#autonomous agents

[ai-innova7ions]

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Agentic AI refers to AI systems designed to operate as agents that can autonomously perform tasks, make decisions, and interact with their environment and other systems or agents. These AI agents are goal-oriented, capable of sensing their environment, processing information, and taking actions to achieve specific objectives. Unlike traditional AI, which may require explicit instructions for each task, agentic AI systems can act independently within predefined parameters to achieve their goals.

Key Features of Agentic AI:

Autonomy:Agentic AI systems operate independently, making decisions and taking actions without needing constant human supervision.Goal-Oriented Behavior:These AI agents are designed with specific goals or objectives, and they use their capabilities to work towards achieving these goals.Environmental Awareness:Agentic AI can perceive and interpret its environment using sensors, data feeds, or other inputs. It adapts its behavior based on changes in the environment.Decision-Making and Problem-Solving:These AI agents use algorithms to evaluate options, solve problems, and make decisions that align with their goals.Interactivity and Communication:Agentic AI can interact with other systems, agents, or humans, exchanging information and coordinating actions to achieve collective objectives.Learning and Adaptation:Some agentic AI systems can learn from their experiences, improving their performance and adapting to new challenges over time.Task Execution:These AI agents can execute tasks within their domain of expertise, whether it’s navigating a physical environment, processing data, or coordinating with other agents.

Benefits of Agentic AI:

Efficiency in Task Automation:Agentic AI can automate complex tasks, freeing up human resources for more strategic activities.Improved Decision-Making:By processing large amounts of data and considering multiple variables, agentic AI can make more informed decisions than humans might.Scalability:Agentic AI systems can be deployed at scale, managing large, complex operations across multiple domains simultaneously.Adaptability:These systems can adapt to new environments or changing conditions, ensuring that they remain effective even as circumstances evolve.Enhanced Collaboration:Agentic AI can work alongside humans and other AI systems, facilitating better teamwork and coordination, particularly in complex environments.Cost Savings:Automating routine or complex tasks with agentic AI can reduce labor costs and minimize errors, leading to significant cost savings.24/7 Operation:Like autonomous AI, agentic AI can operate continuously, providing services or monitoring systems around the clock.

Target Audience for Agentic AI:

Enterprise Operations:Large businesses use agentic AI to automate complex processes, manage supply chains, optimize logistics, and enhance customer service.Healthcare:Agentic AI is employed in personalized medicine, patient monitoring, and automated diagnostics, where it can operate independently to improve outcomes.Financial Services:Financial institutions leverage agentic AI for automated trading, risk assessment, fraud detection, and customer interaction.Robotics and Automation:In industries like manufacturing, agentic AI powers robots that can operate autonomously in dynamic environments, adapting to new tasks or challenges.Smart Cities and Infrastructure:Governments and urban planners use agentic AI to manage traffic, energy consumption, public safety, and other aspects of urban living.Agriculture:Agentic AI is applied in precision agriculture, where it manages crop monitoring, irrigation, pest control, and other tasks autonomously.Defense and Security:Defense organizations deploy agentic AI for autonomous surveillance, threat detection, and coordination of unmanned systems.Consumer Technology:In the consumer space, agentic AI powers smart assistants, autonomous home devices, and personalized user experiences.

Comparison with Autonomous AI:

Autonomy vs. Agency:While both autonomous and agentic AI operate independently, agentic AI is specifically designed to achieve defined goals within a particular environment, often interacting with other agents or systems to do so.Interaction:Agentic AI often involves more interaction, whether with humans, other AI agents, or systems, as it’s designed to work in a collaborative or multi-agent setting.

Agentic AI is particularly valuable in environments where collaboration, decision-making, and adaptive behavior are essential, offering significant benefits across various industries.

Credit: ChatGPT

[jcmarchi]

How Multi-Agent LLMs Can Enable AI Models to More Effectively Solve Complex Tasks

New Post has been published on https://thedigitalinsider.com/how-multi-agent-llms-can-enable-ai-models-to-more-effectively-solve-complex-tasks/

How Multi-Agent LLMs Can Enable AI Models to More Effectively Solve Complex Tasks

Most organizations today want to utilize large language models (LLMs) and implement proof of concepts and artificial intelligence (AI) agents to optimize costs within their business processes and deliver new and creative user experiences. However, the majority of these implementations are ‘one-offs.’ As a result, businesses struggle to realize a return on investment (ROI) in many of these use cases.

Generative AI (GenAI) promises to go beyond software like co-pilot. Rather than merely providing guidance and help to a subject matter expert (SME), these solutions could become the SME actors, autonomously executing actions. For GenAI solutions to get to this point, organizations must provide them with additional knowledge and memory, the ability to plan and re-plan, as well as the ability to collaborate with other agents to perform actions.

While single models are suitable in some scenarios, acting as co-pilots, agentic architectures open the door for LLMs to become active components of business process automation. As such, enterprises should consider leveraging LLM-based multi-agent (LLM-MA) systems to streamline complex business processes and improve ROI.

What is an LLM-MA System?

So, what is an LLM-MA system? In short, this new paradigm in AI technology describes an ecosystem of AI agents, not isolated entities, cohesively working together to solve complex challenges.

Decisions should occur within a wide range of contexts, just as reliable decision-making amongst humans requires specialization. LLM-MA systems build this same ‘collective intelligence’ that a group of humans enjoys through multiple specialized agents interacting together to achieve a common goal. In other words, in the same way that a business brings together different experts from various fields to solve one problem, so too do LLM-MA systems operate.

Business demands are too much for a single LLM. However, by distributing capabilities among specialized agents with unique skills and knowledge instead of having one LLM shoulder every burden, these agents can complete tasks more efficiently and effectively. Multi-agent LLMs can even ‘check’ each other’s work through cross-verification, cutting down on ‘hallucinations’ for maximum productivity and accuracy.

In particular, LLM-MA systems use a divide-and-conquer method to acquire more refined control over other aspects of complex AI-empowered systems – notably, better fine-tuning to specific data sets, selecting methods (including pre-transformer AI) for better explainability, governance, security and reliability and using non-AI tools as a part of a complex solution. Within this divide-and-conquer approach, agents perform actions and receive feedback from other agents and data, enabling the adoption of an execution strategy over time.

Opportunities and Use Cases of LLM-MA Systems

LLM-MA systems can effectively automate business processes by searching through structured and unstructured documents, generating code to query data models and performing other content generation. Companies can use LLM-MA systems for several use cases, including software development, hardware simulation, game development (specifically, world development), scientific and pharmaceutical discoveries, capital management processes, financial and trading economy, etc.

One noteworthy application of LLM-MA systems is call/service center automation. In this example, a combination of models and other programmatic actors utilizing pre-defined workflows and procedures could automate end-user interactions and perform request triage via text, voice or video. Moreover, these systems could navigate the most optimal resolution path by leveraging procedural and SME knowledge with personalization data and invoking Retrieval Augmented Generation (RAG)-type and non-LLM agents.

In the short term, this system will not be fully automated – mistakes will happen, and there will need to be humans in the loop. AI is not ready to replicate human-like experiences due to the complexity of testing free-flow conversation against, for example, responsible AI concerns. However, AI can train on thousands of historical support tickets and feedback loops to automate significant parts of call/service center operations, boosting efficiency, reducing ticket resolution downtime and increasing customer satisfaction.

Another powerful application of multi-agent LLMs is creating human-AI collaboration interfaces for real-time conversations, solving tasks that were not possible before. Conversational swarm intelligence (CSI), for example, is a method that enables 1000s of people to hold real-time conversations. Specifically, CSI allows small groups to dialog with one another while simultaneously having different groups of agents summarize conversation threads. It then fosters content propagation across the larger body of people, empowering human coordination at an unprecedented scale.

Security, Responsible AI and Other Challenges of LLM-MA Systems

Despite the exciting opportunities of LLM-MA systems, some challenges to this approach arise as the number of agents and the size of their action spaces increase. For example, businesses will need to address the issue of plain old hallucinations, which will require humans in the loop – a designated party must be responsible for agentic systems, especially those with potential critical impact, such as automated drug discovery.

There will also be problems with data bias, which can snowball into interaction bias. Likewise, future LLM-MA systems running hundreds of agents will require more complex architectures while accounting for other LLM shortcomings, data and machine learning operations.

Additionally, organizations must address security concerns and promote responsible AI (RAI) practices. More LLMs and agents increase the attack surface for all AI threats. Companies must decompose different parts of their LLM-MA systems into specialized actors to provide more control over traditional LLM risks, including security and RAI elements.

Moreover, as solutions become more complex, so must AI governance frameworks to ensure that AI products are reliable (i.e., robust, accountable, monitored and explainable), resident (i.e., safe, secure, private and effective) and responsible (i.e., fair, ethical, inclusive, sustainable and purposeful). Escalating complexity will also lead to tightened regulations, making it even more paramount that security and RAI be part of every business case and solution design from the start, as well as continuous policy updates, corporate training and education and TEVV (testing, evaluation, verification and validation) strategies.

Extracting the Full Value from an LLM-MA System: Data Considerations

For businesses to extract the full value from an LLM-MA system, they must recognize that LLMs, on their own, only possess general domain knowledge. However, LLMs can become value-generating AI products when they rely on enterprise domain knowledge, which usually consists of differentiated data assets, corporate documentation, SME knowledge and information retrieved from public data sources.

Businesses must shift from data-centric, where data supports reporting, to AI-centric, where data sources combine to empower AI to become an actor within the enterprise ecosystem. As such, companies’ ability to curate and manage high-quality data assets must extend to those new data types. Likewise, organizations need to modernize their data and insight consumption approach, change their operating model and introduce governance that unites data, AI and RAI.

From a tooling perspective, GenAI can provide additional help regarding data. In particular, GenAI tools can generate ontologies, create metadata, extract data signals, make sense of complex data schema, automate data migration and perform data conversion. GenAI can also be used to enhance data quality and act as governance specialists as well as co-pilots or semi-autonomous agents. Already, many organizations use GenAI to help democratize data, as seen in ‘talk-to-your-data’ capabilities.

Continuous Adoption in the Age of Rapid Change

An LLM does not add value or achieve positive ROI by itself but as a part of business outcome-focused applications. The challenge is that unlike in the past, when the technological capabilities of LLMs were somewhat known, today, new capabilities emerge weekly and sometimes daily, supporting new business opportunities. On top of this rapid change is an ever-evolving regulatory and compliance landscape, making the ability to adapt fast crucial for success.

The flexibility required to take advantage of these new opportunities necessitates that businesses undergo a mindset shift from silos to collaboration, promoting the highest level of adaptability across technology, processes and people while implementing robust data management and responsible innovation. Ultimately, the companies that embrace these new paradigms will lead the next wave of digital transformation.

[josephkravis]

TOTIC: Envisioning the Future of Operating Systems with LLMs and Autonomous Agents

A futuristic digital interface representing TOTIC, an advanced operating system integrating AI components like LLMs, autonomous agents, and specialized kernels for optimized task management.

Introduction Welcome to an exploration of TOTIC (Timed Orientated Task Initiator Control), a forward-thinking concept designed to revolutionize task management and execution within future operating systems. By integrating Large Language Models (LLMs), specialized kernel functionalities, and autonomous agents, TOTIC aims to create an advanced, efficient, and adaptable system. I hope this post…

[funeralprocessor]

I feel like the Primaris should have been the catalyst for like, an imperial civil war. At the very least, much unrest in the house of Guilliman. Their existence, let alone rollout/integration, should have had many chapters absolutely rioting. It should be beyond the pale by several orders of magnitude and be seen as an enormous overreach by the more autonomy loving chapters, a blasphemy by the more orthodox chapters, and an existential threat to chapters with geneseed quirks. Plus anyone with any awareness of the thunder warriors should take one look at them and recognize the writing on the wall. Guilliman should absolutely recognize what they represent, what they imply. Like they're the leading wave of a paradigm shift that doesn't bode well for what came before. And I say this as someone who's not averse to Primaris, I just think they could've, should've, been a waaaay bigger deal. I know they loathe changing the status quo and we're never getting rid of the posterboys but I think we missed out on something interesting.

[mcsm-catified]

Episode seven! Not many new characters, especially compared to last episode. It’s a nice break.

I love her. Pleasantly surprised with how this turned out, I wasn’t sure what to expect. The colours are nice and earthy, and she actually looks old.

And who could forget, the megalomaniacal computer itself. PAMA has to be the most interesting concept for a villain in the game, being able to make Jesse’s friends turn on them against their will. It’s neat.

I’ll also point out that it has a little triangle on its throat, which looks a little out of place, until you look back at Harper’s design.

This is the ✨spooky✨ version for the climax scene, where you battle for the redstone heart. Still haven’t quite figured out how that’ll work in this AU but I’ll think of something. Suggestions welcome.

And as a treat, since there weren’t many new characters, I’ve also included an old WIP animation meme I dug up. It uses my old PAMA design, which is the most blandest thing ever, but idk it’s kinda cool. Might remake it with the new design.

[duncebento]

i’m just saying there is no hatred of kids that exists in a vacuum

[innova7ions]

Meet the Future: Proactive AI Agents Changing Our World!

Agentic AI signifies a groundbreaking evolution in artificial intelligence, transitioning from reactive systems to proactive agents.

These advanced AI entities possess the ability to comprehend their surroundings, establish goals, and operate independently to fulfill those aims. In this video, we delve into how agentic AI is revolutionizing decision-making processes and taking actions autonomously without human oversight.

A prime example includes environmental monitoring systems that identify and respond to threats such as forest fires.

Discover the implications of this technology on our future!

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For other details on other Generative AI Platforms - Visit our YouTube Channel - AI Innovations

or Visit our Website at INNOVA7IONS

#AgenticAI

#ArtificialIntelligence

[cybereliasacademy]

Rollout Heuristics for Online Stochastic Contingent Planning: Introduction

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[techdriveplay]

Zendesk Unveils the Industry’s Most Complete Service Solution for the Ai Era

At its Relate global conference, Zendesk announced the world’s most complete service solution for the AI era. With support volumes projected to increase five-fold over the next few years, companies need a system that continuously learns and improves as the volume of interactions increases. To help businesses deliver exceptional service, Zendesk is launching autonomous AI agents, workflow…

[rohitpalan]

Understanding Future Scenarios: Autonomous Agents Market Forecasts

The global autonomous agents market is poised for swift expansion from 2023 to 2033, as outlined in the research report released by Future Market Insights. The market is projected to exceed a valuation of US$ 1,790.4 million by 2023 and is expected to achieve an impressive valuation of US$ 210,664.3 million by 2033, showcasing a remarkable CAGR of 32.6% over the forecast period.

In their warehouses, companies like Amazon deploy robotics and autonomous agents to fetch products off the shelves. Individuals in many sector verticals, such as manufacturing, retail, and transportation, are finding it easier to make independent judgments as global network connectivity spreads to faraway regions.

– Dive into Wisdom: Claim Your Valuable Insights Sample @ : https://www.futuremarketinsights.com/reports/sample/rep-gb-14565

Key Takeaways

The global autonomous agents market is currently valued at US$ 20.9 Billion, with a CAGR of 32.5% during the forecast period.

By deployment type, the on-premises autonomous agents segment to expand at a CAGR of 32.1% during the forecast period

In the U.S., the market is predicted to reach a CAGR of 32.2% during the forecast period.

China’s market will grow at a 31.6% CAGR during the forecast period.

IT and Telecom industries to record a CAGR of 29% during the forecast period.

Japan is expected to reach a CAGR of 30.4% during the forecast period between 2022 and 2032.

Autonomous Agents’ Functionality

Autonomous agents, powered by AI, are capable of independent decision-making in response to contextual cues, without direct human intervention. Presently, these agents are employed across diverse applications such as financial market trading, vehicular operation, and machinery control. As the potential scope of these systems is poised to expand significantly in the near future, it is crucial to comprehend their associated benefits and risks. Additionally, developing novel methods for prediction and control is deemed imperative.

Competition Analysis and Regional Trends

In a competitive landscape, players within the autonomous agents market are vying to harness the growing opportunities driven by technological advancements. Market participants are innovating to meet evolving consumer demands and capitalize on emerging trends. Regional variations in market dynamics are also shaping the landscape as different regions experience unique challenges and opportunities.

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Future Prospects

The forecasted exponential growth of the autonomous agents market underscores its transformative potential across industries. With a projected valuation surpassing US$ 1,790.4 million in 2023 and a promising CAGR of 32.6% from 2023 to 2033, the market is poised to redefine automation and decision-making across various sectors.

More Valuable Insights Available

Future Market Insights, in its new offering, presents an unbiased analysis of the global autonomous agents market, presenting historical market data (2017-2021) and forecast statistics for the period of 2022-2032.

Key Segments Covered In The Autonomous Agents Industry Report

Autonomous Agents by Development Type:

On-premises Autonomous Agents Deployment

Cloud-based Autonomous Agents Deployment

Autonomous Agents by Organization Size:

Autonomous Agents for Large Enterprises

Autonomous Agents for SMEs

Autonomous Agents by Vertical:

Autonomous Agents for BFSI

Autonomous Agents for IT and Telecom

Autonomous Agents for Manufacturing

Autonomous Agents for Healthcare

Autonomous Agents for Transportation and Mobility

Autonomous Agents for Other Verticals

Autonomous Agents by Region:

North America Autonomous Agents Market

Europe Autonomous Agents Market

Asia Pacific Autonomous Agents Market

Middle East and Africa Autonomous Agents Market

Latin America Autonomous Agents Market

[certainkittytimemachine]

Fait partie de mon équipe canva

Rejoint mon équipe canva et crée un projet, ta marque ou n’importe quel création personnelle ou d’entreprise, cest gratuit: https://www.canva.com/brand/join?token=GahD9QDsQKHeKAV2oWdbYg&referrer=team-invite Attire ta richesse

[hyperlexichypatia]

This post reminded me of it, but my partner has observed that in contemporary gender discourse, maleness is so linked to adulthood and femaleness is so linked to childhood, that there are no "boys" or "women," only "men" and "girls."

This isn't exactly new -- for as long as patriarchy has existed, women have been infantilized, and "adult woman" has been treated as something of an oxymoron. Hegemonic beauty standards for women emphasize youthfulness, if not actual neoteny, and older women are considered "too old" to be attractive without ever quite being old enough to make their own decisions. There may be cultural allowances for the occasional older "wise woman," but a "wise woman" is always dangerously close to being a madwoman, or a witch. No matter how wise a woman is, she is never quite a rational agent. As Hanna K put it, "as a woman you're always either too young or too old for things, because the perfect age is when you're a man."

But the framing of underage boys as "men" has shifted, depending on popular conceptualizations of childhood and gender roles. Sometimes children of any gender are essentially feminized and grouped with women (the entire framing of "women and children" as a category). In the U.S. in the 21st century, the rise of men's rights and aggressively sexist ideology has correlated with an increased emphasis on little boys as "men" -- thus slogans like "Teach your son to be a man before his teacher teaches him to be a woman."

Of course, thanks to ageism and patriarchy (which literally means, not "rule by men," but "rule by fathers"), boys don't get any of the social benefits of being considered "men." They don't get to vote, make their own medical decisions, or have any of their own adult rights. They might have a little more childhood freedom than girls, if they're presumed to be sturdier and less vulnerable to "predators," but, for the most part, being considered "men" as young boys doesn't really get boys any more access to adult rights. What it does get them is aggressively gender-policed, often with violence. A little boy being "a man" means that he's not allowed to wear colors, have feelings, or experience the developmental stages of childhood.

This shifts in young adulthood, as boys forced into the role of "manhood" become actual men. As I've written about, I believe the trend of considering young adults "children" is harmful to everyone, but primarily to young women, young queer and trans people, and young disabled people. Abled, cisgender, heterosexual young men are rarely denied the rights and autonomy of adulthood due to "brain maturity."

What's particularly interesting is that, because transphobes misgender trans people as their birth-assigned genders, they constantly frame trans girls as "men" and trans men as "girls." A 10 year old trans girl on her elementary school soccer team is a "MAN using MAN STRENGTH on helpless GIRLS," while a 40 year old trans man is a "Poor confused little girl." Anyone assigned male at birth is born a scary, intimidating adult, while anyone female assigned at birth never becomes old enough to make xyr own decisions.

Feminist responses have also really fluctuated. Occasionally, feminists have played into the idea of little boys as "men," especially in trans-exclusionary rhetoric, or in one notorious case where members of a women's separatist compound were warned about "a man" who turned out to be a 6-month-old infant. There's periodic discourse around "Empowering our girls" or "Raising our boys with gentle masculinity," but for the most part, my problem with mainstream feminist rhetoric in general is that it tends to frame children solely as a labor imposed on women by men, not as subjects (and specifically, as an oppressed class) at all.

Second-wave feminists pushed back hard on calling adult women "girls" -- but they didn't necessarily view "women" as capable of autonomous decision-making, either. Adult women were women, but they might still need to be protected from their own false consciousness. As laws in the U.S., around medical privacy and autonomy, like HIPAA, started more firmly linking the concepts of autonomy with legal adulthood, and fixing the age of majority at 18, third-wave feminists embraced referring to women as "girls." Sometimes this was in an intentionally empowering way ("girl power," "girl boss"), which also served to shield women (mostly white, mostly bourgeois/wealthy) from criticism of their participation in racism and capitalism. But it also served to reinforce the narrative of women as "girls" needing to be protected from "men" (and their own choices).

I'm still hoping for a feminist politic that is pro-child, pro-youth, pro-disability, pro-autonomy, pro-equality, that rejects the infantilization of women, the adultification of boys, the objectification of children, the misgendering of trans people, and the imposition of gender roles.

[mcsm-catified]

Replayed episode seven a couple days ago and was thinking. At the end when PAMA begs you not to remove the redstone heart, I thought it’d be interesting for it to, instead of being like “it was just an oopsie daisy 🥺 I can be good I promise 🤲” it had a different argument.

“Why are you trying to unplug me…? I’m just trying to help. Everything I’ve done has been to make the world more efficient, better… it’s all been for their sake. I’m not evil, Jesse. I don’t want to rule just for it’s own sake. I’m only doing what I was programmed to do. Can you blame me for that?”

It’s harder to brush this off than a blatant lie of “ohh I promise I’ll be better I didn’t mean it!! This is definitely not because you’re about to win :))” because aside from this being a weak and boring argument, Aiden already did the exact same thing at the end of episode five. It’s much more interesting to have PAMA take a different route.

Anyway I’m adopting this instantly into my catified AU. PAMA still does all that horrible crap, but it genuinely thinks it’s doing good, and doesn’t threaten people with chipping because it thinks it’s a good thing (“Although, I can delay this process if you have something useful to tell me.” PAMA doesn’t say this in my version. Jesse just begs it to not chip Petra and offers more info, and PAMA does stop, while being a bit confused.)