#Generative AI Deployment

[rubylogan15]

Empower your enterprise with Gen AI evaluation—explore AI insights that spark innovation and foster a culture of creativity and success.

[dieterziegler159]

How Does Gen AI Evaluation Help Enterprises Innovate?

Original Source: How Does Gen AI Evaluation Help Enterprises Innovate? Generative AI is revolutionizing the digital landscape, offering enterprises innovative solutions to improve efficiency and maintain a competitive edge. However, integrating this technology comes with its own challenges. This is where the Generative AI Evaluation Service becomes invaluable. In this article, we explore how…

[generative-ai-in-bi]

How Does Gen AI Evaluation Help Enterprises Innovate?

Original Source: How Does Gen AI Evaluation Help Enterprises Innovate?

Generative AI is revolutionizing the digital landscape, offering enterprises innovative solutions to improve efficiency and maintain a competitive edge. However, integrating this technology comes with its own challenges. This is where the Generative AI Evaluation Service becomes invaluable. In this article, we explore how this service helps enterprises overcome obstacles and leverage generative AI effectively.

Understanding the Gen AI Evaluation Service: What is it and How Does it Work?

The Generative AI Evaluation Service is an Enterprise Generative AI Consulting Service designed to assist enterprises interested in adopting generative AI technology. This service involves a comprehensive assessment of an organization’s strengths and areas for improvement, identifies potential use cases for generative AI, and provides tailored recommendations for implementation.

The service typically starts with a detailed consultation to understand the enterprise’s goals, challenges, and existing technological infrastructure. This is followed by an in-depth Enterprise Generative AI Analysis of different models and tools to determine their applicability. It also includes a risk assessment, feasibility study, and a strategic plan for seamless integration into the existing environment.

The Challenges Enterprises Face in Adopting Gen AI

Adopting generative AI presents several challenges for enterprises:

Technical Complexity

Integration with Existing Systems: Ensuring compatibility with current IT infrastructure can be intricate.

Data Quality and Management: High-quality, well-managed data is essential for effective generative AI deployment.

Model Selection and Training: Choosing the right models and training them with relevant data requires specialized expertise.

Operational Challenges

Skill Gaps: Many enterprises lack the in-house expertise needed for generative AI implementation.

Change Management: Transitioning to AI-driven processes necessitates significant organizational change.

Strategic Concerns

Cost: Implementing generative AI can be expensive, requiring substantial investment in technology and talent.

Risk Management: Mitigating risks associated with AI, such as data privacy concerns and ethical considerations, is crucial.

How the Gen AI Evaluation Service Addresses Enterprise Needs

The Generative AI Evaluation Service is tailored to address these challenges by providing expert guidance and support throughout the adoption process. Here’s how it meets enterprise needs:

Expert Consultation: Enterprises gain access to AI specialists who provide strategic advice and technical expertise through Enterprise Generative AI Consulting.

Customized Solutions: The service offers tailored recommendations based on the unique requirements and goals of the enterprise.

Risk Management: Comprehensive risk assessments and mitigation strategies ensure that generative AI implementations are secure and compliant.

Cost Efficiency: By optimizing AI model selection and implementation strategies, the service helps reduce overall costs.

Seamless Integration: The service provides detailed integration plans that ensure smooth adoption of generative AI into existing systems.

Key Benefits of the Gen AI Evaluation Service for Enterprises

The Generative AI Evaluation Service offers several key benefits for enterprises:

Informed Decision-Making: Enterprises receive detailed insights and recommendations, enabling them to make informed decisions about generative AI adoption.

Enhanced Innovation: By leveraging generative AI, enterprises can drive innovation, develop new products and services, and improve operational efficiency.

Competitive Advantage: Early adoption of generative AI can provide a significant competitive edge, allowing enterprises to stay ahead of industry trends.

Scalable Solutions: The service ensures that generative AI solutions are scalable and can be expanded across the enterprise as needed.

Risk Reduction: Comprehensive risk assessments and governance frameworks help mitigate potential risks associated with generative AI.

Evaluating the Capabilities and Limitations of Gen AI Models

Understanding the capabilities and limitations of generative AI models is crucial for successful implementation. The Generative AI Assessment Service conducts a thorough evaluation of various models to determine their suitability for specific use cases.

Capabilities:

Content Generation: Creating high-quality text, images, and other content formats.

Predictive Analytics: Making accurate predictions based on data patterns.

Automation: Automating complex tasks and processes.

Limitations:

Data Dependency: Requires large datasets for effective training.

Bias and Fairness: Models can inherit biases from training data, impacting fairness.

Interpretability: Understanding and explaining AI decisions can be challenging.

The Criteria for Choosing the Right Gen AI Evaluation Partner for Your Enterprise

Selecting the right partner for Generative AI evaluation is essential for developing successful AI-related strategies and projects for an organization. The ideal partner should also have experience in the use of generative AI, especially in consulting and should understand the enterprises sector and requirements. They should be able to present consultative services that include diagnostic, model selection, implementation, and maintenance.

Another important factor to consider is their levels of data security and compliance experience. Due to the specifics of data used for training generative AI models, the partner needs to have efficient data management measures and understand all the necessary legislation. Last but not least, the partner should be able to give specific guidance as to how to proceed in the case of the enterprise and how to avoid or solve the challenges that AI introduction may bring, based on the enterprise’s strategic objectives.

Conclusion: Unlocking the Power of Gen AI with the Right Evaluation Service

The Generative AI Evaluation Service is essential for enterprises looking to effectively adopt and implement generative AI technology. By providing expert guidance, customized solutions, and comprehensive support, this service helps businesses overcome the challenges associated with AI adoption. Ultimately, it empowers enterprises to unlock the full potential of generative AI, driving innovation, efficiency, and growth in a competitive landscape.

Original Source: How Does Gen AI Evaluation Help Enterprises Innovate?

[enterprise-cloud-services]

Gen AI evaluation helps enterprises innovate by optimizing processes and driving growth. Transform your business with advanced AI today.

[ai-innova7ions]

Is AI Regulation Keeping Up? The Urgent Need Explained!

AI regulation is evolving rapidly, with governments and regulatory bodies imposing stricter controls on AI development and deployment. The EU's AI Act aims to ban certain uses of AI, impose obligations on developers of high-risk AI systems, and require transparency from companies using generative AI. This trend reflects mounting concerns over ethics, safety, and the societal impact of artificial intelligence. As we delve into these critical issues, we'll explore the urgent need for robust frameworks to manage this technology's rapid advancement effectively. Stay tuned for an in-depth analysis!

#AIRegulation

#EUAIACT

Video Automatically Generated by Faceless.Video

[silence-ofthe-llamas]

I feel I’m VERY late to the party with the mecha AU considering how bone deep Pacific Rim runs within me but I’m chomping at the bit. Gnawing at it. I LOVE YOU ALL. I’ve reactivated my tumblr for this. Good god. @keferon my leige. I'm meant to be SLEEPING.

Anyway, I’m a general nuisance, I wont be following much of the pre-established lore too closely because of who I am as a person, bone app the teeth.

TexAid for the soul is more potent than Chicken soup.

First Aid wakes up in an ice cold sweat.

It’s not the first time. He’d lost count, actually – it seemed that every morning was the same now. He’d wake up, he’d shudder, he’d carefully extract himself from his damp-with-sweat duvet, he’d shower, and then he’d pretend that everything was perfectly fine and normal.

His function first and foremost was one of a medic. He trained to work with live patients. His expertise was with the living, not the cold stares of the dead.

But lately, all he’d been dealing with were corpses, and it all came down to one reason.

Vortex.

Superstition wasn’t something that he bought into, but the theory on base was that the mech was haunted. At the start, he didn’t believe it – mechanics were plagued with stray code, oddly executed scripts. There was nothing supernatural about it. All of the pilots said that they felt another presence within their mechs with them – there wasn’t anything special about Vortex’s AI. If one wanted to look at it that way, all of their mechs were haunted.

But Vortex was different. Of course he fucking was, why wouldn’t he be. No, no, nothing was allowed to be normal. Ever. Firstly, there was the staring. The mechs weren’t meant to stare, but whenever he went close to Vortex, he could feel his piercing gaze against him. It wasn’t normal. They should have been offline without any human input, but Vortex stayed stubbornly awake and studied his every move. Sometimes he’d swear he could hear his internals humming, the rumble of moving parts, his plating trembling and straining against the dock as he tried to move. If someone got too close to him, he’d hear the hum of weapons systems warming up. It was part of their onboarding process that they were warned against approaching him, now. He’d cut them down without a second thought.

There was also the small fact that he had a tendency to kill his pilots. And it wasn’t even an exaggeration – their means of slaughter always came from within. The cameras that filled the insides didn’t show any breaches, no weapons were brought on board, the vital signs monitors from the pilots and their own helm-mounted cameras showed no foul play of an external parties part. No. It was… Vortex. The mech showed his displeasure in a shower of blood and moving parts – and that was if he was being nice. If they weren’t power washing the remains of a digestive tract from his floor, they were manoeuvring a live body that acted like a dead weight, the pilot a stuttering mess, mentally shattered and broken. They’d never managed to get any of them back into active duty – a lot of them First Aid had no idea what had happened to them. They were simply shipped off somewhere, never to be heard of or seen from again. The worst part of it was that they were all missing fingers, as if they’d been cleaved right off by sharp metal as they reached out for something.

An alarm ripped through the base, and he gagged on his morning coffee. He knew what that meant – deployment. And with deployment came another victim, courtesy of Vortex, and all that horrid stench and morbid fascination that sent his spine tingling and brain firing to the point of insanity that paired so closely with it.

Ambulon frowned at him. “Jittery this morning, Aid.”

“I just know I’ll be on Vortex duty again.” He groaned.

Ambulon patted him comfortingly on the shoulder. “Don’t let it get to you, Aid. Pharma only does it because he trusts you.”

Yeah, right. It’s so I haven’t got an excuse to be by the morgue.

You steal one Quintesson body…

He briefly remembered the smell of the grave dirt as he’d re-interred them into the ground instead of the stone cold morgue, and quickly smelled his coffee instead.

The deployment seemed to last an age. First Aid managed to get through all of his deskwork before they returned, and Vortex staggered into his bay. First Aid was waiting patiently by the gate as the docking station clasped around him, holding him in place as cables came down from the ceiling to plug into him.

“How many bets this guys dead?” Someone behind him asked, elbowing the one stood next to him. First Aid ignored them, focusing intently on the mech.

He could see blood behind the glass. It was leaking out down the side – they were more than dead. They’d been eviscerated.

The visor lifted with a loud hiss, and First Aid took a deep breath. He held it so he didn’t have to inhale the initial stench – that part was always the worst, having been left to fester within him – and carefully studied the scene before him.

Organs hung down from the ceiling. Scraps of fabric hung limply from the still locked harness.

“What did he do to them?” First Aid quietly asked himself as he stepped forwards with a bucket.

There was a rule - you never got inside Vortex on your own. First Aid followed it religiously, and he could hear someone behind him, and so he felt perfectly comfortable in getting inside.

Only the visor snapped shut with a sickening crack as their leg was cleaved clean through, the scream barely muffled by the glass.

“No!” First Aid flew to the glass of the visor, pounding against it. “Are you okay?!”

What a stupid question that had been. Of course he wasn’t okay. The smell in the air burned at his throat and turned his stomach, and he looked down at the dismembered leg.

He couldn’t breathe. Or he was breathing too much? He didn’t know, but his chest ached and his head spun and he felt like ice had been injected straight into his veins, every hair stood on end as panic gripped him. It took every ounce of self control he had to not scream from terror when he heard pistons loudly slam into place, firmly locking the visor.

Oh, god, have mercy.

Emergency exits. These things had them, right? He’d had to pull a barely conscious pilot from one once – he’d gotten trapped in it in a malfunctioned ejection sequence. The button would be big and bright red, surely – and with a protective cover so they didn’t smack it by mistake in the middle of a fight and end up launched into the face of a Quintesson. His eyes scanned wildly, breath catching in his chest as he tried to suck in air that didn’t make him want to vomit, hands hovering over the dash. Mental images of the pilots missing their fingers played in his head like an omen.

There. Bright red. The words were worn off, the plastic scratched. The metal around it was worn and faded from use, and the plastic cover was long gone.

Blood crusted it. He smacked it anyway.

Nothing.

He looked back to where it should have been, hyperventilating. What did that mean? The techs had never found anything to be wrong with it before. Everything was functioning as normal – it was why Vortex was still even allowed to be operated. So why didn’t the emergency escape open?

Red light flooded the cockpit. His teeth chattered together as he slowly turned to look at the display that had lit up, white text running across it.

[LEAVING SO SOON?]

“I’m just a medic.” First Aid pathetically said. He almost bit his tongue.

[TAKE A SEAT]

Tears prickled his eyes as he unbuckled the harness and sat down. He tried to ignore the wet squelch as he sat in what remained of the previous human who sat there.

“What do you need from me?” He tried to sound strong as he asked.

The screen remained blank. The lights slowly dimmed, leaving him in the dark with only the sound of Vortex’s hot systems for company. He tried to calm his breathing, timing it to the rhythmic thunk of a nearby fuel pump, and wrung his fingers together.

It would be okay. It would be okay. Everything was going to be okay-

The chair suddenly flew backwards, and First Aid shrieked. His throat felt raw with how hard he’d screamed, clinging on tightly to whatever he could get his hands on. He studiously kept his limbs away from the console – he had a theory on how they’d lost their digits, and he was not keen on finding out if it was true. The chair snapped back upright again, and he whimpered, tears pooling in his eyes and his bottom lip trembling. The mech shuddered, a grinding sound rumbling through the cockpit and rattling his bones.

[PLUG IN] the screen instructed. A cable fell from the ceiling.

Helmet. He needed a helmet. They had the required port for that cable. He scanned the floor, ignoring the rising nausea as he searched for the helmet from the previous pilot.

There. Behind the chair. He picked it up, and had to look away when he realised the head was still inside. He shook it out, humming loudly to block out the sound of it hitting the floor, and kept his eyes closed as he put it on and ignored how much it stank of organic metal. He reached up for the cable, and gently guided it to the port-

Agony. Burning agony. His back arched as he screamed, hands clutching the helmet as if willing it to stay on despite how hard his legs kicked and thrashed. Electricity coursed straight through him, setting him aflame as his brain tried to catch up with his body.

It hurt. It hurt so much.

First Aid gnashed his teeth together as he fought with his conflicting emotions. He wanted to know why. Why Vortex had trapped him in there, why he had gone to this length to do this to him, why him. But he also wanted to run, to run so far away that he was nothing more than a distant memory. He didn’t want to know why Vortex had taken such an interest in him.

But oh, oh he did. He did want to know what he’d done to catch the AI’s attention.

The pain slowly subsided, the fried nerves numbing to the raw energy that charged through them, and he cracked his eyes open.

[GOOD BOY <3]

“Oh, god, I think I broke something.” First Aid whimpered. He suddenly understood just why so many pilots came to them with nerve damage, with extensive burns, and why most of their heads were metal. The connection was. Intense.

“Don’t be such a pussy.” A voice spoke directly into his head. First Aid gasped, sitting up straighter. It was strangely human, yet equally as mechanical.

“What-!”

“I just want to talk, but it’s so irritating to have to wait for you to read the screen. Removing the barriers is so much easier, isn’t it? Now, to business...”

First Aid gasped and whined as he felt pressure in his head, white not points of pain slowly pressing through his brain. His eyesight flickered and faded in and out, his sight shifting from the inside of the cockpit to the chaos right outside – chaos that he couldn’t even hear – and he was glad to see that the man who had been right behind him was receiving medical attention. What a relief. Humour that wasn’t his and that he didn’t recognise pulled at his lips, and he felt a strong urge to smile so wide that his lips split and cracked.

The pressure on his head increased, and he felt his eyes cross, reality slowly slipping through his fingers like thick slime. Red dripped from his nose. Where was he, again? Why was this happening to him? What was even happening to him- Awareness snapped back to him in time with a loud bang on the glass. He heard his name, muffled. Someone was calling to him. He should go to them, right? “Don’t move, I haven’t finished looking at you yet.” First Aid felt phantom sensations of ice cold hands pressing against his skin, a shudder running up his spine. He felt a prickle run down his arm, chasing the feeling of the tips of someone’s fingers running down the bare skin. Obediently, he held still despite how curious he was to go and look. “I can tell you like the good stuff.” An invisible hand patted his cheek and the mech shuddered, loud and clunking. “God, I’m so lucky I found you.” “Found me?” His chest felt weird. His everything felt weird. It was difficult to keep his eyes open. “I’ve been watching you. On the cameras, when you’re in the hangar with me, your files. Fascinating. How wonderful you are to me.” “That’s a bit creepy. You could have asked first.” “I don’t like being told no.” “I would have liked it more if I’d known it was happening.” Why was he so readily admitting this? Where were his carefully constructed walls and defences, keeping the abnormality at bay? He felt like he was an open book and Vortex was just turning to the pages he wanted to read. “Maybe I’d have done something if I knew I had an audience.” The mech shuddered again, harder this time.

“Come on, baby, talk to me wont you? I’ve been so lonely.”

“Maybe if you stopped killing your pilots you wouldn’t struggle so much with that.” He gritted out. Fuck, everything hurt.

“You’ve got a bit of a mouth on you, don’t you.” A sound that felt like anger rumbled through him. “I like it.”

“Can I go now?” He felt woozy. Something was wrong. Something was really, really wrong, his ears felt wet and his face felt wet and he could taste copper-

As if on cue, there was a loud bang on the visor – someone was pounding it with their fist. A shared stab of annoyance flashed through them.

“Question first. How did it feel to have a Quintesson in your bare hands?”

“How did you know about that?”

“Come on, don’t be shy, you know I’ve seen everything.” He crooned. “Tell me. I’m so desperate to know. I know you liked it – I can feel it.” It felt as if he had someone’s arms wrapped around him, their mouth right by his ear. If he closed his eyes and focused, he could feel their warm breath ghosting over it.

“It felt fucking amazing.” He thought back to it. The warmth of the body – an infant, tiny in comparison to the adults that dwarfed their houses. How thick their blood was, how it dripped down through his hands. The burn of the smell, mineral rich and glowing bright blue.

“You fucking tease.”

“You cut through them every day.” First Aid argued. “What’s so special about that?”

“You can really feel it. I’ve got metal between me and my prey.”

The banging was louder, and First Aid’s vision shifted to be through Vortex’s. There was a big group of them now, he had an audience.

“I should go.”

“You’ll be back, honey.”

First Aid ripped the helmet off, and nausea hit him like a truck as he felt a sharp wrench in his head. He loudly gagged, folding in half, and pressed a fist to his mouth to keep himself from spilling his guts into the cockpit. Vortex was certain to kill him if he made a mess. Sucking in a deep breath, he staggered over to the glass and gently placed his hand against it. It felt like half of his consciousness was somewhere else, somewhere he couldn’t reach.

“Please?” He was starting to feel disorientated, the sudden disengaging scrambling his brain. What memories were his, or the previous pilots? Pain suddenly flashed through him and he screamed, his limbs going numb. He felt warm liquid slowly run down his suit, red blooming amongst the white, bone wrenching from bone-

[LATER, DARLING <3]

Vortex’s visor finally opened, laugher echoing in First Aids head, and he fell out face-first onto the catwalk. He was gasping for breath as he scrambled away, shaking and trembling and swallowing back vomit. His hands flew over his body, checking for injures, for limbs he was certain were missing – intact. He was completely intact. His team had their arms around him and were pulling him away faster, leaving a trail of blood smeared after him – was that his? Or was that the pilots? - and were shouting. All of it was just noise. Pure noise.

Giddiness bubbled up in his chest, and he laughed. It started quietly, a little chuckle. Disbelief at the situation, he thought. Pure, utter relief that he was alive. The cannibal mech had eaten him, but here he was – spat out whole and unharmed. His next laugh was a little louder this time, and Ambulon paused, taking notice. First Aid didn’t see him any more, his whole vision taken up by Vortex and the loud snap of his visor clamping back down into place, a hiss as the mechanism locked it back down. He could have sworn he was smiling, but it was ridiculous – the mech didn’t even have a mouth.

He didn’t realise he was still laughing – and hard – until his stomach began to hurt and he felt light headed. Gasping for breath, he let himself fall back onto the floor, staring blindly up at the ceiling. He could see the red lights of Vortex’s visor reflected on the metal there.

“Felix?” The voice of his mentor pierced through his peals of laugher. First Aid looked up and saw Ratchet running towards him, face twisted in agony. He felt himself start to laugh again, and he had to fight to not start punching himself in the stomach to get himself to fucking stop it. It wasn’t funny. None of this was funny. Why was he laughing.

“Is he hurt? Why is he bleeding?” Ratchet demanded as he knelt down next to him. Ambulons response was inaudible, First Aids ears ringing. He felt something dribble from his mouth, and from the acidic taste in the back of his throat he assumed that he’d finally thrown up. He didn’t remember turning – his airway was clear. Two hands gently cupped his face, forcing him to look at someone.

Ratchet.

“Can you hear me?” He gently asked, tension clear in his voice. First Aid could, but he didn’t know how to respond. He slowly blinked, hands reaching up to clasp at his wrists with trembling hands. The adrenaline was burning off, replacing itself with a leaden heaviness that threatened to drown him. Slowly, he nodded.

Get me away from that mech, he tried to say. They get it and I hate that we understand each other.

Ratchet seemed to hear him. “Help me move him.” He was looking at someone else, but First Aid didn’t want to look away from his face. He committed every detail to memory, every line, every grey hair, every follicle and aged scar and flush of colour. It felt like he was seeing him for the very first time.

The world spun and his stomach clenched as he was lifted unceremoniously onto a stretcher, and he took one last glimpse of Vortex before the oxygen mask was fitted over his face and he couldn’t see anything any more.

09090909

It was highly inadvisable.

But he was doing it anyway.

That taste he’d got of Vortex was like a breath of fresh air to him – he hadn’t realised how stifling the company on base was until he’d met him. Ratchet would be so disappointed in him. Pharma would hang him by his guts. Ultra Magnus would try and make it so he never saw the light of day again.

One moment of feeling his teeth at his throat and he was addicted. He wanted him. He wanted physical scars he could touch and remind himself that it hadn’t been a dream, it was real. Carefully sneaking through the base, First Aid crouched and peered around corners, internally humming the Mission Impossible theme. It felt ridiculous, but if he didn’t distract himself he’d make himself vomit from laughing too much again. He had found a random face mask and slapped it on, hoping that obscuring his identity a little would help him get into character.

They hadn’t found a new pilot for Vortex yet – they still went through the usual procedure of finding one with the right personality and skill set, of testing how well the AI meshed with the mind of the pilot outside of the mech before allowing them to go inside. They had a few candidates, but now it was a question of ‘are they more compatible with other bots?’ and ‘how expendable are they really?’ before they stuck them inside of him.

Like lambs for slaughter. They knew they were going to die – but what else could they do? Vortex was their strongest mech. If he went down, their whole operation would crumble with him. Mechs were expensive and difficult to make, the AI’s complicated and prone to disaster.

Pharma didn’t take his eyes off of him for two whole weeks. He’d fallen out of the mech looking like the pilots whose brains had melted under the pressure, his arm marked with a burn that followed the path of a nerve, mapping it onto his skin. Pharma had stared at it, long and hard, brain ticking over. He wasn’t to go near Vortex again. Not for a while, until they figured out why he’d decided to kidnap him, and why he’d decided to spit him back out. They knew why he’d mangled the other medic. He thought it was fun. He’d said so himself, writing messages in the morning memo. They still hadn’t figured out how he was doing it, but if you were early enough in the day you’d see it before they’d caught it. But First Aid didn’t do too well in following instructions, in listening to orders. The Infant he’d plucked from the formaldehyde to get a better look at was evidence enough of that. The fact he was scrambling to get back inside of Vortex right now was yet another reason why First Aid was to be kept under lock and key - god, if they knew anything about him they’d never let him see the light of day again.

The catwalk that lead out to the mechs was a stones throw away. A guard stood watch, hands firmly on their gun.

God damn it.

First Aid rocked on his feet, wondering how he’d get him to move, when he suddenly felt a prickle on the back of his neck as if he were being watched. He shuddered and whipped his head around.

Nobody. Alone. No eerie glow of a camera – not that there were any over on this side of the hall – and no shadowy figures. He held his breath and strained his ears – all he heard was the cough from the guard and their sigh of boredom. He slowly looked back to the guard, and a faint red glow caught his eye.

Vortex’s visor was on. He was watching.

The sound of something falling to the floor caught the guards attention. He quickly turned and ran out onto the catwalk, looking down at the floor. He quickly looked back up at Vortex and scowled.

“I’m not stupid, Vortex. I’m not going down and getting that.”

Vortex did not respond. The guard tutted and turned on his heel.

Something else fell to the floor, a little louder this time.

The guard threw his head back with a sigh.

“You are the worst.”

He marched off, out of sight, and First Aid saw his window of opportunity. He quickly slipped out, thankful for his socks muffling the sound of his steps, and hid behind the terminal the guard was stationed at before he turned back around and walked over to the terminal.

“Yeah, yeah.” He was speaking to someone on the phone, drumming his fingers on the terminal. “It’s Vortex again. I know, I won’t get close – yeah. He’s dropped two this time.” He paused for a moment, listening to what the person on the other end had to say, before making a sound of disgust. “Go and check? I am not getting close to him!”

First Aid could hear a raised voice on the other side, and strained to see if he recognised it. Before he could pin a face to the voice, the guard sighed loudly. “Fine. I’ll go look. You’ve got my will there, right? Take yourself off of it.”

The guard didn’t look back at the terminal as he walked to the stairs and descended down them. First Aid glanced between the stairs and the catwalk, and quickly crawled over. Peering over the side to see where the guard was, he gained an uncharacteristic burst of bravery before he sprinted towards where Vortex was, visor open and waiting for him.

“Can I?” He asked in a hushed whisper. Vortex didn’t respond. He gingerly approached, noticing that every single camera inside his cockpit was trained onto him. He swallowed nervously, and clambered in.

He should have been used to climbing inside of Vortex. He’d done it enough times. Maybe it was because he wasn’t wearing any of his protective gear? Not his uniform, or his helmet, or even his gloves. Just himself and his pyjama shorts, his t-shirt, and his socks with little bears on them.

Mmm. First impressions. Wonderful.

He should have gotten changed first.

[TAKE A SEAT] lit up the screen.

He slipped into the seat obediently, taking care to not touch the controls. He coyly waved at the camera.

“Did I wake you?”

[YOU DIDN’T. I LIKE YOUR SOCKS]

The bears stared back at him. First Aid tried not to think about the rumbling he now recognised as laughter that rolled through the cockpit.

“Thanks.” He replied, red tingeing his cheeks.

[THAT’S A GOOD LOOK ON YOU]

He pressed his legs more tightly together. “The socks?”

[NO, YOU’RE GOING VERY RED]

[MAYBE I SHOULD CALL YOU LITTLE RED INSTEAD]

The helmet dropped from the ceiling, firmly attached to the cable that would connect organic to mechanical.

[I WANT YOU]

[<3]

First aid scrambled with the harness, clipping himself in place, before putting on the helmet. It burned just as badly as the first time, and he saw as the nerves in his arms glowed with the energy of it – without the proper implants, there was nowhere for the current to go but him.

He whined, squirming in the seat. He ground his teeth together and squeezed his eyes shut, counting down from ten and losing his place three times before the connection settled. Vortex was a heavy and oppressive presence in his mind, and he chewed his cheek as he cracked an eye open.

[LET ME TAKE ANOTHER LOOK AT YOU]

The warning wasn’t even a verbal one. He read helplessly as he felt cold hands clasp him once more. Digital fingers made of 1’s and 0’s probed his brain, and First Aid arched in the seat, teeth clenching down over a loud moan of pain. Neurons fired agonisingly and his hands scrambled at the harness, the tips of his fingers raw and torn and bleeding against the rough fabric. Memories were brought to the surface unbidden, dragged out by artificial means, and others flooded in to take their place. He inhaled sharply, eyes going wide as the realisation hit him. Vortex was trying to show him something. He wasn’t a ghost. He wasn’t even an AI.

He’d been entombed in it. In the mech. Vortex had been a real, breathing human being, mocked in a sham trial in the name of obtaining more pilots. Rich men had paid him to do terrible things, and he had taken the entirety of the blame. Hundreds of thousands of pounds of funds, countless hours, blood, sweat, and tears – all for one mech. A prototype, at that.

First Aid blinked as a bright red screen flashed up, text displayed across it. He squinted and rubbed his eyes, grimacing at the drag of sore and exposed flesh against the rough material of his face mask, and blinked.

[LOCKED IN]

“W… what do you mean locked in?” First Aid hesitantly asked. Like… literally, he was locked in? He knew that. He was connected to Vortex’s nervous system – he could feel that there were bolts in place keeping the cockpit well and truly locked down like a fortress, impenetrable except to the override codes the high command kept locked in a vault in their office or the request of the pilot. He felt amusement push at the edge of his awareness, a shudder of a laugh running through the mech, and he clarified.

“I know your dirt, and now you know mine. Do you think high command are going to let you go peacefully?”

Ah. A threat. Of course. Worried he’d run? He wasn’t going to. He was fascinated by this mech – the joy of being caught in his mechanisms was sure to sing in his ears, the pure delight of watching him carefully pick apart his prey like a hawk dismantled a rabbit was like a chorus of cherubs to him. And Vortex knew it, he knew it and he loved it- he was certain of it, the way his mind melded with his, pushing against him and caressing him, a warm blanket around his psyche.

“I’m not going to leave you.” First Aid took a deep breath, the unsettling stench of bleach and cooked meat and rotting oranges filling his lungs. “No, I’m fascinated by you.”

He tensed, eyes briefly widening as he felt a grin that wasn’t his tugging at the corners of his lips, threatening to split his face in two.

“Happy about that?”

“Extremely.” He purred. “I’ve seen what your hands have done, what they’re capable of. I think we’d make a great team.”

“What if I refuse?”

Images flashed in front of his eyes. Bone fragments scattered around the cockpit, blood and guts and gore hanging obscenely from the ceiling. Blood ran thickly on the walls, the smell foul and rotten. First Aid wretched.

“You’ll kill me?” He hated the excitement that bled into his voice, how eager he was to feel the mechanism close down around him, to feel his metal deep inside of him, for his last thought to be about his touch. “It’s a shame you can only do that once, you know. It’s so exciting, all the different ways you could do it to me. You could make me completely unrecognisable, identified by DNA alone. Or maybe flood the cockpit with gas, slowly suffocating me before I realised what was happening.” He bit his bottom lip. “I wish I knew what it all felt like.”

A new image, one of gears and cogs deep inside of him. All sharp angles and straight edges. The presence was probing inside of him, trying to figure out his reactions. He pressed his hand to his mouth and gasped as his teeth pierced his bottom lip without him realising it. He took a deep breath to steady himself, and another. Vortex probed again impatiently. Respond, damn it.

He looked up at the camera, glad that his mask hid his face, the excitement glowing on his cheeks. “I’ll show you.” His voice was breathless. “And if your use for me runs out, give me a little warning before I’m a permanent feature, please?”

“I wont let you run away from me.”

First Aid swallowed hard at the burn of yearning in his chest. “You’d catch me if I tried.”

“Damn fucking right I would.”

He watched the energy sing in his nerves, the pain spreading down his limbs. His digits were starting to go numb. How much longer could he hold out? He never wanted to leave. He felt flayed open and alive. Squirming, screaming, and alive. Red dripped down and stained his pyjama shirt. Damn it. He liked this pair.

“How do you control yourself? You want what I want, you wish you could do it. So why don’t you?”

“I’m a pacifist.”

“Are you? Or is that just what you tell yourself so you can sleep at night?”

First Aid whimpered as the pages in his mind flicked, a burning sensation flaring in his arms. He watched the skin there turn red, the connection starting to be too much. His nose felt wet as he thought of it, as the memories Vortex was looking at came to the forefront of his mind. He liked surgery. He liked anatomy. He liked the cadavers and how they felt under his hands, picking them apart and pulling on tendons and ligaments to move them like puppets. Even earlier, his first pet. A hamster. He had told his parents that he’d buried it in the garden all by himself, and they had praised him for being such a grown up young boy, when really he had picked it apart like he had practised on his teddy bears and then blamed on the dog before shoving it into a hole in the ground to hide the evidence before anyone had seen what he was doing.

Vortex chuckled.

“Oh, let me show you how exciting a Quintesson can be. Little Hamphrey hasn’t got anything on them.”

[sniperct]

Reading the nature thing on gen AI's power and water use and I don't think I've seen the part where OpenAI's CEO admitted we need a breakthrough in cold fusion to meet the power needs of this technology.

That's fucking bonkers!

Yeah lets destroy the environment for shitty stolen art that uses so much power we need the holy grail of energy technology to power, that's just great. Relevant quote:

Last month, OpenAI chief executive Sam Altman finally admitted what researchers have been saying for years — that the artificial intelligence (AI) industry is heading for an energy crisis. It’s an unusual admission. At the World Economic Forum’s annual meeting in Davos, Switzerland, Altman warned that the next wave of generative AI systems will consume vastly more power than expected, and that energy systems will struggle to cope. “There’s no way to get there without a breakthrough,” he said. I’m glad he said it. I’ve seen consistent downplaying and denial about the AI industry’s environmental costs since I started publishing about them in 2018. Altman’s admission has got researchers, regulators and industry titans talking about the environmental impact of generative AI. So what energy breakthrough is Altman banking on? Not the design and deployment of more sustainable AI systems — but nuclear fusion. He has skin in that game, too: in 2021, Altman started investing in fusion company Helion Energy in Everett, Washington.

[thatsonemorbidcorvid]

“By simply existing as women in public life, we have all become targets, stripped of our accomplishments, our intellect, and our activism and reduced to sex objects for the pleasure of millions of anonymous eyes.

Men, of course, are subject to this abuse far less frequently. In reporting this article, I searched the name Donald Trump on one prominent deepfake-porn website and turned up one video of the former president—and three entire pages of videos depicting his wife, Melania, and daughter Ivanka. A 2019 study from Sensity, a company that monitors synthetic media, estimated that more than 96 percent of deepfakes then in existence were nonconsensual pornography of women.”

Recently, a Google Alert informed me that I am the subject of deepfake pornography. I wasn’t shocked. For more than a year, I have been the target of a widespread online harassment campaign, and deepfake porn—whose creators, using artificial intelligence, generate explicit video clips that seem to show real people in sexual situations that never actually occurred—has become a prized weapon in the arsenal misogynists use to try to drive women out of public life. The only emotion I felt as I informed my lawyers about the latest violation of my privacy was a profound disappointment in the technology—and in the lawmakers and regulators who have offered no justice to people who appear in porn clips without their consent. Many commentators have been tying themselves in knots over the potential threats posed by artificial intelligence—deepfake videos that tip elections or start wars, job-destroying deployments of ChatGPT and other generative technologies. Yet policy makers have all but ignored an urgent AI problem that is already affecting many lives, including mine.

Last year, I resigned as head of the Department of Homeland Security’s Disinformation Governance Board, a policy-coordination body that the Biden administration let founder amid criticism mostly from the right. In subsequent months, at least three artificially generated videos that appear to show me engaging in sex acts were uploaded to websites specializing in deepfake porn. The images don’t look much like me; the generative-AI models that spat them out seem to have been trained on my official U.S. government portrait, taken when I was six months pregnant. Whoever created the videos likely used a free “face swap” tool, essentially pasting my photo onto an existing porn video. In some moments, the original performer’s mouth is visible while the deepfake Frankenstein moves and my face flickers. But these videos aren’t meant to be convincing—all of the websites and the individual videos they host are clearly labeled as fakes. Although they may provide cheap thrills for the viewer, their deeper purpose is to humiliate, shame, and objectify women, especially women who have the temerity to speak out. I am somewhat inured to this abuse, after researching and writing about it for years. But for other women, especially those in more conservative or patriarchal environments, appearing in a deepfake-porn video could be profoundly stigmatizing, even career- or life-threatening.

As if to underscore video makers’ compulsion to punish women who speak out, one of the videos to which Google alerted me depicts me with Hillary Clinton and Greta Thunberg. Because of their global celebrity, deepfakes of the former presidential candidate and the climate-change activist are far more numerous and more graphic than those of me. Users can also easily find deepfake-porn videos of the singer Taylor Swift, the actress Emma Watson, and the former Fox News host Megyn Kelly; Democratic officials such as Kamala Harris, Nancy Pelosi, and Alexandria Ocasio-Cortez; the Republicans Nikki Haley and Elise Stefanik; and countless other prominent women. By simply existing as women in public life, we have all become targets, stripped of our accomplishments, our intellect, and our activism and reduced to sex objects for the pleasure of millions of anonymous eyes.

Men, of course, are subject to this abuse far less frequently. In reporting this article, I searched the name Donald Trump on one prominent deepfake-porn website and turned up one video of the former president—and three entire pages of videos depicting his wife, Melania, and daughter Ivanka. A 2019 study from Sensity, a company that monitors synthetic media, estimated that more than 96 percent of deepfakes then in existence were nonconsensual pornography of women. The reasons for this disproportion are interconnected, and are both technical and motivational: The people making these videos are presumably heterosexual men who value their own gratification more than they value women’s personhood. And because AI systems are trained on an internet that abounds with images of women’s bodies, much of the nonconsensual porn that those systems generate is more believable than, say, computer-generated clips of cute animals playing would be.

As I looked into the provenance of the videos in which I appear—I’m a disinformation researcher, after all—I stumbled upon deepfake-porn forums where users are remarkably nonchalant about the invasion of privacy they are perpetrating. Some seem to believe that they have a right to distribute these images—that because they fed a publicly available photo of a woman into an application engineered to make pornography, they have created art or a legitimate work of parody. Others apparently think that simply by labeling their videos and images as fake, they can avoid any legal consequences for their actions. These purveyors assert that their videos are for entertainment and educational purposes only. But by using that description for videos of well-known women being “humiliated” or “pounded”—as the titles of some clips put it—these men reveal a lot about what they find pleasurable and informative.

Ironically, some creators who post in deepfake forums show great concern for their own safety and privacy—in one forum thread that I found, a man is ridiculed for having signed up with a face-swapping app that does not protect user data—but insist that the women they depict do not have those same rights, because they have chosen public career paths. The most chilling page I found lists women who are turning 18 this year; they are removed on their birthdays from “blacklists” that deepfake-forum hosts maintain so they don’t run afoul of laws against child pornography.

Effective laws are exactly what the victims of deepfake porn need. Several states—including Virginia and California—have outlawed the distribution of deepfake porn. But for victims living outside these jurisdictions or seeking justice against perpetrators based elsewhere, these laws have little effect. In my own case, finding out who created these videos is probably not worth the time and money. I could attempt to subpoena platforms for information about the users who uploaded the videos, but even if the sites had those details and shared them with me, if my abusers live out of state—or in a different country—there is little I could do to bring them to justice.

Representative Joseph Morelle of New York is attempting to reduce this jurisdictional loophole by reintroducing the Preventing Deepfakes of Intimate Images Act, a proposed amendment to the 2022 reauthorization of the Violence Against Women Act. Morelle’s bill would impose a nationwide ban on the distribution of deepfakes without the explicit consent of the people depicted in the image or video. The measure would also provide victims with somewhat easier recourse when they find themselves unwittingly starring in nonconsensual porn.

In the absence of strong federal legislation, the avenues available to me to mitigate the harm caused by the deepfakes of me are not all that encouraging. I can request that Google delist the web addresses of the videos in its search results and—though the legal basis for any demand would be shaky—have my attorneys ask online platforms to take down the videos altogether. But even if those websites comply, the likelihood that the videos will crop up somewhere else is extremely high. Women targeted by deepfake porn are caught in an exhausting, expensive, endless game of whack-a-troll.

The Preventing Deepfakes of Intimate Images Act won’t solve the deepfake problem; the internet is forever, and deepfake technology is only becoming more ubiquitous and its output more convincing. Yet especially because AI grows more powerful by the month, adapting the law to an emergent category of misogynistic abuse is all the more essential to protect women’s privacy and safety. As policy makers worry whether AI will destroy the world, I beg them: Let’s first stop the men who are using it to discredit and humiliate women.

Nina Jankowicz is a disinformation expert and the author of How to Be a Woman Online and How to Lose the Information War.

[rubylogan15]

Elevate your enterprise with Gen AI evaluation—uncover insights that drive innovation and shape the future of your business. Start today!

[dieterziegler159]

How Is Gen AI Driving Kubernetes Demand Across Industries?

Understand how Generative AI is accelerating Kubernetes adoption, shaping industries with scalable, automated, and innovative approaches. A new breakthrough in AI, called generative AI or Gen AI, is creating incredible waves across industries and beyond. With this technology rapidly evolving there is growing pressure on the available structure to support both the deployment and scalability of…

[generative-ai-in-bi]

How Is Gen AI Driving Kubernetes Demand Across Industries?

Unveil how Gen AI is pushing Kubernetes to the forefront, delivering industry-specific solutions with precision and scalability.

Original Source: https://bit.ly/4cPS7G0

A new breakthrough in AI, called generative AI or Gen AI, is creating incredible waves across industries and beyond. With this technology rapidly evolving there is growing pressure on the available structure to support both the deployment and scalability of the technology. Kubernetes, an effective container orchestration platform is already indicating its ability as one of the enablers in this context. This article critically analyzes how Generative AI gives rise to the use of Kubernetes across industries with a focus of the coexistence of these two modern technological forces.

The Rise of Generative AI and Its Impact on Technology

Machine learning has grown phenomenally over the years and is now foundational in various industries including healthcare, banking, production as well as media and entertainment industries. This technology whereby an AI model is trained to write, design or even solve business problems is changing how business is done. Gen AI’s capacity to generate new data and solutions independently has opened opportunities for advancements as has never been seen before.

If companies are adopting Generative AI , then the next big issue that they are going to meet is on scalability of models and its implementation. These resource- intensive applications present a major challenge to the traditional IT architectures. It is here that Kubernetes comes into the picture, which provides solutions to automate deployment, scaling and managing the containerised applications. Kubernetes may be deployed to facilitate the ML and deep learning processing hence maximizing the efficiency of the AI pipeline to support the future growth of Gen AI applications.

The Intersection of Generative AI and Kubernetes

The integration of Generative AI and Kubernetes is probably the most significant traffic in the development of AI deployment approaches. Kubernetes is perfect for the dynamics of AI workloads in terms of scalability and flexibility. The computation of Gen AI models demands considerable resources, and Kubernetes has all the tools required to properly orchestrate those resources for deploying AI models in different setups.

Kubernetes’ infrastructure is especially beneficial for AI startups and companies that plan to use Generative AI. It enables the decentralization of workload among several nodes so that training, testing, and deployment of AI models are highly distributed. This capability is especially important for businesses that require to constantly revolve their models to adapt to competition. In addition, Kubernetes has direct support for GPU, which helps in evenly distributing computational intensity that comes with deep learning workloads thereby making it perfect for AI projects.

Key Kubernetes Features that Enable Efficient Generative AI Deployment

Scalability:

Kubernetes excels at all levels but most notably where applications are scaled horizontally. Especially for Generative AI which often needs a lot of computation, Kubernetes is capable of scaling the pods, the instances of the running processes and provide necessary resources for the workload claims without having any human intervention.

Resource Management:

Effort is required to be allocated efficiently so as to perform the AI workloads. Kubernetes assists in deploying as well as allocating resources within the cluster from where the AI models usually operate while ensuring that resource consumption and distribution is efficiently controlled.

Continuous Deployment and Integration (CI/CD):

Kubernetes allows for the execution of CI CD pipelines which facilitate contingency integration as well as contingency deployment of models. This is essential for enterprises and the AI startups that use the flexibility of launching different AI solutions depending on the current needs of their companies.

GPU Support:

Kubernetes also features the support of the GPUs for the applications in deep learning from scratch that enhances the rate of training and inference of the models of AI. It is particularly helpful for AI applications that require more data processing, such as image and speech recognition.

Multi-Cloud and Hybrid Cloud Support:

The fact that the Kubernetes can operate in several cloud environment and on-premise data centers makes it versatile as AI deployment tool. It will benefit organizations that need a half and half cloud solution and organizations that do not want to be trapped in the web of the specific company.

Challenges of Running Generative AI on Kubernetes

Complexity of Setup and Management:

That aid Kubernetes provides a great platform for AI deployments comes at the cost of operational overhead. Deploying and configuring a Kubernetes Cluster for AI based workloads therefore necessitates knowledge of both Kubernetes and the approach used to develop these models. This could be an issue for organizations that are not able to gather or hire the required expertise.

Resource Constraints:

Generative AI models require a lot of computing power and when running them in a Kubernetes environment, the computational resources can be fully utilised. AI works best when the organizational resources are well managed to ensure that there are no constraints in the delivery of the application services.

Security Concerns:

Like it is the case with any cloud-native application, security is a big issue when it comes to running artificial intelligence models on Kubernetes. Security of the data and models that AI employs needs to be protected hence comes the policies of encryption, access control and monitoring.

Data Management:

Generative AI models make use of multiple dataset samples for its learning process and is hard to deal with the concept in Kubernetes. Managing these datasets as well as accessing and processing them in a manner that does not hinder the overall performance of an organization is often a difficult task.

Conclusion: The Future of Generative AI is Powered by Kubernetes

As Generative AI advances and integrates into many sectors, the Kubernetes efficient and scalable solutions will only see a higher adoption rate. Kubernetes is a feature of AI architectures that offer resources and facilities for the development and management of AI model deployment.

If you’re an organization planning on putting Generative AI to its best use, then adopting Kubernetes is non-negotiable. Mounting the AI workloads, utilizing the resources in the best possible manner, and maintaining the neat compatibility across the multiple and different clouds are some of the key solutions provided by Kubernetes for the deployment of the AI models. With continued integration between Generative AI and Kubernetes, we have to wonder what new and exciting uses and creations are yet to come, thus strengthening Kubernetes’ position as the backbone for enterprise AI with Kubernetes. The future is bright that Kubernetes is playing leading role in this exciting technological revolution of AI.

Original Source: https://bit.ly/4cPS7G0

[enterprise-cloud-services]

Understand how Generative AI is accelerating Kubernetes adoption, shaping industries with scalable, automated, and innovative approaches.

[jcmarchi]

Stable Diffusion 3.5: Architectural Advances in Text-to-Image AI

New Post has been published on https://thedigitalinsider.com/stable-diffusion-3-5-architectural-advances-in-text-to-image-ai/

Stable Diffusion 3.5: Architectural Advances in Text-to-Image AI

Stability AI has unveiled Stable Diffusion 3.5, marking yet another advancement in text-to-image AI models. This release represents a comprehensive overhaul driven by valuable community feedback and a commitment to pushing the boundaries of generative AI technology.

Following the June release of Stable Diffusion 3 Medium, Stability AI acknowledged that the model didn’t fully meet their standards or community expectations. Instead of rushing a quick fix, the company took a deliberate approach, focusing on developing a version that would advance their mission to transform visual media while implementing safety measures throughout the development process.

Key Improvements Over Previous Versions

The new release brings substantial improvements in several critical areas:

Enhanced Prompt Adherence: The model generates images with significantly improved understanding of complex prompts, rivaling the capabilities of much larger models.

Architectural Advancements: Implementation of Query-Key Normalization in transformer blocks has helped improve training stability and simplified fine-tuning processes.

Diverse Output Generation: Advanced capabilities in generating images representing different skin tones and features without requiring extensive prompt engineering.

Optimized Performance: Substantial improvements in both image quality and generation speed, particularly in the Turbo variant.

What sets Stable Diffusion 3.5 apart in the landscape of generative AI companies is its unique combination of accessibility and power. The release maintains Stability AI’s commitment to widely accessible creative tools while pushing the boundaries of technical capabilities. This positions the model family as a viable solution for both individual creators and enterprise users, backed by a clear commercial licensing framework that supports medium-sized businesses and larger organizations alike.

Stable Diffusion output (Stability AI)

Three Powerful Models for Every Use Case

Stable Diffusion 3.5 Large

The flagship model of the release, Stable Diffusion 3.5 Large, brings 8 billion parameters of processing power to bear on professional image generation tasks.

Key features include:

Professional-grade output at 1 megapixel resolution

Superior prompt adherence for precise creative control

Advanced capabilities in handling complex image concepts

Robust performance across diverse artistic processes

Large Turbo

The Large Turbo variant represents a breakthrough in efficient performance, offering:

High-quality image generation in just 4 steps

Exceptional prompt adherence despite increased speed

Competitive performance against non-distilled models

Optimal balance of speed and quality for production workflows

Medium Model

Set for release on October 29th, the Medium model with 2.5 billion parameters democratizes access to professional-grade image generation:

Efficient operation on standard consumer hardware

Generation capabilities from 0.25 to 2 megapixel resolution

Optimized architecture for improved performance

Superior results compared to other medium-sized models

Each model has been carefully positioned to serve specific use cases while maintaining Stability AI’s high standards for both image quality and prompt adherence.

Stable Diffusion 3.5 Large (Stability AI)

Next-Generation Architecture Improvements

The architecture of Stable Diffusion 3.5 represents a significant leap forward in image generation technology. At its core, the modified MMDiT-X architecture introduces sophisticated multi-resolution generation capabilities, particularly evident in the Medium variant. This architectural refinement enables more stable training processes while maintaining efficient inference times, addressing key technical limitations identified in previous iterations.

Query-Key (QK) Normalization: Technical Implementation

QK Normalization emerges as a crucial technical advancement in the model’s transformer architecture. This implementation fundamentally alters how attention mechanisms operate during training, providing a more stable foundation for feature representation. By normalizing the interaction between queries and keys in the attention mechanism, the architecture achieves more consistent performance across different scales and domains. This improvement particularly benefits developers working on fine-tuning processes, as it reduces the complexity of adapting the model to specialized tasks.

Benchmarking and Performance Analysis

Performance analysis reveals that Stable Diffusion 3.5 achieves remarkable results across key metrics. The Large variant demonstrates prompt adherence capabilities that rival those of significantly larger models, while maintaining reasonable computational requirements. Testing across diverse image concepts shows consistent quality improvements, particularly in areas that challenged previous versions. These benchmarks were conducted across various hardware configurations to ensure reliable performance metrics.

Hardware Requirements and Deployment Architecture

The deployment architecture varies significantly between variants. The Large model, with its 8 billion parameters, requires substantial computational resources for optimal performance, particularly when generating high-resolution images. In contrast, the Medium variant introduces a more flexible deployment model, functioning effectively across a broader range of hardware configurations while maintaining professional-grade output quality.

Stable Diffusion benchmarks (Stability AI)

The Bottom Line

Stable Diffusion 3.5 represents a significant milestone in the evolution of generative AI models, balancing advanced technical capabilities with practical accessibility. The release demonstrates Stability AI’s commitment to transform visual media while implementing comprehensive safety measures and maintaining high standards for both image quality and ethical considerations. As generative AI continues to shape creative and enterprise workflows, Stable Diffusion 3.5’s robust architecture, efficient performance, and flexible deployment options position it as a valuable tool for developers, researchers, and organizations seeking to leverage AI-powered image generation.

[cognitivejustice]

The International Energy Agency estimates that data centres’ total electricity consumption could double from 2022 levels to 1,000TWh (terawatt hours) in 2026, approximately Japan’s level of electricity demand.

AI will result in data centres using 4.5% of global energy generation by 2030

Data centres play a crucial role in training and operating the models that underpin AI models 

Pledges to reduce CO2 emissions are now coming up against pledges to invest heavily in AI products that require considerable amounts of energy for training and deployment in data centres, along with carbon emissions associated with manufacturing and transporting the computer servers and chips used in that process.

Water usage is another environmental factor in the AI boom, with one study estimating that AI could account for up to 6.6bn cubic metres of water use by 2027 – nearly two-thirds of England’s annual consumption.

[himbeereule]

Орлёнок (Eaglet) Battle System - Dev Diary #2

2.1 The Battle Map

Battles are fought on a 7x7 tiles map, which is randomly generated in terms of terrain - more about that later.

The deployment zone is the second row from the back - the player's is marked green here, and the enemy's red - and represents the tiles on which you can initially deploy your units. Spreading them out to the left/right edges of the map will prevent enemy flanking attemps, but will also make your frontline thinner, increasing the risk of an enemy breakthrough.

The rows marked yellow (player side)/orange (enemy side) are the opposing forces' respective backlines. Only the enemy can move troops there - this is to prevent cheesing the system by simply moving your entire force to the lower edge of the map, which would completely prevent attacks from the rear without cost. Of course, conducting rear attacks on the enemy still requires a breakthrough or successful flanking maneuver, in order to get troops into the enemy backline in the first place.

The blue arrows represent standard weapon ranges in the game.

Melee attacks can only target enemies up to range 1, a tile in direct contact with the tile your attack force is at. Rifles, MGs, Light and Medium Artillery can attack up to range 2, while range 3 is the sole domain of Heavy Artillery. Note that the deployment zones are completely out of range of each other, so both forces are safe initially.

2.2 Terrain

Both in the initial deployment and in the subsequent move orders, the individual tiles' terrain should be considered. In general, terrain comes in four variants: plains, hills, forests and towns/cities. Each has unique effects on how units behave, which will be discussed in a later dev diary.

There are also special features that tiles can have in addition to their terrain type.

Firstly, preexisting features: rivers and railroads. Both can be present at the same time, and both are only ever present in tiles either adjacent to the map border, or to a tile that already has the same feature. Rivers can be major obstacles for troops to move across, while railroads are necessary for the movement of Armored Trains.

Secondly, there are terrain modifiers that the player or enemy AI can set under certain conditions. These are trenches - dug directly before a battle, and giving infantry some cover from enemy fire - and actual fortifications, built in friendly terrain when no hostiles are near. These provide much more substantial bonuses and will be hard to clear out without either artillery or heavy losses.