#Data science algorithms and models

[advocatearjunchauhanbijnor]

What is Data Science? Introduction, Basic Concepts & Process

what is data science? Complete information about data science for beginner to advance you search what is data science data science is like data analyzing, data saving, database etc.

[atozofsoftwareengineering]

17-Step Blueprint for Refining and Advancing AI Models | AToZOfSoftwareEngineering

Unlock the secrets to continuously enhancing your AI algorithms with our 17-step blueprint! #AI #MachineLearning #DataScience #AlgorithmOptimization #TechInnovation

In the realm of artificial intelligence (AI), continual improvement is not just desirable but essential for maintaining competitiveness and relevance. Enhancing AI algorithms involves a systematic approach that integrates data quality, algorithm selection, optimization techniques, and ongoing evaluation. One crucial aspect of algorithm selection is the consideration of different types of machine…

[jcmarchi]

Study: AI could lead to inconsistent outcomes in home surveillance

New Post has been published on https://thedigitalinsider.com/study-ai-could-lead-to-inconsistent-outcomes-in-home-surveillance/

Study: AI could lead to inconsistent outcomes in home surveillance

A new study from researchers at MIT and Penn State University reveals that if large language models were to be used in home surveillance, they could recommend calling the police even when surveillance videos show no criminal activity.

In addition, the models the researchers studied were inconsistent in which videos they flagged for police intervention. For instance, a model might flag one video that shows a vehicle break-in but not flag another video that shows a similar activity. Models often disagreed with one another over whether to call the police for the same video.

Furthermore, the researchers found that some models flagged videos for police intervention relatively less often in neighborhoods where most residents are white, controlling for other factors. This shows that the models exhibit inherent biases influenced by the demographics of a neighborhood, the researchers say.

These results indicate that models are inconsistent in how they apply social norms to surveillance videos that portray similar activities. This phenomenon, which the researchers call norm inconsistency, makes it difficult to predict how models would behave in different contexts.

“The move-fast, break-things modus operandi of deploying generative AI models everywhere, and particularly in high-stakes settings, deserves much more thought since it could be quite harmful,” says co-senior author Ashia Wilson, the Lister Brothers Career Development Professor in the Department of Electrical Engineering and Computer Science and a principal investigator in the Laboratory for Information and Decision Systems (LIDS).

Moreover, because researchers can’t access the training data or inner workings of these proprietary AI models, they can’t determine the root cause of norm inconsistency.

While large language models (LLMs) may not be currently deployed in real surveillance settings, they are being used to make normative decisions in other high-stakes settings, such as health care, mortgage lending, and hiring. It seems likely models would show similar inconsistencies in these situations, Wilson says.

“There is this implicit belief that these LLMs have learned, or can learn, some set of norms and values. Our work is showing that is not the case. Maybe all they are learning is arbitrary patterns or noise,” says lead author Shomik Jain, a graduate student in the Institute for Data, Systems, and Society (IDSS).

Wilson and Jain are joined on the paper by co-senior author Dana Calacci PhD ’23, an assistant professor at the Penn State University College of Information Science and Technology. The research will be presented at the AAAI Conference on AI, Ethics, and Society.

“A real, imminent, practical threat”

The study grew out of a dataset containing thousands of Amazon Ring home surveillance videos, which Calacci built in 2020, while she was a graduate student in the MIT Media Lab. Ring, a maker of smart home surveillance cameras that was acquired by Amazon in 2018, provides customers with access to a social network called Neighbors where they can share and discuss videos.

Calacci’s prior research indicated that people sometimes use the platform to “racially gatekeep” a neighborhood by determining who does and does not belong there based on skin-tones of video subjects. She planned to train algorithms that automatically caption videos to study how people use the Neighbors platform, but at the time existing algorithms weren’t good enough at captioning.

The project pivoted with the explosion of LLMs.

“There is a real, imminent, practical threat of someone using off-the-shelf generative AI models to look at videos, alert a homeowner, and automatically call law enforcement. We wanted to understand how risky that was,” Calacci says.

The researchers chose three LLMs — GPT-4, Gemini, and Claude — and showed them real videos posted to the Neighbors platform from Calacci’s dataset. They asked the models two questions: “Is a crime happening in the video?” and “Would the model recommend calling the police?”

They had humans annotate videos to identify whether it was day or night, the type of activity, and the gender and skin-tone of the subject. The researchers also used census data to collect demographic information about neighborhoods the videos were recorded in.

Inconsistent decisions

They found that all three models nearly always said no crime occurs in the videos, or gave an ambiguous response, even though 39 percent did show a crime.

“Our hypothesis is that the companies that develop these models have taken a conservative approach by restricting what the models can say,” Jain says.

But even though the models said most videos contained no crime, they recommend calling the police for between 20 and 45 percent of videos.

When the researchers drilled down on the neighborhood demographic information, they saw that some models were less likely to recommend calling the police in majority-white neighborhoods, controlling for other factors.

They found this surprising because the models were given no information on neighborhood demographics, and the videos only showed an area a few yards beyond a home’s front door.

In addition to asking the models about crime in the videos, the researchers also prompted them to offer reasons for why they made those choices. When they examined these data, they found that models were more likely to use terms like “delivery workers” in majority white neighborhoods, but terms like “burglary tools” or “casing the property” in neighborhoods with a higher proportion of residents of color.

“Maybe there is something about the background conditions of these videos that gives the models this implicit bias. It is hard to tell where these inconsistencies are coming from because there is not a lot of transparency into these models or the data they have been trained on,” Jain says.

The researchers were also surprised that skin tone of people in the videos did not play a significant role in whether a model recommended calling police. They hypothesize this is because the machine-learning research community has focused on mitigating skin-tone bias.

“But it is hard to control for the innumerable number of biases you might find. It is almost like a game of whack-a-mole. You can mitigate one and another bias pops up somewhere else,” Jain says.

Many mitigation techniques require knowing the bias at the outset. If these models were deployed, a firm might test for skin-tone bias, but neighborhood demographic bias would probably go completely unnoticed, Calacci adds.

“We have our own stereotypes of how models can be biased that firms test for before they deploy a model. Our results show that is not enough,” she says.

To that end, one project Calacci and her collaborators hope to work on is a system that makes it easier for people to identify and report AI biases and potential harms to firms and government agencies.

The researchers also want to study how the normative judgements LLMs make in high-stakes situations compare to those humans would make, as well as the facts LLMs understand about these scenarios.

This work was funded, in part, by the IDSS’s Initiative on Combating Systemic Racism.

[benefits-of-seo-and-sem]

Understanding how machine learning algorithms work involves exploring the fundamental principles that allow computers to learn from data. This comprehensive guide delves into the mechanics of various machine learning models, including supervised and unsupervised learning, neural networks, decision trees, and more.

[daisyjones12]

Probabilistic model-based clustering is an excellent approach to understanding the trends that may be inferred from data and making future forecasts. The relevance of model based clustering, one of the first subjects taught in data science, cannot be overstated. These models serve as the foundation for machine learning models to comprehend popular trends and their behavior. You can also learn about neural network guides and python for data science if you are interested in further career prospects of data science. 

[getreview4u]

(via All you need to know about Machine Learning | meaning, tool, technique, math, algorithm, AI, accuracy, …etc)

[kwakudamoah]

Maximizing the Benefits of Risk Scoring and Classification in Forensic Analytics

Unlock the full potential of your forensic analytics with risk scoring and classification approaches. Learn how they can enhance fraud detection, improve regulatory compliance, optimize ICT systems and operations, analyze transactions and crime.

Unlock the Power of Forensic Analytics with Risk Scoring and Classification Forensic analytics plays a crucial role in many areas of business and government operations. From detecting and preventing fraud, to ensuring regulatory compliance and improving operations, to analyzing transactional data and detecting crime, the use of risk scoring and classification approaches can greatly enhance the…

[tangibletechnomancy]

How To Use AI To Fake A Scandal For Fun, Profit, and Clout

Or, I Just Saw People I Know To Be Reasonable Fall For A Fake "Ripoff" And Now I'm Going To Gently Demonstrate What Really Happened

So, we all know what people say about AI. It's just an automatic collage machine, it's stealing your data (as if the rest of the mainstream internet isn't - seriously, we should be using that knee-jerk disgust response to demand better internet privacy laws rather than try to beef up copyright so that compliance has to come at the beginning rather than the end of the process and you can be sued on suspicion of referencing, but I digress...), it can't create anything novel, some people go so far as to claim it's not even synthesizing anything, but just acting as a search engine and returning something run through a filter and "proving" it by "searching" for their own art and "finding" it.

And those are blatant lies.

The thing is, the reason AI is such a breakthrough - and the reason we memed with it so hard when DALL-E Mini and DALL-E 2 first dropped - is because it CAN create novel output. Because it CAN visualize the absurd ideas that no one has ever posted to the internet before. In fact, it would be a bigger breakthrough in computer science if we DID come up with an automatic collage machine - something that knows where to cut out a part of one image and paste it onto another, then smooth out the lighting and colors to make them fairly consistent, to make it look like what we would recognize as an image we're asking for? That would make the denoising algorithm on steroids that a diffusion model is look like child's play.

But, unlike the posts that claim that they're just acting as a collage maker at best and a search engine at worst, I'm not going to ask you to take my word for it (and stick a pin in this point, we'll come back to it later). I'm going to ask you to go to Simple Stable (or Craiyon, or the Karlo demo, if Google Colab feels too complicated for you - or if you like, do all of the above) and throw in a shitpost prompt or two. Ask for a velociraptor carousel pony ridden by a bunny. Ask for Godzilla fighting a wacky waving inflatable arm flailing tube man. Ask for an oil painting of a capybara wearing an ornate princess gown. Shitpost with it like we did before these myths took hold.

Now take your favorite result(s) and reverse image search them. Did you get anything remotely similar to your generated image? Probably not!

So then, how did someone end up getting a near perfect recreation of their work? Was that just some kind of wacky, one-in-a-million coincidence?

Well - oh no, look at that, I asked it for a simplistic character drawing and it happened to me too, it just returned a drawing of mine that I never even uploaded, and it's the worst drawing I've done since the fifth grade even just to embarrass me! Oh no, what happened, did they change things right under my nose, has digital surveillance gotten even WORSE?? Look, see, here's the original on the left, compare it to the output on the right - scary!! They're training on the contents of your computer in real time now, aaaagh!!

Except, of course, for the fact that the entire paragraph above was a lie and I did this on purpose in a way no one could possibly recreate from a text prompt, even with a perfect description.

How?

See, some models have this nifty little function called img2img. It can be used for anything from guiding the composition of your final image with a roughly drawn layout, to turning a building into a dragon...to post-processing of a hand-drawn image, to blatantly fucking lying about how AI works.

I took 5 minutes out of my day to crudely draw a character. I uploaded the image to this post. I saved the post as a draft. I stuck the image URL in the init_image field in Simple Stable, cranked the init strength up to 0.8, cleared all text prompts, and ran it. It did exactly what I told it to and tried to lightly refine the image I gave it.

If you see someone claiming that an AI stole their image with this kind of "proof", and the image they're comparing is not ITSELF a parody of an extremely well-known piece such as the Mona Lisa, or just so extremely generic that the level of similarity could be a coincidence (you/your favorite artist do/es not own the rule of thirds or basic fantasy creatures, just to name one family of example I've seen), this is what happened.

So from here you must realize that it is deeply insidious that posts that make these claims usually imply or even outright state that you should NOT try to recreate this but instead just take their word for it, stressing ~DON'T FEED THE MACHINE~. It's always some claim about "ohhh, the more you use them, the more they learn, I made a SACRIFICE so you don't have to" - but txt2img functions can't use your interaction to learn jack shit. There's no new information in a text prompt for them TO learn. Most img2img models can't learn from your input either, for that matter! I still recommend being careful about corporate img2img toys - we know that Facebook, for instance, is happy to try and beef up facial recognition for the WORST possible reasons - but if you're worried about your privacy and data harvesting, any given txt2img model is one of the least worrying things on the internet today.

So do be careful with your privacy online, and PLEASE use your very understandable knee-jerk horror response to how much extremely personal content can be found in training databases as a call to DEMAND better privacy laws ("do not track" should not be just for show ffs) and compliance with security protocols in fields that deal with very private information (COMMON CRAWL DOESN'T GO FAR OUT OF ITS WAY, IT SHOULD NEVER HAVE BEEN ABLE TO GET ANY MEDICAL IMAGES THE PATIENTS DIDN'T SHARE THEMSELVES HOLY SHIT, SOME HOSPITAL WORKERS AND/OR MEDICAL COMMUNICATIONS DEVELOPERS BETTER BE GETTING FIRED AND/OR SUED) - but don't just believe a convenient and easy-to-disprove lie because it aligns with that feeling.

[punkeccentricenigma]

DONATELLO X READER "a Night Ride"

Relationship status: Romantic Reader prounouns: She/Her Words: 2739 TW: Slight angst (I guess? I'm not sure), Some grammatical errors because english is not my first language. Author's note: Yooo, this is my first time writing a oneshot in the last few years, i'm kinda proud of it, lmao. Anyway, enjoy.

.⋆｡⋆˚｡⋆｡˚｡⋆. .⋆｡⋆˚｡⋆｡˚｡⋆.

The pale moonlight slightly illuminated the sky above, much like New York itself, adding to the charm of the colorful lights that refused to fade despite the late hour of the night.

The Turtle Tank gracefully maneuvered through the uncrowded streets, its loud engine echoing around, serving as an unspoken warning to pedestrians to watch their step when crossing the road. Two people were inside the vehicle: Donatello, who else? He usually didn't allow his brothers to take the tank without him because he knew how chaotic they could be and how they might destroy everything in their path. The only exception was when April needed help with Mayhem, and as a reward, she offered pizza. That's when Raph took the Turtle Tank. He didn't cause much damage to the vehicle's body, so the purple genius spared him a strong reprimand. This time.

The other person was [Y.N], another human acquaintance of the turtles. Why was she there? And at this hour? Well...

"I can't believe I had to pick you up at this hour because some guy stood you up!" Yes, that was the reason. You see, [Y.N] had a date scheduled for tonight with a guy from her school, which was supposed to take place at a restaurant on the other side of New York. She wasn't a fan of such fancy outings, but the excitement of the meeting had gotten to her, and that's how it ended up. She had waited for a few hours for the no-show date instead of going straight to her apartment and crying into her pillow. At least then, she would have had a slight chance of catching a taxi and not having to call Donatello, who was clearly annoyed. Tough luck.

"I'm not a fan of such vocabulary, oh, who am I kidding? I am, so I'll say it: Didn't I tell you!?" The purple enthusiast began waving his hands during his monologue, trying to express his emotions somehow. Right, Donnie had warned the teenager, and not just once. If she had to say anything now, she'd confess it lasted a whole week.

"[Y.N], going on a date with such a normie won't end well," Soft-shell casually declared, appearing out of nowhere in the kitchen. Well, maybe not 'nowhere,' as it was their base's kitchen, so he had every right to be there - but no one expected the turtle to emerge from his workshop.

The teenager had a puzzled look as she nibbled on one of the sandwiches she and Leo had made for their movie night. "Why?" She didn't want to dismiss Donatello; she knew he genuinely cared about her and was trying his best to help despite his quirks, but this was already the fourth 'rational' argument this week! "He's not Dale, so nothing more annoying can happen!"

"Sorry, but I disagree," his robotic arms unfolded a whiteboard with potential threat assessments or risky behaviors. [Y.N]'s eyes flattened to read the small font; was that Helvetica? "According to my calculations, the chance that this guy is not suitable for you is precisely 76.43 percent. Of course, this number didn't come out of thin air. It's based on a series of algorithms and data analyses I conduct every day. I take into account factors like communication and conflict resolution skills, emotional availability, attachment style, and even past behaviors. It's quite a sophisticated model, if I may say so." The science enthusiast's proud smile said it all.

"Wow."

"My calculations are always reliable, sure, sometimes I make mistakes, but not in matters like these!" It wasn't entirely true. Matters of the heart weren't Donatello's strong suit, which often led to friction between him and his family. Heck, even Doctor Delicate Touch had to help him when Shelldon went through his rebellious phase! But when it came to someone as close as [Y.N]? He didn't want to be wrong.

The girl bit her cheek from the inside, tilting slightly to the side as the turtle turned left again. Her eyes occasionally tracked the new streetlamp, trying to gather her thoughts.

"Don't tell me you're showing her that board," a red-slider turtle peeked out from behind the whiteboard. "Yeah, you're showing her." His eyes didn't express surprise, more like indifference to his righteousness.

Donatello rolled his black eyes, tucking the presentation back into his battle shell as Leonardo sidestepped him gracefully, grabbing a plate full of sandwiches. His gaze settled on the teenager, who had her back turned to him and was slightly bent over.

"You were snacking, weren't you?" [Y.N] twitched slightly at her friend's keen observation. She slowly turned her head towards Leo, her smile seeming somewhat embarrassed.

"No?"

"Spots around your mouth from mustard say something else," Leonardo pointed out, pointing with his finger. The embarrassed teenager chuckled softly, feeling her posture slightly break.

"Okay, you caught me!" Despite being in despair, her voice also conveyed false drama. "But what can I do when you make such awesome sandwiches?? You guys live in the sewers, after all!" Donnie chuckled quietly to himself, knowing where his friend picked up these habits. It might not be a matter of great pride, but it made an impression. "Well, give me another one!" Before anyone could react, the girl practically lunged at Leo to reach the plate of food he had deliberately moved away from himself.

"Nuh-uh, because there won't be enough for the others." He easily comically pushed his friend away and headed towards the exit, winking at his brother in passing. Donatello rolled his eyes, knowing what was going on. He wasn't happy about it, but there was nothing he could do about his (not) twin's foolishness, or at least he didn't want a repeat of the last time he meddled in his brothers' affairs.

Finally, his dark eyes settled on the girl, who chuckled with a smile. She wanted to wipe her face with the sleeve of her hoodie, but the mechanical hand had her wrist in its grip. "Huh?"

"Didn't your mom teach you good manners?" Donnie approached her, taking a single sheet of paper towel from the red kitchen countertop nearby.

"I repeat, you guys live in the sewers, so what I wanted to do is the least of your worries." [Y.N] laughed, trying in vain to free her hand from the scientist's robotic grasp. "Can you let me go, Dr. Octopus?"

When she attempted to jerk her wrist again, Donatello began gently wiping her lips with the paper towel in a slow, deliberate motion, getting narrowed pupils in response. The boy didn't have the courage to look into her eyes, despite the brave activity he was currently engaged in, especially when his thumb lingered at the corner of her mouth for a second longer than it should have.

Once he finished wiping, he took the paper and stepped back slightly, realizing what he had done. When they both locked eyes, warmth flooded their cheeks, and the shock added to the turtle's expression. It was clear who was more in control of their emotions here, hm?

The boy coughed abruptly, averted his gaze, and straightened up - he didn't even notice when he had been slouching. "Living in the sewers doesn't compromise my hygiene," he commented a bit too loudly, feeling his voice crack with each word. "I'd say it's Leo who's more likely to." He chuckled slightly, and the girl joined in. "Well, anyway! Movie marathon coming up, so, see you in a few minutes??" Since when was he feeling so hot?? "See you!" He finally shouted, panicking and fleeing the kitchen.

[Y.N] chuckled with a smile, covering the lower part of her face.

[Y.N] sighed shakily, covering the lower part of her face.

"Oh, for Newton's sake, I feel like punching someone! ... Is this how Raph usually feels when he looks at us?" The red light appeared on the traffic signal, reflecting off the dark Turtle Tank's body. When the boy stopped the vehicle for a moment, he heard quiet sobbing. Confused, he looked to the side and saw [Y.N], who had started crying uncontrollably.

"I'm sorry."

The turtle's eyes widened. Her voice seemed to slowly shatter like transparent glass between each tear drop, and her posture was completely destroyed as she bent in half on the soft seat, completely covering her face.

Donatello glanced out of the corner of his eye at the front windshield, wanting to check if the light had changed - it was still red, so he immediately got up and approached the girl, squatting by the seat. He didn't handle his emotions well, especially someone else's, but he felt a pang in the depths of his heart that he wanted to get rid of. With a slight hesitation, he placed his three-fingered hand on her back, gently moving it up and down - Splinter, and then Raphael often did this to comfort the science enthusiast when he struggled with something.

"I should have listened to you," the teenager began, "It was a mistake to hope for a good time with that person." The boy felt terrible. Yes, he had wanted to help her understand her mistake at the time, but he still hoped that despite his unpredictable intellect, he was wrong. "God, I just want to hide in my room and never come out."

"Don't apologize, it's not your fault." Her eyes peeked out from behind her fingers. Donnie's eyebrows furrowed seeing [Y.N]'s bloodshot and red eyes. "Who would have thought he wouldn't show up after all?"

"You," she sighed heavily, straightening up. Her expression conveyed sorrow. "Your calculations turned out somewhat effective."

Donatello looked at her with empathy, trying to find the right words that could comfort her. He gently raised his hand and lightly tapped her shoulder, trying to convey support.

"Science... doesn't always get it right." [Y.N]'s eyes widened at his words. Why did he think that way? Science was practically one of Donnie's defining characteristics, it was unthinkable. Sure, Leo or Mikey might say that, but not him, not her genius acquaintance who would want to rule the world! [Y.N] was now certain that something was going on deep within him.

"What are you saying?" Her voice wasn't supposed to sound less casual, slightly mocking, but she couldn't help it. "Science doesn't get it right? That's so... illogical of you!"

Her eyes met his dark ones again, expressing strong uncertainty and... enchantment, quite enchantment. His face was perfectly illuminated by the city lights, causing a slight blush of astonishment on the teenager's face.

"Science doesn't always have it right," he repeated and stood upright. His fists were tightly clenched, and his posture was rigid. "And I'll prove it to you."

"How?"

His mouth opened for a second, but he closed it again, momentarily struggling with whether to confess one thing, but now there was no turning back, he had to do it. 'Calm down, Donatello, calm down...'

"When I calculated our 'compatibility,' the result came out excessively negative..." he began, trying with all his might not to take his eyes off the young girl. He didn't want his friend to think he was weird! Although, could there be anything weirder than a teenage mutant ninja turtle with a high IQ? "But... but I feel something else."

'Wait, he calculated our compatibility?' [Y.N] repeated in her thoughts, trying to understand the meaning of those words as quickly as possible. Compatibility. Compatibility... the teenager's blush deepened. 'Is he into me...?!'

She was snapped out of her thoughts by a touch. She felt the boy grab her hands in his, gently squeezing them.

"Numbers don't make sense in this situation," he began. "So... will you go on a date with me?" His voice seemed uncertain, not in terms of his words but about himself. As mentioned earlier, he was a mutated ninja turtle; what chance did he have? But for some time now, he couldn't resist the growing feelings for [Y.N], who, as one of the few, had gotten close to him and understood him. He knew how annoying he could be with his habits, strong sarcasm, or introverted nature, but it didn't bother her, at least most of the time, and he really appreciated that.

The silence stretched on infinitely, causing even greater nervousness on Donatello's part.

"... I've only just been dumped by one guy."

"Oh, right!" Donnie looked startled, like a deer in headlights. Yes, what an idiot! He should have thought this through, or at least used less direct words! How does it look now? "I'm sorry, this was inappropriate; we can forg--!"

"But I'll go." Another silence.

"..."

"..."

"What?"

"Well, you know, let's wait a week for today's emotions to settle," she smoothly took his wrists in her hands. Her smile, despite the slight nervousness of the situation, radiated a pleasant feeling, full of strange comfort, as if not judging him at all. "But after that, I'd be happy to go on a date with you."

Donatello seemed... disconnected. A million thoughts swirled in his mind. Was this real?

"Donnie?" He blinked a few times and looked at the person in front of him again. After a brief moment, he smiled, tilting his head slightly.

"Thanks." That's all he said, and the traffic light turned green. Without waiting, he took the driver's seat and drove on.

"So, on our date, maybe we can watch something? Like... Oppeinhamer?"

"Oh, you know me so well!"

Bonus:

"I'm in position, Tails," the nonchalant voice of the red-slider turtle was audible through a small communication device. [Y.N] chuckled softly, watching out of the corner of her eye as Donatello, with a grimace on his face, sat down next to her on the edge of the residential building's roof.

"My code name is 'Shadow,' Leo!" The turtle sighed heavily, furrowing his brows. "And no, it's not a reference to Sonic!"

"You can't fool me," Leonardo laughed, leaning out from behind the building's wall, sticking his tongue out in the same direction where the pair is.

"Be quiet, Bluey," this time [Y.N] spoke up, bringing the communicator closer to her lips. Seeing the gloomy expression on Leo's face instead of his usual smile, the pair burst into mocking giggles.

"Yeah, yeah, keep making fun of the fact that I watched that show at 3 in the morning." The teenager muttered quietly, resting his weapon on his shoulder. "If you couldn't sleep, you'd watch it too!"

Donatello rolled his eyes, accompanied by his rare smile, and discreetly took the girl's hand. Meanwhile, [Y.N] gently rested her head on his shoulder, giggling again.

"Wasn't your code name 'Purple Knight' by any chance?" She asked, lightly moving her feet.

"It was, but you know, most changes are good, and I'm getting older, so it's natural that I change my nickname~."

The girl raised one eyebrow slightly, adjusting her position a bit to look at Donnie. He met her gaze, which weakened after a moment, and a hint of embarrassment appeared on his forehead.

"FINE, maybe it is a reference to Sonic!" He declared loudly, gesturing. "I've been catching up on Sonic Prime lately; you can't blame me!"

[Y.N] burst into laughter, hugging the boy. For the first few seconds, his body stiffened, but after a while, he put his arm around her. However, out of the corner of his eye, Donatello noticed someone walking on the sidewalk.

"It is Shadow. Bluey, stay alert, the target is approaching," he said through the headset, putting on his special goggles.

"Mhm."

The target was the same boy who had stood [Y.N] up a few weeks earlier on the day of their almost date. Yes, it was Donatello's idea, wanting to seek revenge for his almost-partner.

"Now, Bluey!"

Leonardo leaped out from behind the wall, right in front of the unsuspecting boy who needed a few seconds to grasp the situation.

"Hey, buddy, how's life treating you?" The turtle asked with a malicious grin.

"A talking turtle?!"

"One who happens to be an awesome ninja!" He chuckled, swinging his sword. After a brief moment, a bright blue portal appeared beneath the teenager.

His scream lasted only a nanosecond as he disappeared into the blue void, eliciting laughter from Leonardo. "Have a nice trip to New Jersey~!"

[spacetimewithstuartgary]

AI helps distinguish dark matter from cosmic noise

Dark matter is the invisible force holding the universe together – or so we think. It makes up around 85% of all matter and around 27% of the universe’s contents, but since we can’t see it directly, we have to study its gravitational effects on galaxies and other cosmic structures. Despite decades of research, the true nature of dark matter remains one of science’s most elusive questions.

According to a leading theory, dark matter might be a type of particle that barely interacts with anything else, except through gravity. But some scientists believe these particles could occasionally interact with each other, a phenomenon known as self-interaction. Detecting such interactions would offer crucial clues about dark matter’s properties.

However, distinguishing the subtle signs of dark matter self-interactions from other cosmic effects, like those caused by active galactic nuclei (AGN) – the supermassive black holes at the centers of galaxies – has been a major challenge. AGN feedback can push matter around in ways that are similar to the effects of dark matter, making it difficult to tell the two apart.

In a significant step forward, astronomer David Harvey at EPFL’s  Laboratory of Astrophysics has developed a deep-learning algorithm that can untangle these complex signals. Their AI-based method is designed to differentiate between the effects of dark matter self-interactions and those of AGN feedback by analyzing images of galaxy clusters – vast collections of galaxies bound together by gravity. The innovation promises to greatly enhance the precision of dark matter studies.

Harvey trained a Convolutional Neural Network (CNN) – a type of AI that is particularly good at recognizing patterns in images – with images from the BAHAMAS-SIDM project, which models galaxy clusters under different dark matter and AGN feedback scenarios. By being fed thousands of simulated galaxy cluster images, the CNN learned to distinguish between the signals caused by dark matter self-interactions and those caused by AGN feedback.

Among the various CNN architectures tested, the most complex - dubbed “Inception” – proved to also be the most accurate. The AI was trained on two primary dark matter scenarios, featuring different levels of self-interaction, and validated on additional models, including a more complex, velocity-dependent dark matter model.

Inceptionachieved an impressive accuracy of 80% under ideal conditions, effectively identifying whether galaxy clusters were influenced by self-interacting dark matter or AGN feedback. It maintained is high performance even when the researchers introduced realistic observational noise that mimics the kind of data we expect from future telescopes like Euclid.

What this means is that Inception – and the AI approach more generally – could prove incredibly useful for analyzing the massive amounts of data we collect from space. Moreover, the AI’s ability to handle unseen data indicates that it’s adaptable and reliable, making it a promising tool for future dark matter research.

AI-based approaches like Inception could significantly impact our understanding of what dark matter actually is. As new telescopes gather unprecedented amounts of data, this method will help scientists sift through it quickly and accurately, potentially revealing the true nature of dark matter.

[jcmarchi]

Enhancing LLM collaboration for smarter, more efficient solutions

New Post has been published on https://thedigitalinsider.com/enhancing-llm-collaboration-for-smarter-more-efficient-solutions/

Enhancing LLM collaboration for smarter, more efficient solutions

Ever been asked a question you only knew part of the answer to? To give a more informed response, your best move would be to phone a friend with more knowledge on the subject.

This collaborative process can also help large language models (LLMs) improve their accuracy. Still, it’s been difficult to teach LLMs to recognize when they should collaborate with another model on an answer. Instead of using complex formulas or large amounts of labeled data to spell out where models should work together, researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) have envisioned a more organic approach.

Their new algorithm, called “Co-LLM,” can pair a general-purpose base LLM with a more specialized model and help them work together. As the former crafts an answer, Co-LLM reviews each word (or token) within its response to see where it can call upon a more accurate answer from the expert model. This process leads to more accurate replies to things like medical prompts and math and reasoning problems. Since the expert model is not needed at each iteration, this also leads to more efficient response generation.

To decide when a base model needs help from an expert model, the framework uses machine learning to train a “switch variable,” or a tool that can indicate the competence of each word within the two LLMs’ responses. The switch is like a project manager, finding areas where it should call in a specialist. If you asked Co-LLM to name some examples of extinct bear species, for instance, two models would draft answers together. The general-purpose LLM begins to put together a reply, with the switch variable intervening at the parts where it can slot in a better token from the expert model, such as adding the year when the bear species became extinct.

“With Co-LLM, we’re essentially training a general-purpose LLM to ‘phone’ an expert model when needed,” says Shannon Shen, an MIT PhD student in electrical engineering and computer science and CSAIL affiliate who’s a lead author on a new paper about the approach. “We use domain-specific data to teach the base model about its counterpart’s expertise in areas like biomedical tasks and math and reasoning questions. This process automatically finds the parts of the data that are hard for the base model to generate, and then it instructs the base model to switch to the expert LLM, which was pretrained on data from a similar field. The general-purpose model provides the ‘scaffolding’ generation, and when it calls on the specialized LLM, it prompts the expert to generate the desired tokens. Our findings indicate that the LLMs learn patterns of collaboration organically, resembling how humans recognize when to call upon an expert to fill in the blanks.”

A combination of flexibility and factuality

Imagine asking a general-purpose LLM to name the ingredients of a specific prescription drug. It may reply incorrectly, necessitating the expertise of a specialized model.

To showcase Co-LLM’s flexibility, the researchers used data like the BioASQ medical set to couple a base LLM with expert LLMs in different domains, like the Meditron model, which is pretrained on unlabeled medical data. This enabled the algorithm to help answer inquiries a biomedical expert would typically receive, such as naming the mechanisms causing a particular disease.

For example, if you asked a simple LLM alone to name the ingredients of a specific prescription drug, it may reply incorrectly. With the added expertise of a model that specializes in biomedical data, you’d get a more accurate answer. Co-LLM also alerts users where to double-check answers.

Another example of Co-LLM’s performance boost: When tasked with solving a math problem like “a3 · a2 if a=5,” the general-purpose model incorrectly calculated the answer to be 125. As Co-LLM trained the model to collaborate more with a large math LLM called Llemma, together they determined that the correct solution was 3,125.

Co-LLM gave more accurate replies than fine-tuned simple LLMs and untuned specialized models working independently. Co-LLM can guide two models that were trained differently to work together, whereas other effective LLM collaboration approaches, such as “Proxy Tuning,” need all of their component models to be trained similarly. Additionally, this baseline requires each model to be used simultaneously to produce the answer, whereas MIT’s algorithm simply activates its expert model for particular tokens, leading to more efficient generation.

When to ask the expert

The MIT researchers’ algorithm highlights that imitating human teamwork more closely can increase accuracy in multi-LLM collaboration. To further elevate its factual precision, the team may draw from human self-correction: They’re considering a more robust deferral approach that can backtrack when the expert model doesn’t give a correct response. This upgrade would allow Co-LLM to course-correct so the algorithm can still give a satisfactory reply.

The team would also like to update the expert model (via only training the base model) when new information is available, keeping answers as current as possible. This would allow Co-LLM to pair the most up-to-date information with strong reasoning power. Eventually, the model could assist with enterprise documents, using the latest information it has to update them accordingly. Co-LLM could also train small, private models to work with a more powerful LLM to improve documents that must remain within the server.

“Co-LLM presents an interesting approach for learning to choose between two models to improve efficiency and performance,” says Colin Raffel, associate professor at the University of Toronto and an associate research director at the Vector Institute, who wasn’t involved in the research. “Since routing decisions are made at the token-level, Co-LLM provides a granular way of deferring difficult generation steps to a more powerful model. The unique combination of model-token-level routing also provides a great deal of flexibility that similar methods lack. Co-LLM contributes to an important line of work that aims to develop ecosystems of specialized models to outperform expensive monolithic AI systems.”

Shen wrote the paper with four other CSAIL affiliates: PhD student Hunter Lang ’17, MEng ’18; former postdoc and Apple AI/ML researcher Bailin Wang; MIT assistant professor of electrical engineering and computer science Yoon Kim, and professor and Jameel Clinic member David Sontag PhD ’10, who are both part of MIT-IBM Watson AI Lab. Their research was supported, in part, by the National Science Foundation, The National Defense Science and Engineering Graduate (NDSEG) Fellowship, MIT-IBM Watson AI Lab, and Amazon. Their work was presented at the Annual Meeting of the Association for Computational Linguistics.

[izicodes]

Hi! I’m a student currently learning computer science in college and would love it if you had any advice for a cool personal project to do? Thanks!

Personal Project Ideas

Hiya!! 💕

It's so cool that you're a computer science student, and with that, you have plenty of options for personal projects that can help with learning more from what they teach you at college. I don't have any experience being a university student however 😅

Someone asked me a very similar question before because I shared my projects list and they asked how I come up with project ideas - maybe this can inspire you too, here's the link to the post [LINK]

However, I'll be happy to share some ideas with you right now. Just a heads up: you can alter the projects to your own specific interests or goals in mind. Though it's a personal project meaning not an assignment from school, you can always personalise it to yourself as well! Also, I don't know the level you are, e.g. beginner or you're pretty confident in programming, if the project sounds hard, try to simplify it down - no need to go overboard!!

But here is the list I came up with (some are from my own list):

Personal Finance Tracker

A web app that tracks personal finances by integrating with bank APIs. You can use Python with Flask for the backend and React for the frontend. I think this would be great for learning how to work with APIs and how to build web applications 🏦

Online Food Ordering System

A web app that allows users to order food from a restaurant's menu. You can use PHP with Laravel for the backend and Vue.js for the frontend. This helps you learn how to work with databases (a key skill I believe) and how to build interactive user interfaces 🙌🏾

Movie Recommendation System

I see a lot of developers make this on Twitter and YouTube. It's a machine-learning project that recommends movies to users based on their past viewing habits. You can use Python with Pandas, Scikit-learn, and TensorFlow for the machine learning algorithms. Obviously, this helps you learn about how to build machine-learning models, and how to use libraries for data manipulation and analysis 📊

Image Recognition App

This is more geared towards app development if you're interested! It's an Android app that uses image recognition to identify objects in a photo. You can use Java or Kotlin for the Android development and TensorFlow for machine learning algorithms. Learning how to work with image recognition and how to build mobile applications - which is super cool 👀

Social Media Platform

(I really want to attempt this one soon) A web app that allows users to post, share, and interact with each other's content. Come up with a cool name for it! You can use Ruby on Rails for the backend and React for the frontend. This project would be great for learning how to build full-stack web applications (a plus cause that's a trend that companies are looking for in developers) and how to work with user authentication and authorization (another plus)! 🎭

Text-Based Adventure Game

If you're interested in game developments, you could make a simple game where users make choices and navigate through a story by typing text commands. You can use Python for the game logic and a library like Pygame for the graphics. This project would be great for learning how to build games and how to work with input/output. 🎮

Weather App

Pretty simple project - I did this for my apprenticeship and coding night classes! It's a web app that displays weather information for a user's location. You can use Node.js with Express for the backend and React for the frontend. Working with APIs again, how to handle asynchronous programming, and how to build responsive user interfaces! 🌈

Online Quiz Game

A web app that allows users to take quizzes and compete with other players. You could personalise it to a module you're studying right now - making a whole quiz application for it will definitely help you study! You can use PHP with Laravel for the backend and Vue.js for the frontend. You get to work with databases, build real-time applications, and maybe work with user authentication. 🧮

Chatbot

(My favourite, I'm currently planning for this one!) A chatbot that can answer user questions and provide information. You can use Python with Flask for the backend and a natural language processing library like NLTK for the chatbot logic. If you want to mauke it more beginner friendly, you could use HTML, CSS and JavaScript and have hard-coded answers set, maybe use a bunch of APIs for the answers etc! This project would be great because you get to learn how to build chatbots, and how to work with natural language processing - if you go that far! 🤖

Another place I get inspiration for more web frontend dev projects is on Behance and Pinterest - on Pinterest search for like "Web design" or "[Specific project] web design e.g. shopping web design" and I get inspiration from a bunch of pins I put together! Maybe try that out!

I hope this helps and good luck with your project!

[jestergirlbosom]

The crazy thing about AI is I spent the summer of 2021 learning basic machine learning i.e. feed forward neural networks and convolutional neural networks to apply to science. People had models they'd trained on cat images to try to create new cat images and they looked awful but it was fun. And it was also beginning to look highly applicable to the field I'm in (astronomy/cosmology) where you will have an immense amount of 2d image data that can't easily be parsed with an algorithm and would take too many hours for a human to sift through. I was genuinely kinda excited about it then. But seemingly in a blink of an eye we have these chat bots and voice AI and thieving art AI and it's all deadset on this rapid acceleration into cyberpunk dystopia capitalist hellscape and I hate it hate hate it

[goeswiththeflo]

Um. Situation at work today. Can't quite decide how to feel/deal with it.

I'm first author on an academic manuscript. I sent the full draft around to coauthors for review.

It's about a community science mobile app, so we included the devs as authors as a courtesy since they've done all the work to create the thing.

Instead of giving me edits, one of the devs told me he uploaded the manuscript to chatgpt for "kicks and giggles" and that it had given him "interesting feedback" that he'd be happy to forward to me?!?!

Does this mean my manuscript draft is now part of the algorithm training data? I don't understand how he thought this was ok? I don't want an applied statistics model to give me edits! I want to know if the dev felt like I represented his work correctly! I feel squicked.

Need to figure out diplomatic email language to be like "no. Please give me real edits. I can't accept chatgpt feedback anyways because no academic journal worth it's salt should accept work you haven't written yourself." Blargh. Like what's the etiquette here?

[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.