This hands-on approach solidifies your understanding and allows you to experiment and explore different XAI approaches. 

Ethical Considerations: A data science course that incorporates XAI will also address the ethical considerations surrounding AI development and deployment. You'll learn how XAI can be used to mitigate bias and ensure fairness in AI systems. 

Beyond technical skills, a data science course fosters critical thinking, problem-solving abilities, and the capacity to communicate complex information effectively. These skills are essential for success in the field of XAI, where clear communication of technical concepts to stakeholders is crucial. 

The Future of AI: Transparency and Trust 

As AI continues to evolve and integrate further into our lives, XAI plays a vital role in building trust and ensuring responsible AI development. By fostering transparency and explainability, XAI empowers us to understand how AI systems work, identify potential biases, and ultimately, hold these systems accountable. 

A data science course equips you with the necessary tools and knowledge to become a key player in this critical field. Whether you're interested in developing explainable AI models, interpreting their outputs, or advocating for ethical AI practices, a data science course can pave the way for a rewarding career at the forefront of this transformative technology. 

If you're passionate about artificial intelligence and want to contribute to a future where AI decisions are transparent and trustworthy, then consider enrolling in a well-designed data science course. It can be the first step on your journey to demystifying the black box of AI and unlocking the true potential of this powerful technology. 

[coral-skeleton]

Yep, this one can be enjoyed guilt free.

I'd just also like to point out that AI used for denoising is a very different thing from modern-day generative AI. Denoising AI is, more often than not, a closed system, guided machine learning process, meaning it is trained on a subset of the dataset it is being used on, it does not scrape the internet for training sets, it does not steal from artists, more often than not it can be run completely locally on any mildly decent computer (it might just take a while if you don't have a fancy computer with the latest high end cpu and gpu specs), it does not need those massive servers and computing centers that severly worsen global warming to work.

The type of AI used for denoising is much more similar to the AI that's being used to cure cancer and improve our capabilities to do astronomical and astrophysical research than large language models and other generative AI and tbh, it's still worlds away from the curing cancer AI. It's so different from chat gpt that the same word absolutely should not be used for both of them. I would put a metaphor for trying to compare the two here, but I genuinely can't think of two things in the same category that are this dissimilar from each other in any other context.

Anyway, this type of "AI" is actually very normal mathematics and computer algorithms, and not the evil type of AI

AI in Unification?

If you, like me, couldn't fully enjoy Unification because there was a horrible feeling in your gut the whole time of "is this AI? Did Shatner really let them use AI? That seems like a thing he'd do, because he's kind of awful" then you've come to the right place.

I did a deep dive of the technologies used for Unification and while this isn't a 100% comprehensive guide here's what I've learned:

According to Trekmovie.com's article about the film, the production team used a "team of artists and animators, who combined digital and physical prosthetics with live-action location photography, virtual production, and CG set extensions" and used "OTOY’s “Octane” rendering software and the “Render Network” decentralized GPU rendering platform. Characters and props were digitized using OTOY’s Academy-Award winning “LightStage” scanning system."

So what are all these proprietary names / jargon, and are any of them AI?

LightStage: A scanning tech that allows for digital capture of a human face (probably used to capture the stand-ins faces and superimpose older footage of Spock / Kirk like they would for a video game motion capture or something) = Not AI

OctaneRender: "Fastest unbiased, spectrally correct GPU render engine" (Probably used for sets based on the example I'm seeing on OTOY's website. It DOES use AI for "denoising and lighting" but this is a feature of the program and not the only thing the program does, so it is unclear if this is something they would have employed for the shot film. If they did, this would not be used for character work / deep fakes, and given what little information is written about this tech I'm almost curious if it is even a full AI system at all or just an automatic denoiser that they've dubbed as AI to look impressive. So I'd say results inconclusive here at best.)

The Render Network: "The network connects node operators looking to monetize their idle GPU compute power with artists looking to scale intensive 3D-rendering work and with machine learning developers looking to train and tune AI models. Through a decentralized peer-to-peer network, the Render Network achieves unprecedented levels of scale, speed, and economic efficiency. " (This basically means people can use the platform FOR AI but means nothing in the context of whether AI was used for this project.)

TL;DR: AI is an umbrella term for a lot of technology and it seems if anything, there may have been some AI used in the background rendering process but nothing generative AI / deep fakes. In my cynical opinion, if they HAD used AI in general for this, I feel like they'd be shouting it from the rooftops right now since people who love AI won't shut up about it. I'm tentatively saying this was 99% made with traditional CGI and artist work as is stated in the Trekmovie.com article, but I wouldn't be surprised if that opinion changes as the day goes on and more information is released.

[agent-oo-z]

Just saw a live news feed from the Huston zoo during the eclipse and I have so many animal science questions. Like obviously it’s weird for animals because 1: most of them have never experienced an eclipse, especially at totality and 2: eclipses are just straight up weird.

Like one of the zoo staff reported that most of their birds in the aviary were entering their nesting sites like they would at night time. But what about other animals? How do they react? I’m sure someone somewhere set up some amount of study for this but I have questions! Do eclipses impact migrations? Are there any changes to like. The tides or stuff? That impacts animal behavior???

[reasonsforhope]

"In an unprecedented transformation of China’s arid landscapes, large-scale solar installations are turning barren deserts into unexpected havens of biodiversity, according to groundbreaking research from the Chinese Academy of Sciences. The study reveals that solar farms are not only generating clean energy but also catalyzing remarkable ecological restoration in some of the country’s most inhospitable regions.

The research, examining 40 photovoltaic (PV) plants across northern China’s deserts, found that vegetation cover increased by up to 74% in areas with solar installations, even in locations using only natural restoration measures. This unexpected environmental dividend comes as China cements its position as the global leader in solar energy, having added 106 gigawatts of new installations in 2022 alone.

“Artificial ecological measures in the PV plants can reduce environmental damage and promote the condition of fragile desert ecosystems,” says Dr. Benli Liu, lead researcher from the Chinese Academy of Sciences. “This yields both ecological and economic benefits.”

The economic implications are substantial. “We’re witnessing a paradigm shift in how we view desert solar installations,” says Professor Zhang Wei, environmental economist at Beijing Normal University. “Our cost-benefit analysis shows that while initial ecological construction costs average $1.5 million per square kilometer, the long-term environmental benefits outweigh these investments by a factor of six within just a decade.” ...

“Soil organic carbon content increased by 37.2% in areas under solar panels, and nitrogen levels rose by 24.8%,” reports Dr. Sarah Chen, soil scientist involved in the project. “These improvements are crucial indicators of ecosystem health and sustainability.”

...Climate data from the study sites reveals significant microclimate modifications:

Average wind speeds reduced by 41.3% under panel arrays

Soil moisture retention increased by 32.7%

Ground surface temperature fluctuations decreased by 85%

Dust storm frequency reduced by 52% in solar farm areas...

The scale of China’s desert solar initiative is staggering. As of 2023, the country has installed over 350 gigawatts of solar capacity, with 30% located in desert regions. These installations cover approximately 6,000 square kilometers of desert terrain, an area larger than Delaware.

“The most surprising finding,” notes Dr. Wang Liu of the Desert Research Institute, “is the exponential increase in insect and bird species. We’ve documented a 312% increase in arthropod diversity and identified 27 new bird species nesting within the solar farms between 2020 and 2023.”

Dr. Yimeng Wang, the study’s lead author, emphasizes the broader implications: “This study provides evidence for evaluating the ecological benefit and planning of large-scale PV farms in deserts.”

The solar installations’ positive impact stems from several factors. The panels act as windbreaks, reducing erosion and creating microhabitats with lower evaporation rates. Perhaps most surprisingly, the routine maintenance of these facilities plays a crucial role in the ecosystem’s revival.

“The periodic cleaning of solar panels, occurring 7-8 times annually, creates consistent water drip lines beneath the panels,” explains Wang. “This inadvertent irrigation system promotes vegetation growth and the development of biological soil crusts, essential for soil stability.” ...

Recent economic analysis reveals broader benefits:

Job creation: 4.7 local jobs per megawatt of installed capacity

Tourism potential: 12 desert solar sites now offer educational tours

Agricultural integration: 23% of sites successfully pilot desert agriculture beneath panels

Carbon reduction: 1.2 million tons CO2 equivalent avoided per gigawatt annually

Dr. Maya Patel, visiting researcher from the International Renewable Energy Agency, emphasizes the global implications: “China’s desert solar model could be replicated in similar environments worldwide. The Sahara alone could theoretically host enough solar capacity to meet global electricity demand four times over while potentially greening up to 20% of the desert.”

The Chinese government has responded by implementing policies promoting “solar energy + sand control” and “solar energy + ecological restoration” initiatives. These efforts have shown promising results, with over 92% of PV plants constructed since 2017 incorporating at least one ecological construction mode.

Studies at facilities like the Qinghai Gonghe Photovoltaic Park demonstrate that areas under solar panels score significantly better in environmental assessments compared to surrounding regions, indicating positive effects on local microclimates.

As the world grapples with dual climate and biodiversity crises, China’s desert solar experiment offers a compelling model for sustainable development. The findings suggest that renewable energy infrastructure, when thoughtfully implemented, can serve as a catalyst for environmental regeneration, potentially transforming the world’s deserts from barren wastelands into productive, life-supporting ecosystems.

“This is no longer just about energy production,” concludes Dr. Liu. “We’re witnessing the birth of a new approach to ecosystem rehabilitation that could transform how we think about desert landscapes globally. The next decade will be crucial as we scale these solutions to meet both our climate and biodiversity goals.”"

-via Green Fingers, January 13, 2025