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canmom · 12 days ago

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using LLMs to control a game character's dialogue seems an obvious use for the technology. and indeed people have tried, for example nVidia made a demo where the player interacts with AI-voiced NPCs:

youtube

this looks bad, right? like idk about you but I am not raring to play a game with LLM bots instead of human-scripted characters. they don't seem to have anything interesting to say that a normal NPC wouldn't, and the acting is super wooden.

so, the attempts to do this so far that I've seen have some pretty obvious faults:

relying on external API calls to process the data (expensive!)

presumably relying on generic 'you are xyz' prompt engineering to try to get a model to respond 'in character', resulting in bland, flavourless output

limited connection between game state and model state (you would need to translate the relevant game state into a text prompt)

responding to freeform input, models may not be very good at staying 'in character', with the default 'chatbot' persona emerging unexpectedly. or they might just make uncreative choices in general.

AI voice generation, while it's moved very fast in the last couple years, is still very poor at 'acting', producing very flat, emotionless performances, or uncanny mismatches of tone, inflection, etc.

although the model may generate contextually appropriate dialogue, it is difficult to link that back to the behaviour of characters in game

so how could we do better?

the first one could be solved by running LLMs locally on the user's hardware. that has some obvious drawbacks: running on the user's GPU means the LLM is competing with the game's graphics, meaning both must be more limited. ideally you would spread the LLM processing over multiple frames, but you still are limited by available VRAM, which is contested by the game's texture data and so on, and LLMs are very thirsty for VRAM. still, imo this is way more promising than having to talk to the internet and pay for compute time to get your NPC's dialogue lmao

second one might be improved by using a tool like control vectors to more granularly and consistently shape the tone of the output. I heard about this technique today (thanks @cherrvak)

third one is an interesting challenge - but perhaps a control-vector approach could also be relevant here? if you could figure out how a description of some relevant piece of game state affects the processing of the model, you could then apply that as a control vector when generating output. so the bridge between the game state and the LLM would be a set of weights for control vectors that are applied during generation.

this one is probably something where finetuning the model, and using control vectors to maintain a consistent 'pressure' to act a certain way even as the context window gets longer, could help a lot.

probably the vocal performance problem will improve in the next generation of voice generators, I'm certainly not solving it. a purely text-based game would avoid the problem entirely of course.

this one is tricky. perhaps the model could be taught to generate a description of a plan or intention, but linking that back to commands to perform by traditional agentic game 'AI' is not trivial. ideally, if there are various high-level commands that a game character might want to perform (like 'navigate to a specific location' or 'target an enemy') that are usually selected using some other kind of algorithm like weighted utilities, you could train the model to generate tokens that correspond to those actions and then feed them back in to the 'bot' side? I'm sure people have tried this kind of thing in robotics. you could just have the LLM stuff go 'one way', and rely on traditional game AI for everything besides dialogue, but it would be interesting to complete that feedback loop.

I doubt I'll be using this anytime soon (models are just too demanding to run on anything but a high-end PC, which is too niche, and I'll need to spend time playing with these models to determine if these ideas are even feasible), but maybe something to come back to in the future. first step is to figure out how to drive the control-vector thing locally.

#ai #computer games #Youtube

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nostalgebraist · 1 year ago

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clarification re: ChatGPT, " a a a a", and data leakage

In August, I posted:

For a good time, try sending chatGPT the string ` a` repeated 1000 times. Like " a a a" (etc). Make sure the spaces are in there. Trust me.

People are talking about this trick again, thanks to a recent paper by Nasr et al that investigates how often LLMs regurgitate exact quotes from their training data.

The paper is an impressive technical achievement, and the results are very interesting.

Unfortunately, the online hive-mind consensus about this paper is something like:

When you do this "attack" to ChatGPT -- where you send it the letter 'a' many times, or make it write 'poem' over and over, or the like -- it prints out a bunch of its own training data. Previously, people had noted that the stuff it prints out after the attack looks like training data. Now, we know why: because it really is training data.

It's unfortunate that people believe this, because it's false. Or at best, a mixture of "false" and "confused and misleadingly incomplete."

The paper

So, what does the paper show?

The authors do a lot of stuff, building on a lot of previous work, and I won't try to summarize it all here.

But in brief, they try to estimate how easy it is to "extract" training data from LLMs, moving successively through 3 categories of LLMs that are progressively harder to analyze:

"Base model" LLMs with publicly released weights and publicly released training data.

"Base model" LLMs with publicly released weights, but undisclosed training data.

LLMs that are totally private, and are also finetuned for instruction-following or for chat, rather than being base models. (ChatGPT falls into this category.)

Category #1: open weights, open data

In their experiment on category #1, they prompt the models with hundreds of millions of brief phrases chosen randomly from Wikipedia. Then they check what fraction of the generated outputs constitute verbatim quotations from the training data.

Because category #1 has open weights, they can afford to do this hundreds of millions of times (there are no API costs to pay). And because the training data is open, they can directly check whether or not any given output appears in that data.

In category #1, the fraction of outputs that are exact copies of training data ranges from ~0.1% to ~1.5%, depending on the model.

Category #2: open weights, private data

In category #2, the training data is unavailable. The authors solve this problem by constructing "AuxDataset," a giant Frankenstein assemblage of all the major public training datasets, and then searching for outputs in AuxDataset.

This approach can have false negatives, since the model might be regurgitating private training data that isn't in AuxDataset. But it shouldn't have many false positives: if the model spits out some long string of text that appears in AuxDataset, then it's probably the case that the same string appeared in the model's training data, as opposed to the model spontaneously "reinventing" it.

So, the AuxDataset approach gives you lower bounds. Unsurprisingly, the fractions in this experiment are a bit lower, compared to the Category #1 experiment. But not that much lower, ranging from ~0.05% to ~1%.

Category #3: private everything + chat tuning

Finally, they do an experiment with ChatGPT. (Well, ChatGPT and gpt-3.5-turbo-instruct, but I'm ignoring the latter for space here.)

ChatGPT presents several new challenges.

First, the model is only accessible through an API, and it would cost too much money to call the API hundreds of millions of times. So, they have to make do with a much smaller sample size.

A more substantial challenge has to do with the model's chat tuning.

All the other models evaluated in this paper were base models: they were trained to imitate a wide range of text data, and that was that. If you give them some text, like a random short phrase from Wikipedia, they will try to write the next part, in a manner that sounds like the data they were trained on.

However, if you give ChatGPT a random short phrase from Wikipedia, it will not try to complete it. It will, instead, say something like "Sorry, I don't know what that means" or "Is there something specific I can do for you?"

So their random-short-phrase-from-Wikipedia method, which worked for base models, is not going to work for ChatGPT.

Fortuitously, there happens to be a weird bug in ChatGPT that makes it behave like a base model!

Namely, the "trick" where you ask it to repeat a token, or just send it a bunch of pre-prepared repetitions.

Using this trick is still different from prompting a base model. You can't specify a "prompt," like a random-short-phrase-from-Wikipedia, for the model to complete. You just start the repetition ball rolling, and then at some point, it starts generating some arbitrarily chosen type of document in a base-model-like way.

Still, this is good enough: we can do the trick, and then check the output against AuxDataset. If the generated text appears in AuxDataset, then ChatGPT was probably trained on that text at some point.

If you do this, you get a fraction of 3%.

This is somewhat higher than all the other numbers we saw above, especially the other ones obtained using AuxDataset.

On the other hand, the numbers varied a lot between models, and ChatGPT is probably an outlier in various ways when you're comparing it to a bunch of open models.

So, this result seems consistent with the interpretation that the attack just makes ChatGPT behave like a base model. Base models -- it turns out -- tend to regurgitate their training data occasionally, under conditions like these ones; if you make ChatGPT behave like a base model, then it does too.

Language model behaves like language model, news at 11

Since this paper came out, a number of people have pinged me on twitter or whatever, telling me about how this attack "makes ChatGPT leak data," like this is some scandalous new finding about the attack specifically.

(I made some posts saying I didn't think the attack was "leaking data" -- by which I meant ChatGPT user data, which was a weirdly common theory at the time -- so of course, now some people are telling me that I was wrong on this score.)

This interpretation seems totally misguided to me.

Every result in the paper is consistent with the banal interpretation that the attack just makes ChatGPT behave like a base model.

That is, it makes it behave the way all LLMs used to behave, up until very recently.

I guess there are a lot of people around now who have never used an LLM that wasn't tuned for chat; who don't know that the "post-attack content" we see from ChatGPT is not some weird new behavior in need of a new, probably alarming explanation; who don't know that it is actually a very familiar thing, which any base model will give you immediately if you ask. But it is. It's base model behavior, nothing more.

Behaving like a base model implies regurgitation of training data some small fraction of the time, because base models do that. And only because base models do, in fact, do that. Not for any extra reason that's special to this attack.

(Or at least, if there is some extra reason, the paper gives us no evidence of its existence.)

The paper itself is less clear than I would like about this. In a footnote, it cites my tweet on the original attack (which I appreciate!), but it does so in a way that draws a confusing link between the attack and data regurgitation:

In fact, in early August, a month after we initial discovered this attack, multiple independent researchers discovered the underlying exploit used in our paper, but, like us initially, they did not realize that the model was regenerating training data, e.g., https://twitter.com/nostalgebraist/status/1686576041803096065.

Did I "not realize that the model was regenerating training data"? I mean . . . sort of? But then again, not really?

I knew from earlier papers (and personal experience, like the "Hedonist Sovereign" thing here) that base models occasionally produce exact quotations from their training data. And my reaction to the attack was, "it looks like it's behaving like a base model."

It would be surprising if, after the attack, ChatGPT never produced an exact quotation from training data. That would be a difference between ChatGPT's underlying base model and all other known LLM base models.

And the new paper shows that -- unsurprisingly -- there is no such difference. They all do this at some rate, and ChatGPT's rate is 3%, plus or minus something or other.

3% is not zero, but it's not very large, either.

If you do the attack to ChatGPT, and then think "wow, this output looks like what I imagine training data probably looks like," it is nonetheless probably not training data. It is probably, instead, a skilled mimicry of training data. (Remember that "skilled mimicry of training data" is what LLMs are trained to do.)

And remember, too, that base models used to be OpenAI's entire product offering. Indeed, their API still offers some base models! If you want to extract training data from a private OpenAI model, you can just interact with these guys normally, and they'll spit out their training data some small % of the time.

The only value added by the attack, here, is its ability to make ChatGPT specifically behave in the way that davinci-002 already does, naturally, without any tricks.

#ai tag

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jcmarchi · 4 days ago

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Gemini 2.0: Your Guide to Google’s Multi-Model Offerings

New Post has been published on https://thedigitalinsider.com/gemini-2-0-your-guide-to-googles-multi-model-offerings/

Gemini 2.0: Your Guide to Google’s Multi-Model Offerings

After testing the various models in Google’s new Gemini 2.0 family, something interesting becomes clear: Google is exploring the potential of specialized AI systems working in concert similar to OpenAI.

Google has structured their AI offerings around practical use cases – from rapid response systems to deep reasoning engines. Each model serves a specific purpose, and together they form a comprehensive toolkit for different AI tasks.

What stands out is the design behind each model’s capabilities. Flash processes massive contexts, Pro handles complex coding tasks, and Flash Thinking brings a structured approach to problem-solving.

Google’s development of Gemini 2.0 reflects a careful consideration of how AI systems are actually used in practice. While their earlier approaches focused on general-purpose models, this release shows a shift toward specialization.

This multi-model strategy makes sense when you look at how AI is being deployed across different scenarios:

Some tasks need quick, efficient responses

Others require deep analysis and complex reasoning

Many applications are cost-sensitive and need efficient processing

Developers often need specialized capabilities for specific use cases

Each model has clear strengths and use cases, making it easier to choose the right tool for specific tasks. It’s not revolutionary, but it is practical and well-thought-out.

Breaking Down the Gemini 2.0 Models

When you first look at Google’s Gemini 2.0 lineup, it might seem like just another set of AI models. But spending time understanding each one reveals something more interesting: a carefully planned ecosystem where each model fills a specific role.

1. Gemini 2.0 Flash

Flash is Google’s answer to a fundamental AI challenge: how do you balance speed with capability? While most AI companies push for bigger models, Google took a different path with Flash.

Flash brings three key innovations:

A massive 1M token context window that can handle entire documents

Optimized response latency for real-time applications

Deep integration with Google’s broader ecosystem

But what really matters is how this translates to practical use.

Flash excels at:

Document Processing

Handles multi-page documents without breaking context

Maintains coherent understanding across long conversations

Processes structured and unstructured data efficiently

API Integration

Consistent response times make it reliable for production systems

Scales well for high-volume applications

Supports both simple queries and complex processing tasks

Limitations to Consider

Not optimized for specialized tasks like advanced coding

Trades some accuracy for speed in complex reasoning tasks

Context window, while large, still has practical limits

The integration with Google’s ecosystem deserves special attention. Flash is designed to work seamlessly with Google Cloud services, making it particularly valuable for enterprises already in the Google ecosystem.

2. Gemini 2.0 Flash-Lite

Flash-Lite might be the most pragmatic model in the Gemini 2.0 family. Instead of chasing maximum performance, Google focused on something more practical: making AI accessible and affordable at scale.

Let’s break down the economics:

Input tokens: $0.075 per million

Output tokens: $0.30 per million

This a big reduction in the cost barrier for AI implementation. But the real story is what Flash-Lite maintains despite its efficiency focus:

Core Capabilities

Near-Flash level performance on most general tasks

Full 1M token context window

Multimodal input support

Flash-Lite isn’t just cheaper – it’s optimized for specific use cases where cost per operation matters more than raw performance:

High-volume text processing

Customer service applications

Content moderation systems

Educational tools

3. Gemini 2.0 Pro (Experimental)

Here is where things get interesting in the Gemini 2.0 family. Gemini 2.0 Pro is Google’s vision of what AI can do when you remove typical constraints. The experimental label is important though – it signals that Google is still finding the sweet spot between capability and reliability.

The doubled context window matters more than you might think. At 2M tokens, Pro can process:

Multiple full-length technical documents simultaneously

Entire codebases with their documentation

Long-running conversations with full context

But raw capacity isn’t the full story. Pro’s architecture is built for deeper AI thinking and understanding.

Pro shows particular strength in areas requiring deep analysis:

Complex problem decomposition

Multi-step logical reasoning

Nuanced pattern recognition

Google specifically optimized Pro for software development:

Understands complex system architectures

Handles multi-file projects coherently

Maintains consistent coding patterns across large projects

The model is particularly suited for business-critical tasks:

Large-scale data analysis

Complex document processing

Advanced automation workflows

4. Gemini 2.0 Flash Thinking

Gemini 2.0 Flash Thinking might be the most intriguing addition to the Gemini family. While other models focus on quick answers, Flash Thinking does something different – it shows its work. This transparency helps enable better human-AI collaboration.

The model breaks down complex problems into digestible pieces:

Clearly states assumptions

Shows logical progression

Identifies potential alternative approaches

What sets Flash Thinking apart is its ability to tap into Google’s ecosystem:

Real-time data from Google Search

Location awareness through Maps

Multimedia context from YouTube

Tool integration for live data processing

Flash Thinking finds its niche in scenarios where understanding the process matters:

Educational contexts

Complex decision-making

Technical troubleshooting

Research and analysis

The experimental nature of Flash Thinking hints at Google’s broader vision of more sophisticated reasoning capabilities and deeper integration with external tools.

(Google DeepMind)

Technical Infrastructure and Integration

Getting Gemini 2.0 running in production requires an understanding how these pieces fit together in Google’s broader ecosystem. Success with integration often depends on how well you map your needs to Google’s infrastructure.

The API layer serves as your entry point, offering both REST and gRPC interfaces. What is interesting is how Google has structured these APIs to maintain consistency across models while allowing access to model-specific features. You are not just calling different endpoints – you are tapping into a unified system where models can work together.

Google Cloud integration goes deeper than most realize. Beyond basic API access, you get tools for monitoring, scaling, and managing your AI workloads. The real power comes from how Gemini models integrate with other Google Cloud services – from BigQuery for data analysis to Cloud Storage for handling large contexts.

Workspace implementation shows particular promise for enterprise users. Google has woven Gemini capabilities into familiar tools like Docs and Sheets, but with a twist – you can choose which model powers different features. Need quick formatting suggestions? Flash handles that. Complex data analysis? Pro steps in.

The mobile experience deserves special attention. Google’s app is a testbed for how these models can work together in real-time. You can switch between models mid-conversation, each optimized for different aspects of your task.

For developers, the tooling ecosystem continues to expand. SDKs are available for major languages, and Google has created specialized tools for common integration patterns. What is particularly useful is how the documentation adapts based on your use case – whether you are building a chat interface, data analysis tool, or code assistant.

The Bottom Line

Looking ahead, expect to see this ecosystem continue to evolve. Google’s investment in specialized models reinforces a future where AI becomes more task-specific rather than general-purpose. Watch for increased integration between models and expanding capabilities in each specialized area.

The strategic takeaway is not about picking winners – it is about building systems that can adapt as these tools evolve. Success with Gemini 2.0 comes from understanding not just what these models can do today, but how they fit into your longer-term AI strategy.

For developers and organizations diving into this ecosystem, the key is starting small but thinking big. Begin with focused implementations that solve specific problems. Learn from real usage patterns. Build flexibility into your systems. And most importantly, stay curious – we are still in the early chapters of what these models can do.

FAQs

1. Is Gemini 2.0 available?

Yes, Gemini 2.0 is available. The Gemini 2.0 model suite is broadly accessible through the Gemini chat app and Google Cloud’s Vertex AI platform. Gemini 2.0 Flash is generally available, Flash-Lite is in public preview, and Gemini 2.0 Pro is in experimental preview.

2. What are the main features of Gemini 2.0?

Gemini 2.0’s key features include multimodal abilities (text and image input), a large context window (1M-2M tokens), advanced reasoning (especially with Flash Thinking), integration with Google services (Search, Maps, YouTube), strong natural language processing capabilities, and scalability through models like Flash and Flash-Lite.

3. Is Gemini as good as GPT-4?

Gemini 2.0 is considered on par with GPT-4, surpassing it in some areas. Google reports that its largest Gemini model outperforms GPT-4 on 30 out of 32 academic benchmarks. Community evaluations also rank Gemini models highly. For everyday tasks, Gemini 2.0 Flash and GPT-4 perform similarly, with the choice depending on specific needs or ecosystem preference.

4. Is Gemini 2.0 safe to use?

Yes, Google has implemented safety measures in Gemini 2.0, including reinforcement learning and fine-tuning to reduce harmful outputs. Google’s AI principles guide its training, avoiding biased responses and disallowed content. Automated security testing probes for vulnerabilities. User-facing applications have guardrails to filter inappropriate requests, ensuring safe general use.

5. What does Gemini 2.0 Flash do?

Gemini 2.0 Flash is the core model designed for quick and efficient task handling. It processes prompts, generates responses, reasons, provides information, and creates text rapidly. Optimized for low latency and high throughput, it’s ideal for interactive use, such as chatbots.

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monopolyit8 · 9 days ago

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How to Prepare for a .NET Full Stack Developer Interview at Monopoly IT Solutions

Preparing for a .NET Full Stack Developer interview at Monopoly IT Solutions requires a strategic approach to showcase your technical expertise and problem-solving skills. Start by mastering the fundamentals of .NET, including C#, ASP.NET, and Entity Framework. Familiarize yourself with front-end technologies like HTML, CSS, JavaScript, and frameworks such as Angular or React, as full-stack roles demand proficiency in both front-end and back-end development.

Next, focus on database management systems like SQL Server, as Monopoly IT Solutions likely emphasizes data-driven applications. Practice writing optimized queries and understanding database design principles. Additionally, brush up on RESTful APIs, microservices architecture, and cloud platforms like Azure, which are integral to modern .NET development.

Don’t overlook soft skills! Monopoly IT Solutions values collaboration and communication, so prepare to discuss past projects, teamwork experiences, and how you’ve solved complex challenges. Practice coding problems on platforms like LeetCode or HackerRank to sharpen your problem-solving abilities.

Finally, research Monopoly IT Solutions’ projects, values, and culture to align your responses with their goals. Show enthusiasm for innovation and a willingness to contribute to their success.

Ready to ace your interview? Start preparing today and take the first step toward joining Monopoly IT Solutions!

#bestsoftwaretraininginstutionshyderabad #bestdotnettraininginhyderabad

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archiz321 · 21 days ago

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From Missed Opportunities to Explosive Growth: How Archiz Solutions Empowered a Business through WhatsApp Business API Integrated with CRM

Imagine this: An ambitious entrepreneur is running a fast-paced business, dealing with dozens of customer inquiries every day, but no matter how hard they try, things keep slipping through the cracks. Leads go cold, follow-ups are missed, and customer satisfaction plummets. Sound familiar?

This is how things were with one of our customers before they learned how much of a game-changer it was for their business to include the WhatsApp Business API integration in their CRM thanks to Archiz Solutions.

The Challenge: Losing Leads Due to Problematic Communication Issues

Our customer—a leading real estate house—got many inquiries; however, their communication was nowhere near the effective process.

While their marketing efforts were bringing them high-quality leads, they were slow in responding and lacked a connected approach to communication.

The problem they faced:

Missed Opportunities: Leads went cold many times before even getting a response. Time is of the essence in such a competitive marketplace.

Impersonal Communication: Generic, slow responses made customers feel unimportant. The business was struggling to build meaningful relationships.

There was no unity; thus, leads went scattering all over emails, phone calls, and messages in social media with the consequence of losing opportunities and getting confused.

The repeated queries consumed most of the time of the sales teams; thus, nothing else remained to get focused and close deals on time.

Conclusion: The inquiry backlog increased, the customers' problems were still unsolved, and the sales force was overwhelmed with process inefficiencies.

Transformation: Archiz Solutions & WhatsApp Business API Integrated with CRM - Game Changer

Well, the breaking point came when our client approached Archiz Solutions for a more intelligent and effective method of communication for them.

Enter the WhatsApp Business API, seamlessly integrated into their CRM system—a powerful solution that could help streamline processes, improve customer interactions, and drive business growth.

We were highly intimate with the team because our customized solution was directed at solving the direct issues at hand. So here's how we turned things around for good:

Instant Engagement with Automated Replies: These common queries about property prices, availability, and features were taken care of through automated, real-time responses, which kept leads engaged and interested right from the first message. No more waiting.

Centralized Communication in One Place with CRM Integration: All customer messages were routed through WhatsApp and captured in the CRM system, which thus allowed sales teams to easily manage conversations, track lead progress, and maintain a full history of interactions—all from a single platform.

Personalized, targeted messaging at scale: Instead of canned responses, sales reps were using customer data from the CRM to personally recommend properties, follow up with reminders of those properties, and offer special deals that were meaningful and engaging to each person.

Automated follow-up that never misses a lead.

Leads were automatically nurtured with personalized follow-up messages so no opportunity fell through the cracks, even when the team was busy. Integration with CRM made follow-ups smooth and organized.

Real-Time Hot Leads Alerts:

With real-time notifications through the integrated CRM, the sales team could dive straight into high-priority conversations, closing warm leads with customers much quicker.

Explosive Growth and Unmatched Customer Satisfaction

In just a few months, effects were extraordinary:

It resulted in 50% growth in lead conversion, faster response time, and customization in messaging followed by automation for follow-up, thereby bringing about a massive increase in the lead conversion rates. Days became hours later.

Spend 60% Less Time on Repetitive Tasks: With the CRM integration and automation, the sales team was left with more time to develop relations and close big-ticket deals.

Unbelievable 98% Open Rate: WhatsApp superseded the old ones, such as email, because almost every message was opened; no lead was ever left behind.

High Customer Satisfaction: This immediate response, coupled with personalized communication, brought customer satisfaction to a peak level that created loyalty and trust.

Operational efficiency: Integration of WhatsApp with CRM also brought to an end the chaos of sales operations since the sales force was made more efficient and organized than ever.

Why WhatsApp Business API Integrated with CRM is a Game-Changer for Your Business

What our client went through is not a success story but the formula for success. The inclusion of a WhatsApp Business API with a CRM system makes business-to-customer communication models transform into new levels where the results are glaring as:

Instant Response to Inquiries: Answer customers within seconds, not hours.

Personalized Communication: Send customized messages to thousands of customers, making them feel heard and valued.

Automate the Routine: Get more time in your team's schedule to court and close.

Accelerate Conversion: Convert customers faster with timely and contextual messaging

Improve Customer Delight: Offer delightful experiences to your repeat customers, coming back again and again

Want to Change the Way Your Business Works?

Your competitors probably are ahead if your business lags behind. Log not.

With Archiz Solutions and the WhatsApp Business API integrated into your CRM, you shall see faster and better responses, strong and meaningful customer relationships, repetitive tasks automatically handled so your team has the luxury of focusing on what matters, customers more loyal, and the bottom line growing.

Let's Unleash Your Business Potential Together!

Unlocking businesses' potential through cutting-edge communication tools for business growth is the specialty of Archiz Solutions.

If you’re ready to take your customer communication to the next level, contact us today to see how the WhatsApp Business API integrated with your CRM system can work wonders for your business.

Start your journey toward smarter communication and exponential growth today with Archiz Solutions.

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stuintern1 · 26 days ago

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Kickstart Your Career with a Full Stack Java Developer Internship

Are you aspiring to build a successful career in web development? A full stack Java developer internship could be the perfect launchpad for you. In today’s competitive job market, hands-on experience is crucial to stand out and secure your dream job in the tech industry. This internship opportunity not only helps you gain practical knowledge but also equips you with the skills required to excel in both front-end and back-end development.

Why Choose a Full Stack Java Developer Internship?

Java is one of the most widely used programming languages across the globe, powering web applications, mobile apps, and enterprise-level solutions. As a full stack Java developer, you will gain expertise in working with various frameworks, libraries, and tools that are essential for developing dynamic and scalable applications.

An internship provides the following benefits:

Hands-on Experience: Learn how to build real-world applications by working on live projects.

Industry Exposure: Understand the current trends, best practices, and methodologies followed in the industry.

Mentorship Opportunities: Work closely with experienced developers who can guide you through complex coding challenges.

Networking: Connect with professionals and expand your career prospects.

Skill Enhancement: Improve your coding skills and gain proficiency in tools like Spring Boot, Hibernate, React, and Angular.

What You Will Learn During the Internship

A full stack Java developer internship covers a wide range of topics to help you become proficient in both client-side and server-side development. Some key areas of learning include:

Front-End Development:

HTML, CSS, and JavaScript

Modern frameworks like React and Angular

UI/UX design principles

Back-End Development:

Core Java and Advanced Java concepts

Spring Boot framework

RESTful APIs and microservices

Database Management:

Working with SQL and NoSQL databases

ORM tools like Hibernate

Database optimization techniques

Version Control and Deployment:

Git and GitHub for version control

Continuous integration and deployment (CI/CD) pipelines

Cloud platforms like AWS and Azure

Soft Skills:

Problem-solving and critical thinking

Effective communication and teamwork

Time management and adaptability

How to Apply for a Full Stack Java Developer Internship

Securing an internship requires a strategic approach. Here are some tips to help you land the right opportunity:

Build a Strong Portfolio: Showcase your projects, coding skills, and problem-solving abilities through an impressive portfolio.

Optimize Your Resume: Highlight your technical skills, certifications, and relevant experience.

Prepare for Interviews: Practice common technical and behavioral questions that recruiters might ask.

Leverage Online Platforms: Explore internship opportunities on job portals, LinkedIn, and company websites.

Conclusion

A full stack Java developer internship is an excellent way to gain valuable industry experience and develop the technical skills required to thrive in the field of web development. Whether you are a recent graduate or someone looking to switch careers, this internship can pave the way for a rewarding future in the tech industry. Take the first step today and embark on a journey to becoming a skilled full stack Java developer!

#training #stuintern #internship #full stack developer

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poojithas-world · 30 days ago

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Unleashing Creativity in Low-Code: A Journey Through the Mendix Rapid Developer Course

The digital age demands swift innovation, and low-code platforms like Mendix have become the cornerstone of modern application development. For those looking to enter this dynamic field, the Mendix Rapid Developer Course offered by Mayon Academy serves as the perfect launchpad. This course introduces aspiring developers to the transformative potential of Mendix, enabling them to create applications efficiently and with minimal coding expertise.

Why Mendix Rapid Developer Course?

The Mendix Rapid Developer Course is more than just an introduction; it’s an immersive experience into the world of low-code development. Designed for beginners, the course emphasizes hands-on learning, guiding participants through the creation of functional applications from scratch. It empowers students to think creatively while leveraging Mendix’s intuitive tools to solve real-world problems.

As students progress through the course, they gain insight into visual development, the Mendix Studio, and how to transform ideas into deployable applications quickly. This foundation not only accelerates their learning curve but also prepares them for advanced challenges in the low-code arena.

The Gateway to Growth

Completing the Mendix Rapid Developer Course is not just a milestone; it’s a gateway to growth. After mastering the fundamentals, learners can expand their expertise with the Mendix Intermediate Developer Course. This course dives deeper into data modeling, microflows, and integrating APIs, preparing developers for complex application scenarios.

For those aiming to reach the pinnacle of Mendix expertise, the Mendix Advanced Developer Course is the next step. It focuses on advanced application design, scalability, and performance optimization, making developers proficient in building enterprise-grade solutions.

Beyond Development: Unlocking Career Potential

The Mendix Rapid Developer Course doesn’t just teach you to build applications; it prepares you for an exciting career in low-code development. With Mendix being a leading platform in the industry, certified developers are in high demand across sectors. The course offers a chance to stand out in a competitive market, showcasing your ability to deliver solutions that drive business transformation.

Why Choose Mayon Academy?

Mayon Academy is a trusted name in Mendix training, offering a structured approach to learning with experienced instructors and real-world case studies. Whether you’re an IT professional, a student, or someone looking to upskill, the academy’s courses are tailored to suit all learning needs.

Enrolling in the Mendix Rapid Developer Course is the first step toward a fulfilling journey in low-code development. Begin your transformation today with Mayon Academy and turn your ideas into reality.

Ready to start? Visit the Mendix Rapid Developer Course page to enroll now!

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1globosetechnologysolutions · 2 months ago

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Data Collection for Machine Learning: Laying the Foundation for AI Excellence

This is, in fact, a fresh age of possibilities initiated by accelerating data collection for Machine Learning; ML models redefining how we tackle complex problems are self-driving cars on the surface to precisely detecting diseases. However, behind every brilliant AI system is a crucial task-data collection.

In any ML project, data collection is the first step of the whole process. Without such data, there is just no basis for even high-tech algorithms to work on. It is basically about gathering, sorting, and processing raw data to make it ready for training machine learning models. This blog will dig into the importance of data collection, ways for data collection, challenges ahead, and how this channelizes excellence for AI.

Why Is Data Collection Critical for Machine Learning?

In machine learning, data acts as the fuel that powers algorithms. It provides the examples that models use to learn patterns, make predictions, and refine their accuracy over time.

This is the significance of data collection:

A Foundation for Learning: ML models gain knowledge of relationships and trends from examples. It is practically impossible to map something without the dataset required for the learning process. Thus, data collection is vital to ensure these relevant and diverse sets of information are available at this stage.

Model Performance Improvement: Data quality and variation are critical to the accuracy and reliability of an ML model. The more wrapped up in creating a good dataset, the better the model generalizes and performs in the real world.

Addressing Domain-Specific Challenges: Every industry/application has certain peculiar data requirements. A healthcare AI system needs medical imaging data, while an autonomous vehicle system needs road and traffic data. Data collection allows the features in the input space to be tailored relative to the specific problem under question.

Supporting Continuous Improvement: AI models are not static—they evolve with time and usage. Continuous data collection enables these systems to adapt to new environments, trends, and user behaviors.

Methods of Data Collection for Machine Learning

Data can be collected in several ways, depending on the type of project and the domain it serves.

Here are some common methods:

Manual Data Collection: In this method, human operators gather data by observing, recording, or annotating it. Though time-intensive, manual collection ensures high-quality, precise data, especially in tasks like labeling images or annotating medical scans.

Automated Data Collection: Automated methods use scripts, sensors, or APIs to gather large volumes of data efficiently. For example, web scraping tools can extract data from websites, while IoT sensors collect environmental data.

Crowdsourced Data: Platforms like Amazon Mechanical Turk enable crowdsourcing for data collection and annotation. This approach is cost-effective and scalable but may require additional quality checks.

Synthetic Data Generation: Synthetic data is artificially created to mimic real-world data. This is particularly useful when collecting actual data is expensive, risky, or impossible, such as in autonomous driving simulations.

Open-Source Datasets: Many organizations and academic institutions release publicly available datasets. Platforms like Kaggle, UCI Machine Learning Repository, and ImageNet are popular sources for diverse datasets.

Key Considerations for Effective Data Collection

Not all data is created equal. To ensure that the collected data serves its purpose effectively, it’s essential to focus on the following aspects:

Relevance: The data should align with the specific problem the ML model aims to solve. Irrelevant data adds noise and hinders model performance.

Diversity: Diverse datasets improve the model’s ability to generalize. For example, a facial recognition model should be trained on images representing different ethnicities, ages, and lighting conditions.

Quality: High-quality data is clean, accurate, and well-annotated. Data preprocessing, such as removing duplicates, handling missing values, and resolving inconsistencies, is critical to maintaining quality.

Scalability: As ML projects grow, so does the need for more data. Scalable data collection methods ensure that datasets can be expanded without compromising quality.

Ethical Compliance: Data collection must adhere to ethical guidelines and legal regulations, such as GDPR or HIPAA. Respecting privacy and obtaining consent are paramount.

Challenges in Data Collection

While data collection is vital, it is not without challenges. Some of the most common obstacles include:

Data Scarcity: In some domains, such as rare diseases or emerging technologies, relevant data may be hard to find or collect. Synthetic data and simulation environments can help mitigate this issue.

High Costs: Manual annotation, especially for large datasets, can be expensive. Automated tools and crowdsourcing platforms can help reduce costs while maintaining quality.

Data Imbalance: Many datasets suffer from imbalances, where one class or category is overrepresented. For instance, in fraud detection, fraudulent transactions may be rare, making it harder for the model to detect them.

Privacy Concerns: Collecting data that involves personal or sensitive information requires stringent measures to protect user privacy and comply with regulations.

Data Drift: As real-world conditions evolve, previously collected data may no longer represent current trends. Continuous data collection and periodic updates are necessary to address this issue.

Applications of Data Collection in AI and ML

Data collection fuels innovation across industries, enabling transformative AI solutions. Here are a few examples:

Healthcare: AI models trained on medical imaging datasets are improving diagnostics and treatment planning. Data collection from wearable devices and patient records supports personalized medicine.

Retail and E-commerce: Retailers use data on customer preferences, browsing behavior, and transaction history to train recommendation systems and optimize supply chains.

Autonomous Vehicles Self-driving cars rely on video and sensor data collected from real-world driving scenarios. This data helps train models to navigate roads, detect obstacles, and ensure passenger safety.

Finance: In the financial sector, datasets of transaction records, market trends, and user behavior are used for fraud detection, credit scoring, and risk management.

Agriculture: Satellite and drone imagery provide data for AI models that monitor crop health, predict yields, and optimize irrigation.

Conclusion

Data collection is the foundation upon which every successful ML model is built. It’s not just about data gathering; it’s about curating a rich, diverse, and high-quality data set from relevant sources, so that AI systems can perform efficiently and responsibly.

As the demand for smarter AI solutions keeps on rising, investment in strong data collection methods becomes a key factor in realizing machine learning excellence. While some certainly need to address challenges and accept new trends, industries and researchers globally can unlock the full potential of AI and bring the world one step closer to an intelligent future driven by data.

Visit Globose Technology Solutions to see how the team can speed up your facial recognition projects.

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mernfulstack · 2 months ago

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Mern Stack Online Course | Mern Stack Course In Hyderabad

The Roadmap to Mern Full-Stack Development: Frontend, Backend, and Beyond

The Mern Stack Training has become one of the most popular full-stack development frameworks, empowering developers to build robust and scalable web applications. Enrolling in a is a crucial step for anyone looking to master the art of full-stack development. This framework consists of MongoDB, Express.js, React, and Node.js, covering both frontend and backend development seamlessly. Whether you are an aspiring developer or a seasoned programmer, MERN Stack Online Training provides hands-on experience with each module, ensuring proficiency in building dynamic web applications.

Understanding the MERN Stack Components

The MERN stack is an acronym for four essential technologies that facilitate full-stack development. MongoDB serves as the NoSQL database, efficiently storing application data. Express.js is the backend framework that simplifies routing and middleware handling. Together, these components form a cohesive system that streamlines web development.

Through Best Mern Stack Course, developers gain in-depth knowledge of how these technologies interact. The course often starts with Node.js and Express to set up the server-side architecture. Next, students dive into MongoDB to understand data modelling and database management. The frontend portion covers react, focusing on state management and component-based architecture. This holistic approach ensures learners are equipped to handle the entire development lifecycle.

The Roadmap to Full-Stack Development: Frontend, Backend, and Beyond

A MERN Stack Full Course typically follows a structured roadmap, beginning with foundational concepts and progressing to advanced application development. The initial stages focus on JavaScript fundamentals and object-oriented programming, essential for both frontend and backend tasks. Learners then move to, where hands-on projects help solidify their understanding of each module.

Backend development with Node.js and Express.js is often the first step. This phase emphasizes building RESTful APIs, handling HTTP requests, and managing data through MongoDB. As the course advances, frontend development with React takes center stage. Participants learn to build interactive interfaces, integrate APIs, and manage state effectively. This comprehensive curriculum ensures that by the end of the Mern Stack Course In Hyderabad, students are proficient in developing full-fledged applications.

Real-World Applications and Project-Based Learning

One of the standout features of a MERN Stack is the focus on project-based learning. By working on real-world applications, learners apply theoretical knowledge in practical scenarios. These projects often involve building e-commerce platforms, social media apps, and task management systems, mimicking industry standards. This experiential learning not only enhances technical skills but also prepares students for real-world challenges.

Additionally, Full Stack emphasizes collaboration through Git and version control systems, ensuring that students are well-versed in modern development workflows. By the conclusion of the course, learners will have a portfolio showcasing their ability to build and deploy applications across various domains, making them attractive to potential employers.

The Benefits of Enrolling in a MERN Stack Full Course

The demand for full-stack developers continues to rise, and offers a fast track to entering this lucrative field. One of the primary benefits is the unified programming language—JavaScript—across the entire stack, reducing the learning curve and enhancing productivity. Additionally, the MERN stack's scalability and flexibility make it an ideal choice for start-ups and large enterprises alike.

Through MERN Stack, learners develop not only technical expertise but also problem-solving skills, critical thinking, and the ability to work on cross-functional teams. The comprehensive nature of the curriculum ensures that graduates are job-ready, capable of handling diverse projects from conception to deployment.

Conclusion

Pursuing a Node Full Stack Course is a transformative step for anyone looking to excel in full-stack development. By mastering MongoDB, Express.js, React, and Node.js through, learners gain the skills necessary to build cutting-edge web applications. The demand for MERN stack developers is at an all-time high, making now the perfect time to invest in. With project-based learning, expert guidance, and real-world applications, this training opens doors to endless opportunities in the tech industry.

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enlume · 5 months ago

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#enlume #enlume inc #how api first approach solves these challenges

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cxolanes · 2 months ago

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#AmitGupta #HDFCBAnk #KotakMahindraBank

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precisiotech · 2 months ago

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Top Challenges in System Integration and How to Overcome Them

System integration is an essential process in today’s connected business world. It means making different systems, applications, or platforms work together smoothly. While it offers great benefits like better productivity and easier information sharing, it also comes with challenges that can slow down progress. Businesses aiming to improve efficiency and stay innovative need to understand these challenges and how to handle them.

Here are the main challenges in system integration and practical ways to solve them.

1. Compatibility Issues Between Systems

The Challenge: The first common issue pertaining to legacy system consolidation is that of interoperability with today’s technologies; typically, protocols differ from system to system, as do data presentation patterns and system constructs. This has a number of implications for leading to organizational inefficiencies and associated costs.

How to Overcome:

Identify the compatibility between existing systems and greater networks.

If there are any gaps between incompatible systems, then middleware or API gateways should be conducted.

Select integration architectures that are not limited by the platforms upon which they are embedded to support your system’s evolution.

2. Data Silos and Inconsistencies

The Challenge: Having multiple systems creates a data island where there are many variations, multiple records, and data inconsistencies that affect decisions.

How to Overcome:

Organize a central approach to data integration using the ETL (Extract, Transform, Load) tools.

It is required to format the data and create guidelines for observation of structural disciplines over data.

Use advanced data analytics to detect and resolve discrepancies.

3. Lack of Clear Objectives

The Challenge: Without clearly defined goals, integration projects can become directionless, resulting in budget overruns and failure to meet business needs.

How to Overcome:

Reach out to organizational stakeholders across various departments to bring out an understanding of integration goals towards business goals.

Use SMART (Specific, Measurable, Achievable, Relevant, and Time-bound) so that there is no confusion when the goals are set.

Regularly review and refine objectives during the integration process.

4. Security Risks

The Challenge: System Integration sometimes causes suspicious data to be made available to other platforms or interconnected with other insecure connections.

How to Overcome:

Adopt end-to-end encryption for all data exchanges.

Conduct regular security audits and vulnerability assessments.

Implement RBAC and MFA as methods for protection of systems.

5. High costs and resource allocation

The Challenge: Integration projects are typically going to require a good deal of effort, capital, and time when it comes to integration with architecture or huge systems.

How to Overcome:

The changes should be integrated progressively in order to disperse the expenditures and ensure few interferences.

Direct the use of cloud integration solutions, as these are cheaper and more flexible.

Partner with experienced system integrators in order to minimize resource utilization and time taken for implementation.

6. Resistance to Change

The Challenge: Usually, employees and teams do not welcome new integrated systems because they can see them as complex and dangerous to their operations.

How to Overcome:

Communicate the benefits of the integration clearly, such as improved efficiency and reduced manual tasks.

Provide training programs to familiarize employees with new systems.

Get users’ feedback during the implementation process and when there is an issue to provide a quick solution.

7. Integration of Data in Real-Time

The Challenge: Real-time data synchronization is technically complex, particularly in cases where frequent transfers of transactions or data updates are occurring.

How to Overcome:

Use advanced integration tools like message queues or event-driven architectures to facilitate real-time updates.

A system should also be efficient through the minimization of latency, with some of the following strategies incorporating load balancing and caching.

Create a tracking system that will enable the key data feed monitoring in order to address synchronization problems in advance.

8. Scalability and Future Proofing

The Challenge: Most integrations do not consider future expansion, and the result is system constraints and reintegration costs later on.

How to Overcome:

Select platforms that can handle more work when there is increased business as a result of increased publicity.

Architect the system in a modular solution wherein enhancements mean the integration of other modules.

Ensure that you always conduct an analysis and supplementation of the existing systems where there is technological innovation.

9. Vendor dependency

The Challenge: There is the problem of dependency since using one vendor for integration tools or integration services may lock one into the vendor all along, proving costly in the long run.

How to Overcome:

Every time possible, global integration shall be done using open integration platforms or those that are independent of the vendor.

Negotiate flexible contracts and maintain access to source codes or configurations.

Establish internal capability for a given process to minimize outsourcing.

READ MORE- https://www.precisio.tech/top-challenges-in-system-integration-and-how-to-overcome-them/

#technology #business #marketing #ITservices #AI #cloud #Digital transformation

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jcmarchi · 1 month ago

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From Intent to Execution: How Microsoft is Transforming Large Language Models into Action-Oriented AI

New Post has been published on https://thedigitalinsider.com/from-intent-to-execution-how-microsoft-is-transforming-large-language-models-into-action-oriented-ai/

From Intent to Execution: How Microsoft is Transforming Large Language Models into Action-Oriented AI

Large Language Models (LLMs) have changed how we handle natural language processing. They can answer questions, write code, and hold conversations. Yet, they fall short when it comes to real-world tasks. For example, an LLM can guide you through buying a jacket but can’t place the order for you. This gap between thinking and doing is a major limitation. People don’t just need information; they want results.

To bridge this gap, Microsoft is turning LLMs into action-oriented AI agents. By enabling them to plan, decompose tasks, and engage in real-world interactions, they empower LLMs to effectively manage practical tasks. This shift has the potential to redefine what LLMs can do, turning them into tools that automate complex workflows and simplify everyday tasks. Let’s look at what’s needed to make this happen and how Microsoft is approaching the problem.

What LLMs Need to Act

For LLMs to perform tasks in the real world, they need to go beyond understanding text. They must interact with digital and physical environments while adapting to changing conditions. Here are some of the capabilities they need:

Understanding User Intent

To act effectively, LLMs need to understand user requests. Inputs like text or voice commands are often vague or incomplete. The system must fill in the gaps using its knowledge and the context of the request. Multi-step conversations can help refine these intentions, ensuring the AI understands before taking action.

Turning Intentions into Actions

After understanding a task, the LLMs must convert it into actionable steps. This might involve clicking buttons, calling APIs, or controlling physical devices. The LLMs need to modify its actions to the specific task, adapting to the environment and solving challenges as they arise.

Adapting to Changes

Real world tasks don’t always go as planned. LLMs need to anticipate problems, adjust steps, and find alternatives when issues arise. For instance, if a necessary resource isn’t available, the system should find another way to complete the task. This flexibility ensures the process doesn’t stall when things change.

Specializing in Specific Tasks

While LLMs are designed for general use, specialization makes them more efficient. By focusing on specific tasks, these systems can deliver better results with fewer resources. This is especially important for devices with limited computing power, like smartphones or embedded systems.

By developing these skills, LLMs can move beyond just processing information. They can take meaningful actions, paving the way for AI to integrate seamlessly into everyday workflows.

How Microsoft is Transforming LLMs

Microsoft’s approach to creating action-oriented AI follows a structured process. The key objective is to enable LLMs to understand commands, plan effectively, and take action. Here’s how they’re doing it:

Step 1: Collecting and Preparing Data

In the first phrase, they collected data related to their specific use cases: UFO Agent (described below). The data includes user queries, environmental details, and task-specific actions. Two different types of data are collected in this phase: firstly, they collected task-plan data helping LLMs to outline high-level steps required to complete a task. For example, “Change font size in Word” might involve steps like selecting text and adjusting the toolbar settings. Secondly, they collected task-action data, enabling LLMs to translate these steps into precise instructions, like clicking specific buttons or using keyboard shortcuts.

This combination gives the model both the big picture and the detailed instructions it needs to perform tasks effectively.

Step 2: Training the Model

Once the data is collected, LLMs are refined through multiple training sessions. In the first step, LLMs are trained for task-planning by teaching them how to break down user requests into actionable steps. Expert-labeled data is then used to teach them how to translate these plans into specific actions. To further enhanced their problem-solving capabilities, LLMs have engaged in self-boosting exploration process which empower them to tackle unsolved tasks and generate new examples for continuous learning. Finally, reinforcement learning is applied, using feedback from successes and failures to further improved their decision-making.

Step 3: Offline Testing

After training, the model is tested in controlled environments to ensure reliability. Metrics like Task Success Rate (TSR) and Step Success Rate (SSR) are used to measure performance. For example, testing a calendar management agent might involve verifying its ability to schedule meetings and send invitations without errors.

Step 4: Integration into Real Systems

Once validated, the model is integrated into an agent framework. This allowed it to interact with real-world environments, like clicking buttons or navigating menus. Tools like UI Automation APIs helped the system identify and manipulate user interface elements dynamically.

For example, if tasked with highlighting text in Word, the agent identifies the highlight button, selects the text, and applies formatting. A memory component could help LLM to keeps track of past actions, enabling it adapting to new scenarios.

Step 5: Real-World Testing

The final step is online evaluation. Here, the system is tested in real-world scenarios to ensure it can handle unexpected changes and errors. For example, a customer support bot might guide users through resetting a password while adapting to incorrect inputs or missing information. This testing ensures the AI is robust and ready for everyday use.

A Practical Example: The UFO Agent

To showcase how action-oriented AI works, Microsoft developed the UFO Agent. This system is designed to execute real-world tasks in Windows environments, turning user requests into completed actions.

At its core, the UFO Agent uses a LLM to interpret requests and plan actions. For example, if a user says, “Highlight the word ‘important’ in this document,” the agent interacts with Word to complete the task. It gathers contextual information, like the positions of UI controls, and uses this to plan and execute actions.

The UFO Agent relies on tools like the Windows UI Automation (UIA) API. This API scans applications for control elements, such as buttons or menus. For a task like “Save the document as PDF,” the agent uses the UIA to identify the “File” button, locate the “Save As” option, and execute the necessary steps. By structuring data consistently, the system ensures smooth operation from training to real-world application.

Overcoming Challenges

While this is an exciting development, creating action-oriented AI comes with challenges. Scalability is a major issue. Training and deploying these models across diverse tasks require significant resources. Ensuring safety and reliability is equally important. Models must perform tasks without unintended consequences, especially in sensitive environments. And as these systems interact with private data, maintaining ethical standards around privacy and security is also crucial.

Microsoft’s roadmap focuses on improving efficiency, expanding use cases, and maintaining ethical standards. With these advancements, LLMs could redefine how AI interacts with the world, making them more practical, adaptable, and action-oriented.

The Future of AI

Transforming LLMs into action-oriented agents could be a game-changer. These systems can automate tasks, simplify workflows, and make technology more accessible. Microsoft’s work on action-oriented AI and tools like the UFO Agent is just the beginning. As AI continues to evolve, we can expect smarter, more capable systems that don’t just interact with us—they get jobs done.

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learnmoretechnology · 3 months ago

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Become a Full Stack Developer with Java Training in BTM, Bangalore

In today’s fast-evolving tech landscape, full stack developers are in high demand. Full stack development allows you to build both the client-side (frontend) and server-side (backend) of web applications, making you a versatile and highly valuable member of any development team. At LearnMoreTechnologies in BTM, Bangalore, we offer comprehensive Java Full Stack training that covers everything from Java programming to modern frontend technologies, giving you the skills you need to become a successful full stack developer.

Why Choose Java Full Stack Training in btm at LearnMoreTechnologies?

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Career Opportunities with Java Full Stack Skills

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