#automated process discovery

[techtoio]

How Big Data Analytics is Changing Scientific Discoveries

Introduction

In the contemporary world of the prevailing sciences and technologies, big data analytics becomes a powerful agent in such a way that scientific discoveries are being orchestrated. At Techtovio, we explore this renewed approach to reshaping research methodologies for better data interpretation and new insights into its hastening process. Read to continue

[hellokrutz-blog]

Process Discovery

Every Automation development has a lifecycle that begins with the Process Discovery phase and ends with the release and support phase. In which the design, development, testing, deployment, and maintenance phase happens in between.What makes the end product shine? It’s not only the release phase,but the phases that come before releasing the product are equally important. As Gordon B Hinckley…

[materialsscienceandengineering]

Self-driving lab transforms electronic polymers discovery

Plastic that conducts electricity might sound impossible. But there is a special class of materials known as "electronic polymers" that combines the flexibility of plastic with the functionality of metal. This type of material opens the door for breakthroughs in wearable devices, printable electronics and advanced energy storage systems. Yet, making thin films from electronic polymers has always been a difficult task. It takes a lot of fine-tuning to achieve the right balance of physical and electronic properties. Researchers at the U.S. Department of Energy's (DOE) Argonne National Laboratory have created an innovative solution to this challenge with artificial intelligence (AI). They used an AI-driven, automated materials laboratory, a tool called Polybot, to explore processing methods and produce high-quality films. Polybot is located at the Center for Nanoscale Materials, a DOE Office of Science user facility at Argonne.

Read more.

[yupuffin]

I don't disagree with the take that "GenAI bros are just lazy," but I've also read and agreed with the take that laziness is a nonexistent (and lowkey ableist) concept. So I think there's a little more depth and nuance to the sentiment than mere "laziness."

Put succinctly, I think GenAI users erroneously devalue the process of making art relative to the product. This is closely associated with the arbitrary assignment of value to an artistic product based on the perceived skill level required to create it. (i.e. the belief in existence of "good" and "bad" art)

The motivations I've heard from people who use what's commonly called "generative AI" (hereafter "GenAI users") can indeed be summarized as that it's "easier" or that it allows them to create something that they otherwise do not have the skill or the means to create. Basically, they think that using GenAI will allow them to create something "better" with relatively less personal commitment than, well, creating it themselves. Effectively, they want "instant gratification."

While I sympathize with this sort of rationale, I also think it reflects a fundamental misunderstanding of creativity as a concept. When it comes to artistic creation -- or really, any activity that necessitates the development of a skill -- the product is never the only relevant aspect; the creative process, while often less publicly visible than the product, can be at least as meaningful, if not more.

Hence, my tentative hypothesis is that the motivation to use GenAI, specifically to produce an equivalent of visual art, is potentially derived from specific misconceptions about the creative process. Below, I have deconstructed said motivation into several potential misconceptions and included corresponding analysis disproving them. My goal is to further elucidate the purported "laziness" behind GenAI usage and illustrate how genuine artistic creation, while potentially more difficult, is certainly more productive and rewarding.

Myth: The work you put into making art is a "struggle" you have to survive. Fact: I'm putting this misconception first, because it is perhaps both the most common and the most essential. I've seen this sentiment inadvertently and widely perpetuated even by the most staunchly anti-AI artists, in the form of memes such as "In order to get this [highly refined art], I had to survive this [art I made much longer ago that is hence implied to be intrinsically worse]." Frankly, this is also incredibly insulting and discouraging to newer artists, or, really, anyone perceived to have a lower skill level regardless of how "new" they are.

The reality is that the creative process should, overall, be enjoyable. That's where the desire to create comes from. As with the development of any skill, some aspects of the creative journey will inevitably be less enjoyable than others, hence the perceived difficulty; however, in order to be both productive and genuine, the enjoyable aspects of the process should make the "struggle" worth it. I believe that determining how best to enjoy the creative process requires continuous and deliberate self-reflection by the artist -- which can, indeed, be a rewarding journey of self-discovery.

Which leads me to the next point:

Myth: The fear of making "bad" art. Fact: Every artist will sometimes create art with which they are deeply unsatisfied. When people ask me how to deal with artistic disappointment, I remind them first that "good" is highly subjective, and second, that in order to make "good" art, one must also inevitably sometimes make "bad" art. It's okay to give yourself space to feel disappointed in your art, as long as you likewise allow yourself to celebrate when you create something you're really happy with.

Consider: GenAI can also provide an unsatisfactory product. Though GenAI is commonly treated as a "shortcut" to "good" art, it is not actually so infallible. Because the process is highly automated and, likewise, involves so little human input, getting a product that's sufficiently to your liking can be a genuine headache.

Effectively, you will make "bad" art regardless of if you are using GenAI or your own skills.

If the product and process are both your own, the process can be deliberately fine-tuned and optimized, and then applied to your future works to continue your artistic journey. Again, it's work, but it's rewarding work! (You may start to notice a theme here...)

Myth: Creating "good" art is expensive. Fact: Expensive materials and tools certainly have their place, but an infinite number of possible artistic journeys can be made without them. In fact, it's often unnecessary or even counterproductive to spend copious amounts of money on highly specialized tools if you don't have the training or skillset to know how to use them.

I'm sure many artists out there will be willing to demonstrate the possibilities behind even the most basic and accessible tools; in this case, I'll provide a personal anecdote to illustrate: As an adolescent, I used to use professional-grade, highly specialized Prismacolor pencils. Now, as an adult, despite having over twice as many years of artistic experience, my go-to traditional art tool is a tiny box of "school-grade" colored pencils. I'm happier with the results at a tiny fraction of the cost, because I realized that having limited color options and more generic applicability works better with my thinking, drawing and coloring process.

When it comes to classes and lessons, which can likewise be expensive and hence inaccessible... While they can serve specific artistic goals depending on what you want to do, the number of "self-taught" artists out there proves that these are far from obligatory. Plus, right now, there's more knowledge out in the world for free than there has been ever before. (If you're reading this soon after I posted it, I just reblogged a post full of visual art resources 😆)

Myth: I'm not creative or imaginative enough to make my own art. Fact: Creativity and imagination are also skills that can be learned and cultivated; as with any other skill, this requires practice. As expanded upon above, skill cultivation is certainly work, but it can be intrinsically rewarding when done right. This is scientifically proven: learning and accomplishing things, as happens when one develops a skill, releases the neurotransmitter serotonin, which is directly associated with mood stabilization and feelings of happiness and contentment. Likewise, having pursued multiple forms of creativity over the course of my life, I strongly believe that enjoying the creative process, whatever that means for you personally, is the most crucial factor for cultivating a skill. Maximizing enjoyment likewise maximizes the amount of time you can put into a skill, and, as a general trend, invested time is directly proportional to skill improvement.

GenAI usage denies the user this opportunity to develop their creative skills, as it prioritizes the artistic product at the expense of the process. Because it's arguably easier, it likewise lacks that intrinsic reward.

This can be associated with the point above about making "bad" art; art that is mundane or unoriginal is often arbitrarily called "bad." In reality, each person's creative endeavors, regardless of their perceived skill level, are a unique synthesis of their individual lived experiences. Compare this to GenAI, the output of which is based solely on what it most statistically probable, and is hence quite literally unoriginal.

Honestly, as an artist, I recommend making art yourself in part because the rush of happy chemicals you get, either when someone compliments it or when you're happy with it yourself -- because YOU MADE THAT! THAT'S WORK YOU PUT IN! -- is so worth it. And for that to happen, you have to give yourself the space to make art you're disappointed with and art you're happy with.

As a disabled artist myself, I think it's also worth noting that there may be some very specific forms of art that are less accessible to you, but I also think, from my experience, that for each of these forms, there are countless possibilities and approaches for utilizing the same medium or general type of art that can still be just as fulfilling.

How do I start? A crash course...

To make "good" art, you must first make art. Pick up a pencil and some paper. Doesn't have to be a specific kind -- it can be generic and inexpensive. Now, draw anything. Draw something you want to draw. It can be super simple. What do you like about it? What do you not like about it? Draw it again, making changes accordingly. If you can't think of anything in particular to change, just keep drawing what makes you feel fulfilled. Repeat! If you ever find yourself demotivated out of self-criticality, try focusing more on what you do like -- "playing to your strengths" is just as worthy a pursuit as "compensating for your weaknesses."

When people tell me they use GenAI because it's "easier," I think back to a conversation my coworker talked about having with her significant other. Her significant other would ask her to cook, because she was so much better at it, so wasn't it a waste of time for him to do it? A little bit under the influence of THC (as she claims), she took a breath, and asked him:

"How are you ever going to get better if you don't try?"

[mindblowingscience]

A fully automated process, including a brand-new artificial intelligence (AI) tool, has successfully detected, identified and classified its first supernova. Developed by an international collaboration led by Northwestern University, the new system automates the entire search for new supernovae across the night sky—effectively removing humans from the process. Not only does this rapidly accelerate the process of analyzing and classifying new supernova candidates, it also bypasses human error. The team alerted the astronomical community to the launch and success of the new tool, called the Bright Transient Survey Bot (BTSbot), this week. In the past six years, humans have spent an estimated total of 2,200 hours visually inspecting and classifying supernova candidates. With the new tool now officially online, researchers can redirect this precious time toward other responsibilities in order to accelerate the pace of discovery.

Continue Reading.

[elbiotipo]

Notes on technology in Campoestela:

Most spaceships are single-stage-to-orbit. They have rather standard jet engines to lift off from the ground like a standard plane.

To get into orbit, they use a rocket engine that uses a solid fuel made of a HIGHLY combustible (yet stable) carbon-nitrogen compound which allows a better fuel than anything previous. This was first discovered by Iranian scientists who named it "Nafta".

(sí, Beto tiene que estacionar su camión espacial para cargar nafta)

Nafta was a big discovery on its time, allowing cheap SSTO rockets. Nowadays it's produced in many worlds and widely available. It also has uses as weaponry, but it's not that efficient.

Nafta is used for lift-off and orbital burns. For manuevering in space, there are small jets on the nose and tail of spaceships, similar to the Space Shuttle.

Spaceship piloting is still not an easy task, but it's comparable to being a jet pilot, about 4 or 5 years to master. Hard, but something on the reach of many people. People from the generation ship clans are a bit more used to it and often represent an outsized part of space pilots, but there's always many wellers (from down the gravity well) who get their licenses too.

The hardest thing is always landing. Especially given all the different gravities, atmospheres, orbits and such you have to learn in each different case, even with all the automation in the world. Many spacers feel confident sticking to one or at most two or three planets they know.

Pilots that only do shuttle or cargo runs in the same star system or planet are called "Starters", because they go around the same star. It's rude, but many spacers do it.

FTL travel is another thing. FTL travel is done using a ring-like structure that projects a bubble around the ship and takes it to a (completely made-up for the setting) dimension called the Aether. The Aether is one of the meta-dimensions (there might be more) that uphold reality. Conveniently, you can use it as a shortcut to travel between stars, which project "shadows" on the Aether.

The Aether has its own navigation, with currents and whirpools and areas of thick dark matter (which, for cinematic purposes, actually look like bright nebulae) There are routes that are easier to travel and navigate, and these are where the most visited worlds are. Even stars that are close in real space might be very hard to get in Aetheric space, so there's routes that can take you all over the galaxy in a week, while many other places are out of reach.

Navigating the Aether is very similar to flying a plane through a cloudy sky. Some spacer says it's even easier than flying in real space.

Staying on the aether depends on how much you can keep the fields upholding your "bubble". This depends on the energy of your ship. Big ships can travel all over the galaxy but they have enormous energy consumption requirements.

Smaller ships (such as Beto's Mastropiero) dock with a ring-like structure that allows them to make short jumps. The average jump in an explored route is about 12-48 hours, so it's much like aircraft flights.

Exploring new aetheric routes is something that is very romanticized but in reality is a tedious process of jumping, cataloguing new systems (many of them empty and useful only as refuelling stations), seeing where the streams go and end, how they change, and more.

There is no FTL radio or live communication. There is a kind of aetheric radar that allows you to see incoming ships and do some morse-like communication, but it's not very efficient, there is no such thing as a galactic internet (though it's said ancient civilizations had one)

Aether travel engines require very sophisticated manufacturing and materials, which were hard for humans to develop. This was long only in the hands of governments and corporations, but after the Machine War, accessible aether starships hit the civilian market.

Smaller ships are still used by governments (more like loose "leagues") to do what big ships can't: supply satellites and equipment to remote bases, small-scale transport of engineers, researchers, aether "meteorology" and exploration, etc. This is very much like bush planes in remote regions or the role of Aeroflot in developing the USSR.

While humans in the setting, like most species, are composed of many different leagues, cultures and organizations, their technology is remarkably consistent. This is because cheap and reliable spaceflight depends on very reliable standarization. Some of the spaceship parts used six centuries after Gagarin are still the same used in the Soyuz. The ISO is perhaps one of the most enduring legacies of human civilization, along with FIFA.

[silkwhim]

Kopos Kos 🪿 for your travels

(wip content)

Kopos Kos, a proud and quickly developing nation located across what we know as Vietnam and Cambodia. The current emblem is definitely not my proudest work😓

The bird featured is a great cormorant, domesticated by our sophont wallabies (now called Walaba), used to aid in fishing to supply the Kepa-ka-o-o populations which base a lot of seafood in their diet as opposed to Walaba whom are completely herbivorous. The great cormorant represents initiative, resourcefulness and union between peoples, and is worn as a symbol of patriotism in Kopos Kos.

Kopos Kos was originally established as a farming colony, taking advantage of tropical soils and the abundance of wildlife but quickly established itself as a trade center, bringing in many Lau merchants and immigrants who began making their own settlements. The name Kopos Kos originally referred to a single town, though it is now a giant web of interconnected cities and villages across large stretches of land. By human terms it would be a country, however this concept doesn't yet exist on Beatha.

Because Walaba are herbivorous, livestock is generally uneeded in terms of meat, however domestic animals remain a common sight. The water pig, the equivalent of a Central Asian boar, is a new discovery that has been quickly integrated into agricultural life.

While many wild boars are aggressive when threatened or nervous, this domestic species tends to be docile and flees rather than fights if it comes down to it. Water pigs live in forests and jungles, preferring darker environments. Because of their excellent sense of smell, they are particularly good at locating food and water sources in areas that would be almost impossible to navigate through if you were unfamiliar with them, proving helpful to early settlers who often found the pigs near streams which lent them their name. Coincidentally, it was later discovered they have an excellent swimming ability. In Kopos Kos, water pigs are kept for their manure which is an incredibly efficient fertiliser for crops. The pigs also dig up root vegetables, and can be trained not to eat the roots and allow an owner to collect them instead. This process also aerates soil, which means automated tilling of small fields. Of course, even if they're not kept for meat, water pigs will eventually die. The skin can be harvested for leather, bones for jewellery and tools, hair for clothing and paintbrushes, etc, while the meat is sold off to Kepa-ka-o-o and Lau or used as food for meat eating domestic animals.

[apexbyte]

What is artificial intelligence (AI)?

Imagine asking Siri about the weather, receiving a personalized Netflix recommendation, or unlocking your phone with facial recognition. These everyday conveniences are powered by Artificial Intelligence (AI), a transformative technology reshaping our world. This post delves into AI, exploring its definition, history, mechanisms, applications, ethical dilemmas, and future potential.

What is Artificial Intelligence? Definition: AI refers to machines or software designed to mimic human intelligence, performing tasks like learning, problem-solving, and decision-making. Unlike basic automation, AI adapts and improves through experience.

Brief History:

1950: Alan Turing proposes the Turing Test, questioning if machines can think.

1956: The Dartmouth Conference coins the term "Artificial Intelligence," sparking early optimism.

1970s–80s: "AI winters" due to unmet expectations, followed by resurgence in the 2000s with advances in computing and data availability.

21st Century: Breakthroughs in machine learning and neural networks drive AI into mainstream use.

How Does AI Work? AI systems process vast data to identify patterns and make decisions. Key components include:

Machine Learning (ML): A subset where algorithms learn from data.

Supervised Learning: Uses labeled data (e.g., spam detection).

Unsupervised Learning: Finds patterns in unlabeled data (e.g., customer segmentation).

Reinforcement Learning: Learns via trial and error (e.g., AlphaGo).

Neural Networks & Deep Learning: Inspired by the human brain, these layered algorithms excel in tasks like image recognition.

Big Data & GPUs: Massive datasets and powerful processors enable training complex models.

Types of AI

Narrow AI: Specialized in one task (e.g., Alexa, chess engines).

General AI: Hypothetical, human-like adaptability (not yet realized).

Superintelligence: A speculative future AI surpassing human intellect.

Other Classifications:

Reactive Machines: Respond to inputs without memory (e.g., IBM’s Deep Blue).

Limited Memory: Uses past data (e.g., self-driving cars).

Theory of Mind: Understands emotions (in research).

Self-Aware: Conscious AI (purely theoretical).

Applications of AI

Healthcare: Diagnosing diseases via imaging, accelerating drug discovery.

Finance: Detecting fraud, algorithmic trading, and robo-advisors.

Retail: Personalized recommendations, inventory management.

Manufacturing: Predictive maintenance using IoT sensors.

Entertainment: AI-generated music, art, and deepfake technology.

Autonomous Systems: Self-driving cars (Tesla, Waymo), delivery drones.

Ethical Considerations

Bias & Fairness: Biased training data can lead to discriminatory outcomes (e.g., facial recognition errors in darker skin tones).

Privacy: Concerns over data collection by smart devices and surveillance systems.

Job Displacement: Automation risks certain roles but may create new industries.

Accountability: Determining liability for AI errors (e.g., autonomous vehicle accidents).

The Future of AI

Integration: Smarter personal assistants, seamless human-AI collaboration.

Advancements: Improved natural language processing (e.g., ChatGPT), climate change solutions (optimizing energy grids).

Regulation: Growing need for ethical guidelines and governance frameworks.

Conclusion AI holds immense potential to revolutionize industries, enhance efficiency, and solve global challenges. However, balancing innovation with ethical stewardship is crucial. By fostering responsible development, society can harness AI’s benefits while mitigating risks.

[socksbro1]

Date: march 14th, 2025

Discovery

I decided that while Remington-Gede is working on his memoir, I’ll work on Task 1. By examining the help file for the default cube, I figured out that there’s an automated system that is in charge of spawning and despawning them. It seems to rely on new interactions.

Essentially, as the simulation runs, new events occur that never occurred before. When that happens, two things are activated. One spawns a cube, and the other, I am unsure of. When the process is finished, the cube is supposed to despawn. Something seems to be preventing that with the one I use, but that’s perfectly acceptable for my purposes.

I decided to try querying the second process from the cubes DataFile. I got a type 4 error accidentally, then an unrecognized error that I’m now calling a type 5, which I’m pretty sure is calling a function without the necessary input values.

I looked at its help page, and it seems that it requires a new data type I’m calling a Soft Error Trigger. A soft error, as I’m calling it, is when the simulation is required to render the result of an interaction that has no recorded result. A Soft Error Trigger is a compressed form of that interaction, removing all extraneous data values. It seems to be able to compress any data type I discovered.

I was able to artificially generate a Soft Error Trigger by utilizing an interesting quirk I discovered in the help files. A Soft Error Trigger always follows a distinct format, starting with “Te4DTe” and ending with “Te4ETe”. The trigger also always contains at least 2 file references, often directly referencing Data Values. Finally, there is always a file references within that references an AdvUript that is at least a type 2.

I entered some values in this format, then pasted the command into the Cube. I used myself as the AdvUript, as Gede was busy. The Trigger I used referenced material 6 and the Cube’s UPV. I started daydreaming what it could cause. Would the UPV make another cube there? Would I receive an error? Would material 6 be visible, but not cause headaches? I decided that the most likely outcome was that a cube would spawn, made out of material 6, then would likely error itself out, since Material 6 doesn’t seem to work right.

Almost immediately, I felt a burst of pain that disappeared moments after. I then felt an urge to think of a 3rd tier AdvUript. My mind went to Gede instantly, and Remington froze where he was typing. A few minutes later, another cube appeared next to the original. Remington-Gede then trotted up to me and bit me.

Apperantly that second script is a sort of AI generation for possible interactions. What I was imaging was what would be tested. The burst of pain was the second cube spawning, then deleting itself since it occupied a space that was already filled by the first cube. Then, I forwarded the interaction, plus my idea to Gede, who had to figure out how to make it work. The bite was because he and Remington were in the middle of a discussion, and my idea had so many issues he was temporarily overloaded.

I now know I likely need Gede’s help with spawning new cubes. I also now know not to interrupt him.

Pleasant day,

Tester

[hypocrite-human]

AI & IT'S IMPACT

Unleashing the Power: The Impact of AI Across Industries and Future Frontiers

Artificial Intelligence (AI), once confined to the realm of science fiction, has rapidly become a transformative force across diverse industries. Its influence is reshaping the landscape of how businesses operate, innovate, and interact with their stakeholders. As we navigate the current impact of AI and peer into the future, it's evident that the capabilities of this technology are poised to reach unprecedented heights.

1. Healthcare:

In the healthcare sector, AI is a game-changer, revolutionizing diagnostics, treatment plans, and patient care. Machine learning algorithms analyze vast datasets to identify patterns, aiding in early disease detection. AI-driven robotic surgery is enhancing precision, reducing recovery times, and minimizing risks. Personalized medicine, powered by AI, tailors treatments based on an individual's genetic makeup, optimizing therapeutic outcomes.

2. Finance:

AI is reshaping the financial industry by enhancing efficiency, risk management, and customer experiences. Algorithms analyze market trends, enabling quicker and more accurate investment decisions. Chatbots and virtual assistants powered by AI streamline customer interactions, providing real-time assistance. Fraud detection algorithms work tirelessly to identify suspicious activities, bolstering security measures in online transactions.

3. Manufacturing:

In manufacturing, AI is optimizing production processes through predictive maintenance and quality control. Smart factories leverage AI to monitor equipment health, reducing downtime by predicting potential failures. Robots and autonomous systems, guided by AI, enhance precision and efficiency in tasks ranging from assembly lines to logistics. This not only increases productivity but also contributes to safer working environments.

4. Education:

AI is reshaping the educational landscape by personalizing learning experiences. Adaptive learning platforms use AI algorithms to tailor educational content to individual student needs, fostering better comprehension and engagement. AI-driven tools also assist educators in grading, administrative tasks, and provide insights into student performance, allowing for more effective teaching strategies.

5. Retail:

In the retail sector, AI is transforming customer experiences through personalized recommendations and efficient supply chain management. Recommendation engines analyze customer preferences, providing targeted product suggestions. AI-powered chatbots handle customer queries, offering real-time assistance. Inventory management is optimized through predictive analytics, reducing waste and ensuring products are readily available.

6. Future Frontiers:

A. Autonomous Vehicles: The future of transportation lies in AI-driven autonomous vehicles. From self-driving cars to automated drones, AI algorithms navigate and respond to dynamic environments, ensuring safer and more efficient transportation. This technology holds the promise of reducing accidents, alleviating traffic congestion, and redefining mobility.

B. Quantum Computing: As AI algorithms become more complex, the need for advanced computing capabilities grows. Quantucm omputing, with its ability to process vast amounts of data at unprecedented speeds, holds the potential to revolutionize AI. This synergy could unlock new possibilities in solving complex problems, ranging from drug discovery to climate modeling.

C. AI in Creativity: AI is not limited to data-driven tasks; it's also making inroads into the realm of creativity. AI-generated art, music, and content are gaining recognition. Future developments may see AI collaborating with human creators, pushing the boundaries of what is possible in fields traditionally associated with human ingenuity.

In conclusion, the impact of AI across industries is profound and multifaceted. From enhancing efficiency and precision to revolutionizing how we approach complex challenges, AI is at the forefront of innovation. The future capabilities of AI hold the promise of even greater advancements, ushering in an era where the boundaries of what is achievable continue to expand. As businesses and industries continue to embrace and adapt to these transformative technologies, the synergy between human intelligence and artificial intelligence will undoubtedly shape a future defined by unprecedented possibilities.

[spacetimewithstuartgary]

More than 10,000 supernovae counted in stellar census

Since 2018 the Zwicky Transient Facility, an international astronomical collaboration based at the Palomar Observatory in California, has scanned the entire sky every two to three nights. As part of this mission, the ZTF's Bright Transient Survey has been counting and cataloging supernovae—flashes of light in the sky that are the telltale signs of stars dying in spectacular explosions.

On Dec. 4, ZTF researchers—including astronomers at the University of Washington—announced that they have identified more than 10,000 of these stellar events, the largest number ever identified by an astronomical survey.

"There are trillions of stars in the universe, and about every second, one of them explodes," said Christoffer Fremling, an astronomer at Caltech who leads the Bright Transient Survey. "ZTF detects hundreds of these explosions per night and a handful are then confirmed as supernovae. Systematically doing this for seven years has led to the most complete record of confirmed supernovae to date."

The Bright Transient Survey is currently the primary discovery pipeline for cosmic flashes—also known as astronomical transients—in the world. To determine which transients are supernovae, ZTF shares a stream of nightly transient detections with the wider astronomical community so that other telescopes around the world can conduct follow-up observations of candidate transients.

This includes conducting a spectral analysis, in which instruments on observatory telescopes split the light from a transient object into its individual colors to reveal its distance from Earth and other properties.

"Classifying 10,000 supernovae is a tremendous achievement and will enable unprecedented scientific studies of explosive transients," said ZTF team member Eric Bellm, a UW research associate professor of astronomy and scientist with the UW's DiRAC Institute. "Reaching this milestone required careful technical work on scheduling and processing the ZTF discovery images, human and machine vetting of the alerts and obtaining timely follow-up spectra."

For the Bright Transient Survey, a 60-megapixel wide-field camera mounted on Palomar's Samuel Oschin telescope scanned the entire visible sky every two nights. To detect new astronomical events, astronomers subtracted images of the same portion of the sky from subsequent scans. Next, members of the ZTF team studied the subtracted images and triggered follow-up spectral observations by a second telescope at Palomar or other observatories.

Bellm, UW research scientist Melissa Graham and Mario Jurić, UW professor of astronomy and director of the DiRAC Institute, all contributed to the Bright Transient Survey. Bellm managed alerts of new transients and scheduled imaging for the survey. Jurić helped set up the ZTF's automated system to alert team members around the world of new transients.

Developing automated analysis pipelines and alert systems are critical for the field as more powerful imaging technologies and new generations of observatories continue to transform astronomy into a "big data" endeavor. Fritz Zwicky, a 20th century astronomer who first coined the term "supernova," identified 120 supernovae in 52 years. The Bright Transient Survey by the ZTF—named for Zwicky—found 10,000 in a fraction of that time.

"The Bright Transient Survey program serves as an exemplar for the kinds of science we hope to do with the Vera C. Rubin Observatory in the near future," said Bellm.

Under construction in Chile, the Vera C. Rubin Observatory is the future home of the Legacy Survey of Space and Time, or LSST, a mission that will take deep images of the sky nightly and detect even more cosmic transients than ZTF. UW scientists with the DiRAC Institute have been heavily involved in planning for the launch of the LSST. Collaborations like the ZTF have been a proving ground for developing and testing methods for use in the LSST.

For the Bright Transient Survey, Graham conducted follow-up spectral analyses of transients at Apache Point Observatory in New Mexico. These efforts were especially valuable in catching some of the fainter, fading supernovae that would have been missed at Palomar.

"As UW astronomers, we are so fortunate to have access to the Apache Point Observatory for our research," said Graham. "One of the most impactful—and fun—parts of obtaining optical spectra is being surprised by rare transients with peculiar characteristics, which often reveal more about supernova physics than hundreds of ordinary objects. Figuring out how to do this work with the even larger number of LSST supernovae is the next big challenge."

Most of the transients in the Bright Transient Survey are classified as one of two common types of supernovae: Type Ia, when a white dwarf steals so much material from another nearby star that it explodes, or Type II, when massive stars collapse and die under their own gravity. Thanks to the treasure trove of data from the Bright Transient Survey, astronomers are now better equipped to answer questions about how stars grow and die, as well as how dark energy drives the expansion of the universe.

After its expected 2025 commissioning, the Vera Rubin C. Observatory could discover millions more supernovae.

"The machine learning and AI tools we have developed for ZTF will become essential when the Vera Rubin Observatory begins operations," said ZTF team member Daniel Perley, an astronomer at Liverpool John Moores University. "We have already planned to work closely with Rubin to transfer our machine learning knowledge and technology."

ZTF will continue to scan the night sky for the next two years.

"The period in 2025 and 2026 when ZTF and Vera Rubin can both operate in tandem is fantastic news for time-domain astronomers," said Mansi Kasliwal, an astronomy professor at Caltech who will lead ZTF in the next two years. "Combining data from both observatories, astronomers can directly address the physics of why supernovae explode and discover fast and young transients that are inaccessible to ZTF or Rubin alone. I am excited about the future."

TOP IMAGE: SN 1987a, a Type II supernova remnant first observed in 1987, is visible in this image taken by the Hubble Space Telescope in 2017. The bright ring around the exploded star is material it had ejected approximately 20,000 years before its demise. Credit: NASA/ESA/Robert Kirshner/Max Mutchler/Roberto Avila

LOWER IMAGE: Key moments in the discovery of supernovae, including the recent discovery of more than 10,000 of these cosmic events by the Zwicky Transient Facility. Credit: Caltech

[tech4bizsolutions]

MedAI by Tech4Biz Solutions: Pioneering Next-Gen Medical Technologies

The healthcare industry is undergoing a seismic shift as advanced technologies continue to transform the way care is delivered. MedAI by Tech4Biz Solutions is at the forefront of this revolution, leveraging artificial intelligence and cutting-edge tools to develop next-generation medical solutions. By enhancing diagnostics, personalizing patient care, and streamlining operations, MedAI is empowering healthcare providers to deliver better outcomes.

1. AI-Driven Medical Insights

MedAI harnesses the power of artificial intelligence to analyze complex medical data and generate actionable insights. Its advanced algorithms can detect anomalies, predict disease progression, and recommend treatment pathways with unprecedented accuracy.

Case Study: A large medical center integrated MedAI’s diagnostic platform, leading to:

Faster identification of rare conditions.

A 30% reduction in misdiagnoses.

Enhanced clinician confidence in treatment decisions.

These capabilities underscore MedAI’s role in advancing clinical decision-making.

2. Personalized Patient Care

Personalization is key to modern healthcare, and MedAI’s data-driven approach ensures treatment plans are tailored to individual needs. By analyzing patient histories, lifestyle factors, and genetic data, MedAI offers more targeted and effective interventions.

Example: A chronic disease management clinic used MedAI to create personalized care plans, resulting in:

Improved medication adherence.

Decreased hospital readmission rates.

Greater patient satisfaction and engagement.

MedAI’s solutions allow providers to offer more precise, patient-centered care.

3. Enhanced Operational Efficiency

MedAI goes beyond clinical improvements by optimizing healthcare operations. Its automation tools reduce administrative burdens, freeing healthcare professionals to focus on patient care.

Insight: A regional hospital implemented MedAI’s workflow automation system, achieving:

A 40% reduction in administrative errors.

Faster patient registration and billing processes.

Streamlined appointment scheduling.

These improvements enhance overall operational efficiency and patient experiences.

4. Advanced Predictive Analytics

Predictive analytics play a vital role in preventive care. MedAI’s algorithms identify patients at high risk of developing chronic conditions, enabling early interventions.

Case Study: A primary care network used MedAI’s predictive models to monitor high-risk patients, leading to:

Early lifestyle adjustments and medical interventions.

A 25% drop in emergency room visits.

Higher enrollment in wellness programs.

By shifting to proactive care, MedAI helps reduce healthcare costs and improve long-term outcomes.

5. Revolutionizing Telemedicine

The rise of telemedicine has been accelerated by MedAI’s AI-powered virtual care solutions. These tools enhance remote consultations by providing real-time patient insights and symptom analysis.

Example: A telehealth provider adopted MedAI’s platform and reported:

Improved diagnostic accuracy during virtual visits.

Reduced wait times for consultations.

Increased access to care for rural and underserved populations.

MedAI’s telemedicine tools ensure equitable, high-quality virtual care for all.

6. Streamlining Drug Development

MedAI accelerates the drug discovery process by analyzing clinical trial data and simulating drug interactions. Its AI models help identify promising compounds faster and improve trial success rates.

Case Study: A pharmaceutical company partnered with MedAI to enhance its drug development process, achieving:

Faster identification of viable drug candidates.

Shorter trial durations.

Reduced costs associated with trial phases.

These innovations are driving faster development of life-saving medications.

7. Natural Language Processing for Clinical Data

MedAI’s natural language processing (NLP) capabilities extract insights from unstructured medical data, such as physician notes and discharge summaries. This allows for faster retrieval of vital patient information.

Insight: A healthcare system implemented MedAI’s NLP engine and experienced:

Improved documentation accuracy.

Quicker clinical decision-making.

Enhanced risk assessment for high-priority cases.

By automating data extraction, MedAI reduces clinician workloads and improves care quality.

8. Robust Data Security and Compliance

Data security is paramount in healthcare. MedAI employs advanced encryption, threat monitoring, and regulatory compliance measures to safeguard patient information.

Example: A hospital using MedAI’s security solutions reported:

Early detection of potential data breaches.

Full compliance with healthcare privacy regulations.

Increased patient trust and confidence in data protection.

MedAI ensures that sensitive medical data remains secure in an evolving digital landscape.

Conclusion

MedAI by Tech4Biz Solutions is redefining healthcare through its pioneering medical technologies. By delivering AI-driven insights, personalized care, operational efficiency, and robust security, MedAI empowers healthcare providers to navigate the future of medicine with confidence.

As healthcare continues to evolve, MedAI remains a trailblazer, driving innovation that transforms patient care and outcomes. Explore MedAI’s comprehensive solutions today and discover the next frontier of medical excellence.

For More Reachout :https://medai.tech4bizsolutions.com/

[michaelespositostatenisland]

Michael Esposito Staten Island: Innovative AI Solutions for Influencer Marketing in the Digital Age

In the ever-evolving landscape of digital marketing, influencer marketing has emerged as a powerful strategy for brands to connect with their target audience and drive engagement. With the rise of social media platforms, influencers have become key players in shaping consumer preferences and purchasing decisions. Michael Esposito Staten Island — Influence in the Digital Age exemplifies this trend, highlighting how digital influencers can significantly impact marketing strategies and outcomes. However, as the digital space becomes increasingly saturated with content, brands are turning to innovative AI solutions to enhance their influencer marketing efforts and stay ahead of the curve.

AI-Powered Influencer Discovery

One of the biggest challenges brands face in influencer marketing is finding the right influencers to collaborate with. Traditional methods of influencer discovery often involve manual research and outreach, which can be time-consuming and inefficient. However, AI-powered influencer discovery platforms leverage advanced algorithms to analyze vast amounts of data and identify influencers who are the best fit for a brand's target audience and campaign objectives. Michael Esposito Staten Island: An Influencer Marketer Extraordinaire, exemplifies how effective influencer collaboration can transform marketing strategies. By harnessing the power of AI, brands can streamline the influencer discovery process and identify high-potential collaborators with greater accuracy and efficiency.

Predictive Analytics for Campaign Optimization

Once influencers have been identified and partnerships established, brands can leverage AI-powered predictive analytics to optimize their influencer marketing campaigns. Predictive analytics algorithms analyze historical campaign data, audience demographics, and engagement metrics to forecast the performance of future campaigns. By leveraging these insights, brands can make data-driven decisions about content strategy, audience targeting, and campaign optimization, maximizing the impact of their influencer collaborations and driving measurable results.

AI-Driven Content Creation

Content creation is a critical component of influencer marketing campaigns, and AI is revolutionizing the way brands create and optimize content for maximum impact. AI-powered content creation tools can generate personalized, high-quality content at scale, helping brands maintain a consistent brand voice and aesthetic across their influencer collaborations. From automated image and video editing to natural language processing for caption generation, AI-driven content creation tools empower brands to create compelling, on-brand content that resonates with their target audience and drives engagement.

Sentiment Analysis for Brand Monitoring

Influencer marketing campaigns can have a significant impact on brand perception, and it's essential for brands to monitor and manage their online reputation effectively. AI-powered sentiment analysis tools analyze social media conversations and user-generated content to gauge public sentiment towards a brand or campaign. By tracking mentions, sentiment trends, and key themes, brands can quickly identify and address any potential issues or negative feedback, allowing them to proactively manage their brand reputation and maintain a positive online presence.

Automated Performance Reporting

Measuring the success of influencer marketing campaigns is crucial for determining ROI and informing future strategies. However, manual performance reporting can be time-consuming and prone to human error. AI-powered analytics platforms automate the process of performance reporting by aggregating data from multiple sources, analyzing key metrics, and generating comprehensive reports in real-time. By providing brands with actionable insights into campaign performance, audience engagement, and ROI, AI-driven analytics platforms enable brands to optimize their influencer marketing efforts and drive continuous improvement.

In conclusion, as influencer marketing continues to evolve in the digital age, brands must leverage innovative AI solutions to stay competitive and maximize the impact of their campaigns. From AI-powered influencer discovery and predictive analytics to automated content creation and sentiment analysis, AI is revolutionizing every aspect of influencer marketing, enabling brands to connect with their target audience more effectively and drive measurable results. By embracing these innovative AI solutions, brands can unlock the full potential of influencer marketing and achieve success in the digital era.