#Behavioral AI

[psychichomie]

And Behavior AI is the behavioral mapping and proceedings of video game characters within their virtual words.

(I've seen some people in modding communities get angry at seeing "AI" in the title of some mods, so I wanted to make it clear that not all Behavioral "NPC" AI is in reference to Generative AI. Some of it is just the artificial "intelligence" of characters within a game, usually NPC's mapping and interacting with the game world so they function correctly. When in doubt make sure to read things carefully and do research into where exactly the artificial intelligence is being created from. Good rule of thumb? If the intelligence has to "generate" a product or answer from "nothing", it's worthless. If a computer is being taught pattern recognition or a series of programmed instructions to run through (i.e. identifying cancer cells or getting blorbo from video game to walk through doors correctly) it isn't necessarily "generative").

when talking about AI remember the different versions:

Analytical AI, is the one that can detect cancer and save lives

Generative AI is the one that steals art to make it worse, and gives you a wrong answer every time you google something

Weird Al is the one who got his ponysona to canonically have children with a pony from my little pony

[yikesy]

and like I don't even blame the other gods for not doubting or thinking too much of apollo's personality change despite how obvious it should have been that something was up if you looked at the gap between his domains and his behavior and just thought about it for a minute

and that's because despite everything, the idea of apollo having learnt to lie is such an existentially terrifying concept that I do not blame their brains for refusing to even consider it

[benotafraid111]

Allow yourselves to believe that anything can be possible.

[mondstalgia]

I MISSED THE WAY CHEN YI LITERALLY TONGUED AIDI DOWN?

[hungwy]

Sometimes I see people on here reply to posts extremely adjacently. Like if someone said "let's talk about horses" and another person responded with "I think what you mean is birds. Here are the things I've learned about birds" like no bro. We're talking about horses right now. We don't need the bird script you memorized for bird conversations. Start writing a horse script or giddyup on out of here

[riotcat103]

BEACH EPISODE LETSSS FUCKING GOOOOOO

[nokk0]

Lots of doodles of Fal with Starstruck Dee!! @starflungwaddledee

Originally supposed to be for the Shipganza event but since I picture their interactions as platonic friendship, these don't count in the event...

[deathbypufferfish]

I forgot I blocked the inzoi tag because everyone was being so damn annoying 😭

[robo-dino-puppy]

the sunhawk, and sunwing

↓ wider version bc i couldn't decide:

i didn't really like the composition here (unbalanced and why? is the roof not straight???) but it gives more perspective on size of sunwing vs. lodge

[jcmarchi]

Beyond Large Language Models: How Large Behavior Models Are Shaping the Future of AI

New Post has been published on https://thedigitalinsider.com/beyond-large-language-models-how-large-behavior-models-are-shaping-the-future-of-ai/

Beyond Large Language Models: How Large Behavior Models Are Shaping the Future of AI

Artificial intelligence (AI) has come a long way, with large language models (LLMs) demonstrating impressive capabilities in natural language processing. These models have changed the way we think about AI’s ability to understand and generate human language. While they are excellent at recognizing patterns and synthesizing written knowledge, they struggle to mimic the way humans learn and behave. As AI continues to evolve, we are seeing a shift from models that simply process information to ones that learn, adapt, and behave like humans.

Large Behavior Models (LBMs) are emerging as a new frontier in AI. These models move beyond language and focus on replicating the way humans interact with the world. Unlike LLMs, which are trained primarily on static datasets, LBMs learn continuously through experience, enabling them to adapt and reason in dynamic, real-world situations. LBMs are shaping the future of AI by enabling machines to learn the way humans do.

Why Behavioral AI Matters

LLMs have proven to be incredibly powerful, but their capabilities are inherently tied to their training data. They can only perform tasks that align with the patterns they’ve learned during training. While they excel in static tasks, they struggle with dynamic environments that require real-time decision-making or learning from experience.

Additionally, LLMs are primarily focused on language processing. They can’t process non-linguistic information like visual cues, physical sensations, or social interactions, which are all vital for understanding and reacting to the world. This gap becomes especially apparent in scenarios that require multi-modal reasoning, such as interpreting complex visual or social contexts.

Humans, on the other hand, are lifelong learners. From infancy, we interact with our environment, experiment with new ideas, and adapt to unforeseen circumstances. Human learning is unique in its adaptability and efficiency. Unlike machines, we don’t need to experience every possible scenario to make decisions. Instead, we extrapolate from past experiences, combine sensory inputs, and predict outcomes.

Behavioral AI seeks to bridge these gaps by creating systems that not only process language data but also learn and grow from interactions and can easily adapt to new environments, much like humans do. This approach shifts the paradigm from “what does the model know?” to “how does the model learn?”

What Are Large Behavior Models?

Large Behavior Models (LBMs) aim to go beyond simply replicating what humans say. They focus on understanding why and how humans behave the way they do. Unlike LLMs which rely on static datasets, LBMs learn in real time through continuous interaction with their environment. This active learning process helps them adapt their behavior just like humans do—through trial, observation, and adjustment. For instance, a child learning to ride a bike doesn’t just read instructions or watch videos; they physically interact with the world, falling, adjusting, and trying again—a learning process that LBMs are designed to mimic.

LBMs also go beyond text. They can process a wide range of data, including images, sounds, and sensory inputs, allowing them to understand their surroundings more holistically. This ability to interpret and respond to complex, dynamic environments makes LBMs especially useful for applications that require adaptability and context awareness.

Key features of LBMs include:

Interactive Learning: LBMs are trained to take actions and receive feedback. This enables them to learn from consequences rather than static datasets.

Multimodal Understanding: They process information from diverse sources, such as vision, sound, and physical interaction, to build a holistic understanding of the environment.

Adaptability: LBMs can update their knowledge and strategies in real time. This makes them highly dynamic and suitable for unpredictable scenarios.

How LBMs Learn Like Humans

LBMs facilitate human-like learning by incorporating dynamic learning, multimodal contextual understanding, and the ability to generalize across different domains.

Dynamic Learning: Humans don’t just memorize facts; we adapt to new situations. For example, a child learns to solve puzzles not just by memorizing answers, but by recognizing patterns and adjusting their approach. LBMs aim to replicate this learning process by using feedback loops to refine knowledge as they interact with the world. Instead of learning from static data, they can adjust and improve their understanding as they experience new situations. For instance, a robot powered by an LBM could learn to navigate a building by exploring, rather than relying on pre-loaded maps.

Multimodal Contextual Understanding: Unlike LLMs that are limited to processing text, humans seamlessly integrate sights, sounds, touch, and emotions to make sense of the world in a profoundly multidimensional way. LBMs aim to achieve a similar multimodal contextual understanding where they can not only understand spoken commands but also recognize your gestures, tone of voice, and facial expressions.

Generalization Across Domains: One of the hallmarks of human learning is the ability to apply knowledge across various domains. For instance, a person who learns to drive a car can quickly transfer that knowledge to operating a boat. One of the challenges with traditional AI is transferring knowledge between different domains. While LLMs can generate text for different fields like law, medicine, or entertainment, they struggle to apply knowledge across various contexts. LBMs, however, are designed to generalize knowledge across domains. For example, an LBM trained to help with household chores could easily adapt to work in an industrial setting like a warehouse, learning as it interacts with the environment rather than needing to be retrained.

Real-World Applications of Large Behavior Models

Although LBMs are still a relatively new field, their potential is already evident in practical applications. For example, a company called Lirio uses an LBM to analyze behavioral data and create personalized healthcare recommendations. By continuously learning from patient interactions, Lirio’s model adapts its approach to support better treatment adherence and overall health outcomes. For instance, it can pinpoint patients likely to miss their medication and provide timely, motivating reminders to encourage compliance.

In another innovative use case, Toyota has partnered with MIT and Columbia Engineering to explore robotic learning with LBMs. Their “Diffusion Policy” approach allows robots to acquire new skills by observing human actions. This enables robots to perform complex tasks like handling various kitchen objects more quickly and efficiently. Toyota plans to expand this capability to over 1,000 distinct tasks by the end of 2024, showcasing the versatility and adaptability of LBMs in dynamic, real-world environments.

Challenges and Ethical Considerations

While LBMs show great promise, they also bring up several important challenges and ethical concerns. A key issue is ensuring that these models could not mimic harmful behaviors from the data they are trained on. Since LBMs learn from interactions with the environment, there is a risk that they could unintentionally learn or replicate biases, stereotypes, or inappropriate actions.

Another significant concern is privacy. The ability of LBMs to simulate human-like behavior, particularly in personal or sensitive contexts, raises the possibility of manipulation or invasion of privacy. As these models become more integrated into daily life, it will be crucial to ensure that they respect user autonomy and confidentiality.

These concerns highlight the urgent need for clear ethical guidelines and regulatory frameworks. Proper oversight will help guide the development of LBMs in a responsible and transparent way, ensuring that their deployment benefits society without compromising trust or fairness.

The Bottom Line

Large Behavior Models (LBMs) are taking AI in a new direction. Unlike traditional models, they don’t just process information—they learn, adapt, and behave more like humans. This makes them useful in areas like healthcare and robotics, where flexibility and context matter.

But there are challenges. LBMs could pick up harmful behaviors or invade privacy if not handled carefully. That’s why clear rules and careful development are so important.

With the right approach, LBMs could transform how machines interact with the world, making them smarter and more helpful than ever.

[yikesy]

okay so apollo vs python round one time!!

bear with me because this got really long

so the thing I've long wondered about this event is just Why The Fuck did he do this. at four days old. alone.

because it makes no sense right??

even if he wants to take revenge and protect his mom, at this time he's a duo with artemis and isn't she by all means more qualified for this than him?? she's the more martially inclined of the two and represents lawlessness and wildness, I don't know if she is yet but still, when she comes into her divinity (only a few days later!!!) she'll literally be known as the huntress of wild beasts

so that is one point. the other is just what possessed him to think he could do this?? python is a child of gaia who has been openly tormenting a well connected titanness and has taken over delphi the center of the world and dictator of fate and hasn't been defeated yet

apollo is, again, four days old and not the martial one of the pair, in either powers or disposition, he doesn't have any experience using his powers in general, let alone offensively what made him think he could do this??

why him, why now

well my answer is that I think it makes perfect sense if we take into account two things 1) that he is the four day old embodiment of Light as a concept and 2) the reason python was chasing leto around in the first place

right, what started this in the first place, python received a prophecy that the unborn son of leto would be his murderer, that's why he was trying to kill her before she could give birth

and again apollo is four days old meaning his nature has not been,, "tainted" by much of anything yet, be it humanity, belief, other domains or even social interaction. let us remember what he developed into in the future when he's more of a person and not only a pure concept, an avatar of relentless seeking and revelation and knowledge and truth. light is not restrained or subtle at all and this was the time when that was all he was

so basically I think apollo knew there was a prophecy that said he would kill python and just fucking went for it, the winning condition was already met just by being him so why wait right? did he think he was in any way qualified otherwise? no, did he have a plan or any idea of how he would manage? not at all, but it literally did not matter since his victory was already written in fate and confirmed by python, it must have looked like a bright point a to point b to him

and I think that's how he beat him, by leveraging prophecy and using it as a weapon, he was by all means no match at all but it was fated by a prophecy that scared python enough to confirm it's validity and they were at delphi that he usurped from his grandmother so he had the right of inheritance on his side and with his faith and steadfastness on this one thing, apollo won by literally muscling the domain of prophecy away from python

you know the fate string apollo uses on his bow in canon? my headcanon is that this is where it came from, that deep in the fight, he physically took the string of the prophecy and literally used it as the arrow that killed python and later used it to string his bow and now all his shots have literal reality piercing power

[benotafraid111]

[gojoest]

Satoru would be the type would lean down to thank your pussy for delivering his baby safely😂 when you guys get back into it he would praise it say how strong it has been for nine months and how warm and stretchy it is

truer words have never been spoken

[alqueni]

Matt spitting straight bars🔥🔥🔥

Be careful, he RULES the streets. (his words)

[ben-txt]

some BEN things!! (i didn't make the last image, just thought it was silly)

[hungergameshyperfixation]

Before anyone says that the things Snow does are unrealistic and petty, I want to remind people of the U.S. government’s current “leadership” and how they spend their free time whilst being in power.