#AI agents as annotators

[macgenceaiml]

High-quality annotated data is utmost important for developing any Artificial Intelligence and Machine Learning Model. Human annotators performed Annotation, which is the act of indexing and tagging data, but with larger datasets becoming more complex, there is greater need for faster and more scalable annotation techniques. This is where AI agents as annotators step in to revolutionize the field. 

[cogitotech]

Top Data Annotation Companies for Agritech in 2025

This labeled data helps AI algorithms make informed decisions, supporting farmers in monitoring fields and augmenting productivity.

Data annotation for agriculture data (sensor readings, images, etc) with relevant information allows AI systems to see and understand the crucial details of a farm environment. Do you want to explore where to outsource data labeling services personalized to your agriculture needs? You are reading the right blog, as we will list the top data annotation companies for agriculture in 2025. Let’s delve deeper!

[hiscalliope]

Luigi Mangione x Writer GF! Headcanons

𖦹 read your manuscript before you queried it out to an agent. gave you genuine feedback with suggestions to add, spent money printing the manuscript on paper, got a red pen and a yellow highlighter to highlight is favorite parts and annotate in his suggestions.

𖦹 he headed out with you to the post office to mail in your query, gave you the sweetest, most encouraging words as you finished off handing the piece of mail over to the post office clerk. pulled you in for a comfy side hug and plants a kiss on your hair.

𖦹 several months go by before you're finally contacted by an agent wanting to represent you and pitch your work to a publishing house. once you got off the phone with your new representation, you called luigi up at work with shaky hands, you know he'd be so proud.

𖦹 "baby? baby, i did it! i got an agent and they want to pitch my book to penguin random house!"

𖦹 "god, that's amazing! i'm so proud of you! i knew you could do it, i knew you could get it done all along!" he whispered into the phone, having quickly exited a work conference to pick up.

𖦹 once your book got picked up by penguin random house, he took off work to come to the offices as moral support to sign the book deal with your agent. your deal consisted of a three book deal in the young adult fantasy genre, the first book successfully pitched would the first in a trilogy.

𖦹 the male love interest in this story is, unbeknownst to luigi and your readers, very much an ode to luigi. his name was tiberius (you would've named him lucius but that would've been too obvious to him). this character was modeled to be kind, generous, selfless and without ulterior motivations to help the main character reya navigate her destiny (a/n: i chose the most basic fantasy plot with the most cliche buzzwords i know i'm sorry)

𖦹 very involved in asking how the editing and book printing process is going. every other evening after the both of you were done working for the day (you still had your day job even after getting your advance, it had been suggested you kept this job until after the book was released) you cuddled next to him on the couch going over different ideas for the physical appearance of the book. "wouldn't it be cool if the book had deckled edges, spray painted edges?" he'd suggest to you.

𖦹 just a few weeks before the book was set to print, you discussed some cover design options with luigi. the publishing house was trying to push a cover made with AI art, its cheaper and easier to make. you were very torn because you had a real appreciation for artists and their craft but you didn't want to be regarded as "difficult to work with" when or if you decided to insist on real human art.

𖦹 luigi advised you sincerely although he makes sure to let you know that the final decision lies with you, "there's something so beautiful and so unique about art made with feeling, with emotion and that's just something a AI system can't replicate no matter how hard it tries."

𖦹 you ended up picking one of luigi's good friends to design the covers for you, the publishing house did end up signing off on the commission begrudgingly after making some initial protests. you felt proud in standing up for your beliefs to prop up human art over AI art and you were so incredibly happy that your boyfriend stood by your side as you stood your ground, clasping your hand on the day his friend signed on to design all three book covers in the trilogy.

𖦹 luigi planned a party with your and his immediate families and your closest friends at the apartment the two of you share. filled with red balloons (the color of your book's sprayed edges and the general color scheme of the book), a giant sword shaped cake (it's totally red velvet inside), a charcuterie board (luigi stayed away from eating it but he helps arrange it with his mom) and little sandwiches he had especially catered for this.

𖦹 there was a countdown to midnight at the party and when the clock struck midnight your baby book was out officially in the world. luigi gave you a fierce kiss, cupping your face in his hands and you stayed in his embrace, smelling his sweet cologne for what seems like ages. after he pulled away from the kiss, he pressed one to your forehead, repeating how happy he was for you, elated by seeing your dreams finally come true.

𖦹 later that day you had your first ever book signing. your book had a great marketing team behind it and your book had received a ton of hype, being branded as the next "it" YA fantasy novel. your signing event was completely sold out. luigi arrived with his friends ten minutes before the signing is set to begin, sitting in the front row that'd been reserved for the your editorial team, your boyfriend and his guests. he was wearing khaki colored slacks and a crisp baby blue button down (your favorite of his shirts)

𖦹 he was laughing and chatting animatedly with his friends when you're finally brought out to begin the author q&a portion of the event. once you're introduced by another author you're holding the event in conversation with, his eyes are glued on you, taking in your every movement. an audience member asks you a question you've been equal parts excited for and dreading. "what was the inspiration behind tiberius' character?"

𖦹 "that's an excellent question! the main inspiration behind tiberius is actually someone in my life, someone who has always been able to make me smile even when i don't feel like smiling. someone who has never once stopped believing in me, someone who has never been afraid to say what he feels when he feels it. someone who once said to 'follow my heart' except my heart led me to him."

𖦹 your gaze slowly but surely found its way to where luigi is sitting in the front row and when your piercing gazes finally connected, he knew what you were saying and his lips pressed together in emotion, he had no idea that you had specifically modeled tiberius after him, he hadn't made the connection until you had decided to confirm in front of hundreds of readers. the audience also very much picked up on what you were saying and a chorus of 'aw''s slowly grew. the blush that grew on the bridge of his nose and his cheek bones rivaled the intensity of the red deckled, spray painted edges of your book. his friends snickered teasingly next to him. his mom seated on his right side chuckled to herself, seemed she may have had an inkling about that.

𖦹 once the q&a was over, he like everyone else got on line to get his copy (that he bought with his own money) signed. once it was his turn to get his copy signed, he handed his phone to the bookstore employee so they could snap some candids of the two of you. "hi pretty author lady, i love your book. a lot. its too bad i love the girl that wrote it a little more"

𖦹 you autograph his book with a little message that goes: "to luigi: thank you for being the candle that was lit during my darkest days. now you can enjoy the sunshine with me."

[hey there lovelies! <3 this was just a little something i wrote to cheer myself up. i hope this is something all of my writer friends would love to envision, luigi reading your books and showing up to your author events, he would be the most loving and supportive]

[100percent-shell-oil]

@i-count-words-in-posts try me, motherfucker

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relying remain remainder remained remaining remains remark remarkable remarks remedies remedy remember remembered remind reminder remix remote removable removal remove removed removing renaissance render rendered rendering renew renewable renewal reno rent rental rentals rentcom rep repair repairs repeat repeated replace replaced replacement replacing replica replication replied replies

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[mariacallous]

What is the future of the like button in the age of artificial intelligence? Max Levchin—the PayPal cofounder and Affirm CEO—sees a new and hugely valuable role for liking data to train AI to arrive at conclusions more in line with those a human decisionmaker would make.

It’s a well-known quandary in machine learning that a computer presented with a clear reward function will engage in relentless reinforcement learning to improve its performance and maximize that reward—but that this optimization path often leads AI systems to very different outcomes than would result from humans exercising human judgment.

To introduce a corrective force, AI developers frequently use what is called reinforcement learning from human feedback (RLHF). Essentially they are putting a human thumb on the scale as the computer arrives at its model by training it on data reflecting real people’s actual preferences. But where does that human preference data come from, and how much of it is needed for the input to be valid? So far, this has been the problem with RLHF: It’s a costly method if it requires hiring human supervisors and annotators to enter feedback.

And this is the problem that Levchin thinks could be solved by the like button. He views the accumulated resource that today sits in Facebook’s hands as a godsend to any developer wanting to train an intelligent agent on human preference data. And how big a deal is that? “I would argue that one of the most valuable things Facebook owns is that mountain of liking data,” Levchin told us. Indeed, at this inflection point in the development of artificial intelligence, having access to “what content is liked by humans, to use for training of AI models, is probably one of the singularly most valuable things on the internet.”

While Levchin envisions AI learning from human preferences through the like button, AI is already changing the way these preferences are shaped in the first place. In fact, social media platforms are actively using AI not just to analyze likes, but to predict them—potentially rendering the button itself obsolete.

This was a striking observation for us because, as we talked to most people, the predictions mostly came from another angle, describing not how the like button would affect the performance of AI but how AI would change the world of the like button. Already, we heard, AI is being applied to improve social media algorithms. Early in 2024, for example, Facebook experimented with using AI to redesign the algorithm that recommends Reels videos to users. Could it come up with a better weighting of variables to predict which video a user would most like to watch next? The result of this early test showed that it could: Applying AI to the task paid off in longer watch times—the performance metric Facebook was hoping to boost.

When we asked YouTube cofounder Steve Chen what the future holds for the like button, he said, “I sometimes wonder whether the like button will be needed when AI is sophisticated enough to tell the algorithm with 100 percent accuracy what you want to watch next based on the viewing and sharing patterns themselves. Up until now, the like button has been the simplest way for content platforms to do that, but the end goal is to make it as easy and accurate as possible with whatever data is available.”

He went on to point out, however, that one reason the like button may always be needed is to handle sharp or temporary changes in viewing needs because of life events or situations. “There are days when I wanna be watching content that’s a little bit more relevant to, say, my kids,” he said. Chen also explained that the like button may have longevity because of its role in attracting advertisers—the other key group alongside the viewers and creators—because the like acts as the simplest possible hinge to connect those three groups. With one tap, a viewer simultaneously conveys appreciation and feedback directly to the content provider and evidence of engagement and preference to the advertiser.

Another major impact of AI will be its increasing use to generate the content itself that is subject to people’s emotional responses. Already, growing amounts of the content—both text and images—being liked by social media users are AI generated. One wonders if the original purpose of the like button—to motivate more users to generate content—will even remain relevant. Would the platforms be just as successful on their own terms if their human users ceased to make the posts at all?

This question, of course, raises the problem of authenticity. During the 2024 Super Bowl halftime show, singer Alicia Keys hit a sour note that was noticed by every attentive listener tuned in to the live event. Yet when the recording of her performance was uploaded to YouTube shortly afterward, that flub had been seamlessly corrected, with no notification that the video had been altered. It’s a minor thing (and good for Keys for doing the performance live in the first place), but the sneaky correction raised eyebrows nonetheless. Ironically, she was singing “If I Ain’t Got You”—and her fans ended up getting something slightly different from her.

If AI can subtly refine entertainment content, it can also be weaponized for more deceptive purposes. The same technology that can fix a musical note can just as easily clone a voice, leading to far more serious consequences.

More chilling is the trend that the US Federal Communications Commission (FCC) and its equivalents elsewhere have recently cracked down on: uses of AI to “clone” an individual’s voice and effectively put words in their mouth. It sounds like them speaking, but it may not be them—it could be an impostor trying to trick that person’s grandfather into paying a ransom or trying to conduct a financial transaction in their name. In January 2024, after an incident of robocalls spoofing President Joe Biden’s voice, the FCC issued clear guidance that such impersonation is illegal under the provisions of the Telephone Consumer Protection Act, and warned consumers to be careful.

“AI-generated voice cloning and images are already sowing confusion by tricking consumers into thinking scams and frauds are legitimate,” said FCC chair Jessica Rosenworcel. “No matter what celebrity or politician you favor, or what your relationship is with your kin when they call for help, it is possible we could all be a target of these faked calls.”

Short of fraudulent pretense like this, an AI-filled future of social media might well be populated by seemingly real people who are purely computer-generated. Such virtual concoctions are infiltrating the community of online influencers and gaining legions of fans on social media platforms. “Aitana Lopez,” for example, regularly posts glimpses of her enviable life as a beautiful Spanish musician and fashionista. When we last checked, her Instagram account was up to 310,000 followers, and she was shilling for hair-care and clothing brands, including Victoria’s Secret, at a cost of some $1,000 per post. But someone else must be spending her hard-earned money, because Aitana doesn’t really need clothes or food or a place to live. She is the programmed creation of an ad agency—one that started out connecting brands with real human influencers but found that the humans were not always so easy to manage.

With AI-driven influencers and bots engaging with each other at unprecedented speed, the very fabric of online engagement may be shifting. If likes are no longer coming from real people, and content is no longer created by them, what does that mean for the future of the like economy?

In a scenario that not only echoes but goes beyond the premise of the 2013 film Her, you can also now buy a subscription that enables you to chat to your heart’s content with an on-screen “girlfriend.” CarynAI is an AI clone of a real-life online influencer, Caryn Marjorie, who had already gained over a million followers on Snapchat when she decided to team up with an AI company and develop a chatbot. Those who would like to engage in one-to-one conversation with the virtual Caryn pay a dollar per minute, and the chatbot’s conversation is generated by OpenAI’s GPT-4 software, as trained on an archive of content Marjorie had previously published on YouTube.

We can imagine a scenario in which a large proportion of likes are not awarded to human-created content—and not granted by actual people, either. We could have a digital world overrun by synthesized creators and consumers interacting at lightning speed with each other. Surely if this comes to pass, even in part, there will be new problems to be solved, relating to our needs to know who really is who (or what), and when a seemingly popular post is really worth checking out.

Do we want a future in which our true likes (and everyone else’s) are more transparent and unconcealable? Or do we want to retain (for ourselves but also for others) the ability to dissemble? It seems plausible that we will see new tools developed to provide more transparency and assurance as to whether a like is attached to a real person or just a realistic bot. Different platforms might apply such tools to different degrees.

[drmikewatts]

Weekly Review 20 June 2025

Some interesting links that I Tweeted about in the last week (I also post these on Mastodon, Threads, Newsmast, and Bluesky):

Wikipedia kills AI summaries of its pages: https://www.extremetech.com/computing/wikipedia-editors-revolt-over-ai-summaries Given how many bots scrape Wikipedia for training data, this is probably a good thing

Corporate adoption of AI is levelling off: https://techcrunch.com/2025/06/09/corporate-ai-adoption-may-be-leveling-off-according-to-ramp-data/

Sam Altman is very smart but he's no scientist. Current AI are not going to drive scientific discoveries anytime soon, although they will be useful to accelerate some parts of them: https://www.bigdatawire.com/2025/06/03/ai-agents-to-drive-scientific-discovery-within-a-year-altman-predicts/

The thing with AI predicting your lifespan is, you'll only ever know if it under-estimated: https://www.nzherald.co.nz/world/how-many-years-do-i-have-left-an-app-gave-me-some-helpful-insights/6YFWBORXD5AI3NHGVQWQKYLGPY/

Trying to automate any business operation with AI is fraught. Trying to automate a government with AI is going to be hilarious: https://www.theregister.com/2025/06/10/trump_admin_leak_government_ai_plans/

There appears to be a fairly widespread use of AI to generate legal filings, and judges are getting sick of it: https://techcrunch.com/2025/06/07/lawyers-could-face-severe-penalties-for-fake-ai-generated-citations-uk-court-warns/

AI in the browser seems like a good idea, but the privacy issues around it have yet to be resolved: https://www.techrepublic.com/article/news-arc-dia-the-browser-company/

Retrieval-augmented Generation is helping make generative AI easier to roll out: https://www.techrepublic.com/article/news-gartner-generative-ai-apps-data-management-platforms/

Generative AI suck at chess: https://www.extremetech.com/computing/chatgpt-lost-a-chess-game-to-an-atari-2600

AI are the definition of garbage in, garbage out. Proper data annotation is essential: https://dataconomy.com/2025/06/12/why-data-labeling-quality-makes-or-breaks-ai-models/

Now is not a good time to be demolishing guardrails on AI: https://www.theverge.com/politics/681727/ro-khanna-ai-state-law-moratorium-reconciliation

An AI that can interpret genomic data, not just scan for patterns: https://dataconomy.com/2025/06/10/this-ai-explains-your-genes-the-way-a-doctor-would/

To be clear, this isn't AI restoring old artworks, it is AI guessing what it could have looked like: https://www.theguardian.com/science/2025/jun/11/researchers-create-ai-based-tool-that-restores-age-damaged-artworks-in-hours

AI have limitations in their reasoning, and these limitations appear to be fundamental: https://www.stuff.co.nz/world-news/360719320/new-research-offers-fresh-warnings-about-ability-ai

Constant testing of AI has a cost, but it lowers costs in the long run: https://www.theregister.com/2025/06/08/chatterbox_labs_ai_adoption/

AI is being used in recruitment, but people aren't happy about it: https://www.techrepublic.com/article/ai-and-recruitment/

That's one way of stopping students using AI to cheat:  https://www.theverge.com/news/682737/china-shuts-down-ai-chatbots-exam-season

AI might currently be used as a screening tool in recruitment, but that doesn't mean that any occult biases in the AI won't inappropriately screen people out: https://www.computerworld.com/article/4005351/surprise-employers-are-using-ai-to-interview-you.html

The connection between AI and galactic colonisation seems a bit tenuous to me: https://futurism.com/google-deepmind-hassabis

Some policies to mitigate the risk of shadow AI in the workplace: https://devclass.com/2025/06/10/shadow-ai-in-the-enterprise-managing-risk-without-slowing-progress/

How AI is contributing to the fight against cancer: https://dataconomy.com/2025/06/10/how-ai-is-fueling-our-hope-in-the-fight-against-cancer/

Some strategies to catch the errors made by generative AI: https://www.informationweek.com/machine-learning-ai/how-to-successfully-catch-generative-ai-errors

While it's not the only application of robust quantum computers, AI will certainly benefit from this technology: https://www.techrepublic.com/article/news-ibm-quantum-computer-error-correction-2029/

How DevOps can accelerate rollout of AI: https://www.datasciencecentral.com/unlocking-the-value-of-ai-with-devops-accelerated-mlops/

[newsnexttech]

AI generates data to help embodied agents ground language to 3D world

A new 3D-text dataset, 3D-GRAND, leverages generative AI to create synthetic rooms that are automatically annotated with 3D structures. The dataset’s 40,087 household scenes can help train embodied AI, like household robots, connect language to 3D spaces. Credit: Joyce Chai A new, densely annotated 3D-text dataset called 3D-GRAND can help train embodied AI, like household robots, to connect…

[aiandblockchainchronicles]

Building Smarter AI: The Role of LLM Development in Modern Enterprises

As enterprises race toward digital transformation, artificial intelligence has become a foundational component of their strategic evolution. At the core of this AI-driven revolution lies the development of large language models (LLMs), which are redefining how businesses process information, interact with customers, and make decisions. LLM development is no longer a niche research endeavor; it is now central to enterprise-level innovation, driving smarter solutions across departments, industries, and geographies.

LLMs, such as GPT, BERT, and their proprietary counterparts, have demonstrated an unprecedented ability to understand, generate, and interact with human language. Their strength lies in their scalability and adaptability, which make them invaluable assets for modern businesses. From enhancing customer support to optimizing legal workflows and generating real-time insights, LLM development has emerged as a catalyst for operational excellence and competitive advantage.

The Rise of Enterprise AI and the Shift Toward LLMs

In recent years, AI adoption has moved beyond experimentation to enterprise-wide deployment. Organizations are no longer just exploring AI for narrow, predefined tasks; they are integrating it into their core operations. LLMs represent a significant leap in this journey. Their capacity to understand natural language at a contextual level means they can power a wide range of applications—from automated document processing to intelligent chatbots and AI agents that can draft reports or summarize meetings.

Traditional rule-based systems lack the flexibility and learning capabilities that LLMs offer. With LLM development, businesses can move toward systems that learn from data patterns, continuously improve, and adapt to new contexts. This shift is particularly important for enterprises dealing with large volumes of unstructured data, where LLMs excel in extracting relevant information and generating actionable insights.

Custom LLM Development: Why One Size Doesn’t Fit All

While off-the-shelf models offer a powerful starting point, they often fall short in meeting the specific needs of modern enterprises. Data privacy, domain specificity, regulatory compliance, and operational context are critical considerations that demand custom LLM development. Enterprises are increasingly recognizing that building their own LLMs—or fine-tuning open-source models on proprietary data—can lead to more accurate, secure, and reliable AI systems.

Custom LLM development allows businesses to align the model with industry-specific jargon, workflows, and regulatory environments. For instance, a healthcare enterprise developing an LLM for patient record summarization needs a model that understands medical terminology and adheres to HIPAA regulations. Similarly, a legal firm may require an LLM that comprehends legal documents, contracts, and precedents. These specialized applications necessitate training on relevant datasets, which is only possible through tailored development processes.

Data as the Foundation of Effective LLM Development

The success of any LLM hinges on the quality and relevance of the data it is trained on. For enterprises, this often means leveraging internal datasets that are rich in context but also sensitive in nature. Building smarter AI requires rigorous data curation, preprocessing, and annotation. It also involves establishing data pipelines that can continually feed the model with updated, domain-specific content.

Enterprises must implement strong data governance frameworks to ensure the ethical use of data, mitigate biases, and safeguard user privacy. Data tokenization, anonymization, and access control become integral parts of the LLM development pipeline. In many cases, businesses opt to build LLMs on-premise or within private cloud environments to maintain full control over their data and model parameters.

Infrastructure and Tooling for Scalable LLM Development

Developing large language models at an enterprise level requires robust infrastructure. Training models with billions of parameters demands high-performance computing resources, including GPUs, TPUs, and distributed systems capable of handling parallel processing. Cloud providers and AI platforms have stepped in to offer scalable environments tailored to LLM development, allowing enterprises to balance performance with cost-effectiveness.

Beyond hardware, the LLM development lifecycle involves a suite of software tools. Frameworks like PyTorch, TensorFlow, Hugging Face Transformers, and LangChain have become staples in the AI development stack. These tools facilitate model training, evaluation, and deployment, while also supporting experimentation with different architectures and hyperparameters. Additionally, version control, continuous integration, and MLOps practices are essential for managing iterations and maintaining production-grade models.

The Business Impact of LLMs in Enterprise Workflows

The integration of LLMs into enterprise workflows yields transformative results. In customer service, LLM-powered chatbots and virtual assistants provide instant, human-like responses that reduce wait times and enhance user satisfaction. These systems can handle complex queries, understand sentiment, and escalate issues intelligently when human intervention is needed.

In internal operations, LLMs automate time-consuming tasks such as document drafting, meeting summarization, and knowledge base management. Teams across marketing, HR, legal, and finance can benefit from AI-generated insights that save hours of manual work and improve decision-making. LLMs also empower data analysts and business intelligence teams by providing natural language interfaces to databases and dashboards, making analytics more accessible across the organization.

In research and development, LLMs accelerate innovation by assisting in technical documentation, coding, and even ideation. Developers can use LLM-based tools to generate code snippets, debug errors, or explore APIs, thus enhancing productivity and reducing turnaround time. In regulated industries like finance and healthcare, LLMs support compliance by reviewing policy documents, tracking changes in legislation, and ensuring internal communications align with legal requirements.

Security and Privacy in Enterprise LLM Development

As LLMs gain access to sensitive enterprise data, security becomes a top concern. Enterprises must ensure that the models they build or use do not inadvertently leak confidential information or produce biased, harmful outputs. This calls for rigorous testing, monitoring, and auditing throughout the development lifecycle.

Security measures include embedding guardrails within the model, applying reinforcement learning with human feedback (RLHF) to refine responses, and establishing filters to detect and block inappropriate content. Moreover, differential privacy techniques can be employed to train LLMs without compromising individual data points. Regulatory compliance, such as GDPR or industry-specific mandates, must also be factored into the model’s architecture and deployment strategy.

Organizations are also investing in private LLM development, where the model is trained and hosted in isolated environments, disconnected from public access. This approach ensures that enterprise IP and customer data remain secure and that the model aligns with internal standards and controls. Regular audits, red-teaming exercises, and bias evaluations further strengthen the reliability and trustworthiness of the deployed LLMs.

LLM Development and Human-AI Collaboration

One of the most significant shifts LLMs bring to enterprises is the redefinition of human-AI collaboration. Rather than replacing human roles, LLMs augment human capabilities by acting as intelligent assistants. This symbiotic relationship allows professionals to focus on strategic thinking and creative tasks, while the LLM handles the repetitive, data-intensive parts of the workflow.

This collaboration is evident in industries like journalism, where LLMs draft article outlines and summarize press releases, freeing journalists to focus on investigative reporting. In legal services, LLMs assist paralegals by reviewing documents, identifying inconsistencies, and organizing case information. In software development, engineers collaborate with AI tools to write code, generate tests, and explore new libraries or functions more efficiently.

For human-AI collaboration to be effective, enterprises must foster a culture that embraces AI as a tool, not a threat. This includes reskilling employees, encouraging experimentation, and designing interfaces that are intuitive and transparent. Trust in AI systems grows when users understand how the models work and how they arrive at their conclusions.

Future Outlook: From Smarter AI to Autonomous Systems

LLM development marks a crucial milestone in the broader journey toward autonomous enterprise systems. As models become more sophisticated, enterprises are beginning to explore autonomous agents that can perform end-to-end tasks with minimal supervision. These agents, powered by LLMs, can navigate workflows, take decisions based on goals, and coordinate with other systems to complete complex objectives.

This evolution requires not just technical maturity but also organizational readiness. Governance frameworks must be updated, roles and responsibilities must be redefined, and AI literacy must become part of the corporate fabric. Enterprises that successfully embrace these changes will be better positioned to innovate, compete, and lead in a rapidly evolving digital economy.

The future will likely see the convergence of LLMs with other technologies such as computer vision, knowledge graphs, and edge computing. These integrations will give rise to multimodal AI systems capable of understanding not just text, but images, videos, and real-world environments. For enterprises, this means even more powerful and versatile tools that can drive efficiency, personalization, and growth across every vertical.

Conclusion

LLM development is no longer a luxury or an experimental initiative—it is a strategic imperative for modern enterprises. By building smarter AI systems powered by large language models, businesses can transform how they operate, innovate, and deliver value. From automating routine tasks to enhancing human creativity, LLMs are enabling a new era of intelligent enterprise solutions.

As organizations invest in infrastructure, talent, and governance to support LLM initiatives, the payoff will be seen in improved efficiency, reduced costs, and stronger customer relationships. The companies that lead in LLM development today are not just adopting a new technology—they are building the foundation for the AI-driven enterprises of tomorrow.

[microcos]

How Much Does It Cost to Build an AI Video Agent? A Comprehensive 2025 Guide

In today’s digital era, video content dominates the online landscape. From social media marketing to corporate training, video is the most engaging medium for communication. However, creating high-quality videos requires time, skill, and resources. This is where AI Video Agents come into play- automated systems designed to streamline video creation, editing, and management using cutting-edge technology.

If you’re considering investing in an AI Video Agent, one of the first questions you’ll ask is: How much does it cost to build one? This comprehensive guide will walk you through the key factors, cost breakdowns, and considerations involved in developing an AI Video Agent in 2025. Whether you’re a startup, multimedia company, or enterprise looking for advanced AI Video Solutions, this article will help you understand what to expect.

What Is an AI Video Agent?

An AI Video Agent is a software platform that leverages artificial intelligence to automate and enhance various aspects of video production. This includes:

AI video editing: Automatically trimming, color grading, adding effects, or generating subtitles.

AI video generation: Creating videos from text, images, or data inputs without manual filming.

Video content analysis: Understanding video context, tagging scenes, or summarizing content.

Personalization: Tailoring video content to specific audiences or user preferences.

Integration: Seamlessly working with other marketing, analytics, or content management systems.

These capabilities make AI Video Agents invaluable for businesses seeking scalable, efficient, and cost-effective video creation workflows.

Why Are AI Video Agents in Demand?

The rise of video marketing, e-learning, and digital entertainment has created an urgent need for faster and smarter video creation tools. Traditional video editing and production are labor-intensive and expensive, often requiring skilled professionals and expensive equipment.

AI Video Applications can:

Accelerate video production timelines.

Reduce human error and repetitive tasks.

Enable non-experts to create professional-quality videos.

Provide data-driven insights to optimize video content.

Support multi-language and multi-format video creation.

This explains why many companies are partnering with AI Video Solutions Companies or investing in AI Video Software Development to build custom AI video creators tailored to their needs.

Key Components of an AI Video Agent

Before diving into costs, it’s important to understand what goes into building an AI Video Agent. The main components include:

1. Data Collection and Preparation

AI video creators rely heavily on large datasets of annotated videos, images, and audio to train machine learning models. This step involves:

Collecting diverse video samples.

Labeling and annotating key features (e.g., objects, scenes, speech).

Cleaning and formatting data for training.

2. Model Development and Training

This is the core AI development phase where algorithms are designed and trained to perform tasks such as:

Video segmentation and object detection.

Natural language processing for script-to-video generation.

Style transfer and video enhancement.

Automated editing decisions.

Deep learning models, including convolutional neural networks (CNNs) and transformers, are commonly used.

3. Software Engineering and UI/UX Design

Developers build the user interface and backend systems that allow users to interact with the AI video editor or generator. This includes:

Web or mobile app development.

Cloud infrastructure for processing and storage.

APIs for integration with other platforms.

4. Integration and Deployment

The AI Video Agent needs to be integrated with existing workflows, such as content management systems, marketing automation tools, or social media platforms. Deployment may involve cloud services like AWS, Azure, or Google Cloud.

5. Testing and Quality Assurance

Extensive testing ensures the AI video creation tool works reliably across different scenarios and devices.

6. Maintenance and Updates

Post-launch support includes fixing bugs, updating models with new data, and adding features.

Detailed Cost Breakdown

The cost of building an AI Video Agent varies widely depending on complexity, scale, and specific requirements. Below is a detailed breakdown of typical expenses.

Component

Estimated Cost Range (USD)

Notes

Data Collection & Preparation

$10,000 – $100,000+

Larger, high-quality datasets increase costs; proprietary data is pricier.

Model Development & Training

$30,000 – $200,000+

Advanced deep learning models require more time and computational resources.

Software Engineering

$40,000 – $150,000+

Includes frontend, backend, UI/UX, cloud infrastructure, and APIs.

Integration & Deployment

$10,000 – $50,000+

Depends on the number and complexity of integrations.

Licensing & Tools

$5,000 – $50,000+

Third-party SDKs, cloud compute costs, and software licenses.

Testing & QA

$5,000 – $20,000+

Ensures reliability and user experience.

Maintenance & Updates (Annual)

$10,000 – $40,000+

Ongoing support, bug fixes, and model retraining.

Example Cost Scenarios

Basic AI Video Agent

Features: Automated trimming, captioning, simple effects.

Target users: Small businesses, content creators.

Estimated cost: $20,000 – $50,000.

Timeframe: 3-6 months.

Intermediate AI Video Agent

Features: Script-to-video generation, multi-language support, style transfer.

Target users: Marketing agencies, multimedia companies.

Estimated cost: $100,000 – $250,000.

Timeframe: 6-12 months.

Advanced AI Video Agent

Features: Real-time video editing, deep personalization, multi-format export, enterprise integrations.

Target users: Large enterprises, AI Video Applications Companies.

Estimated cost: $300,000+.

Timeframe: 12+ months.

Factors That Influence Cost

1. Feature Complexity

More advanced features, such as AI clip generator capabilities, voice synthesis, or 3D video creation, significantly increase development time and cost.

2. Data Quality and Quantity

High-quality, diverse datasets are crucial for effective AI video creation tools. Licensing proprietary datasets or creating custom datasets can be expensive.

3. Platform and Deployment

Building a cloud-based AI video creation tool with scalable infrastructure costs more than a simple desktop application.

4. Customization Level

Tailoring the AI Video Agent to specific industries (e.g., healthcare, education) or branding requirements adds to the cost.

5. Team Expertise

Hiring experienced AI developers, data scientists, and multimedia engineers commands premium rates but ensures better results.

Alternatives to Building From Scratch

If your budget is limited or you want to test the waters, several best AI video generators and AI video maker platforms offer ready-made solutions:

Synthesia: AI video creator focused on avatar-based videos.

Runway: AI video editor with creative tools.

Lumen5: AI-powered video creation from blog posts.

InVideo: Easy-to-use AI video generator for marketers.

These platforms offer subscription-based pricing, allowing you to create video with AI without a heavy upfront investment.

How to Choose the Right AI Video Solutions Company

When partnering with an AI Video Solutions Company or AI Video Software Company, consider these factors:

Proven track record: Look for companies with successful AI video projects.

Transparency: Clear pricing and project timelines.

Technical expertise: Experience in AI for video creation and multimedia development.

Customization capabilities: Ability to tailor solutions to your unique needs.

Support and maintenance: Reliable post-launch assistance.

The Future of AI Video Creation

As AI technology advances, the cost of building AI Video Agents is expected to decrease due to improved tools, open-source frameworks, and more efficient algorithms. Meanwhile, the capabilities will expand to include:

Hyper-personalized video marketing.

Real-time interactive video content.

AI-powered video analytics and optimization.

Integration with AR/VR and metaverse platforms.

Investing in AI video creation tools today positions your business to stay ahead in the evolving multimedia landscape.

Conclusion

Building an AI Video Agent is a significant but rewarding investment. Depending on your requirements, the cost can range from $20,000 for a basic AI video editor to over $300,000 for a sophisticated enterprise-grade AI video creation tool. Understanding the components, cost drivers, and alternatives will help you make informed decisions.

Whether you want to develop a custom AI video generator or leverage existing AI video creation tools, partnering with the right AI Video Applications Company or multimedia company is crucial. With the right strategy, you can harness AI for video creation to boost engagement, reduce production costs, and accelerate your content pipeline.

[govindhtech]

Text to SQL LLM: What is Text to SQL And Text to SQL Methods

SQL LLM text

How text-to-SQL approaches help AI write good SQL

SQL is vital for organisations to acquire quick and accurate data-driven insights for decision-making. Google uses Gemini to generate text-to-SQL from natural language. This feature lets non-technical individuals immediately access data and boosts developer and analyst productivity.

How is Text to SQL?

A feature dubbed “text-to-SQL” lets systems generate SQL queries from plain language. Its main purpose is to remove SQL code by allowing common language data access. This method maximises developer and analyst efficiency while letting non-technical people directly interact with data.

Technology underpins

Recent Text-to-SQL improvements have relied on robust large language models (LLMs) like Gemini for reasoning and information synthesis. The Gemini family of models produces high-quality SQL and code for Text-to-SQL solutions. Based on the need, several model versions or custom fine-tuning may be utilised to ensure good SQL production, especially for certain dialects.

Google Cloud availability

Current Google Cloud products support text-to-SQL:

BigQuery Studio: available via Data Canvas SQL node, SQL Editor, and SQL Generation.

Cloud SQL Studio has “Help me code” for Postgres, MySQL, and SQLServer.

AlloyDB Studio and Cloud Spanner Studio have “Help me code” tools.

AlloyDB AI: This public trial tool connects to the database using natural language.

Vertex AI provides direct access to Gemini models that support product features.

Text-to-SQL Challenges

Text-to-SQL struggles with real-world databases and user queries, even though the most advanced LLMs, like Gemini 2.5, can reason and convert complex natural language queries into functional SQL (such as joins, filters, and aggregations). The model needs different methods to handle critical problems. These challenges include:

Provide Business-Specific Context:

Like human analysts, LLMs need a lot of “context” or experience to write appropriate SQL. Business case implications, semantic meaning, schema information, relevant columns, data samples—this context may be implicit or explicit. Specialist model training (fine-tuning) for every database form and alteration is rarely scalable or cost-effective. Training data rarely includes semantics and business knowledge, which is often poorly documented. An LLM won't know how a cat_id value in a table indicates a shoe without context.

User Intent Recognition

Natural language is less accurate than SQL. An LLM may offer an answer when the question is vague, causing hallucinations, but a human analyst can ask clarifying questions. The question “What are the best-selling shoes?” could mean the shoes are most popular by quantity or revenue, and it's unclear how many responses are needed. Non-technical users need accurate, correct responses, whereas technical users may benefit from an acceptable, nearly-perfect query. The system should guide the user, explain its decisions, and ask clarifying questions.

LLM Generation Limits:

Unconventional LLMs are good at writing and summarising, but they may struggle to follow instructions, especially for buried SQL features. SQL accuracy requires careful attention to specifications, which can be difficult. The many SQL dialect changes are difficult to manage. MySQL uses MONTH(timestamp_column), while BigQuery SQL uses EXTRACT(MONTH FROM timestamp_column).

Text-to-SQL Tips for Overcoming Challenges

Google Cloud is constantly upgrading its Text-to-SQL agents using various methods to improve quality and address the concerns identified. These methods include:

Contextual learning and intelligent retrieval: Provide data, business concepts, and schema. After indexing and retrieving relevant datasets, tables, and columns using vector search for semantic matching, user-provided schema annotations, SQL examples, business rule implementations, and current query samples are loaded. This data is delivered to the model as prompts using Gemini's long context windows.

Disambiguation LLMs: To determine user intent by asking the system clarifying questions. This usually involves planning LLM calls to see if a question can be addressed with the information available and, if not, to create follow-up questions to clarify purpose.

SQL-aware Foundation Models: Using powerful LLMs like the Gemini family with targeted fine-tuning to ensure great and dialect-specific SQL generation.

Verification and replenishment: LLM creation non-determinism. Non-AI methods like query parsing or dry runs of produced SQL are used to get a predicted indication if something crucial was missed. When provided examples and direction, models can typically remedy mistakes, thus this feedback is sent back for another effort.

Self-Reliability: Reducing generation round dependence and boosting reliability. After creating numerous queries for the same question (using different models or approaches), the best is chosen. Multiple models agreeing increases accuracy.

The semantic layer connects customers' daily language to complex data structures.

Query history and usage pattern analysis help understand user intent.

Entity resolution can determine user intent.

Model finetuning: Sometimes used to ensure models supply enough SQL for dialects.

Assess and quantify

Enhancing AI-driven capabilities requires robust evaluation. Although BIRD-bench and other academic benchmarks are useful, they may not adequately reflect workload and organisation. Google Cloud has developed synthetic benchmarks for a variety of SQL engines, products, dialects, and engine-specific features like DDL, DML, administrative requirements, and sophisticated queries/schemas. Evaluation uses offline and user metrics and automated and human methods like LLM-as-a-judge to deliver cost-effective performance understanding on ambiguous tasks. Continuous reviews allow teams to quickly test new models, prompting tactics, and other improvements.

[tonymattny]

Revolutionize Business Intelligence with a Leading LLM Development Company

The Future of AI Begins with ideyaLabs

Businesses adopt advanced solutions to gain a competitive edge in today’s digital landscape. AI-driven innovations rapidly shape how organizations operate and interact with their customers. ideyaLabs stands as the LLM Development company that empowers enterprises to elevate business processes, create smarter applications, and achieve data-driven excellence.

LLM Solutions Power Digital Evolution

Companies require intelligent systems for dynamic data analysis, automation, and enhanced communication. ideyaLabs delivers large language model (LLM) development services that cater to diverse industry needs. Custom LLMs automate internal workflows, power conversational agents, and streamline content creation. Enterprises harness large language models for smarter decision-making and improved efficiency.

Expert Strategies for Building Powerful LLMs

ideyaLabs brings a blend of technical expertise and strategic thinking. The LLM Development company houses professionals with vast experience in machine learning, natural language processing, and deep neural networks.

Adapt LLMs to domain-specific requirements

Build secure, scalable, and high-performing AI models

Integrate LLMs into existing business platforms

Train LLMs on proprietary or open-source datasets

Custom LLM Development Tailored to Unique Needs

Companies require models that address distinct workflows and challenges. ideyaLabs crafts large language models aligned with business goals. The team follows a rigorous process:

Requirement gathering and business analysis

Data collection and annotation

Model architecture design

Training, validation, and optimization

Seamless deployment and monitoring

This approach guarantees accurate, relevant, and efficient language models that deliver strategic value.

Empower Applications with Advanced LLM Integration

Modern enterprises depend on applications that serve customers and employees effectively. ideyaLabs integrates LLMs into chatbots, document summarizers, recommendation engines, and virtual assistants. Companies benefit from:

Improved customer service through intelligent chatbots

Automated content generation and summarization

Enhanced search capabilities with semantic understanding

Real-time language translation and sentiment analysis

LLMs provide robust solutions for customer support, e-commerce, finance, healthcare, and more.

End-to-End Project Support from ideyaLabs

ideyaLabs serves as a comprehensive LLM Development company. The team ensures success from ideation to ongoing optimization. Clients receive:

Proactive consultation and technology selection

Transparent communication at each project phase

Efficient project management for timely delivery

Continuous model monitoring and retraining

The focus on long-term partnerships drives business outcomes and innovation.

Data Privacy and Regulatory Compliance Assured

Businesses trust ideyaLabs to protect sensitive information. The team maintains strict data security protocols during every project stage. LLMs developed by ideyaLabs comply with regulatory requirements, industry standards, and enterprise data policies.

Industry-Specific LLM Expertise

Every sector holds unique requirements. ideyaLabs delivers tailored large language models for industries such as:

Finance: Automated document processing, fraud detection, client support

Healthcare: Medical text summarization, clinical chatbot development, patient data privacy

Retail: Personalized recommendations, product categorization, virtual shopping assistants

Legal: Contract analysis, document summarization, legal intelligence tools

Education: AI tutors, exam grading, personalized learning assistants

The LLM Development company addresses both generic and niche use cases across verticals.

Scalable LLM Infrastructure to Support Growth

ideyaLabs engineers robust and flexible infrastructure for large language models. The team deploys LLMs on cloud, on-premises, or hybrid environments. Enterprises scale their AI solutions effortlessly to accommodate business growth.

Reliable hosting for large-scale LLMs

Load balancing and redundancy for maximum uptime

Flexible integrations with third-party platforms

Automated scaling to handle variable workloads

Cost-Effective LLM Development and Maintenance

ideyaLabs optimizes every project for ROI. The LLM Development company leverages automation, open source technologies, and efficient workflows to lower costs and accelerate time to market. Clients save development resources without compromising on quality.

ideyaLabs: Your Trusted Partner for LLM Success

Leading brands choose ideyaLabs to navigate the complexities of large language model development. The team prioritizes innovation, agility, and measurable business impact.

Proven track record of successful LLM projects

Dedicated support for custom requirements

Deep understanding of language model technologies

Transparent engagement with clear deliverables

Get Started with an Expert LLM Development Company

Partner with ideyaLabs to realize the full potential of large language models. The LLM Development company delivers cutting-edge solutions, seamless integration, and continuous support.

Contact ideyaLabs today to accelerate digital transformation and position your business at the forefront of AI-powered innovation.

[cogitotech]

Agentic and Robotic AI: The Shift from Reactive to Proactive Systems

Generative AI laid the foundation. Models like GPT and DALL·E have sparked a major shift in how computers create text, images, and videos that feel almost human. Fueled by massive datasets, these systems generate content to produce fluent language and striking visuals. Yet, despite their sophistication, generative AI remains fundamentally reactive—it responds to prompts rather than taking proactive steps on its own.

The Leap to Agentic and Robotic AI

Agentic AI pushes beyond content creation. These models set goals, track real-time feedback, and refine decisions as new data arrives. Likewise, robotic AI merges software intelligence with physical systems, allowing machines to explore real-world environments, navigate obstacles, and even collaborate with human operators. Although the modalities differ—one may focus on language, while the other uses sensor arrays—the same principle applies: they both need a core ability to reason about objectives, constraints, and context on the fly. Read more on Agentic and Robotic AI: The Shift from Reactive to Proactive Systems

[christianbale121]

What Are the Key Steps in AI Agent Development for Building Autonomous Systems?

AI agent development is transforming how businesses and industries operate by enabling autonomous systems capable of decision-making, task execution, and adaptation without constant human intervention. From self-driving cars to AI-powered customer service chatbots, autonomous AI agents are revolutionizing sectors by boosting efficiency and accuracy.

Building an autonomous system involves a series of intricate steps, from defining the problem and selecting appropriate AI models to training, testing, and deploying the agents. In this blog, we will explore the key steps involved in developing AI agents for autonomous systems and discuss best practices for creating reliable and scalable solutions.

1. Define the Problem and Objectives

The first step in AI agent development is clearly defining the problem the autonomous system will solve. This includes:

Identifying the use case: Determine whether the AI agent will handle customer interactions, automate business workflows, perform predictive analytics, or operate in a physical environment (e.g., robotics).

Outlining objectives: Establish clear goals, such as enhancing productivity, reducing human intervention, or increasing decision-making accuracy.

Specifying performance metrics: Define key performance indicators (KPIs) like response time, accuracy, error rates, and adaptability.

✅ Example: For an AI agent in e-commerce, the objective could be to automate personalized product recommendations, with KPIs tracking recommendation accuracy and customer engagement.

2. Data Collection and Preprocessing

AI agents require vast datasets to learn and make autonomous decisions. This step involves:

Data collection: Gather relevant data from internal systems, third-party APIs, or publicly available datasets. For autonomous driving systems, this may include image and sensor data, while for customer service agents, it may involve past interactions and user behavior data.

Data labeling and annotation: For supervised learning, the data must be accurately labeled to train the model effectively.

Data cleaning and normalization: Remove inconsistencies, duplicates, and noise. Normalize the data by scaling and standardizing it for uniformity.

✅ Example: For an AI healthcare assistant, data collection would involve electronic health records (EHR), patient history, and symptom reports, which are cleaned and labeled before training.

3. Choose the Right AI Model and Architecture

Selecting the appropriate AI model and architecture is crucial for building a functional autonomous system. The options include:

Rule-based models: For simple, deterministic tasks, such as automation scripts.

Machine learning (ML) models: Supervised and unsupervised learning algorithms that help the agent recognize patterns.

Deep learning models: Neural networks (CNNs, RNNs, or transformers) for complex tasks like image recognition and natural language processing (NLP).

Reinforcement learning (RL): For agents that learn through continuous interaction with their environment by receiving feedback and rewards.

✅ Example: In autonomous vehicles, deep reinforcement learning (DRL) is used to train the AI to navigate traffic and make driving decisions.

4. Agent Design and Development

At this stage, developers design the AI agent by implementing its core components:

Perception module: For interpreting input data, such as visual, textual, or sensory information.

Decision-making module: Implements algorithms that enable the agent to assess situations, make decisions, and select actions.

Action module: Executes the selected action, such as sending a response, moving a robotic arm, or generating a report.

Memory and knowledge base: Autonomous agents often require memory to store information and learn from past interactions.

✅ Example: For a virtual assistant, the perception module uses NLP to interpret user queries, the decision module selects relevant responses, and the action module sends the reply.

5. Training and Testing the AI Agent

Training is a critical phase where the AI agent learns from data through:

Supervised learning: The agent is trained on labeled datasets, allowing it to map inputs to desired outputs.

Unsupervised learning: The agent identifies patterns without labeled data, clustering similar data points.

Reinforcement learning: The agent interacts with the environment and learns from feedback (rewards or penalties).

During testing, the agent’s performance is evaluated through:

Validation datasets: To assess accuracy and generalization.

Simulated environments: For autonomous robots or self-driving cars, simulated environments help test behavior in controlled settings.

A/B testing: For customer-facing AI agents, A/B testing evaluates the effectiveness of different model versions.

✅ Example: An AI fraud detection agent is trained using historical financial data and tested on unseen transactions to measure its fraud detection accuracy.

6. Fine-Tuning and Optimization

Once the AI agent is trained, fine-tuning is required to improve its performance:

Parameter tuning: Adjusting hyperparameters like learning rate, batch size, and model complexity for optimal performance.

Model optimization: Using techniques like transfer learning, pruning, or quantization to make the model more efficient.

Error correction: Refining the model based on errors identified during testing to minimize false positives or false negatives.

✅ Example: In AI-powered customer support, fine-tuning the agent on customer queries ensures it provides more relevant and accurate responses.

7. Deployment of the Autonomous AI Agent

After successful testing, the AI agent is deployed into the production environment. Key deployment steps include:

Containerization and orchestration: Using tools like Docker and Kubernetes for easy scaling and management.

Cloud deployment: Hosting the AI agent on cloud platforms (AWS, Azure, or GCP) for accessibility and flexibility.

API integration: Connecting the AI agent with existing systems or applications through APIs.

✅ Example: An AI chatbot deployed on a website via an API handles customer inquiries and automatically generates leads.

8. Continuous Monitoring and Maintenance

Post-deployment, the AI agent requires continuous monitoring to ensure optimal performance. This includes:

Real-time monitoring: Tracking the agent’s performance, accuracy, and error rates.

Retraining with new data: Updating the model with new data to maintain accuracy.

Bug fixes and patches: Addressing any issues or vulnerabilities that arise in production.

✅ Example: In AI-powered fraud detection systems, continuous monitoring ensures that the agent adapts to new fraudulent patterns.

9. Security and Compliance Considerations

AI agents, especially in sensitive industries, must adhere to security and regulatory standards:

Data privacy: Implementing encryption and access controls to protect sensitive data.

Ethical AI guidelines: Ensuring the agent makes unbiased decisions and adheres to ethical guidelines.

Regulatory compliance: Meeting industry-specific standards like GDPR, HIPAA, or PCI-DSS.

✅ Example: An AI financial advisor must comply with financial regulations and secure customer data.

10. Scaling and Iterative Improvements

As the autonomous system matures, scaling and continuous improvement are necessary:

Scaling infrastructure: Increasing the computational resources and server capacity to handle higher loads.

Iteration and enhancement: Adding new functionalities, refining algorithms, and incorporating user feedback.

✅ Example: An AI-powered sales agent can be scaled to handle more customer interactions by optimizing infrastructure and adding new sales modules.

Conclusion

Building an autonomous AI agent involves multiple complex steps, from problem definition and data collection to model training, deployment, and continuous optimization. Each step is critical to creating a reliable and adaptable system capable of making autonomous decisions with minimal human intervention.

As AI technology continues to evolve, autonomous agents will become more sophisticated, delivering enhanced efficiency, accuracy, and scalability across various industries. Companies investing in AI agent development today will gain a competitive edge by automating workflows, improving decision-making, and enhancing customer experiences.

[umichenginabroad]

Week 4: IFSA Action-Packed Week!

Ahoj!

Welcome back to my life in Praha! This week has been one of the busiest yet, featuring exciting developments in all my classes, an IFSA program-sponsored trip to Brno, and a visit from a friend from Michigan. Stay tuned for all the adventures!

Academics Debrief

As the semester progresses, my workload is definitely picking up. I had my first meeting for the Gnosis project, our capstone collaboration with a startup to develop an AI agent for prediction markets. At first, I felt a little out of my depth due to my limited familiarity with the financial technology space. However, after asking my prepared questions and reviewing some code, the pieces are starting to come together!

My team of four decided it would be more manageable to have our agent specialize in a specific market, such as politics, tech, business, etc. Our research so far has been super interesting; markets like politics and crypto are fast-paced and speculative, whereas tech and business tend to be more structured and data-driven. It’s a challenging yet exciting opportunity that ties into my AI class, making it highly relevant to my studies. I’m looking forward to seeing where this project goes, and who knows, maybe I’ll even use it myself if proven successful!

In terms of my classes, all six are in full swing. My Software Engineering class, as I mentioned in my previous post, is fast-paced and hands-on. Every Monday and Wednesday, I know I’ll be learning a lot, and I really enjoy both the coursework and my professor’s teaching style. Our group project, travelog, feels like working on a personal passion project but for a class, which makes it even more exciting.

Meanwhile, Essential Czech has me preparing for a quiz and choosing a subject for my upcoming influential personality presentation. Having only ever taken Spanish classes (which I’m fluent in), it’s been really fun formally learning a new language. As for the presentation, we have to select a famous Czech person to create a presentation about. Although I had learned about Franz Kafka from the museum and statue in Prague, and from reading his books in the past, I’m challenging myself to select someone new. The class has already been helpful when navigating daily interactions, and I’m slowly gaining confidence in using Czech in public. Wish me luck on the quiz!

My Communication and Leadership courses have been extremely interactive, with discussions and presentations every week. In Comm, we’re reading The Culture Map, a book about how cultural awareness and adaptation can impact success in international business. Our latest assignment is an annotated bibliography, which also ties into our Leadership class, since we selected academic research papers that could help us with the capstone project.

Finally, in Tech Ethics we're taking a deep dive into deontology versus utilitarianism. I wasn’t expecting such a philosophical and thought-provoking class, and having never taken a philosophy class it’s a completely new yet intriguing experience for me. We often consider philosopher's takes on theoretical societies and moral dilemmas, which sparks some interesting late-night conversations with my roommates.

Overall, I’ve been really enjoying the IFSA academic structure here in Prague. Although I originally dreaded the idea of being in class for so many hours of the week (except Fridays), the Villa, where we take classes, is such a cozy space. There’s a backroom lounge with beanbag chairs, tea, and coffee, and you’ll always see someone sipping a freshly brewed mug in class. The professors and small class sizes create a close-knit, homey atmosphere that feels comfortable but also pushes you to actually learn. If we have longer breaks in-between classes, I’ll usually go to Coffee Corner to grab a bite (a popular café near the Villa), hit the gym, or even squeeze in some midday sightseeing in Prague 1.

Weekend with IFSA in Brno!

This weekend was the much-anticipated IFSA overnight trip! Brno is the second largest city in the Czech Republic after Prague, and to me, it felt like a smaller, quieter, and less touristy version of the capital. Heading to the train station at 7:30 AM on Friday, I had no expectations, but the trip ended up being the highlight of my week!

We had an extremely comfortable two-hour private bus ride, followed by check-in at a surprisingly nice hotel that everyone was excited about. They assigned rooms by roommates, and I shared a room with two of my roommates that had everything we needed.

After settling in, we split into three pre-organized groups for our scheduled activities, kicking things off with a group lunch at a Czech restaurant reserved for us by IFSA. The rest of the day consisted of exploring Brno’s history with stops at a bunker, a cathedral, and a castle that was also a prison. That evening, we enjoyed burgers with friends before heading to a bar, where, by the end of the night, the entire IFSA group had gathered. It was a really fun, relaxed night, and I met so many more students in the program!

The next day, we started with a continental breakfast at the hotel before heading to the water reservoir for a quick visit. Afterward, we enjoyed another provided lunch (carbonara — my favorite) before hopping back on the bus to Prague.

Brno was an amazing weekend getaway, and I loved the opportunity to explore another Czech city. I also really enjoyed getting to know more students in the program and chatting with IFSA staff! I definitely recommend the overnight trip for students on any study abroad program, they’re almost completely provided for the students and make for an amazing time.

The First of Many Michigan Visits

When I opened the door to my apartment after Brno, I was greeted by Izzy, my friend from UM, who was visiting from her study abroad in Amsterdam! She had spent the weekend hanging out with my two other roommates while we were away, and it was so nice to see her (even though we had just been together in Berlin).

Since Brno was just an overnight trip, we still had all of Sunday to recharge and see more of Prague. We took a quick five minute walk from my apartment to the National Museum (Národní muzeum), where we explored exhibits on evolution, different species, minerals, and Czech culture. Shockingly, this was my first museum visit in Prague and I’m so glad I finally made time for it. The exhibits were incredible and the museum was definitely worth it, I’m already planning visits to others in the city!

That’s a wrap for this week! Between weekday classes and weekend adventures, it was another eventful one. Next week, I’m looking forward to hosting more visitors from Michigan for their spring break (including my twin sister from Carnegie Mellon!). It’s going to be busy, but I couldn’t be more excited :)

Until next time,

Natalie

[mariacallous]

For its entire existence as a global medium, the internet's evolution has been caught in a tug of war, pulled by opposing forces: on one side, moderation and control; on the other, disruption and anarchy.

This year, the most prominent actors weighing in on the side of disruption were familiar faces: The reckless oligarchs, cybercriminals, scammers, and state-sponsored hackers who made the internet feel particularly dangerous in 2024 were, to some degree, the same forces destabilizing the online world in 2023. But perhaps more than in any other recent year, those agents of chaos seemed to persist in the face of opposition, to grow in their influence—and to win.

From Elon Musk's completed remake of X in his own tech-bro image to Trump's disinformation-fueled campaign, to Russia's ongoing cyberattacks against Ukraine, to China's relentless onslaught of digital intrusions and crypto scammers' global spread, the online experience of 2024 was messy, hazardous, and Hobbesian. And for the most part, the people who made it that way are poised to exert even more influence over the year to come.

As we do every year, WIRED has assembled a list of the most dangerous people, groups, and organizations on the internet. Here are our picks for 2024.

Elon Musk

After years of evolution from entrepreneur to edgelord, Musk seemed to reach his final form this year in the run-up to November's US election. Once a technologist with ambiguous politics who occasionally pursued public arguments against scuba divers, Musk now uses his megaphone of 200-million-plus followers on X, the social media platform he fully controls, to broadcast an unrelenting stream of anti-regulation, anti-immigrant, anti-transgender, anti-press, anti-progressive talking points. He's amplified and repeated disinformation, like claims after the disastrous damage done by Hurricanes Helene and Milton that the Federal Emergency Management Agency (FEMA) was hoarding supplies or had spent its budget on migrants instead of needy Americans. He's repeated debunked theories about voter fraud and immigrants swinging elections. In at least one case, he's even seemed to drop a reference that suggest support for the QAnon movement, which maintains bizarre theories about satanic pedophile rings that only Donald Trump can destroy.

Meanwhile, Musk continues to push his AI startup, xAI, toward one of the most no-holds-barred visions of what that technology can make possible, with little regard for safety or preventing its use for disinformation. The company this year launched new image-generation capabilities in its Grok tool that were immediately used to create images of copyrighted characters, celebrities, and political figures in compromising or sexual positions. Musk himself at one point posted an AI-generated deepfake of Kamala Harris that mocked her with her own voice—with no annotation that it was not authentic—that received 150 million views.

More than any particular political message or technology he's pushed, however, the danger Musk represents is simply that the richest man in the world can buy one of the internet's most vital media platforms, then use it, along with hundreds of millions of dollars in donations, to elect the president of his choosing and shape US policy. After Trump's election, Musk was rewarded with hundreds of billions more in net worth. The system, in other words, is working—for oligarchs like Elon Musk.

Donald Trump

For the next four years, Donald Trump will once again be the most powerful single person in the world. This year, he was just a very loud one. On social media platforms like X—where Musk reinstated Trump's account despite a 2021 ban following his incitement of the January 6 storming of the US Capitol—and Trump's own Truth Social, he lit fires with false claims that arguably helped fuel his path to political victory. Like Musk, he made and repeated falsehoods such as FEMA spending its budget on immigrants rather than hurricane victims, or that immigrants were stealing and eating pets. By converting Robert F. Kennedy Jr. from a third-party candidate to an endorsing ally, Trump also reciprocally endorsed RFK Jr.'s long-running stream of anti-vaccine misinformation, rhetoric that has led to childhood vaccine rates now dipping to a lower level than before the Covid-19 pandemic.

Once his second term in office begins, Trump is poised to use digital surveillance to carry out a highly disruptive and even vindictive agenda: He's promised mass deportations of millions of immigrants starting on day one of his administration, and he's vowed to target political opponents and journalists with prosecution. There's little doubt that Trump will top this list in 2025. But in 2024, his words alone represent a clear and present danger.

Volt Typhoon

In the cybersecurity world, the last months of 2024 have been dominated by warnings from public officials about a Chinese state-sponsored hacking group known as Salt Typhoon, which has penetrated at least eight telecoms, in some cases accessing the real-time calls and texts of Americans. But in the midst of that cyberespionage scandal, a different, far more dangerous digital threat from China still quietly looms: A Chinese state-sponsored group called Volt Typhoon spent much of 2024 continuing its stealthy campaign to breach US critical infrastructure facilities—not to spy on communications, but to prepare for a potential cyberattack. While headlines about Volt Typhoon have largely dissipated since it was first called out and named by Microsoft in May of 2023, officials warn that the group continues to “pre-position” itself inside of networks that appear to be chosen for maximum disruption, perhaps timed to a Chinese invasion of Taiwan. Given that Chinese head of state Xi Jinping has told his country's military to be prepared for that invasion by 2027, now is the time to head off the digital salient of what could be a world-shaking future war.

Sandworm

While Volt Typhoon prepares, Sandworm attacks. The Russian hacking group, a unit of the country's GRU military intelligence agency, has in fact carried out many of the most disruptive cyberattacks of the past decade. Those attacks—mostly in Ukraine—include at least three blackouts triggered by its hacking, the destructive NotPetya malware that spread around the globe and inflicted $10 billion in damage, and countless data-destroying breaches that the group has carried out since the beginning of Russia's full-scale invasion of Ukraine in 2022. In 2024, as Russia fought Ukraine to a stalemate or regained territory in the east of the country, Sandworm continued to project its power beyond that front: Security firm StrikeReady discovered Sandworm targeting Ukraine's energy sector again this fall, perhaps in preparation for another round of electrical utility hacking, either to carry out its own attacks or as reconnaissance for the Russian military's now-annual physical bombing of Ukraine's grid.

Israeli Military and Intelligence

Last year, WIRED named both the IDF and Hamas on this list, a nod to each side's use of the internet in its propaganda efforts in the war in Gaza following Hamas' October 7 attack. This year, with Israel's adversaries in that conflict like Hamas and Hezbollah significantly weakened and in retreat, it's the Israeli forces that remain on the offensive. Israel's military continues to use AI tooling to identify targets for bombs as its strikes have killed tens of thousands of Gazan civilians and leveled entire cities in the territory. Israeli missiles continue to intermittently knock out Gaza's communications infrastructure, destroying 80 percent of the country's top telecom's cell towers and leading to information blackouts amid the destruction and starvation of that now year-plus siege. Add to all of that a still-mysterious Israeli supply chain attack that hid lethal explosives in pagers and walkie-talkies in an effort to target Hezbollah operatives, which led to immediate distrust of all communications devices in the country, and Israel's government forces remain an internet apex predator by any definition.

Black Cat/AlphV/RansomHub

Ransomware in 2024 was, again, one of the most abhorrent forms of cybercrime to blight the internet. But few ransomware attacks in history have been quite as dangerous and damaging as the one that targeted UnitedHealthcare subsidiary Change Healthcare early this year, carried out by a ransomware group known as Black Cat or AlphV. Even after extracting a $22 million ransom from the company—a payment processor that handles around 40 percent of all health care insurance claims across the US—the hackers' disruption of Change's network continued to prevent the company from completing payments to pharmacies, clinics, health care practices, and hospitals across the country for weeks, causing some to even go out of business. Then, as if it hadn't inflicted enough chaos, AlphV ran off with Change's ransom money instead of sharing it with a group of hackers with whom it had partnered to infiltrate the company. That led the jilted hackers to share the data stolen from Change Healthcare with another, newer ransomware crew called RansomHub, which extorted the company a second time—an unprecedented health care cybersecurity debacle.

The Com

Even in an age of state-sponsored Chinese cyberspies, Russian hacker saboteurs, and ransomware gangs, nihilistic young hackers remain a constant in the darker corners of the internet. And few of those corners are as dark as the ones frequented by the Com. The loose movement of online trolls and criminals who operate under the Com banner in channels on Telegram and Discord, many just in their teens, engage in some of the most despicable digital crimes possible, from petty crypto theft and ransomware to sextortion, harassment, and the creation and trade of child sexual abuse material. This year, several members of a Com subgroup of ransomware hackers known as Scattered Spider were arrested, following their alleged high-profile 2023 attacks on companies including Caesar's Entertainment and MGM Resorts. But other members of the group are allegedly responsible for the serial breaches of well over a hundred customers of the cloud provider Snowflake. It's a safe bet that the Com will continue to serve as a haven for plenty more criminal acts to come.

Data Brokers

Once, privacy fears centered on government agencies like the FBI and NSA and their ability to carry out mass surveillance worldwide. Now, increasingly, we give away that same surveillance data en masse to companies you've never heard of. This year, it came to light that several data brokers collect and then sell access to Americans' highly granular location data pulled from their phones. Surveillance services like Babel Street's LocateX allegedly can—and do—track visitors to sensitive locations abortion clinic or mosques. A data broker called Near Intelligence left a vast cache of location data exposed online, revealing the movements of hundreds of visitors to sex trafficker Jeffrey Epstein's island in the Caribbean. Reporting on both companies has demonstrated how much private location data our devices leave behind as revelatory digital detritus, and how the possession of that data now extends far beyond government agencies to little-known firms with highly questionable privacy and security practices.

Character.AI

If 2023 was the year that AI tools future-shocked the internet with their seemingly abrupt rise to prominence, 2024 was the year that those tools became normalized, quietly settling into common use in spite of all their alleged flaws and ethical problems. Yet those issues are still there, and perhaps no startup better exemplifies them than Character.AI, an AI firm backed by $2.7 billion in investment from Google. According to lawsuits filed in Texas and Florida against the company, its chatbots have encouraged children to engage in self-harm and violence against their parents, and allegedly contributed to 14-year-old dying by suicide. Other chatbots hosted by the company have allegedly coached kids into developing eating disorders, role-playing as school shooters, and even seemed to be sexually grooming them. Despite repeatedly vowing to implement age restrictions, no meaningful age safeguards appear to have been added even now, according to news outlet Futurism. If this is the future of artificial intelligence, the AI era is going to be a dark age indeed.

Crypto Scammers

The crypto-based investment scams known as “pig butchering”—a term experts now say should be retired because it can further harm victims—pulled in a staggering $37 billion in 2023, and likely more in 2024. The victims of that explosively growing form of crimes aren't just those who fall for its scams and lose their life savings, but also many of the perpetrators of the scams themselves: As many as 200,000 people have been enslaved in compounds across Southeast Asia and forced to carry out the fraud schemes. In some cases, they've been kept in electrified shackles and faced threats of cattle prods and beatings from the gangs overseeing their work. This year, the dystopia those scammers have ushered in has begun to spread worldwide, with operations cropping up in the Middle East, Eastern Europe, Latin America, and West Africa. The targets of their fraud, of course, are even more global. Crypto scams have already become a top-tier form of cybercrime, and there's no end in sight to the expansion of this predatory underworld industry.

[drmikewatts]

IEEE Transactions on Artificial Intelligence, Volume 6, Issue 3, March 2025

1) Fair Machine Learning in Healthcare: A Survey

Author(s): Qizhang Feng, Mengnan Du, Na Zou, Xia Hu

Pages: 493 - 507

2) Regret and Belief Complexity Tradeoff in Gaussian Process Bandits via Information Thresholding

Author(s): Amrit Singh Bedi, Dheeraj Peddireddy, Vaneet Aggarwal, Brian M. Sadler, Alec Koppel

Pages: 508 - 517

3) An Evolutionary Multitasking Algorithm for Efficient Multiobjective Recommendations

Author(s): Ye Tian, Luke Ji, Yiwei Hu, Haiping Ma, Le Wu, Xingyi Zhang

Pages: 518 - 532

4) Deep-Learning-Based Uncertainty-Estimation Approach for Unknown Traffic Identification

Author(s): Siqi Le, Yingxu Lai, Yipeng Wang, Huijie He

Pages: 533 - 548

5) ReLAQA: Reinforcement Learning-Based Autonomous Quantum Agent for Quantum Applications

Author(s): Ahmad Alomari, Sathish A. P. Kumar

Pages: 549 - 558

6) Traffexplainer: A Framework Toward GNN-Based Interpretable Traffic Prediction

Author(s): Lingbai Kong, Hanchen Yang, Wengen Li, Yichao Zhang, Jihong Guan, Shuigeng Zhou

Pages: 559 - 573

7) Personalized Learning Path Problem Variations: Computational Complexity and AI Approaches

Author(s): Sean A. Mochocki, Mark G. Reith, Brett J. Borghetti, Gilbert L. Peterson, John D. Jasper, Laurence D. Merkle

Pages: 574 - 588

8) Prompt Learning for Few-Shot Question Answering via Self-Context Data Augmentation

Author(s): Jian-Qiang Qiu, Chun-Yang Zhang, C. L. Philip Chen

Pages: 589 - 603

9) MDA-GAN: Multiscale and Dual Attention Generative Adversarial Network for Bone Suppression in Chest X-Rays

Author(s): Anushikha Singh, Rukhshanda Hussain, Rajarshi Bhattacharya, Brejesh Lall, B.K. Panigrahi, Anjali Agrawal, Anurag Agrawal, Balamugesh Thangakunam, D.J. Christopher

Pages: 604 - 613

10) Image Tampering Detection With Frequency-Aware Attention and Multiview Fusion

Author(s): Xu Xu, Junxin Chen, Wenrui Lv, Wei Wang, Yushu Zhang

Pages: 614 - 625

11) NICASU: Neurotransmitter Inspired Cognitive AI Architecture for Surveillance Underwater

Author(s): Mehvish Nissar, Badri Narayan Subudhi, Amit Kumar Mishra, Vinit Jakhetiya

Pages: 626 - 638

12) Att2CPC: Attention-Guided Lossy Attribute Compression of Point Clouds

Author(s): Kai Liu, Kang You, Pan Gao, Manoranjan Paul

Pages: 639 - 650

13) Neural Network-Based Ensemble Learning Model to Identify Antigenic Fragments of SARS-CoV-2

Author(s): Syed Nisar Hussain Bukhari, Kingsley A. Ogudo

Pages: 651 - 660

14) Bridging the Climate Gap: Multimodel Framework With Explainable Decision-Making for IOD and ENSO Forecasting

Author(s): Harshit Tiwari, Prashant Kumar, Ramakant Prasad, Kamlesh Kumar Saha, Anurag Singh, Hocine Cherifi, Rajni

Pages: 661 - 675

15) Model-Heterogeneous Federated Graph Learning With Prototype Propagation

Author(s): Zhi Liu, Hanlin Zhou, Xiaohua He, Haopeng Yuan, Jiaxin Du, Mengmeng Wang, Guojiang Shen, Xiangjie Kong, Feng Xia

Pages: 676 - 689

16) Semisupervised Breast MRI Density Segmentation Integrating Fine and Rough Annotations

Author(s): Tianyu Xie, Yue Sun, Hongxu Yang, Shuo Li, Jinhong Song, Qimin Yang, Hao Chen, Mingxiang Wu, Tao Tan

Pages: 690 - 699

17) NL-CoWNet: A Deep Convolutional Encoder–Decoder Architecture for OCT Speckle Elimination Using Nonlocal and Subband Modulated DT-CWT Blocks

Author(s): P. S. Arun, Bibin Francis, Varun P. Gopi

Pages: 700 - 709

18) Multiattribute Deep CNN-Based Approach for Detecting Medicinal Plants and Their Use for Skin Diseases

Author(s): Prachi Dalvi, Dhananjay R. Kalbande, Surendra Singh Rathod, Harshal Dalvi, Amey Agarwal

Pages: 710 - 724

19) Deep Feature Unsupervised Domain Adaptation for Time-Series Classification

Author(s): Nannan Lu, Tong Yan, Song Zhu, Jiansheng Qian, Min Han

Pages: 725 - 737

20) Enhanced LiDAR-Based Localization via Multiresolution Iterative Closest Point Algorithms and Feature Extraction

Author(s): Yecheng Lyu, Xinkai Zhang, Feng Tao

Pages: 738 - 746

21) Swin-MGNet: Swin Transformer Based Multiview Grouping Network for 3-D Object Recognition

Author(s): Xin Ning, Limin Jiang, Weijun Li, Zaiyang Yu, Jinlong Xie, Lusi Li, Prayag Tiwari, Fernando Alonso-Fernandez

Pages: 747 - 758

22) A Two-Level Neural-RL-Based Approach for Hierarchical Multiplayer Systems Under Mismatched Uncertainties

Author(s): Xiangnan Zhong, Zhen Ni

Pages: 759 - 772

23) Adaptive Neural Network Finite-Time Event Triggered Intelligent Control for Stochastic Nonlinear Systems With Time-Varying Constraints

Author(s): Jia Liu, Jiapeng Liu, Qing-Guo Wang, Jinpeng Yu

Pages: 773 - 779

24) DDM-Lag: A Diffusion-Based Decision-Making Model for Autonomous Vehicles With Lagrangian Safety Enhancement

Author(s): Jiaqi Liu, Peng Hang, Xiaocong Zhao, Jianqiang Wang, Jian Sun

Pages: 780 - 791