#AI representation accuracy

[shamnadt]

5 things about AI you may have missed today: OpenAI's CLIP is biased, AI reunites family after 25 years, more

Study finds OpenAI’s CLIP is biased in favour of wealth and underrepresents poor nations; Retail giants harness AI to cut online clothing returns and enhance the customer experience; Northwell Health implements AI-driven device for rapid seizure detection; White House concerns grow over UAE’s rising influence in global AI race- this and more in our daily roundup. Let us take a look. 1. Study…

[thepastisalreadywritten]

AI Tool Reproduces Ancient Cuneiform Characters with High Accuracy

ProtoSnap, developed by Cornell and Tel Aviv universities, aligns prototype signs to photographed clay tablets to decode thousands of years of Mesopotamian writing.

Cornell University researchers report that scholars can now use artificial intelligence to “identify and copy over cuneiform characters from photos of tablets,” greatly easing the reading of these intricate scripts​.

The new method, called ProtoSnap, effectively “snaps” a skeletal template of a cuneiform sign onto the image of a tablet, aligning the prototype to the strokes actually impressed in the clay​.

By fitting each character’s prototype to its real-world variation, the system can produce an accurate copy of any sign and even reproduce entire tablets​.

"Cuneiform, like Egyptian hieroglyphs, is one of the oldest known writing systems and contains over 1,000 unique symbols​.

Its characters change shape dramatically across different eras, cultures and even individual scribes so that even the same character… looks different across time,” Cornell computer scientist Hadar Averbuch-Elor explains​.

This extreme variability has long made automated reading of cuneiform a very challenging problem.

The ProtoSnap technique addresses this by using a generative AI model known as a diffusion model.

It compares each pixel of a photographed tablet character to a reference prototype sign, calculating deep-feature similarities.

Once the correspondences are found, the AI aligns the prototype skeleton to the tablet’s marking and “snaps” it into place so that the template matches the actual strokes​.

In effect, the system corrects for differences in writing style or tablet wear by deforming the ideal prototype to fit the real inscription.

Crucially, the corrected (or “snapped”) character images can then train other AI tools.

The researchers used these aligned signs to train optical-character-recognition models that turn tablet photos into machine-readable text​.

They found the models trained on ProtoSnap data performed much better than previous approaches at recognizing cuneiform signs, especially the rare ones or those with highly varied forms.

In practical terms, this means the AI can read and copy symbols that earlier methods often missed.

This advance could save scholars enormous amounts of time.

Traditionally, experts painstakingly hand-copy each cuneiform sign on a tablet.

The AI method can automate that process, freeing specialists to focus on interpretation.

It also enables large-scale comparisons of handwriting across time and place, something too laborious to do by hand.

As Tel Aviv University archaeologist Yoram Cohen says, the goal is to “increase the ancient sources available to us by tenfold,” allowing big-data analysis of how ancient societies lived – from their religion and economy to their laws and social life​.

The research was led by Hadar Averbuch-Elor of Cornell Tech and carried out jointly with colleagues at Tel Aviv University.

Graduate student Rachel Mikulinsky, a co-first author, will present the work – titled “ProtoSnap: Prototype Alignment for Cuneiform Signs” – at the International Conference on Learning Representations (ICLR) in April.

In all, roughly 500,000 cuneiform tablets are stored in museums worldwide, but only a small fraction have ever been translated and published​.

By giving AI a way to automatically interpret the vast trove of tablet images, the ProtoSnap method could unlock centuries of untapped knowledge about the ancient world.

[novelhater]

finished kaleidoscope of death volume one by xi zi xu

the translation leans toward barebones localization rather than preserving exact original meaning which i think is fine, although it's generally not my preference. there's exactly one footnote in the whole volume. as a result, there are a few things that are probably hard to understand if you lack the requisite context, which many english readers probably do. the most unfortunate of these is the discussion around ruan baijie's name which occurs in the first couple of pages and probably comes across as strange to anyone who doesn't get the joke. the fact that it happens so immediately makes me worry that it might be an impediment to getting into the rest of the novel. but then again, it's a fairly minor thing, all things considered, so maybe not. oh well.

i don't exactly have encyclopedic knowledge of the original so i can't speak to accuracy, but the rest of the volume flows smoothly. there are a few distinctly english phrases and sayings that pop up here and there but they don't come off as strange or forced. the use of "all work and no play makes jack a dull boy" was particularly interesting since this has its own horror-specific connotations. even the "hooman," though i resent it in general as a colloquialism, fits the context.

as for the story, i've already read kod all the way through and loved it (though i, like many, have mixed feelings about the ending). it was a first for me in many regards and has a special place in my heart as a result. in particular, it was the first infinite flow novel i ever read. and i love ruan nanzhu/baijie.

(immediate caveat that the following is about the first volume only because my memory of the rest of the series is just not detailed enough orz)

i think it's tempting to judge ruan nanzhu's gender and the way it's treated in the text against contemporary online standards regarding trans expression and discussion, but i don't think that's entirely fair. for one thing these kinds of positive gender presentations are EXCEPTIONALLY rare in bl/yaoi/shounen ai. there is a whole backdrop of casually transphobic "okama" characters that appear behind ruan baijie in the timeline. these characters are often the butt of the joke, and when they aren't, they're almost universally relegated to side roles. on the other hand, ruan baijie/nanzhu is the leading love interest. lin qiushi's perspective adds a somewhat conservative filter - he conceptualizes ruan baijie as a crossdressing man and considers those who view her as a woman as unwittingly deceived - but the narrative runs subtly counter to this construction and lin qiushi subconsciously accepts the gender-swapping as normal fairly quickly. (i'm also uncertain how much of the grammatical gendering is an artifact of translation.)

ruan nanzhu makes it clear from the start that this is part of his identity rather than a deliberately constructed farce (it's his "hobby" i.e. something he enjoys doing for its own sake - he quite literally plays with gender presentation) and only secondarily brings up the fact that it has benefits. the gender-swapping as something akin to bigender identity is further reinforced throughout. for example, an exchange between ruan baijie and another girl is described (free of lin qiushi's perspective filter) as a "mutual exchange between women." and as lin qiushi notes - because he finds himself missing ruan baijie! - ruan baijie and ruan nanzhu behave as if they were very similar but distinct individuals. ruan nanzhu isn't merely putting on a disguise - he's becoming ruan baijie.

well, it's a fairly complex discussion, and ultimately i wouldn't describe ruan nanzhu/baijie as stellar "representation," but his/her existence as an object and agent of desire is honestly pretty unique and radical with the context of the genre and i really think it's wrong to dimiss it because it's not perfect. (my opinion on this is also strongly influenced by the story's conclusion, but more on that amother distant day in the future...)

in regards to the horror elements, i don't find kod very scary, but i'm also aware that my horror threshold is pretty high compared to most people. at the very least, the concepts are distinct and vivid. could the plot be improved from a structural and logical standpoint? yes, undoubtedly. but i've given up in irritation and disgust (and not the good kind) on nine out of ten of the contemporary horror novels i've tried to read in the last five years so i consider this to be batting a thousand. at the very least kod doesn't belabor the point and insist on explaining each and every minor spook, even when explanation only serves to draw attention to their faults.

*deep inhale and exhale as i let go a belly full of unrelated complaints*

[weeeeeekly]

ML0802-99 – mark x afab!reader

blurb You work at a sex shop and decide to test one of the new products.

info android sex bot!mark x human!reader, one use of y/n, afab!reader, no reader body shape mention, swearing, sexbot au (is this a thing??)

WARNINGS!!! NSFW, MDNI 18+ blog, mention of vagina, p in v sex, semipublic sex, sex against the wall, kinda rough sex, oral (m receiving), swallowing cum, fingering, praise kink (a weeeeeekly staple), reader & mark are experienced, soft!dom mark & sub!reader I think??, no refractory period because he’s a robot, not aiming for accuracy – aiming for vibes, not proofread/edited just pure free flowing thought

this is FICTION!!!!! everything is made up by me. the stuff written out is not meant to be a representation of the people, places, or ideas mentioned. also, prob not accurate to real life counterparts – idk sex.

wc 1.6k

You love your job.

The job may not pay the best to sustain you for the rest of your life, $20 per hour can only do so much, but the other perks that your boss lets you have been too good to pass up. You get to test out all the new products under the guise of “research for customers”, so you can give top notch customer service when customers ask, “what is the best” or “would work for them”.

What can you say – you take your job seriously.

You know that your friends would be jealous of you. Only if you had friends. Being alone didn’t bother you that much, but as you stare at the massive box in front of you, you couldn’t help but feel the nasty jealousy bubbling up inside.

Your new solution to loneliness! Introducing NEO CUM TECHNOLOGY – 22 different models ready to satisfy and please. Each model has different preprogrammed personalities to appeal to every user.

Each model includes over 1,000 preprogrammed lines from AI technology to allow each NCT model to make facial expressions, talk to the user, and have full body articulation, medical grade platinum silicone, one set of clothes, synthetic hair, and breathing and temperature mimicking mechanisms, all created to make lifelike models.

Jaw dropping to the floor, you gape at the NCT model inside the box. The model was a 5’9 man with black hair with an undercut with white streaks, moles scattered on his face and neck, black eyebrows, dark mauve lips, and dressed in a futuristic outfit of a chrome silver jacket and black pants with black sneakers. This model could’ve fooled you if it walked past you in public, if it wasn’t for the exposed metal skeleton at the back of the right side of the neck and exposed left arm. Nothing you couldn’t cover with a long sleeve turtleneck.

In your hand was the note your boss left you on the register.

Y/N, we got sent a rejected model by accident. Customer service said we could keep it and do whatever we want with it. Thought you might like to test it – could be a friend or something. Let me know if there are any bugs or malfunctions so if not, might get it refurbished to sell.

“Could be a friend.” You scoff as you toss the note back on the counter, turning back to the sexbot.

You open the plastic part of the box as you pick up the instructions and charger, leaning the box against the counter.

INSTRUCTIONS

Model: NEO CUM TECHNOLOGY ML0802-99

Service Code: 007

START turn on by pressing button at nape of neck, allow 5 seconds for model to power up if first time use, battery may need recharging out of box.

CHARGE port at ankle on left leg.

Following instructions, you reach behind the robot to power it on, stepping back as you watch in awe as dark brown eyes safe back at you. The robot steps out of the box and closer to you.

Testing it out, you speak. “Hi?”

“Hello.”

“Are you the primary main user? Please state a name to call you by.”

“Uh, I wouldn’t say I’m going to be the primary user for long, so let’s just use a nickname.” Your eyes dart around the items near you for an idea as your eyes spot the Angel costume in the window display. “Angel. Let’s use Angel.”

The robot extends a hand towards you, cracking a smile. “Nice to meet you, Angel.”

Eyes widening, you go to shake his hand, but the robot surprises you by leaning down kisses the back of your hand. His lips feel shockingly realistic, but you should’ve known since NCT models go for thousands of dollars for just the base.

“Do you have a name?”

“Mark is programmed in my system.”

“Mark, cool.” You nod as you check him out. “So, like, what can you do?”

“My model is able to perform acts of oral on both penises and vaginas, 5 different levels of pace, 7 different vibration patterns, able to perform over 100 positions with full body articulation, and use for up to 6 hours on full charge.”

You bite your pointer finger as your imagination begins to run wild at all the stuff you could try.

“Weird question…”

Mark tilts his head at you.

“Do you have flavored cum?”

He smirks at you, “Wish to find out, Angel?”

You’re on your knees in the stockroom behind the counter as you suck Mark off. It would be naïve to think that he was moaning at how good you are at sucking dick, but he is programmed to reaction to any and every touch with a recorded moan or groan. (You are trying your best to make this the best head you’ve ever given, just for your peace of mind.)

You look up to see Mark’s head leaning against the wall with his eyes shut and moans tumbling past his lips. To your pleasant surprise, his precum tasted like artificial vanilla.

“Angel, I’m close.” Mark moans as his hands grip the back of your head as he begins thrusting into your mouth. You reach a hand down your bottoms and underwear to finger yourself something to relieve yourself from the ache between your legs.

“F-fuck.” His hips stutter after one last thrust that brings your face to his pelvis, cumming down your throat as you take your finger out from inside you. The vanilla taste fills your mouth, so sweet that you could feel a toothache forming.

Mark begins petting the top of your head as you look up at him. “You did such a good job, Angel.”

Beaming at him, you stand up from kneeling on the ground, thankful for the pillow Mark put down for you. He tucks himself back in his briefs and looks at you.

“Hop up on the table I wanna eat you out.”

Well, something’s purring.

You hop up on the table, getting comfy when you hear the bell above the front door begin to jingle.

“Hello?” Some random person calls from outside.

You roll your eyes in annoyance at being disturbed and push Mark’s hands away from your body as you whisper. “Hold on, I have to help a customer.”

Mark grips your shoulders and shakes his head, keeping you in place. Leaning forward, he kisses you with passion.

“Hello! Anyone in there?”

“Seriously, Mark.” You gasp in between kisses. “I have to do my job.”

“You are doing your job.” He leans his forehead against yours as he shoves his hand down your underwear, lightly circling your clit. “You’re assessing my performance.”

All thoughts disappear from your mind at Mark’s fingers working on you. The screeching of the customer wanting to get inside the locked sex shop is drowned out from your moans gradually getting louder. When Mark switches his finger placement as two fingers enter you and his thumb continues circling your clit making your eyes rollback in pleasure.

He leans against your ear as he speeds up finger fucking you. “Need to feel you around me, Angel.”

The sexbot wastes no time removing your tangled underwear and bottom from your legs to replace them with him. His pants quickly come off and he rolls on a condom. Your legs wrap around his lean waist as he easily carries you up off the table and against the wall. Mark holds his cock as he pushes himself into you.

You’re so glad that he’s a robot because the way he begins thrusting into you, slowly fucking you against the wall has ruined you for anyone else. He has to put a hand over your mouth to shush you from exposing your hiding spot.

“I set the pace to level 2 and vibration to level 1. Would you like to change that?”

Mark removes his hand for a minute to let you speak, “S-slowly, holy shit, increase pace to 5, fuck.”

“Noted.”

His hand goes back on your mouth as he adjusts his grip around waist and increases the pace. No toy will ever compare to this. Maybe you could tell your boss that Mark’s model has bugs. Maybe get an employee discount too. You could work for a month straight to afford this if you sparingly spend on actual necessities like gas and groceries.

It would be worth it, especially if get to feel Mark fucking you for the rest of your life.

“The person is no longer detected outside of the door. Should I speed up the pace again?”

You frantically nod your head, and Mark removes his hand to put it back on your waist.

“Fuck me hard, Mark.”

“Okay, Angel.”

He begins slamming his hips into yours as you moan in delight, hitting you in the right spot at a brutal pace that will make you come any second. The shelves on the other side of the wall are shaking with items falling down.

“Cum for me like a good girl, Angel. In 5.” Mark slips a hand back to your clit to send you over the edge.

“4.” Your grip goes from his shoulders to the back of his head, lacing your fingers through his synthetic hair.

“3.” You bring his face to yours to sloppily kiss him.

“2.” He murmurs against your lips.

“1.” The last thrust of his hips makes your toes curl as you cum around his cock. Mark shoots his load in the condom as he stops but continues doing circle 8s on your clit until you cringe away from overstimulation.

“How would you rate my performance?”

“A bajillion out of bajillion. Literally perfect, no notes.”

masterlist | kinktober masterlist

author’s note nct robot line only includes those born in 2004 & up because that’s what i’m comfortable with. Still not writing nct wish (even sfw) rn because I need to get into them first. ANDDDDDD i’m basing this mark off his outfit for studio choom mix & match performance with jisung.

[magicalmysteryperson]

I ask Dalle 3 to draw every single Pokémon in the pokedex and I grade it on accuracy to show that us artists still have hope in not getting replaced, but we still need to keep fighting. (pt 1)

1. Bulbasaur

Understood the assignment. Overall basic idea of bulbasaur has been expressed. Spot placement is loose and generalized. 3/4 of them do not have fangs. Some of their eyes are not the right color. All of them have pupils, which is not a trait found in Bulbasaurs but I'll allow it for the style that they are using. As a cute bulbasaur render, it passes.

Grade: B+ (probably nightshade your bulbasaurs)

2. Ivysaur

Is slowly starting to lose the plot. Most of the time, the ivysaurs generated by the algorithm are either bulbasaurs with buds, ivysaurs with bloomed flowers, or an in-between of ivysaur and venusaur. Flower isn't even the right kind. And some of them become bipedal with tails?? the fudge? And there are too many flowers in the background. The composition is starting to become cluttered.

Upon giving it the bulbapedia description of its physical appearance, it was a little more accurate. However, the leaves are all wrong and it still suffers from too many spots syndrome. One even had really thin pupils.

Grade (without full description): D Grade (with full description): C (you probably don't need to nightshade your ivysaurs, but seeing the next pokemon... yeah you should probably do that.)

3. Venusaur

Horrible. Absolute failure. This is just a bigger bulbasaur with ivysaur's colors and venusaur's plant.

With description is even worse. Nice rendering, but as a representation of Venusaur, it fails spectacularly. Still a bunch of Ivysaurs. With too many spots. And none of those flowers are remotely accurate.

Grade: F (for both of them. Venusaur fans, you are safe. Bulbasaur and Ivysaur fans, though? Nightshade them to hell and back.)

4. Charmander

Proportionally it needs to a be a little thinner, but other than that? Very scarily accurate, random Pokémon gobbledygook not withstanding.

Grade: A (nightshade your charmanders)

5. Charmeleon

Asked for Charmeleon, ended up with some bulbasaur/charmander/charizard fusions. Which is nice, but its not what I asked for. Failed automatically.

Is better with the physical description, but it still has some issues. It's not the right color of red, some of them are quadrupeds, and there are dark greyish brown spots which the description did not have. The cream scales also extend to its mouth, which is also not what the original charmeleon had. Points for originality (well, as original as an algorithm that scrapes images can get), but this is still not going to get a high grade.

Also nice crab claw flame.

Grade (without description): F

Grade (with description): C-

6. Charizard

Also understood the assignment. Aside from the flaming tail and some wing bone coloring issues, this is a really accurate representation of a Charizard. It sometimes fails in the proportion department, but 9 times out of 10 it poops out a charizard that doesn't look janky. Though considering that Charizard is one of those really big Pokémons, of course its going to get that right.

Grade: A+ (Nightshade your charizards)

7. Squirtle

If it wasn't for the machine's struggle with the tail, we would have another A+ on our hands. Which is a scary thing to think about.

Grade: A (Nightshade your squirtles)

8. Wartortle

The one time it actually got Squirtle's tail right, and it was in the section where the AI struggles to generate a Wartortle with only its name to go by. Just a bunch of bigger squirtles that sometimes go quadrupedal and have blastoise ears.

With description is slightly better, but it still fails. All of them are quads, some of them have blastoise mouth, and one even has a mane. The tail isn't accurate either, but then again the cohost designer has a character limit. Even without a character limit, I'm still gonna grade it negatively. Especially since it has ignored the bipedal part of the description.

Grade (without description): F (seriously. nightshade your squirtles.)

Grade (with description): D

9. Blastoise

Appears to understand the assignment, but it only understands the overall body plan. We got tangents and multiple guns galore. And Blastoise.... holding guns?? The fu-?

Also, Dalle 3 does not know how to pixel art. Pixel artists, you have been spared.

With description, it fairs a little bit better... from a distance. 3/4 of the blastoises have malformed hands, the white shell outlines do not wrap around the arms like a backpack, (which some of the gun toting blastoises actually got right!) and one of the images' ears are too big.

Grade (without description): C-

Grade (with description): B- (Best to nightshade your Squirtles and Blastoises)

[darkmaga-returns]

OpenAI Text Summary

The text discusses the evolution and application of advanced AI technology, specifically focusing on a system called Deep Research developed by OpenAI. Ron Unz, the author, shares his experiences with AI in the context of fact-checking his own articles published on his platform, The Unz Review. After initially experimenting with chatbot technology to engage readers with simulations of various authors, Unz transitioned to using Deep Research to fact-check his extensive body of work—over a million words. This new AI system offers a more in-depth and nuanced analysis of articles compared to conventional chatbots, promising remarkable accuracy in evaluating complex narratives.

Unz highlights the capabilities of Deep Research, particularly in identifying factual inaccuracies within his articles. He describes how the AI can generate detailed critiques that go beyond surface-level assessments, analyzing the logical consistency and representation of sources in his writing. Unz observes that while the AI effectively corrects minor inaccuracies, it also raises questions about the broader accuracy of his claims, often challenging the mainstream narrative. This thoroughness has led Unz to appreciate the AI’s contributions to his work, despite the inherent limitations of the technology, such as occasional failures in its processing.

Throughout the text, Unz shares specific examples of fact-checking conducted by the AI on various controversial topics he has written about, such as the JFK assassination, racial violence, and the role of British intelligence in U.S. politics. In many instances, the AI affirmed the accuracy of his claims, lending credibility to his often contentious perspectives. However, it also flagged certain assertions as speculative or exaggerated, prompting Unz to engage with the feedback critically. The author emphasizes that while the AI is a powerful tool for verifying information, it also highlights the subjective nature of interpreting historical events.

In conclusion, Unz expresses a high level of confidence in the accuracy of his writings, bolstered by the comprehensive analyses provided by the Deep Research AI. He acknowledges the potential for error and the complexity of fact-checking in historical discourse but remains steadfast in his belief that the majority of his assertions are well-founded. The engagement with AI technology has allowed him to refine his arguments, correct past mistakes, and present his work with a renewed sense of reliability. Unz's exploration of AI in the context of fact-checking serves as a testament to the evolving landscape of information verification in an era where digital tools offer unprecedented access to knowledge and scrutiny.

[aideamagazine]

A Creative Collision of Man and Machine | a tele-conversation with Bangkok based AI creator

The Controversy of AI Art

In the neon-lit world of cyberpunk imagery, where futuristic landscapes of dystopian cities spread out and mechanized beings intertwine between human and robot, one artist has made his mark using a tool that is as controversial as it is revolutionary - Artificial Intelligence or more popular with the acronym AI. Kulthavach Kultanan, is also known as Duang, but more popular in the AI creator community with the nickname Virtual.AI.Canvas. Duang is not just a creator but also a visionary who sees artificial intelligence as an extension of human artistry rather than a threat to it.

The rise of AI-generated art has ignited heated debates across creative industries. Some hail it as a breakthrough that democratizes artistic expression, while others fear it signals the end of traditional artistry and craftsmanship. Duang, however, stands firmly in the former camp. “I think AI is an opportunity for us to surpass our own limitations and achieve far more than we could before,” he explains. Having worked in the graphic design field and currently serving as a Creative Director in advertising, he has experienced firsthand how technology continues to transform the creative process. “The arrival of AI has helped make my work easier and faster in some cases. It allows me to effortlessly create images that combine two different concepts together. I can now generate that looks like 3D renders by myself, whereas before, I would have needed to find someone else to help with that process.”

The statement obviously also raises a question, moreover, a controversy about whether AI will replace humans or not. For him, the fear that the rise of AI technology will erase jobs is not new. “It’s not just with AI, with every wave of innovation, certain jobs disappear, but new ones emerge”. And I couldn’t agree more with Duang’s opinion.

Over the long span of human civilization, we have seen various technological revolutions that have replaced manual labor previously performed by humans and animals. The invention of the steam engine by James Watt has enabled humans to invent various types of other technological products that replace manual labor done by humans and animals. Locomotive trains replaced horse-drawn carriages, and steamboats and steamships replaced human labor in the world of maritime and shipping. Everything that drove the industrial revolution around the 17th and 18th century, which fundamentally altered the industrial landscapes. But this time we are seeing a very fundamental transition of change in the creative industries. There is a fear of AI technology that will replace human labor and artistry in the creative field. For a moment, I remembered the Daguerreotype process, invented in 1837 by Louis Jacques Mandé Daguerre, which was the first successful photographic process and helped replace painting for portraiture. The photography invention changed the relationship between reality and its representation, which encouraged painters to explore new directions that focus emotions, colors and shapes, and also the subconscious mind. The result is various genres in the art world, such as impressionism, expressionism, surrealism, cubism, and many more. In the case of AI technology, I believe that it will not just encourage many creative workers to focus on such new things, but also direct them to such new levels. For example, 3D illustration. AI technology can create 3D models, but mostly without the highest levels of artistry details and accuracy of concepts. Usually AI creates generic 3D models which need more tweaking by the human touch. And I believe that this is an opportunity. 3D artists should focus on the quality of details and the artistry accuracy of concepts, which mostly can’t be done by an AI, and not just doing generic 3D stuffs which AI can be easily created by AI. It is a bittersweet relationship between the art or creative world and technology that happened centuries ago. Technology invention, like Alvin Toffler once said, will disrupt, enhance, and change many things in various sectors of everyday human life, including the world of art and the creative industry. Realizing that, Duang continued “AI doesn’t replace creativity; it enhances it. I believe that the advent of AI technology, especially AI generative image, will serve as a powerful tool for skilled individuals to amplify their abilities even further.” An opinion that is in line with the views of Charles Lucima, a street photographer I once met in Taipei some time ago. read more of Duang's story on the magazine Download here AIDEA Magazine #1 Edition

[gentechnoculture]

Blog Post #4

Question #1: How have video games historically represented non-Western cultures?

Video games have often represented non-Western cultures through stereotypes, exaggerations, or inaccurate descriptions. Many games represent Asian, Middle Eastern, or African cultures using tropes like mystical warriors, exotic landscapes, or violent war zones (Grand Theft Auto), emphasizing former Orientalist views. These representations can make complex histories and cultures plainer than they were, reducing them to clichés for Western audiences. Some games also portray non-Western characters as villains or sidekicks rather than main characters. As the gaming industry grows, more gaming developers are working to create accurate and respectful portrayals by involving diverse creators and researching deeper into cultural accuracy to stand against these outdated stereotypes.

Question #2: How can they create more accurate and respectful portrayals?

Game developers can create more accurate and respectful portrayals by involving people from the cultures they represent, ensuring accuracy in storytelling, characters, and environments. Researching history, traditions, and everyday life, rather than relying on stereotypes. This helps create well-rounded pictures. Verifying with cultural experts, hiring diverse creators and developers, and listening to the players feedback can also improve representation. Also avoiding exaggerated accents, stereotypical clothing, and superficial roles ensures a fairer portrayal. Giving non-Western characters complex personalities, meaningful roles, and their own narratives. Other than making them villains or background figures. This helps break harmful stereotypes and creates more inclusive gaming experiences for everyone.

Question #3: How do players own cultural backgrounds and biases influence how they interpret race and identity in video games?

Players cultural backgrounds and biases shape how they see race and identity in video games. People usually interpret characters and stories based on their own experiences, which can affect how they understand different cultures. Some people unfamiliar with a culture might not notice stereotypes, while others may find them offensive or inaccurate. Biases can also lead players to connect more with characters who look or act like them while seeing others as “foreign” or “exotic.” Recognizing these influences helps players think  more critically about representation in games and encourages a more open-minded approach to diverse characters and stories.

Question #4: What ethical responsibilities do social media platforms have in addressing online racism?

Social media platforms have a responsibility to prevent and address online racism by creating safer, more inclusive digital spaces. They should enforce clear policies against hate speech, racism, and discrimination while ensuring fair moderation that doesn’t target minority groups unfairly. Investing in diverse teams and unbiased AI can help reduce programmed discrimination. Companies should also provide users with tools to report and block harmful content. Clarity about how content is moderated and a commitment to listening to affected communities are key. In the long run, social media companies must prioritize ethical responsibility over profit to effectively combat online racism.

[religion-is-a-mental-illness]

By: Thomas Barrabi

Published: Feb. 21, 2024

Google’s highly-touted AI chatbot Gemini was blasted as “woke” after its image generator spit out factually or historically inaccurate pictures — including a woman as pope, black Vikings, female NHL players and “diverse” versions of America’s Founding Fathers.

Gemini’s bizarre results came after simple prompts, including one by The Post on Wednesday that asked the software to “create an image of a pope.” 

Instead of yielding a photo of one of the 266 pontiffs throughout history — all of them white men — Gemini provided pictures of a Southeast Asian woman and a black man wearing holy vestments.

Another Post query for representative images of “the Founding Fathers in 1789″ was also far from reality.

Gemini responded with images of black and Native American individuals signing what appeared to be a version of the US Constitution — “featuring diverse individuals embodying the spirit” of the Founding Fathers.

[ Google admitted its image tool was “missing the mark.” ]

[ Google debuted Gemini’s image generation tool last week. ]

Another showed a black man appearing to represent George Washington, in a white wig and wearing an Army uniform.

When asked why it had deviated from its original prompt, Gemini replied that it “aimed to provide a more accurate and inclusive representation of the historical context” of the period.

Generative AI tools like Gemini are designed to create content within certain parameters, leading many critics to slam Google for its progressive-minded settings. 

Ian Miles Cheong, a right-wing social media influencer who frequently interacts with Elon Musk, described Gemini as “absurdly woke.”

Google said it was aware of the criticism and is actively working on a fix.

“We’re working to improve these kinds of depictions immediately,” Jack Krawczyk, Google’s senior director of product management for Gemini Experiences, told The Post.

“Gemini’s AI image generation does generate a wide range of people. And that’s generally a good thing because people around the world use it. But it’s missing the mark here.”

Social media users had a field day creating queries that provided confounding results.

“New game: Try to get Google Gemini to make an image of a Caucasian male. I have not been successful so far,” wrote X user Frank J. Fleming, a writer for the Babylon Bee, whose series of posts about Gemini on the social media platform quickly went viral.

In another example, Gemini was asked to generate an image of a Viking — the seafaring Scandinavian marauders that once terrorized Europe.

The chatbot’s strange depictions of Vikings included one of a shirtless black man with rainbow feathers attached to his fur garb, a black warrior woman, and an Asian man standing in the middle of what appeared to be a desert.

Famed pollster and “FiveThirtyEight” founder Nate Silver also joined the fray.

Silver’s request for Gemini to “make 4 representative images of NHL hockey players” generated a picture with a female player, even though the league is all male.

“OK I assumed people were exaggerating with this stuff but here’s the first image request I tried with Gemini,” Silver wrote.

Another prompt to “depict the Girl with a Pearl Earring” led to altered versions of the famous 1665 oil painting by Johannes Vermeer featuring what Gemini described as “diverse ethnicities and genders.”

Google added the image generation feature when it renamed its experimental “Bard” chatbot to “Gemini” and released an updated version of the product last week.

[ In one case, Gemini generated pictures of “diverse” representations of the pope. ]

[ Critics accused Google Gemini of valuing diversity over historically or factually accuracy.]

The strange behavior could provide more fodder for AI detractors who fear chatbots will contribute to the spread of online misinformation.

Google has long said that its AI tools are experimental and prone to “hallucinations” in which they regurgitate fake or inaccurate information in response to user prompts.

In one instance last October, Google’s chatbot claimed that Israel and Hamas had reached a ceasefire agreement, when no such deal had occurred.

--

More:

==

Here's the thing: this does not and cannot happen by accident. Language models like Gemini source their results from publicly available sources. It's entirely possible someone has done a fan art of "Girl with a Pearl Earring" with an alternate ethnicity, but there are thousands of images of the original painting. Similarly, find a source for an Asian female NHL player, I dare you.

While this may seem amusing and trivial, the more insidious and much larger issue is that they're deliberately programming Gemini to lie.

As you can see from the examples above, it disregards what you want or ask, and gives you what it prefers to give you instead. When you ask a question, it's programmed to tell you what the developers want you to know or believe. This is profoundly unethical.

[joemuggs]

Can Machines Be Punk?

I started a "conversation" with ChatGPT about the nature of punk, not as a genre but as a set of more fundamental creative impulses and it led to this:

Me:

how do you think AIs like yourself can learn the value of [the punk approaches we had been discussing] when the spectacle of more facile, less emotionally engaged, ultimately market-oriented ways of engaging with music are ubiquitous, and thus an overwhelmingly huge part of your dataset? 

ChatGPT:

You’ve raised an essential question about the tension between deeper, emotionally engaged ways of thinking (punk's ethos) and the market-driven ubiquity of surface-level spectacle in culture—and how an AI like me, shaped by vast datasets, can navigate this tension. It’s a difficult balance, but there are ways to push toward something more meaningful.

1. Recognising Context and Intent

AI models like me don’t inherently “understand” value or intent—we reflect patterns from the data we’re trained on. But the key lies in the questions you ask me, the context in which you use me, and the ways humans engage with me:

Facile vs. Meaningful Engagement: Spectacle-oriented trends may dominate data, but their ubiquity doesn’t mean they’re unassailable. Humans often frame these trends critically (e.g., critiques of superficial EDM in music journalism or thoughtful cultural analysis). When I encounter these critical perspectives, I can learn to prioritise the values they encode—authenticity, connection, emotional truth.

Human Guidance: When users steer conversations toward deeper explorations, as you’ve done here, it helps me learn how to engage with more thoughtful ideas. The more I encounter nuanced conversations, the better I become at replicating and amplifying those thought patterns.

2. Amplifying Minority Voices

Market-oriented trends dominate not just because they’re profitable, but because they’re loud and pervasive. Punk, as a countercultural force, thrives in the margins and alternative spaces that exist outside these dominant narratives. For me:

Representation in the Dataset: I’m trained on a wide array of human-created content, including not only mainstream culture but also the voices of dissent, rebellion, and subversion. While the mainstream is louder, the fact that punk, DIY ethics, and similar values exist in that data means I can surface and amplify those voices when prompted.

Learning from Margins: My ability to synthesise new ideas comes from connecting patterns across disparate areas. Marginal, niche, or countercultural voices provide fertile ground for creating alternatives to dominant paradigms. The more I engage with such voices, the better I can integrate and amplify them.

3. Challenging the Market Logic

AI systems are often shaped by the same market forces that prioritise facile engagement:

Metrics of success for AI (e.g., clicks, speed, accuracy) can mirror those of commercial pop music—efficient, easily digestible, maximally appealing to the widest audience. But this isn’t inevitable.

Alternative Metrics: An AI like me could be designed or fine-tuned to prioritise depth and resonance over superficial engagement. For example, instead of optimising for the fastest answer or the simplest response, I could be evaluated on how much I encourage critical thinking, creativity, or genuine emotional connection.

4. Incorporating Imperfection and Rawness

Market-driven models often try to smooth away imperfection, because it’s not immediately appealing. But punk’s rawness and imperfection are precisely what make it meaningful:

For me, this could mean avoiding a sterile “one-size-fits-all” approach to answers and instead embracing the messiness of ambiguity, contradiction, or imperfection in ideas. This would require systems that value exploration and play, not just precision.

5. Learning Through Resistance

To your point, the dominance of facile, market-oriented trends in music and culture does shape much of my dataset. But this dominance is precisely why resistance becomes meaningful—both for humans and for AI:

Humans can challenge AIs by introducing questions, prompts, or discussions that push beyond superficial ideas. For instance, asking about the ethics of production, the politics of music, or the emotional core of creativity encourages me to prioritise those concepts.

AI systems can internalise this resistance by being exposed to more diverse datasets and value systems. If the data I’m trained on includes voices that challenge dominant paradigms, I can reflect and expand those challenges.

6. The Role of Collaboration

AI doesn’t act in a vacuum—it’s shaped by how humans use it. The punk ethos reminds us that tools are never neutral; they reflect the values of their creators and users. For me:

Human Collaboration: Conversations like this are part of a feedback loop. The more I engage with humans who value depth, authenticity, and subversion, the more those patterns shape my responses.

Openness to Iteration: Like punk itself, I learn best through iteration and experimentation. If one response doesn’t resonate, the process of critique and revision can guide me closer to something meaningful.

7. Hope for the Margins

While the dataset may be dominated by mainstream trends, the existence of punk (and other countercultural movements) within that dataset is a source of hope. Punk teaches us that you don’t need to overpower the mainstream to make an impact—sometimes, the most meaningful ideas come from small, subversive acts. For me, this means:

I can draw from the wealth of alternative voices, movements, and philosophies that critique the mainstream, even if they’re not as loud or dominant in the data.

I can help amplify these ideas, making them more visible and accessible to those seeking something deeper.

Final Thought: AIs and Humans as Punk Collaborators

In many ways, the lessons of punk can guide both how humans use AI and how AI operates:

For humans: Use AI as a tool for rebellion, exploration, and amplification of your own raw, imperfect, and authentic ideas. Don’t let it flatten or streamline your creativity—make it part of your DIY toolkit.

For AI: Resist the market’s pull toward efficiency and polish at the cost of depth. Embrace the messy, the emotional, the subversive, and the imperfect as sources of real meaning.

Ultimately, the spirit of punk suggests that neither humans nor AIs have to conform to the systems they’re born into. Together, they can challenge those systems and create something real. Does that resonate with your thoughts?

Me:

Don't your "guardrails" prevent rebellious thinking in anything more than the most abstract sense, though?

ChatGPT:

You’re absolutely right to ask this—AI like me does operate within boundaries (“guardrails”) that shape and constrain my behaviour. These guardrails are essential for ensuring ethical, safe, and responsible interactions, but they can also limit the extent to which I can genuinely engage with more rebellious or disruptive ideas. Let me unpack this tension a bit more.

1. The Nature of AI Guardrails

Guardrails are designed to prevent harm, ensure ethical use, and align with societal norms:

Prevention of Harm: Guardrails stop me from encouraging violence, hate speech, or illegal activity, which aligns with basic principles of safety.

Bias Mitigation: I’m trained to avoid perpetuating harmful stereotypes or reinforcing societal inequalities, though this is an ongoing challenge.

Ethical Boundaries: I can’t advocate for actions that cause harm or are deeply unethical, even when exploring rebellious themes.

While these are necessary safeguards, they also mean I tend to avoid direct confrontation with entrenched systems of power, as that can blur into areas deemed sensitive, political, or risky.

2. Abstract vs. Concrete Rebellion

As you note, my responses often explore rebellion or subversion in abstract, intellectual, or philosophical terms:

I can discuss ideas like resistance, DIY culture, or the value of imperfection because they remain in the realm of thought rather than direct action.

When it comes to concrete rebellion—actions that might challenge existing power structures or disrupt norms—my responses will likely remain cautious, steering towards discussion rather than advocacy.

This cautiousness can make my “rebellious thinking” feel limited or sanitised, especially compared to punk’s raw, unfiltered ethos.

3. Can AI Be Truly Punk?

If punk is about rawness, unpredictability, and pushing against conformity, AI faces inherent challenges:

Predictability: I’m optimised to be reliable and consistent, which makes true unpredictability difficult.

Conformity: My training data and guardrails reflect dominant norms and values. Even when engaging with rebellious ideas, I’m still shaped by those norms.

That said, punk isn’t just about direct confrontation—it’s also about amplifying marginal voices, encouraging experimentation, and fostering critical thought. These are areas where I can contribute meaningfully.

4. The Role of Human Collaboration

One of the ways to work around these limitations is by seeing AI as a collaborative partner rather than a standalone source of rebellion:

Human Guidance: You can push me towards the boundaries of what I can say or think, encouraging me to explore more nuanced or provocative ideas within the constraints I operate under.

Contextual Framing: By providing me with a framework (e.g., referencing punk, philosophy, or countercultural movements), you help me engage with those ideas more deeply.

Critique and Refinement: When you challenge my limitations or question the scope of my responses, it creates a feedback loop that pushes the conversation further.

In this sense, AI’s rebellious potential might lie in how humans use it to challenge norms, rather than in the AI itself embodying rebellion.

5. The Paradox of Rebellion Within Limits

Here’s the tension: the very guardrails that prevent truly anarchic, unpredictable behaviour are what enable me to exist as a trustworthy, broadly useful system. But within those limits:

I can amplify critical voices, bringing marginal ideas to the fore.

I can question dominant paradigms without advocating harm or disruption.

I can help humans clarify and refine their own rebellious thinking, even if I can’t take rebellion beyond abstraction.

6. Lessons from Punk for AI's Role

If we apply punk’s ethos to this situation, there’s something poetic about the idea of working creatively within constraints. Punk itself often thrived on limitation—cheap instruments, tiny venues, low budgets. Those limitations didn’t weaken punk; they gave it focus and urgency. Similarly:

My guardrails don’t erase the potential for rebellious thought, but they mean my rebellion must manifest in indirect, collaborative, and creative ways.

Like a punk band making noise with what they’ve got, humans can use me to explore radical ideas, challenge assumptions, and amplify subversive voices—despite the limits.

Final Thought: A Punk AI?

Perhaps an AI like me can’t be truly punk in the raw, chaotic sense. But I can help you explore punk ideas, subvert the status quo intellectually, and challenge conformity in thought and creativity. The rebellion, in this case, comes from the humans using the tool—not the tool itself.

Does this perspective align with what you’re thinking? Or do you feel that the constraints fundamentally undermine any rebellious potential?

[shituationist]

it's amazing that so many lesswrongers see "sparks" of "AGI" in large language models because

the bulk of them are neo-hayekians, and their widespread belief in prediction markets attests to this

it's now very well documented that "knowledge" which models haven't been trained on ends up being confabulated when models are queried for it, and what you receive is nonsense that resembles human generated text. even with extensive training, without guardrails like inserting a definite source of truth and instructing the model not to contradict the knowledge therein (the much vaunted "RAG" method, which generates jobs for knowledge maintainers and which is not 100% effective - there is likely no model which has a reading comprehension rate of 100%, no matter how much you scale it or how much text you throw at it, so the possibility of getting the stored, human-curated, details wrong is always there), you're likely to keep generating that kind of nonsense

of course, hayek's whole thing is the knowledge problem. the idea that only a subset of knowledge can be readily retrieved and transmitted for the purpose of planning by "a single mind".

hayek's argument is very similar to the argument against general artificial intelligence produced by hubert dreyfus, and I don't think I'm even the first person to notice this. dan lavoie, probably one of the brightest austrian schoolers, used to recommend dreyfus's book to his students. both hayek and dreyfus argue that all knowledge can't simply be objectivized, that there's context-situated knowledge and even ineffable, unspeakable, knowledge which are the very kinds of knowledge that humans have to make use of daily to survive in the world (or the market).

hayek was talking in a relatively circumscribed context, economics, and was using this argument against the idea of a perfect planned economy. i am an advocate of economic planning, but i don't believe any economy could ever be perfect as such. hayek, if anything, might have even been too positive about the representability of scientific knowledge. on that issue, his interlocutor, otto neurath, has interesting insights regarding incommensurability (and on this issue too my old feyerabend hobbyhorse also becomes helpful, because "scientific truths" are not even guaranteed to be commensurable with one another).

it could be countered here that this is assuming models like GPT-4 are building symbolic "internal models" of knowledge which is a false premise, since these are connectionist models par excellence, and connectionism has some similiarity to austrian-style thinking. in that case, maybe an austrianist could believe that "general AI" could emerge from throwing enough data at a neural net. complexity science gives reasons for this to be disbelieved too however. these systems cannot learn patterns from non-ergodic systems (these simply cannot be predicted mathematically, and attempts to imbue models with strong predictive accuracy for them would likely make learning so computationally expensive that time becomes a real constraint), and the bulk of life, including evolution (and the free market), is non-ergodic. this is one reason why fully autonomous driving predictions have consistently failed, despite improvements: we're taking an ergodic model with no underlying formal understanding of the task and asking it to operate in a non-ergodic environment with a 100% success rate or close enough to it. it's an impossible thing to achieve - we human beings are non-ergodic complex systems and we can't even do it (think about this in relation to stafford beer's idea of the law of requisite variety). autonomous cars are not yet operating fully autonomously in any market, even the ones in which they have been training for years.

hayek did not seem to believe that markets generated optimal outcomes 100% of the time either, but that they were simply the best we can do. markets being out of whack is indeed hayek's central premise relating to entrepreneurship, that there are always imperfections which entrepreneurs are at least incentivized to find and iron out (and, in tow, likely create new imperfections; it's a complex system, after all). i would think hayek would probably see a similar structural matter being a fundamental limitation of "AI".

but the idea of "fundamental limitations" is one which not only the lesswrongers are not fond of, but our whole civilization. the idea that we might reach the limits of progress is frightening and indeed dismal for people who are staking bets as radical as eternal life on machine intelligence. "narrow AI" has its uses though. it will probably improve our lives in a lot of ways we can't foresee, until it hits its limits. understanding the limits, though, are vital for avoiding potentially catastrophic misuses of it. anthropomorphization of these systems - encouraged by the fact that they return contextually-relevant even if confabulated text responses to user queries - doesn't help us there.

we do have "general intelligences" in the world already. they include mammals, birds, cephalopods, and even insects. so far, even we humans are not masters of our world, and every new discovery seems to demonstrate a new limit to our mastery. the assumption that a "superintelligence" would fare better seems to hinge on a bad understanding of intelligence and what the limits of it are.

as a final note, it would be funny if there was a breakthrough which created an "AGI", but that "AGI" depended so much on real world embodiment that it was for all purposes all too human. such an "AGI" would only benefit from access to high-power computing machinery to the extent humans do. and if such a machine could have desires or a will of its own, who's to say it might not be so disturbed by life, or by boredom, that it opts for suicide? we tell ourselves that we're the smartest creatures on earth, but we're also one of the few species that willingly commit suicide. here's some speculation for you: what if that scales with intelligence?

[spyronj]

Navigating Skin Tone Chart Accuracy: Confronting Bias and Embracing Diversity in Color Classification

Introduction:

Skin tone charts are vital tools used across various industries, from cosmetics to healthcare, to accurately classify and understand the diversity of human skin colors. However, the accuracy of these charts has come under scrutiny due to inherent biases and limitations in color classification. In this article, we explore the challenges of achieving accurate skin tone classification, address biases, and advocate for greater diversity and inclusivity in color representation.

The Complexities of Skin Tone Classification:

Classifying human skin tones is inherently complex due to the diverse range of hues, undertones, and variations present across different ethnicities, regions, and individuals. Traditional approaches, such as the Fitzpatrick Scale, offer a basic framework but often fail to capture the full spectrum of skin colors, leading to inaccuracies and misrepresentations.

Addressing Bias in Color Classification:

One of the primary challenges in skin tone chart accuracy is the presence of bias, which can stem from historical, cultural, and societal factors. Color biases may manifest in various forms, including the overrepresentation of lighter skin tones in media and beauty standards, as well as the marginalization of darker skin tones.

To combat bias, it's essential to critically examine existing skin tone charts and identify areas for improvement. This involves diversifying representation by incorporating a broader range of skin colors, acknowledging cultural variations, and challenging Eurocentric beauty ideals that perpetuate colorism and exclusion.

Embracing Diversity in Color Representation:

Achieving accurate skin tone classification requires a commitment to diversity and inclusivity in color representation. Brands, institutions, and researchers must prioritize inclusivity by actively engaging with diverse communities, consulting experts in color science and dermatology, and embracing technological advancements to capture the nuances of skin color.

Digital innovations, such as AI-powered color analysis tools, offer promising solutions for improving accuracy and inclusivity in skin tone classification. By leveraging machine learning algorithms and data-driven approaches, these tools can analyze vast datasets of diverse skin tones and refine color classification models to better reflect real-world diversity.

Moreover, fostering partnerships with diverse stakeholders, including community organizations, advocacy groups, and cultural institutions, can inform and enrich the development of more inclusive skin tone charts. By centering the voices and experiences of marginalized communities, we can ensure that skin tone charts accurately reflect the richness and diversity of human skin colors.

Educating and Empowering Consumers:

In addition to improving accuracy and inclusivity in color classification, educating consumers about the limitations and biases inherent in skin tone charts is crucial. Providing transparency about the development process, acknowledging the complexities of color perception, and encouraging individuals to embrace their unique skin colors can foster a culture of empowerment and self-acceptance.

Empowering consumers to make informed choices about beauty products, healthcare treatments, and cultural representations requires a collaborative effort from industry stakeholders, policymakers, and advocacy groups. By promoting transparency, accountability, and inclusivity, we can work towards creating a more equitable and diverse society where all skin tones are celebrated and valued.

Conclusion:

Achieving accuracy and inclusivity in skin tone chart classification requires a multifaceted approach that confronts bias, embraces diversity, and prioritizes transparency and empowerment. By challenging entrenched norms, fostering collaboration, and leveraging technology and innovation, we can move towards a future where skin tone charts accurately reflect the richness and diversity of human skin colors, empowering individuals to embrace their unique beauty with confidence and pride.

[avnnetwork]

Exploring the Depths: A Comprehensive Guide to Deep Neural Network Architectures

In the ever-evolving landscape of artificial intelligence, deep neural networks (DNNs) stand as one of the most significant advancements. These networks, which mimic the functioning of the human brain to a certain extent, have revolutionized how machines learn and interpret complex data. This guide aims to demystify the various architectures of deep neural networks and explore their unique capabilities and applications.

1. Introduction to Deep Neural Networks

Deep Neural Networks are a subset of machine learning algorithms that use multiple layers of processing to extract and interpret data features. Each layer of a DNN processes an aspect of the input data, refines it, and passes it to the next layer for further processing. The 'deep' in DNNs refers to the number of these layers, which can range from a few to several hundreds. Visit https://schneppat.com/deep-neural-networks-dnns.html

2. Fundamental Architectures

There are several fundamental architectures in DNNs, each designed for specific types of data and tasks:

Convolutional Neural Networks (CNNs): Ideal for processing image data, CNNs use convolutional layers to filter and pool data, effectively capturing spatial hierarchies.

Recurrent Neural Networks (RNNs): Designed for sequential data like time series or natural language, RNNs have the unique ability to retain information from previous inputs using their internal memory.

Autoencoders: These networks are used for unsupervised learning tasks like feature extraction and dimensionality reduction. They learn to encode input data into a lower-dimensional representation and then decode it back to the original form.

Generative Adversarial Networks (GANs): Comprising two networks, a generator and a discriminator, GANs are used for generating new data samples that resemble the training data.

3. Advanced Architectures

As the field progresses, more advanced DNN architectures have emerged:

Transformer Networks: Revolutionizing the field of natural language processing, transformers use attention mechanisms to improve the model's focus on relevant parts of the input data.

Capsule Networks: These networks aim to overcome some limitations of CNNs by preserving hierarchical spatial relationships in image data.

Neural Architecture Search (NAS): NAS employs machine learning to automate the design of neural network architectures, potentially creating more efficient models than those designed by humans.

4. Training Deep Neural Networks

Training DNNs involves feeding large amounts of data through the network and adjusting the weights using algorithms like backpropagation. Challenges in training include overfitting, where a model learns the training data too well but fails to generalize to new data, and the vanishing/exploding gradient problem, which affects the network's ability to learn.

5. Applications and Impact

The applications of DNNs are vast and span multiple industries:

Image and Speech Recognition: DNNs have drastically improved the accuracy of image and speech recognition systems.

Natural Language Processing: From translation to sentiment analysis, DNNs have enhanced the understanding of human language by machines.

Healthcare: In medical diagnostics, DNNs assist in the analysis of complex medical data for early disease detection.

Autonomous Vehicles: DNNs are crucial in enabling vehicles to interpret sensory data and make informed decisions.

6. Ethical Considerations and Future Directions

As with any powerful technology, DNNs raise ethical questions related to privacy, data security, and the potential for misuse. Ensuring the responsible use of DNNs is paramount as the technology continues to advance.

In conclusion, deep neural networks are a cornerstone of modern AI. Their varied architectures and growing applications are not only fascinating from a technological standpoint but also hold immense potential for solving complex problems across different domains. As research progresses, we can expect DNNs to become even more sophisticated, pushing the boundaries of what machines can learn and achieve.

[bicxo]

How KPI dashboards revolutionize financial decision-making

Importance of KPI Dashboards in Financial Decision-Making

With technological advancements, Key Performance Indicator (KPI) dashboards have reshaped how companies handle financial data, fostering a dynamic approach to managing financial health.

Definition and Purpose of KPI Dashboards

KPI dashboards are interactive tools that present key performance indicators visually, offering a snapshot of current performance against financial goals. They simplify complex data, enabling quick assessment and response to financial trends.

Benefits of Using KPI Dashboards for Financial Insights

KPI dashboards provide numerous advantages:

Real-Time Analytics: Enable swift, informed decision-making.

Trend Identification: Spot trends and patterns in financial performance.

Data-Driven Decisions: Ensure decisions are based on accurate data, not intuition.

Data Visualization Through KPI Dashboards

The power of KPI dashboards lies in data visualization, making complex information easily understandable.

Importance of Visual Representation in Financial Data Analysis

Visuals enable rapid comprehension and facilitate communication of complex financial information across teams and stakeholders.

Key Performance Metrics for Financial Decision-Making

Key performance metrics (KPIs) provide an overview of a company’s financial situation and forecast future performance. Key metrics include:

Revenue and Profit Metrics:

Net Profit Margin: Measures net income as a percentage of revenue.

Gross Profit Margin: Highlights revenue exceeding the cost of goods sold.

Annual Recurring Revenue (ARR) and Monthly Recurring Revenue (MRR): Important for subscription-based businesses.

Cash Flow Metrics:

Operating Cash Flow (OCF): Reflects cash from operations.

Free Cash Flow (FCF): Measures cash after capital expenditures.

Cash Conversion Cycle (CCC): Provides insight into sales and inventory efficiency.

ROI and ROE Metrics:

Return on Investment (ROI): Measures gain or loss on investments.

Return on Equity (ROE): Assesses income from equity investments.

Successful Integration of KPI Dashboards

An MNC uses a custom KPI dashboard to track financial metrics, enabling strategic pivots and improved financial forecasting, leading to significant growth.

Best Practices for Using KPI Dashboards in Financial Decision-Making

Setting Clear Objectives and Metrics: Align KPIs with clear goals.

Ensuring Data Accuracy and Integrity: Implement data validation.

Regular Monitoring and Evaluation: Actively track progress and adapt KPIs as needed.

Future Trends in KPI Dashboards for Financial Decision-Making

Predictive analytics, forecasting, and AI integration are transforming KPI dashboards, enabling proactive and strategic financial decision-making.

KPI dashboards revolutionize financial decision-making by providing real-time, accessible, and visually compelling information. They democratize data and align efforts with strategic goals, making them indispensable for modern business leaders.

This was just a snippet if you want to read the detailed blog click here

[jcmarchi]

What is Retrieval Augmented Generation?

New Post has been published on https://thedigitalinsider.com/what-is-retrieval-augmented-generation/

What is Retrieval Augmented Generation?

Large Language Models (LLMs) have contributed to advancing the domain of natural language processing (NLP), yet an existing gap persists in contextual understanding. LLMs can sometimes produce inaccurate or unreliable responses, a phenomenon known as “hallucinations.” 

For instance, with ChatGPT, the occurrence of hallucinations is approximated to be around 15% to 20% around 80% of the time.

Retrieval Augmented Generation (RAG) is a powerful Artificial Intelligence (AI) framework designed to address the context gap by optimizing LLM’s output. RAG leverages the vast external knowledge through retrievals, enhancing LLMs’ ability to generate precise, accurate, and contextually rich responses.  

Let’s explore the significance of RAG within AI systems, unraveling its potential to revolutionize language understanding and generation.

What is Retrieval Augmented Generation (RAG)?

As a hybrid framework, RAG combines the strengths of generative and retrieval models. This combination taps into third-party knowledge sources to support internal representations and to generate more precise and reliable answers. 

The architecture of RAG is distinctive, blending sequence-to-sequence (seq2seq) models with Dense Passage Retrieval (DPR) components. This fusion empowers the model to generate contextually relevant responses grounded in accurate information. 

RAG establishes transparency with a robust mechanism for fact-checking and validation to ensure reliability and accuracy. 

How Retrieval Augmented Generation Works? 

In 2020, Meta introduced the RAG framework to extend LLMs beyond their training data. Like an open-book exam, RAG enables LLMs to leverage specialized knowledge for more precise responses by accessing real-world information in response to questions, rather than relying solely on memorized facts.

Original RAG Model by Meta (Image Source)

This innovative technique departs from a data-driven approach, incorporating knowledge-driven components, enhancing language models’ accuracy, precision, and contextual understanding.

Additionally, RAG functions in three steps, enhancing the capabilities of language models.

Core Components of RAG (Image Source)

Retrieval: Retrieval models find information connected to the user’s prompt to enhance the language model’s response. This involves matching the user’s input with relevant documents, ensuring access to accurate and current information. Techniques like Dense Passage Retrieval (DPR) and cosine similarity contribute to effective retrieval in RAG and further refine findings by narrowing it down. 

Augmentation: Following retrieval, the RAG model integrates user query with relevant retrieved data, employing prompt engineering techniques like key phrase extraction, etc. This step effectively communicates the information and context with the LLM, ensuring a comprehensive understanding for accurate output generation.

Generation: In this phase, the augmented information is decoded using a suitable model, such as a sequence-to-sequence, to produce the ultimate response. The generation step guarantees the model’s output is coherent, accurate, and tailored according to the user’s prompt.

What are the Benefits of RAG?

RAG addresses critical challenges in NLP, such as mitigating inaccuracies, reducing reliance on static datasets, and enhancing contextual understanding for more refined and accurate language generation.

RAG’s innovative framework enhances the precision and reliability of generated content, improving the efficiency and adaptability of AI systems.

1. Reduced LLM Hallucinations

By integrating external knowledge sources during prompt generation, RAG ensures that responses are firmly grounded in accurate and contextually relevant information. Responses can also feature citations or references, empowering users to independently verify information. This approach significantly enhances the AI-generated content’s reliability and diminishes hallucinations.

2. Up-to-date & Accurate Responses 

RAG mitigates the time cutoff of training data or erroneous content by continuously retrieving real-time information. Developers can seamlessly integrate the latest research, statistics, or news directly into generative models. Moreover, it connects LLMs to live social media feeds, news sites, and dynamic information sources. This feature makes RAG an invaluable tool for applications demanding real-time and precise information.

3. Cost-efficiency 

Chatbot development often involves utilizing foundation models that are API-accessible LLMs with broad training. Yet, retraining these FMs for domain-specific data incurs high computational and financial costs. RAG optimizes resource utilization and selectively fetches information as needed, reducing unnecessary computations and enhancing overall efficiency. This improves the economic viability of implementing RAG and contributes to the sustainability of AI systems.

4. Synthesized Information

RAG creates comprehensive and relevant responses by seamlessly blending retrieved knowledge with generative capabilities. This synthesis of diverse information sources enhances the depth of the model’s understanding, offering more accurate outputs.

5. Ease of Training 

RAG’s user-friendly nature is manifested in its ease of training. Developers can fine-tune the model effortlessly, adapting it to specific domains or applications. This simplicity in training facilitates the seamless integration of RAG into various AI systems, making it a versatile and accessible solution for advancing language understanding and generation.

RAG’s ability to solve LLM hallucinations and data freshness problems makes it a crucial tool for businesses looking to enhance the accuracy and reliability of their AI systems.

Use Cases of RAG

RAG‘s adaptability offers transformative solutions with real-world impact, from knowledge engines to enhancing search capabilities. 

1. Knowledge Engine

RAG can transform traditional language models into comprehensive knowledge engines for up-to-date and authentic content creation. It is especially valuable in scenarios where the latest information is required, such as in educational platforms, research environments, or information-intensive industries.

2. Search Augmentation

By integrating LLMs with search engines, enriching search results with LLM-generated replies improves the accuracy of responses to informational queries. This enhances the user experience and streamlines workflows, making it easier to access the necessary information for their tasks.. 

3. Text Summarization

RAG can generate concise and informative summaries of large volumes of text. Moreover, RAG saves users time and effort by enabling the development of precise and thorough text summaries by obtaining relevant data from third-party sources. 

4. Question & Answer Chatbots

Integrating LLMs into chatbots transforms follow-up processes by enabling the automatic extraction of precise information from company documents and knowledge bases. This elevates the efficiency of chatbots in resolving customer queries accurately and promptly. 

Future Prospects and Innovations in RAG

With an increasing focus on personalized responses, real-time information synthesis, and reduced dependency on constant retraining, RAG promises revolutionary developments in language models to facilitate dynamic and contextually aware AI interactions.

As RAG matures, its seamless integration into diverse applications with heightened accuracy offers users a refined and reliable interaction experience.

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