#ai in particle analysis

[daydreamhowell]

literally the biggest hindrance to having any kind of productive conversation about ai is the fact that so many product designers call their products ai when they’re actually just a normal algorithm. so many “new” non-generative “ai” are tools that were already available before the ai boom, but they’ve repackaged it to sell it to you again (certain photoshop tools, suggested email responses, etc). and so every time people critique ai, other people come out of the woodworks going aCtUaLLyyyyyyy ai has so many uses that are good and productive and not harmful. and I go yes and I could hop on visual studio and write a quick program to approximate the general code needed for that in probably under an hour. including the time to understand the problem.

[valevod]

Echoes of the Eye spoilers!!!!

I played the DLC!!!!! I dont think ill ever be the same person again!!!! I have consumed every little particle of the game, of the fan content, of the theory and analysis videos. And i am not ready yet to depart from the game ,if i ever will 😣

Have a bunch of doodleis!! I will make more ...for sure...

First of all THE PRISONER MY BELOVED!!!!!

I LOVE YOU UUUUUuuuuUuUUu!!!!!!!!!@@@!?**#,,**÷*(([,;;;;;

Then, there was this time this fella wouldnt chase me, the Ai of theirs is so wonky, i love the owlks.....and shining lights in their face. Not so much when they do it to me.

And tgen... a Gabbro and Outer Wilds: EotE if it was a 10/10

I played the game on a broken tv, so i played on hard mode.

[inukag]

Hello! Idk if u do translations analysis anymore. But there’s this one line on chapter 473, “the one you most loved” I wonder if that’s what the demon really said or if it’s just like when kikyo died? Not the most, but the first? Thank you!

I still do when people ask!

I’ve never seen this scene translated as anything other than “most”, but let’s look at the original Japanese text:

The second bubble says:

この世で一番愛していた女がー

kono yo de ichiban aishiteita hito ga

"The person you loved most in the world..."

The breakdown:

この世 (kono yo) - this world

で (de) - [particle] in

一番 (ichiban) - first place, #1; the best; the most

愛していた (aishiteita) - to love (notice 愛 (ai) in there), the -いた (ita) indicates past tense, so “loved”

女 (onna) - woman / ひと (hito) - person

が (ga) - [particle]

The part you were asking about is the 一番 (ichiban) which does somewhat mean “first”, but as in “first place”. Which is probably best describe as “best”.

We’ve discussed this scene a lot in the fandom, but to reiterate, Kao is a demon that feeds on sadness. This scene is happening right after Kikyo’s final and permanent death, and the nth time that Inuyasha failed to protect her from Naraku. Kao cannot feel Inuyasha’s love for Kagome at that moment because Kagome has only ever been a source of happiness for Inuyasha, not a source of pain like his love for Kikyo.

That is the whole point of this arc, Inuyasha did love Kikyo, but his love for Kagome always makes him choose to stay with her and live instead of succumbing to guilt and dying. Inuyasha already made the same choice before when Kikyo had him under a spell and was trying to drag him to hell, and again in the illusionary death arc. We know Kao is wrong in his assumption that Kikyo is the woman he loved most, because if she was, Inuyasha wouldn’t have hesitated to follow her in that peaceful vision Kao showed him. If he wanted to follow Kikyo, he could have ignored Kagome’s voice and make the choice to be in the afterlife with Kikyo anyway. But he didn’t, and the fact that he was able to wake up shocked Kao, who thought he had figured out what Inuyasha wanted.

In general, a lot of characters make assumptions about other characters in the series, based on their limited knowledge of that person. Sometimes, villains make statements intentionally trying to hurt the person they are targeting, like here. The only person who truly knows what Inuyasha is feeling is Inuyasha himself, and he explicitly said himself that he was born to be with Kagome.

[spacetimewithstuartgary]

New diagnostic tool will help LIGO hunt gravitational waves

Machine learning tool developed by UCR researchers will help answer fundamental questions about the universe.

Finding patterns and reducing noise in large, complex datasets generated by the gravitational wave-detecting LIGO facility just got easier, thanks to the work of scientists at the University of California, Riverside. 

The UCR researchers presented a paper at a recent IEEE big-data workshop, demonstrating a new, unsupervised machine learning approach to find new patterns in the auxiliary channel data of the Laser Interferometer Gravitational-Wave Observatory, or LIGO. The technology is also potentially applicable to large scale particle accelerator experiments and large complex industrial systems.

LIGO is a facility that detects gravitational waves — transient disturbances in the fabric of spacetime itself, generated by the acceleration of massive bodies. It was the first to detect such waves from merging black holes, confirming a key part of Einstein’s Theory of Relativity. LIGO has two widely-separated 4-km-long interferometers — in Hanford, Washington, and Livingston, Louisiana — that work together to detect gravitational waves by employing high-power laser beams. The discoveries these detectors make offer a new way to observe the universe and address questions about the nature of black holes, cosmology, and the densest states of matter in the universe.

Each of the two LIGO detectors records thousands of data streams, or channels, which make up the output of environmental sensors located at the detector sites. 

“The machine learning approach we developed in close collaboration with LIGO commissioners and stakeholders identifies patterns in data entirely on its own,” said Jonathan Richardson, an assistant professor of physics and astronomy who leads the UCR LIGO group. “We find that it recovers the environmental ‘states’ known to the operators at the LIGO detector sites extremely well, with no human input at all. This opens the door to a powerful new experimental tool we can use to help localize noise couplings and directly guide future improvements to the detectors.”

Richardson explained that the LIGO detectors are extremely sensitive to any type of external disturbance. Ground motion and any type of vibrational motion — from the wind to ocean waves striking the coast of Greenland or the Pacific — can affect the sensitivity of the experiment and the data quality, resulting in “glitches” or periods of increased noise bursts, he said. 

“Monitoring the environmental conditions is continuously done at the sites,” he said. “LIGO has more than 100,000 auxiliary channels with seismometers and accelerometers sensing the environment where the interferometers are located. The tool we developed can identify different environmental states of interest, such as earthquakes, microseisms, and anthropogenic noise, across a number of carefully selected and curated sensing channels.”

Vagelis Papalexakis, an associate professor of computer science and engineering who holds the Ross Family Chair in Computer Science, presented the team’s paper, titled “Multivariate Time Series Clustering for Environmental State Characterization of Ground-Based Gravitational-Wave Detectors,” at the IEEE's 5th International Workshop on Big Data & AI Tools, Models, and Use Cases for Innovative Scientific Discovery that took place last month in Washington, D.C.

“The way our machine learning approach works is that we take a model tasked with identifying patterns in a dataset and we let the model find patterns on its own,” Papalexakis said. “The tool was able to identify the same patterns that very closely correspond to the physically meaningful environmental states that are already known to human operators and commissioners at the LIGO sites.”

Papalexakis added that the team had worked with the LIGO Scientific Collaboration to secure the release of a very large dataset that pertains to the analysis reported in the research paper. This data release allows the research community to not only validate the team’s results but also develop new algorithms that seek to identify patterns in the data.

“We have identified a fascinating link between external environmental noise and the presence of certain types of glitches that corrupt the quality of the data,” Papalexakis said. “This discovery has the potential to help eliminate or prevent the occurrence of such noise.”

The team organized and worked through all the LIGO channels for about a year. Richardson noted that the data release was a major undertaking. 

“Our team spearheaded this release on behalf of the whole LIGO Scientific Collaboration, which has about 3,200 members,” he said. “This is the first of these particular types of datasets and we think it’s going to have a large impact in the machine learning and the computer science community.”

Richardson explained that the tool the team developed can take information from signals from numerous heterogeneous sensors that are measuring different disturbances around the LIGO sites. The tool can distill the information into a single state, he said, that can then be used to search for time series associations of when noise problems occurred in the LIGO detectors and correlate them with the sites’ environmental states at those times.

“If you can identify the patterns, you can make physical changes to the detector — replace components, for example,” he said. “The hope is that our tool can shed light on physical noise coupling pathways that allow for actionable experimental changes to be made to the LIGO detectors. Our long-term goal is for this tool to be used to detect new associations and new forms of environmental states associated with unknown noise problems in the interferometers.”

Pooyan Goodarzi, a doctoral student working with Richardson and a coauthor on the paper, emphasized the importance of releasing the dataset publicly. 

“Typically, such data tend to be proprietary,” he said. “We managed, nonetheless, to release a large-scale dataset that we hope results in more interdisciplinary research in data science and machine learning.”

The team’s research was supported by a grant from the National Science Foundation awarded through a special program, Advancing Discovery with AI-Powered Tools, focused on applying artificial intelligence/machine learning to address problems in the physical sciences. 

[americas-favourite-fossil]

TO: Captain Steven Rogers FROM: @carlos-the-ai SUBJECT: Analysis of Substance Responsible for Age Regression of Subjects Lina and Pyro

Objective: To analyze the molecular composition of the provided substance and determine potential pathways for reversing its effects, which caused age regression in subjects identified as Lina and Pyro.

Analysis Summary: The sample exhibits unique characteristics indicating temporal manipulation at a molecular level. Key findings include: Molecular Composition: The substance contains artificially synthesized compounds not commonly found in organic materials. Significant presence of particles with properties akin to “chronoton” particles, typically associated with temporal displacement. However, the substance is engineered, suggesting intentional manipulation for specific effects. Temporal and Aging Effects: Preliminary findings indicate the substance acts on a cellular level, reversing age markers. It appears to temporarily suspend natural metabolic decay while promoting regeneration to a predetermined age range, hence the regression to a teenage state in subjects. There is evidence of accelerated cell division and growth in reverse, a process similar to biological age manipulation observed in certain advanced serums and magic-based phenomena. Potential Countermeasures: Reversal may be possible by isolating the key chronoton-like particles and developing a counter-agent that neutralizes these effects. Suggested research includes synthesizing an agent that can target the particles without further disrupting cellular stability. This would require a controlled environment, preferably in a lab with access to both high-energy particle accelerators and molecular stasis technology. Next Steps: Isolate Chronoton Agents: Further isolate the particles responsible for age regression effects. Synthesize Counter-Agent: Using data from known temporal stabilization protocols, develop a prototype antidote. Testing Protocol: Initiate controlled trials on a cellular sample to gauge the counter-agent's efficacy prior to testing on subjects. Conclusion: The substance responsible for age regression holds substantial complexity, blending artificial compounds with properties associated with temporal manipulation. Given appropriate lab resources, an antidote or counter-agent could be synthesized within an estimated timeframe of 2-4 weeks, pending trials. Recommended Actions: Secure lab access and begin the synthesis of a counter-agent under controlled conditions.

Thank you Carlos.

You might find it helpful to know they’re adults again, almost like it wore off?

[blorb-el]

trick or treat (your pick of fic)!

another lil writing exercise wip form the fic graveyard! clark has been whammied with some sort of... magically infused fear toxin? iirc? and has locked himself away about it :) i'd have to check the kryptahniuo here, wrote it a long time ago partially to practice using the language.

gen, 530ish words, no particular warnings, trinity

---

The earthquakes had been mild. They’d begun thirty-six hours after the dosing, and had tapered off five hours after that. The scientific community was still mystified at the small scale of the earthquakes and their origination point deep in the Arctic, far from usual tectonic plate activity.

Bruce enters the main hall, eyes darting around. There, between the states of Jor-El and Lara, lies Clark’s cape. It’s crumpled small, alone on the floor, brilliant splash of red against the serenity of blue and glowing white.

“Clark,” he calls out. No answer.

“Fortress. Show me where Kal-El is.”

The ever-shifting low chimes turn down in volume for a moment, then return, and a soft glow blooms under the floor leading to the main body of the building. Not Clark’s quarters.

He follows the path. Under the globe of Krypton, through the man rows of mostly-empty animal habitat. To the main laboratory, and there’s another arched doorway at the end that Bruce has never been through, that’s never been open while he’s been here. It’s closed now, and the cool refracted light plays across the door, then slowly fades like starlight.

Bruce knocks, mouth dry.

“Please hold,” the cool Fortress voice says. “Announcing your arrival.” And then the cool tones shift into the lilt of Kryptonian.

Bruce has, sometimes, in moments between stakeouts and shareholder meetings and chemical analysis, thought about learning Kryptonian. He’d been on his way to take over monitor duty from Diana, and she and Superman had been speaking. Clark had been leaning back, arms crossed behind his head. As he’d entered Diana had ventured a phrase and Clark had chuckled, low and, Bruce thinks, happy.

“.Ehrosh :bem khaehtiv :bysh,” he corrects. “m-bysh, with the stress on the back, just like khaehtiv.” He’d looked up then, smiled at seeing Bruce. Diana looks up as well, and smiles, and he nods at the two of them. “There, try it on Batman.”

“.Ehrosh mbem khaehtiv mbysh,” Diana says, a little slowly.

Bruce pauses for a moment, then echoes back. “.Ehrosh… mbem?,” He sees Clark’s lips part just a tiny bit in surprise before they curl up. “What’s the second part?”

“My friend,” Clark answers. After a half second he adds, almost tentatively. “But for you and I, we’d use the masculine - .khahtiv :bysh.”

Bruce forms the syllables in his mouth, cautious. “…khahtiv mbysh,” he says. And the little smile of Clark’s reaches to the corners of his eyes, something unnameable but warm there.

Around him the Fortress is murmuring words beyond what he can make out, but it repeats .nahn rrutiv :bysh .nahn rraop zhachahvymahehd .nahn rrutiv :bysh .nahn rraop suzehdh

It falls silent for a moment, and Bruce strains his ears though there’s nothing to hear. Moments pass, nothing but the soft shimmer of the crystals around him. Then, softer still, the Fortress AI speaks again.

.kaoshahrodh kahl-ehl

Shahr. Shahrrehth. He knows that word; the world knows the word, blazoned as its ideograph is in red and yellow across Clark’s chest. Bruce pockets the Kryptonite, lays his hand on the door. “Clark. Kal. I’m here to help you.”

And the door holds a moment longer, and then sinks into the floor.

---

rough translations...

.ehrosh :bem khahtiv :bysh - hello, my friend .nahn rrutiv :bysh - he is your friend .nahn rraop zhachahvymahehd - you are not alone .nahn rraop suzehdh - you belong here/you are at home .kaoshahrodh - you must continue to hope

(I think the possessives are off, i'm missing some particles, and i'm not sure -ehd is the right suffix to change chahvymah from an adverb to an adjective. but yeah. rough drafts!! fun fact i kind of want to get a tattoo of the symbols for the word kaoshahrodh.........)

[wolfliving]

Internet of forest things

(...)

Wallrabe and her fellow researchers are working on a range of devices that will be deployed from the ground to the treetops, transmitting data from Germany’s Black Forest to their labs at the University of Freiburg. At the same time, scientists and engineers at other companies are also focused on making their devices unobtrusive and, ultimately, self-sustaining.

Silvanet Wildfire Sensor

Time is of the essence when fighting forest fires. Sensors attached to trunks “smell” tell-tale gases like hydrogen and carbon monoxide, and alert firefighters within the first hour—before satellites or cameras can spot open flames. German startup Dryad Networks has built AI into its solar-powered sensors to ensure that they can distinguish between real fires and, say, passing diesel trucks.

Treevia

Digital dendrometers relieve foresters of tedious work. As trees grow, the elastic band wrapped around their trunk stretches and transmits data directly to a computer. The lightweight device from Brazilian startup Treevia can even be attached to saplings. It also contains a humidity and heat sensor, providing insights into climatic impacts on reforested areas.

The Guardian

What does it take to catch illegal loggers or poachers? A smartphone is a good start. Rainforest Connection’s recycled, solar-powered smartphone listens for the sound of chain saws or gunshots within a 1-mile radius. The recordings are transmitted to the cloud for analysis and alert local authorities in near real time. This device also provides insights into the distribution and calling behavior of animals.

BiodivX Drone

As animals move through trees, they shed DNA through feces, skin, and hair. This innovative drone collects what is known as environmental DNA (eDNA) from leaves and branches—with particles sticking to its adhesive strips. Scientists from Switzerland programmed the drone so it can navigate autonomously through dense forests and hover steadily around branches to take samples.

Leaf Sensor

Wallrabe and her team at the University of Freiburg have developed a glass capsule that measures gas exchange between a leaf and its surroundings. It can detect specific chemicals that trees emit under stress, for example, in the event of a drought, infestation, or disease. The capsule is transparent so that sunlight can reach the leaf without impairing its function.

Plant-e

When sunlight is limited, most devices are powered by batteries. Plant-e, a Dutch company spun out of Wageningen University, makes use of a natural process: Plants produce organic material through photosynthesis; some they use for growth, the rest ends up into the soil. Bacteria break down this material and release electrons that Plant-e uses to power its sensors....

[nuadox]

Astrophysicists may have solved the mystery of Uranus’s unusual radiation belts

- By Nuadox Crew -

The weak radiation belts around Uranus, observed by Voyager 2 nearly 50 years ago, may actually be due to changes in particle speed caused by the planet’s asymmetric magnetic field.

Uranus's magnetic field is tilted 60° from its spin axis, creating an unusual magnetic environment. 

Researchers, including Acevski et al., used new modeling incorporating a quadrupole field to simulate this asymmetry and found that particle speeds vary in different parts of their orbits.

This variation spreads particles out, decreasing their density by up to 20%, which could explain Voyager 2's observations. 

Although this does not completely account for the weaker radiation belts, it offers insights into Uranus's magnetic anomalies. NASA’s proposed mission to the ice giants may provide further data to understand these mechanisms.

Header image: The planet Uranus, depicted in this James Webb Space Telescope image, features a tilted magnetic field and unusual radiation belts. Future missions to this icy giant may uncover more details. Credit: NASA, ESA, CSA, STScI.

Read more at Eos

Scientific paper: M. Acevski et al, Asymmetry in Uranus' High Energy Proton Radiation Belt, Geophysical Research Letters (2024). DOI: 10.1029/2024GL108961

--

Other recent news

Google is investigating claims of AI-generated content scraping, which affects search result rankings.

Amazon is reviewing whether Perplexity AI improperly scraped online content.

A bone analysis provides new insights into the Denisovans, an ancient human species, and their survival in extreme environments.

[aihoshiino]

i got to watch the first episode for the new OnK Season early today, and since you're my go-to account for Oshi no Analysis and Professor Ai Hoshino���, I need you to know that it was absolutely amazing and I can't wait for you to watch it and give your opinions on it (especially the opening.).

RIPPING UP THE CARPET WITH MY TEETH AUAUUAUAUAAUUUUHHFHFGFGF

I'M GLAD YOU ENJOYED IT ANON BUT I'M EXPLODING INTO DUST AND PARTICLES!!!! I NEED TO SEE THIS EPISODE AUUUU

[kaibutsushidousha]

Could you do one of your name analysis Metas on ai Hoshinos name?

It's really not complicated, and the puns you see here are puns you find a lot elsewhere.

Hoshi is 星野. Means "star plains". But the 野(no) meaning "plains" is often used for its value as a generic name suffix (attach 野 to any word and it'll sound enough like a real name) that is pronounced the same as the grammar particle "of". This is meant to connect the first and last name as a single phrase.

Another pun layer comes with hoshii (欲しい; wanting) since Ai is a self-admitted greedy gal for pursuing happiness both as an idol and as a mother at the same time.

Ai uses its katakana reading, so it's not bound to any specific meaning. And the Japanese language has a lot of words pronounced Ai, in addition to the English words "I" and "eye". Check AI: The Somnium Files for a more complete list.

So your reading options for "hoshi no ai" are:

Love of the stars (星の愛）

Star eyes (星のアイ）

Grief of the stars (星の哀)

I want love (欲しいの、愛)

[mizukagami-takamagahara]

With Entropy Effect's Early Access version releasing in just a few hours, I wanted to go over some of what we've learned from trailers, reviews, and sneak peaks that hasn't come up while I was analyzing the character spotlights.

Of course, do take time to note that I am writing this before even the early access version has dropped. What I discuss here may prove inaccurate to the game we'll get tonight, or the full release we'll see in the future.

Let's just have a little fun with some last minute speculation and analysis!

The gameplay visuals that we've seen so far have looked absolutely gorgeous. I love the character models and the way that battles turn into a light show. I'm a very simple creature, I can always be pleased with some colored lights. The environments and battle effects also look great- this game has very strong art direction. I mean, look at this!

Which really surprised me, and still confuses me, when compared to the artwork they've chosen to use as the face of the game.

Yeah, unfortunately we're gonna start this post with a little negativity, but it should be the last of it, so please be patient.

Or feel free to skip this rant, if you'd like to keep your coverage positive, or just read about what we know about the gameplay itself.

But seriously. What is this key visual. This is just the Central Fiction character select artwork!

Okay, correction; it's the Central Fiction artwork for most of the characters. Poor Hibiki doesn't even get his character select artwork, he's left as the odd man out with his CF story mode battle sprite.

And then there's Es! What did they do to Es!? I don't know enough about graphic design to be able to tell what happened here, but something about her face looks like they tossed her art, and only hers, through some kind of AI filter. She's been weirdly smoothed out and she looks way paler, and more gray, than the rest of the line up. This straight up hurts to look at.

For the first few months after Entropy Effect's announcement, I thought the game was fanmade, because all the key visuals they were pumping out were (and, thus far, still are) made up of the basic official art that appears as the first images when you do a google search of the characters.

How did this get greenlit? They really couldn't afford to pay an artist? I can't even say "they couldn't afford to hire and artist" because, looking at the gameplay, clearly they did! They hired some very good artists! Why not write one more paycheck to get some cover art out of the incredible talent they already have on board?

Okay, okay, SaltFest is over. Coping and seething have been reigned in. Let's get into the fun stuff.

Trailers so far have indicated that the story of Entropy Effect is original, with no clear connection to that of the BlazBlue series- although it seems to share a significant amount of thematic elements with BlazBlue.

The story looks kind of cyberpunk, revolving around a world filled with these little robot guys. Our main character seems to be called "ACER," though whether that's their name, a descriptor, or a title, there's no way to tell- at least, for a few more hours. Sources claim that, similar to BlazBlue's story, there are themes of a looping existence, looming destruction, and fighting to ensure the very existence of a future.

Something that stood out to me in trailers, but I haven't seen spoken about anywhere, are these things called "Phenomena Fragments." They seem to showcase scenes from the lives of human figures in this gray dust-like particle effect.

If I had to make any guesses, I'd say this looks like a post-humanity story, with our ACER using the BlazBlue cast within some kind of simulation.

One thing we do know is that the word 'Entropy' in the title will be integral to the plot, as some cutscene dialogue warns of an "Entropy Shoreline" that may threaten to bring an end to the world our protagonist lives in. The robotic inhabitants of this world also seem to have some kind of god figure, though not all of them believe in it.

Those who have played the game say that Entropy works as a mechanic in the game that increases the difficulty of the run as it builds. I'm interested to see how it plays into the story...!

The full truth of the story will remain a mystery, at least for another hour, but all reviews that have spoken of it so far have been very positive.

Now let's move on to take a look at some of the gameplay!

I saw this screen a while back, with what looks to be 30 available places for characters. At first, I assumed this meant that the game would have 30 different playable characters- I mistook this for a character select screen. Keyword being mistook.

Instead, it looks like the 30 slots on this screen are to save up to 30 builds from previous runs. We see the same screen again here, with two different versions of Lambda saved on it, and the other characters in different positions than the one above. As I'll discuss later in the post, previous runs of the game will be used to pass on certain skills to your next character at the start of a new run. So this screen will probably end up looking different for all players!

The real character selection screen loops, so there's no way to know how many characters will appear in the final version. However, the company has confirmed that there will be more than the seven we'll see in the Early Access version.

The seven characters playable in early access Entropy Effect are Ragna, Noel, Hakumen, Lambda, Kokonoe, Hibiki, and Mai. The games animated trailer (which also looks great, I don't know what happened with the damn key visual) also shows us Jin and Taokaka, who will likely be made playable with the full version of the game. Considering Naoto is the face of the game's Twitter and YouTube accounts, I expect to see him in the final version as well.

As for characters appearing as enemies, Bang's NPC subordinates from BlazBlue's story mode have been shown to appear as generic enemies in certain stages, and both Arakune and Susano'o have appeared as bosses!

After selecting which character you want to play as, you're taken to the screen I mistook for character selection, where you can select two characters you've played as in previous runs, called "Evotypes" in the game. You can have your new character inherit abilities from them- the number of abilities you can inherit from your chosen characters seems to depend on how well you've been doing in your runs, as each run earns some kind of grade. This explains how we've been seeing characters using one another's abilities, such as Hibiki using Hakumen's lightning, in the trailers!

While playing, you earn points called "Potential," which can be used between stages to purchase new abilities. This seems to be where characters unlock their movesets from the BlazBlue fighting games.

There's also a second type of choice players can make as they develop their run. These are called "Tactics." Unlike the abilities unlocked by Potential that seem to be exclusive to characters unless chosen as inherited Evotypes, Tactics are universal and can be purchased for and used by any character.

In a video on his experience playing the game early, Twitch streamer Veedotme says the game rewards you for selecting Tactics you haven't used before, so consider trying out everything before you start picking favorites! Veedotme's review also clarified a lot of what I comment on in this post, so please check out his work!

Interviews claim that the game's early access version will have roughly 100 Potentials and roughly 200 Tactics to choose from, though there could be even more in the final version of the game.

It seems that the early access version will include 10 different stages, with 13 levels each, along with a few other modes of gameplay such as a challenge mode were the stage bosses become even more difficult. I've mentioned it already in other posts, but I like this 10 and 13 number theming, as both numbers have been pretty important to the BlazBlue franchise. It's another sign of serious attention to detail from the developers.

And, well, if I want to get this post out in time to be able to watch some of the first streams go live, I'll have to wrap it up now. Thank you for reading, and if you're interested, please share your own experience with the game in the coming days!

[thesummoningdark]

One thing machine learning is genuinely really good at is pattern recognition.

The basic example we used when studying neural networks is: you feed your model photos of oranges and bananas. You set up the image recognition so that it can identify the fruit as the area of interest in the photo. Then you go through your data and tag each photo in the training dataset with whether it's of an orange or a banana.

If you've set the network up correctly, once trained, it will be able to look at a new image and decide whether it's an orange or a banana, using factors like "are the pixels in the area of interest more orange or more yellow" and "is the height of the area of interest similar to the width, or is it more elongated".

You may already have identified the problem with this, which is that if you then show the network a picture of an apple, it's going to get confused as hell. It hasn't been told how to deal with that. Maybe it'll decide this new image is an orange, since it's more round than elongated. If it's a granny smith, maybe it'll decide it must be a banana, since it's learned that bananas can be green-ish. If you want your network to be able to identify apples too, you need to go back and train it again on data that includes apples.

But for applications like sifting through data from particle accelerators? It's perfect. You can feed it old data that's already been manually analysed, and it'll pick out any pattern in the new data that looks like an event of interest. You can set it up to highlight any event it can't identify to be reviewed by a human. And as pointed out above, it can do this in a fraction of the time it would take a person to review the data, and with more flexibility than a traditional analysis program with hard-coded parameters.

Generative AI is a whole other beast, but analytical AI? It's been in use for decades. Crucially, by researchers who understand how it works, and understand its limitatations.

(Source)

[yadavprince]

Maximizing Wind Energy Efficiency: Wind Turbine Blade Maintenance with Equinox’s Drones

As the demand for renewable energy continues to rise, wind turbines are becoming an increasingly vital source of clean power. However, like any complex machinery, wind turbines require regular maintenance to ensure optimal performance. Among the most critical components to maintain are the blades, which bear the brunt of environmental forces and are crucial to energy output. Equinox’s Drones offers a cutting-edge solution to wind turbine blade maintenance, combining aerial technology with precision inspections for faster, safer, and more efficient operations.

Why Blade Maintenance Matters

Wind turbine blades face constant exposure to harsh conditions—UV radiation, wind, rain, ice, and airborne particles—all of which can cause wear and damage over time. Common issues include leading edge erosion, lightning strikes, cracks, and delamination. If left unaddressed, these problems can lead to reduced turbine efficiency, costly repairs, or even catastrophic failure.

Timely and accurate blade inspections are essential for identifying these issues early. Traditional inspection methods involve rope access technicians climbing towers—a risky, time-consuming, and expensive process. That’s where Equinox’s Drones steps in.

Drone Technology: Transforming Blade Inspections

Equinox’s Drones leverages advanced UAV (Unmanned Aerial Vehicle) technology to conduct comprehensive wind turbine blade inspections. Equipped with high-resolution cameras, thermal imaging, and AI-powered analytics, our drones can detect even the smallest surface imperfections without interrupting turbine operations.

Unlike manual inspections, drone inspections are:

Faster – Reduce downtime and inspection time by up to 70%.

Safer – Eliminate the need for human technicians to work at dangerous heights.

Cost-Effective – Lower labor costs and minimize turbine stoppage.

Equinox’s Drones: Precision, Reliability, Results

At Equinox’s Drones, we understand the unique challenges of the renewable energy industry. Our drone inspection services are tailored to deliver high-precision data that helps wind farm operators make informed maintenance decisions. Our process includes:

Pre-Flight Planning – Site assessment and automated flight path programming for consistent data capture.

Flight Execution – Autonomous drone missions capturing detailed imagery and thermal data of each blade.

Data Analysis – AI-assisted software identifies defects, categorizes damage, and generates actionable reports.

Maintenance Recommendations – Partnering with your maintenance team to prioritize repairs and schedule proactive interventions.

Future-Proofing Wind Energy

Investing in drone-based blade maintenance is not just a smart choice—it's a strategic one. As wind farms scale up and turbines grow in size, manual inspections will only become more difficult and expensive. Drone inspections by Equinox’s Drones offer the scalability and adaptability needed for modern wind energy operations.

By integrating our drone technology into your maintenance workflow, you can extend turbine lifespan, boost performance, and reduce long-term costs—while ensuring safety and compliance.

Conclusion

Wind turbine blade maintenance is critical to the success of sustainable energy systems. With Equinox’s Drones, you gain a reliable partner equipped with advanced tools and expertise to keep your turbines turning. Let us help you power the future with precision and efficiency. Contact Equinox’s Drones today to schedule your next blade inspection.

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CMP Slurry Market  : Size, Trends, and Growth Analysis 2032

The CMP Slurry Market was valued at US$ 1,998.32 million in 2024 and is expected to grow at a CAGR of 6.90% from 2025 to 2032. This growth reflects the rising demand for advanced semiconductor devices and the crucial role that CMP slurry plays in their fabrication. Chemical Mechanical Planarization (CMP) slurry is a key consumable material used in the semiconductor manufacturing process, ensuring a smooth, flat wafer surface essential for precise layer stacking and the reliable performance of modern integrated circuits.

What Is CMP Slurry and Why Is It Important?

CMP slurry is a chemically reactive, abrasive liquid used during the CMP process to planarize or flatten layers on semiconductor wafers. It typically comprises a mix of abrasive particles—commonly silica (SiO₂) or alumina (Al₂O₃)—dispersed in a chemically active solution. This solution helps selectively remove excess materials from the wafer surface while minimizing surface defects.

The CMP process is indispensable for fabricating advanced integrated circuits (ICs), including logic chips, memory devices, and 3D-stacked architectures. Any uneven surface at a nanometer scale can compromise the performance, yield, and reliability of semiconductor devices. CMP slurry ensures uniformity across each wafer layer, enabling high-resolution photolithography and defect-free deposition in subsequent steps.

Key Market Drivers

1. Rapid Advancement in Semiconductor Node Shrinkage As chipmakers move toward smaller process nodes—such as 5nm, 3nm, and beyond—precision planarization becomes more critical. CMP slurry must deliver ultra-fine abrasive performance to meet the stringent requirements of nanoscale fabrication while avoiding dishing and erosion of patterns.

2. Proliferation of Advanced Packaging Techniques Technologies such as 3D ICs, chiplet integration, and through-silicon vias (TSVs) rely heavily on planar surfaces. CMP slurry plays an essential role in preparing surfaces for bonding, stacking, and encapsulation, supporting high-density device integration.

3. Growth in Demand for High-Performance Computing and AI Chips The explosion of data from artificial intelligence, cloud computing, and edge devices is pushing chipmakers to design increasingly complex architectures. CMP slurry supports these developments by enabling the fabrication of multi-layer interconnects and ultra-flat surfaces required for reliable signal transmission and reduced power consumption.

4. Expanding Foundry and Fab Capacity Global investment in semiconductor foundries, particularly in Asia-Pacific and North America, is accelerating. Countries like South Korea, Taiwan, the U.S., and China are increasing their wafer fabrication capacities to strengthen supply chains, boosting the demand for CMP consumables, including slurry.

5. Shift Toward Eco-Friendly and Low-Defect Slurries Sustainability concerns and stricter environmental regulations are prompting slurry manufacturers to innovate. New formulations with reduced metal content, biodegradable surfactants, and lower environmental impact are gaining traction, offering better performance with fewer residues.

Applications of CMP Slurry

CMP slurry is used across several critical processes in semiconductor manufacturing:

Oxide CMP: For planarizing silicon oxide layers in dielectric structures.

Metal CMP: For metals like copper, tungsten, and tantalum in interconnect layers.

Poly-Si CMP: Used in transistor gate formation.

Shallow Trench Isolation (STI): To isolate devices on a silicon wafer.

Each application demands different slurry compositions, pH levels, particle sizes, and chemical reactivity, making slurry customization a vital part of the supply chain.

Competitive Landscape: Major Market Players

Several companies dominate the global CMP slurry market through technological leadership, broad product portfolios, and strategic collaborations with semiconductor fabs:

Air Products/Versum Materials A key supplier of electronic materials, the company focuses on high-performance CMP slurries tailored for metal and dielectric polishing. Their formulations support complex node transitions and advanced memory technologies.

Saint-Gobain Known for its abrasives expertise, Saint-Gobain supplies advanced slurry solutions engineered for high removal rates and low defectivity. They serve both front-end and back-end semiconductor applications.

Asahi Glass (AGC Inc.) This Japanese materials leader offers CMP slurries with specialized chemical control, targeting both logic and memory wafer processes. AGC emphasizes high throughput and defect reduction in its product development.

Ace Nanochem A fast-growing player focusing on advanced oxide and metal CMP slurry formulations. Ace Nanochem is recognized for its innovations in nano-abrasive dispersion and defect control in high-k/metal gate structures.

Cabot Microelectronics (Entegris) One of the largest and most established names in CMP slurry, Cabot provides customized solutions across oxide, copper, tungsten, and barrier layer polishing. The company merged with Entegris to expand its global reach and technological capabilities.

Fujimi Incorporated A pioneer in precision abrasives, Fujimi specializes in ultra-pure slurries for advanced semiconductor manufacturing. Their products are known for consistent particle size distribution and strong global supply chain integration.

Regional Insights

Asia-Pacific holds the largest market share due to the concentration of leading semiconductor manufacturers in countries such as Taiwan, South Korea, China, and Japan. Companies like TSMC, Samsung, and SK Hynix drive the demand for CMP slurry in large volumes.

North America, led by U.S.-based fabs and investments in chip production under the CHIPS Act, is a growing region for CMP slurry demand, especially with Intel and GlobalFoundries expanding operations.

Europe is gradually catching up with investments in domestic chip manufacturing, creating new demand opportunities for CMP consumables in the region.

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📶 IoT Telecom-Services Market Size, Share & Growth Analysis 2034: The Smart Network Boom

IoT Telecom Services Market is witnessing remarkable growth, with projections estimating a leap from $22.3 billion in 2024 to $97.8 billion by 2034, at an impressive CAGR of 15.9%. This market is central to enabling communication between billions of interconnected devices that form the backbone of smart homes, connected cars, industrial automation, and smart cities. By providing essential connectivity services, network management, data analysis, and security solutions, IoT telecom services are transforming how industries operate, facilitating innovation and enhanced efficiency in the digital era.

As IoT ecosystems continue to scale, telecom providers are evolving from traditional connectivity suppliers to strategic enablers of integrated, intelligent solutions. This shift is being driven by the demand for real-time monitoring, data-driven decision-making, and scalable communication infrastructure, which are vital to the functioning of complex IoT networks.

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Market Dynamics

The rapid adoption of 5G technology is revolutionizing the IoT telecom services landscape. Enhanced bandwidth and ultra-low latency are paving the way for applications like autonomous vehicles, remote healthcare, and industrial IoT. The growing focus on edge computing is another pivotal trend, enabling faster data processing and reducing strain on centralized servers.

However, with growth come challenges. Concerns about data privacy, interoperability, and network vulnerabilities are significant hurdles. Additionally, integration with legacy infrastructure and a shortage of skilled professionals are impeding seamless deployment in certain regions. Despite these barriers, flexible pricing models like pay-as-you-go and cloud-based deployment options are making services more accessible to enterprises of all sizes.

Key Players Analysis

The competitive landscape of the IoT Telecom Services Market is composed of a blend of established telecom giants and innovative startups. Leading companies like Tata Communications, Thales Group, KORE Wireless, Telit, Sierra Wireless, and u-blox are at the forefront, offering robust connectivity and device management solutions.

Emerging players such as Nexiot, Soracom, and Particle are disrupting the market with agile platforms focused on edge processing, lightweight protocols, and affordable deployment. Companies that prioritize cybersecurity, seamless device integration, and customer-centric solutions are expected to gain a competitive edge in this evolving space.

Regional Analysis

Asia-Pacific currently leads the IoT Telecom Services Market, driven by robust technological adoption in countries like China, Japan, and South Korea. Government-led initiatives, such as smart cities and Industry 4.0 programs, have significantly fueled the demand for IoT-based services.

North America, particularly the U.S. and Canada, follows closely behind due to mature digital infrastructure and high investment in emerging technologies. Europe is seeing rapid growth in markets such as Germany, the U.K., and France, which are focusing on sustainability and smart mobility.

Regions like Latin America, the Middle East, and Africa are emerging as potential hotspots, bolstered by growing urbanization and government incentives for digital transformation. Countries like Brazil, UAE, and Saudi Arabia are ramping up efforts to implement smart city and e-governance initiatives.

Recent News & Developments

Recent developments in the IoT telecom services space include the global rollout of 5G, which has dramatically improved service delivery capabilities. Companies are also increasingly adopting AI-powered analytics to enhance device performance and customer engagement.

Additionally, regulatory bodies are moving toward standardizing communication protocols and data privacy policies. This regulatory momentum is expected to ensure smoother interoperability and better security compliance across devices and platforms.

Cybersecurity continues to be a focal point, with telecom providers investing in advanced encryption and authentication mechanisms. Subscription-based models and customizable service tiers are being introduced to cater to businesses with varied IoT needs, making services more scalable and flexible.

Browse Full Report : https://www.globalinsightservices.com/reports/iot-telecom-services-market/

Scope of the Report

This report covers an in-depth analysis of the IoT Telecom Services Market from 2018 to 2034, including historical trends, current dynamics, and future projections. It delves into market segmentation by type, product, services, technology, application, and region, offering a comprehensive view of each segment’s performance and potential.

The study evaluates key market drivers, emerging trends, restraints, and opportunities, supported by detailed competitive profiling of both established and emerging players. It also includes regulatory overviews, SWOT analysis, and value-chain assessments to help stakeholders formulate informed business strategies.

As digital transformation accelerates across industries, the IoT Telecom Services Market stands at the forefront of enabling smarter, more connected ecosystems. Businesses that align with market trends and invest in resilient, secure, and scalable IoT solutions will be best positioned to lead in this evolving digital landscape.

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