#chatbot overview

[r0bita]

Hahaha, yeah! Google's AI Overview is telling women that it's okay to smoke while pregnant and that we should eat rocks-

People will probably die from this.

If a chatbot can encourage a man to "sacrifice himself to stop climate change" only last year, I can't imagine what other fucked up things could have arised from this shit.

Misinformation on the internet already caused enough grief for a lot of vulnerable people LONG before ai and chatbots were implemented. A lot of us who grew up dependant on search browsers and internet access are going to be screwed by this. AI Overview may very well have already started killing people. And guess what? No amount of intelligence and experience is gonna save you, your aging grandmother, or your impressionable children from this nonsense because even if you don't fall for it, that doesn't mean it won't effect you one way or another.

I think it goes without saying that whoever is responsible for this should immediately start burning in Hell.

[dnschmidt]

Google is killing the web

Google's AI Overviews and AI Mode are killing the web. The goal is to turn the internet into a handful of megacorp AI "answer engines." No independent voices. No independent thought. Everything you know and believe will be determined by a billionaire's robot.

[collapsedsquid]

Structurally speaking, platforms are marketplaces. They’re built around the appearance of transparency and the illusion of choice, and participants generally play along with these fictions even if the truth is somewhat more complicated. LLMs--in particular when instantiated as chatbots--as decidedly not marketplaces. There are no other groups into which the LLM has brought you into contact for mutually beneficial exchange. There is not even the veneer of transparency or choice: The workings of the LLM are constitutively opaque and what you are given is not a scroll of posts or search results to pick from but a single-source, semi-regurgitated synthesis to be treated as mystically authoritative. After two decades of having their lives mediated by volatile and disappointing platforms, people may be clamoring for a new kind of mediation--knowledge obtained not via neoliberal software marketplace but from the mouth of the occult computer prophet-god. For all my misgivings about platforms, I am not personally ready to make that leap. I was happy when Google was just a list of results, and my general preference would be to make it again as close to a list of results as possible. It’s not that I think the “AI Overview” results can never be helpful or accurate, or that LLMs in general are junk outside of this context. It’s that Google and other platforms play a particular role in the web ecosystem, and shifting away from an even nominally transparent model to an increasingly obfuscated one is movement in the wrong direction.

[mariacallous]

Let’s say you just searched Google to learn more about this new AI Overview feature that everyone’s talking about. If your query triggers an AI Overview—and not every query will—then you might see an AI-generated summary of this very article at the top of your results. These new AI Overviews in Google Search present users with AI-generated answers to queries that are culled from information on the web, and they’re presented in a way that gives you the answer you seek without ever requiring you to click on a web link or even learn the names of the publications that the AI used as sources.

Google has already released a nascent version of AI Overviews within something called the Search Generative Experience, but it was only available to users who opted in. However, at the company’s I/O developer conference this week, Google announced that the newly renamed AI Overviews is now receiving a wider launch; everyone in the US who uses Google to search the web or ask a question will now see AI Overviews at the top of their results—again, if their question can be answered by a summary.

Can I Turn Off AI Overviews?

What if you’d rather just see web links? Unfortunately, AI Overviews are baked into the default search results page now. You can’t opt out of seeing them in your results. While there’s no way to fully disable AI Overviews for your Google account, there are a couple of methods you can use to get a search results page filled with web links.

First, there's the manual method of selecting a special filtered view after each individual query. After landing on the search result page topped with the AI Overview, click on the More tab—it should now appear among other filter options like Images, Videos, Shopping—and click Web. You’ll see a results page just showing links to actual websites.

Second, you can install a browser extension that automatically forces this web-only view of the search results page. Developers have been hard at work since I/O; there are already options available for Chrome and Vivaldi, as well as add-ons for Firefox. Other browsers will likely get extensions soon.

What's in an AI Overview?

When can you expect your query to trigger an AI-generated summary of the results? “AI Overviews appear for complex queries,” says Mallory De Leon, a Google spokesperson. “You'll find AI Overviews in your Google Search results when our systems determine that generative AI can be especially helpful–for example, when you want to quickly understand information from a range of sources.” During my initial tests, it felt like the AI Overviews popped up almost at random for queries, and the summaries appeared for simple questions as well as more complicated asks.

According to De Leon, the AI Overview is powered by a customized version of Google’s Gemini model that’s supplemented with aspects of the company’s Search system, like the Knowledge Graph that has billions of general facts.

One of my core hesitations about this feature as it rolls out is the continued potential for AI hallucinations, more commonly known as lies. When you interact with Google’s Gemini chatbot, a disclaimer at the bottom reads: “Gemini may display inaccurate info, including about people, so double-check its responses.” There’s no such disclaimer added to the bottom of the AI Overview, which often simply reads, “Generative AI is experimental.”

When asked why there’s no mention of potential hallucinations for AI Overviews, De Leon emphasizes that Google wants to still offer high-quality search results and mentions that the company did adversarial red-teaming tests to uncover potential weak points for the feature.

“This implementation of generative AI is rooted in Search’s core quality and safety systems, with built-in guardrails to prevent low-quality or harmful information from surfacing,” she says. “AI Overviews are designed to highlight information that can be easily verified by the supporting information that we surface.”

Knowing this, you might still want to click through the webpage links to double-check that the information is actually correct. Though it’s hard to imagine many users, who are often looking for quick answers, will spend extra time reading over the source material for Google’s AI-generated answer.

Liz Reid, Google’s head of Search, recently told my colleague Lauren Goode that AI Overviews are expected to arrive for countries outside of the United States before the end of 2024, so over a billion people will likely soon encounter this new feature. As someone whose job relies on readers actually clicking links and spending time reading the articles, of course I’m apprehensive about this change—and I’m not alone.

Beyond concerns from publishers, it also remains unclear what additional impacts might trickle down to users from Google’s AI Overviews. Yes, OpenAI’s ChatGPT and other AI tools are quite popular in Silicon Valley tech circles, but this feature will likely expose billions of people, who have never used a chatbot before, to AI-generated text. Even though AI Overviews are designed to save you time, they might lead to less trustworthy results.

[kynvillingur]

are there any slopless search engines. google has ai overview bing has copilot and duckduckgo has ai summaries for search results ecosia has an ai chatbot (lying hypocritical fucks)

are there good search engines. using the internet is driving me mad nowadays im going to bite a chunk out of my laptop

[seonethub]

AEO is the NEW SEO!

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📢 Future-Proof Your Strategy: Combine SEO + AEO to dominate both traditional and AI search.

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[acuvate-updates]

How Agentic AI & RAG Revolutionize Autonomous Decision-Making

In the swiftly advancing realm of artificial intelligence, the integration of Agentic AI and Retrieval-Augmented Generation (RAG) is revolutionizing autonomous decision-making across various sectors. Agentic AI endows systems with the ability to operate independently, while RAG enhances these systems by incorporating real-time data retrieval, leading to more informed and adaptable decisions. This article delves into the synergistic relationship between Agentic AI and RAG, exploring their combined impact on autonomous decision-making.

Overview

Agentic AI refers to AI systems capable of autonomous operation, making decisions based on environmental inputs and predefined goals without continuous human oversight. These systems utilize advanced machine learning and natural language processing techniques to emulate human-like decision-making processes. Retrieval-Augmented Generation (RAG), on the other hand, merges generative AI models with information retrieval capabilities, enabling access to and incorporation of external data in real-time. This integration allows AI systems to leverage both internal knowledge and external data sources, resulting in more accurate and contextually relevant decisions.

Read more about Agentic AI in Manufacturing: Use Cases & Key Benefits

What is Agentic AI and RAG?

Agentic AI: This form of artificial intelligence empowers systems to achieve specific objectives with minimal supervision. It comprises AI agents—machine learning models that replicate human decision-making to address problems in real-time. Agentic AI exhibits autonomy, goal-oriented behavior, and adaptability, enabling independent and purposeful actions.

Retrieval-Augmented Generation (RAG): RAG is an AI methodology that integrates a generative AI model with an external knowledge base. It dynamically retrieves current information from sources like APIs or databases, allowing AI models to generate contextually accurate and pertinent responses without necessitating extensive fine-tuning.

Know more on Why Businesses Are Embracing RAG for Smarter AI

Capabilities

When combined, Agentic AI and RAG offer several key capabilities:

Autonomous Decision-Making: Agentic AI can independently analyze complex scenarios and select effective actions based on real-time data and predefined objectives.

Contextual Understanding: It interprets situations dynamically, adapting actions based on evolving goals and real-time inputs.

Integration with External Data: RAG enables Agentic AI to access external databases, ensuring decisions are based on the most current and relevant information available.

Enhanced Accuracy: By incorporating external data, RAG helps Agentic AI systems avoid relying solely on internal models, which may be outdated or incomplete.

How Agentic AI and RAG Work Together

The integration of Agentic AI and RAG creates a robust system capable of autonomous decision-making with real-time adaptability:

Dynamic Perception: Agentic AI utilizes RAG to retrieve up-to-date information from external sources, enhancing its perception capabilities. For instance, an Agentic AI tasked with financial analysis can use RAG to access real-time stock market data.

Enhanced Reasoning: RAG augments the reasoning process by providing external context that complements the AI's internal knowledge. This enables Agentic AI to make better-informed decisions, such as recommending personalized solutions in customer service scenarios.

Autonomous Execution: The combined system can autonomously execute tasks based on retrieved data. For example, an Agentic AI chatbot enhanced with RAG can not only answer questions but also initiate actions like placing orders or scheduling appointments.

Continuous Learning: Feedback from executed tasks helps refine both the agent's decision-making process and RAG's retrieval mechanisms, ensuring the system becomes more accurate and efficient over time.

Read more about Multi-Meta-RAG: Enhancing RAG for Complex Multi-Hop Queries

Example Use Case: Customer Service

Customer Support Automation Scenario: A user inquiries about their account balance via a chatbot.

How It Works: The Agentic AI interprets the query, determines that external data is required, and employs RAG to retrieve real-time account information from a database. The enriched prompt allows the chatbot to provide an accurate response while suggesting payment options. If prompted, it can autonomously complete the transaction.

Benefits: Faster query resolution, personalized responses, and reduced need for human intervention.

Example: Acuvate's implementation of Agentic AI demonstrates how autonomous decision-making and real-time data integration can enhance customer service experiences.

2. Sales Assistance

Scenario: A sales representative needs to create a custom quote for a client.

How It Works: Agentic RAG retrieves pricing data, templates, and CRM details. It autonomously drafts a quote, applies discounts as instructed, and adjusts fields like baseline costs using the latest price book.

Benefits: Automates multi-step processes, reduces errors, and accelerates deal closures.

3. Healthcare Diagnostics

Scenario: A doctor seeks assistance in diagnosing a rare medical condition.

How It Works: Agentic AI uses RAG to retrieve relevant medical literature, clinical trial data, and patient history. It synthesizes this information to suggest potential diagnoses and treatment options.

Benefits: Enhances diagnostic accuracy, saves time, and provides evidence-based recommendations.

Example: Xenonstack highlights healthcare as a major application area for agentic AI systems in diagnosis and treatment planning.

4. Market Research and Consumer Insights

Scenario: A business wants to identify emerging market trends.

How It Works: Agentic RAG analyzes consumer data from multiple sources, retrieves relevant insights, and generates predictive analytics reports. It also gathers customer feedback from surveys or social media.

Benefits: Improves strategic decision-making with real-time intelligence.

Example: Companies use Agentic RAG for trend analysis and predictive analytics to optimize marketing strategies.

5. Supply Chain Optimization

Scenario: A logistics manager needs to predict demand fluctuations during peak seasons.

How It Works: The system retrieves historical sales data, current market trends, and weather forecasts using RAG. Agentic AI then predicts demand patterns and suggests inventory adjustments in real-time.

Benefits: Prevents stockouts or overstocking, reduces costs, and improves efficiency.

Example: Acuvate’s supply chain solutions leverage predictive analytics powered by Agentic AI to enhance logistics operations

How Acuvate Can Help

Acuvate specializes in implementing Agentic AI and RAG technologies to transform business operations. By integrating these advanced AI solutions, Acuvate enables organizations to enhance autonomous decision-making, improve customer experiences, and optimize operational efficiency. Their expertise in deploying AI-driven systems ensures that businesses can effectively leverage real-time data and intelligent automation to stay competitive in a rapidly evolving market.

Future Scope

The future of Agentic AI and RAG involves the development of multi-agent systems where multiple AI agents collaborate to tackle complex tasks. Continuous improvement and governance will be crucial, with ongoing updates and audits necessary to maintain safety and accountability. As technology advances, these systems are expected to become more pervasive across industries, transforming business processes and customer interactions.

In conclusion, the convergence of Agentic AI and RAG represents a significant advancement in autonomous decision-making. By combining autonomous agents with real-time data retrieval, organizations can achieve greater efficiency, accuracy, and adaptability in their operations. As these technologies continue to evolve, their impact across various sectors is poised to expand, ushering in a new era of intelligent automation.

[meret118]

From the very first use, however, AI Mode crystallized something about Google’s priorities and in particular its relationship to the web from which the company has drawn, and returned, many hundreds of billions of dollars of value. AI Overviews demoted links, quite literally pushing content from the web down on the page, and summarizing its contents for digestion without clicking:

. . .

Meanwhile, AI Mode all but buries them, not just summarizing their content for reading within Google’s product but inviting you to explore and expand on those summaries by asking more questions, rather than clicking out. In many cases, links are retained merely to provide backup and sourcing, included as footnotes and appendices rather than destinations:

. . .

Now, both AI Overviews and AI Mode, when they aren’t occasionally hallucinating, produce relatively clean answers that benefit in contrast to increasingly degraded regular search results on Google, which are full of hyperoptimized and duplicative spamlike content designed first and foremost with the demands of Google’s ranking algorithms and advertising in mind. AI Mode feels one step further removed from that ecosystem and once again looks good in contrast, a placid textual escape from Google’s own mountain of links that look like ads and ads that look like links (of course, Google is already working on ads for both Overviews and AI Mode). In its drive to embrace AI, Google is further concealing the raw material that fuels it, demoting links as it continues to ingest them for abstraction. Google may still retain plenty of attention to monetize and perhaps keep even more of it for itself, now that it doesn’t need to send people elsewhere; in the process, however, it really is starving the web that supplies it with data on which to train andfrom which to draw up-to-date details.

------

My biggest fear is that you're getting what google wants you to know, rather than the actual information. Now maybe the two are one in the same. But maybe they're not.

[darkmaga-returns]

Google has a “vision of a universal assistant,” but Mariner falls short. AI Agents are reputed to be the future of AI which autonomously “takes actions, adapts in real-time, and, solves multi-step problems based on context and objectives.” This is the technology that will destroy massive numbers of jobs in the future. ⁃ Patrick Wood, Editor.

Today, chatbots can answer questions, write poems and generate images. In the future, they could also autonomously perform tasks like online shopping and work with tools like spreadsheets.

Google on Wednesday unveiled a prototype of this technology, which artificial intelligence researchers call an A.I. agent.

Google is among the many tech companies building A.I. agents. Various A.I. start-ups, including OpenAI and Anthropic, have unveiled similar prototypes that can use software apps, websites and other online tools.

Google’s new prototype, called Mariner, is based on Gemini 2.0, which the company also unveiled on Wednesday. Gemini is the core technology that underpins many of the company’s A.I. products and research experiments. Versions of the system will power the company’s chatbot of the same name and A.I. Overviews, a Google search tool that directly answers user questions.

“We’re basically allowing users to type requests into their web browser and have Mariner take actions on their behalf,” Jaclyn Konzelmann, a Google project manager, said in an interview with The New York Times.

Gemini is what A.I researchers call a neural network — a mathematical system that can learn skills by analyzing enormous amounts of data. By recognizing patterns in articles and books culled from across the internet, for instance, a neural network can learn to generate text on its own.

The latest version of Gemini learns from a wide range of data, from text to images to sounds. That might include images showing how people use spreadsheets, shopping sites and other online services. Drawing on what Gemini has learned, Mariner can use similar services on behalf of computer users.

“It can understand that it needs to press a button to make something happen,” Demis Hassabis, who oversees Google’s core A.I. lab, said in an interview with The Times. “It can take action in the world.”

Mariner is designed to be used “with a human in the loop,” Ms. Konzelmann said. For instance, it can fill a virtual shopping cart with groceries if a user is in an active browser tab, but it will not actually buy the groceries. The user must make the purchase.

[kirtivishwakarmadigital]

Digital marketing

1. Introduction

Define digital marketing.

Briefly explain its importance in today’s tech-driven world.

Use a hook: statistics like "Over 60% of the global population is online!"

2. What is Digital Marketing?

Overview of the concept.

Difference between traditional marketing and digital marketing.

Explain its components (SEO, SEM, Social Media, Content Marketing, etc.).

3. Importance of Digital Marketing

Its role in connecting brands with audiences globally.

Benefits like cost-effectiveness, real-time analytics, and wider reach.

4. Key Components of Digital Marketing

SEO (Search Engine Optimization): The art of ranking higher on search engines.

Content Marketing: Blogs, videos, infographics, etc.

Social Media Marketing: Using platforms like Instagram and Facebook.

Email Marketing: Building personal connections.

Pay-Per-Click Advertising (PPC): Paid strategies for immediate visibility.

5. How Digital Marketing Helps Businesses

Building brand awareness.

Increasing website traffic.

Boosting sales and revenue.

Reaching targeted audiences.

6. Challenges in Digital Marketing

Competition and market saturation.

Keeping up with trends and algorithms.

Creating engaging and authentic content.

7. Future Trends in Digital Marketing (2024 and Beyond)

Growth of AI tools (e.g., chatbots, analytics tools).

Personalization and user experience.

The rise of video content and short-form reels.

Focus on voice search and local SEO.

8. Tips for Beginners

Start with a strong social media presence.

Learn SEO basics and use tools like Google Analytics.

Create quality, user-focused content consistently.

Experiment with small ad campaigns.

9. Conclusion

Summarize why digital marketing is a game-changer.

Call-to-action: “Start your digital marketing journey today with ‘Kirtivishwakarma Digital’!”

[manishsparktg]

Boosting Engagement with Click-to-Call: A Game-Changer for Businesses

In today’s fast-paced digital world, businesses are constantly seeking innovative ways to engage with customers and drive conversions. One such innovation that has revolutionized customer interaction is the click to call feature. This simple yet powerful tool has proven to be a game-changer for businesses, enhancing customer engagement, improving satisfaction, and ultimately driving growth. In this blog, we will explore the click to call service, its benefits, how to implement it effectively, and conclude with the success of SparkTG in leveraging this service.

If you want to know more about Whatsapp Business API — Schedule a Free Demo

If you want to read more about our Latest Blogs , then check it Now!! —

Capitalizing on Financial Institutions Integrated IVR Solution: An Overview

Beyond Button Mashing: How Conversational IVR Revolutionizes IVRs

Top 10 WhatsApp Business API Use Cases Reshaping Real Estate Communication

Beyond the Hype: The Real-World Power of Chatbots for Lead Generation

#business #sparktg #technology #ivrservice #cloudtelephony #sparktechnology #cloud #whatsappbusinessapi #ivrsolution #click2call @click2callsolution #click2callservice #click2callserviceprovider

[mariacallous]

A week after its algorithms advised people to eat rocks and put glue on pizza, Google admitted Thursday that it needed to make adjustments to its bold new generative AI search feature. The episode highlights the risks of Google’s aggressive drive to commercialize generative AI—and also the treacherous and fundamental limitations of that technology.

Google’s AI Overviews feature draws on Gemini, a large language model like the one behind OpenAI’s ChatGPT, to generate written answers to some search queries by summarizing information found online. The current AI boom is built around LLMs’ impressive fluency with text, but the software can also use that facility to put a convincing gloss on untruths or errors. Using the technology to summarize online information promises can make search results easier to digest, but it is hazardous when online sources are contractionary or when people may use the information to make important decisions.

“You can get a quick snappy prototype now fairly quickly with an LLM, but to actually make it so that it doesn't tell you to eat rocks takes a lot of work,” says Richard Socher, who made key contributions to AI for language as a researcher and, in late 2021, launched an AI-centric search engine called You.com.

Socher says wrangling LLMs takes considerable effort because the underlying technology has no real understanding of the world and because the web is riddled with untrustworthy information. “In some cases it is better to actually not just give you an answer, or to show you multiple different viewpoints,” he says.

Google’s head of search Liz Reid said in the company’s blog post late Thursday that it did extensive testing ahead of launching AI Overviews. But she added that errors like the rock eating and glue pizza examples—in which Google’s algorithms pulled information from a satirical article and jocular Reddit comment, respectively—had prompted additional changes. They include better detection of “nonsensical queries,” Google says, and making the system rely less heavily on user-generated content.

You.com routinely avoids the kinds of errors displayed by Google’s AI Overviews, Socher says, because his company developed about a dozen tricks to keep LLMs from misbehaving when used for search.

“We are more accurate because we put a lot of resources into being more accurate,” Socher says. Among other things, You.com uses a custom-built web index designed to help LLMs steer clear of incorrect information. It also selects from multiple different LLMs to answer specific queries, and it uses a citation mechanism that can explain when sources are contradictory. Still, getting AI search right is tricky. WIRED found on Friday that You.com failed to correctly answer a query that has been known to trip up other AI systems, stating that “based on the information available, there are no African nations whose names start with the letter ‘K.’” In previous tests, it had aced the query.

Google’s generative AI upgrade to its most widely used and lucrative product is part of a tech-industry-wide reboot inspired by OpenAI’s release of the chatbot ChatGPT in November 2022. A couple of months after ChatGPT debuted, Microsoft, a key partner of OpenAI, used its technology to upgrade its also-ran search engine Bing. The upgraded Bing was beset by AI-generated errors and odd behavior, but the company’s CEO, Satya Nadella, said that the move was designed to challenge Google, saying “I want people to know we made them dance.”

Some experts feel that Google rushed its AI upgrade. “I’m surprised they launched it as it is for as many queries—medical, financial queries—I thought they’d be more careful,” says Barry Schwartz, news editor at Search Engine Land, a publication that tracks the search industry. The company should have better anticipated that some people would intentionally try to trip up AI Overviews, he adds. “Google has to be smart about that,” Schwartz says, especially when they're showing the results as default on their most valuable product.

Lily Ray, a search engine optimization consultant, was for a year a beta tester of the prototype that preceded AI Overviews, which Google called Search Generative Experience. She says she was unsurprised to see the errors that appeared last week given how the previous version tended to go awry. “I think it’s virtually impossible for it to always get everything right,” Ray says. “That’s the nature of AI.”

Even if blatant errors like suggesting people eat rocks become less common, AI search can fail in other ways. Ray has documented more subtle problems with AI Overviews, including summaries that sometimes draw on poor sources such as sites that are from another region or even defunct websites—something she says could provide less useful information to users who are hunting for product recommendations, for instance. Those who work on optimizing content for Google’s Search algorithm are still trying to understand what’s going on. “Within our industry right now, the level of confusion is on the charts,” she says.

Even if industry experts and consumers get more familiar with how the new Google search behaves, don’t expect it to stop making mistakes. Daniel Griffin, a search consultant and researcher who is developing tools to make it easy to compare different AI-powered search services, says that Google faced similar problems when it launched Featured Snippets, which answered queries with text quoted from websites, in 2014.

Griffin says he expects Google to iron out some of the most glaring problems with AI Overviews, but that it’s important to remember no one has solved the problem of LLMs failing to grasp what is true, or their tendency to fabricate information. “It’s not just a problem with AI,” he says. “It’s the web, it’s the world. There’s not really a truth, necessarily.”

[redtail-lol]

The discussion about Google's Gemini and AI overviews needs to not start and stop at laughing at the AI for being wrong. The AI is working perfectly fine. Google is using large language model technology for a purpose that technology fundamentally was not made for and cannot perform and we should both be laughing at them, and seriously criticizing them for not knowing or caring enough about the technology and how it worked before implementing it. They just wanted to get on the hype train when they should have waited for when an AI that could actually meet their needs was developed

Ignoring the ethical concerns of LLMs, because believe me I know there are many, an LLM is a neat little piece of technology. It's just "look I taught a robot how to mimic human speech patterns! You can talk to the robot now and get responses that are relevant to what you said and feel like they were said by a person. It's basically a super sophisticated version of predictive text. Pretty cool, right?" This fundamental concept is what LLMs are made for. Character roleplay apps, though again having the same ethical concerns, are using the technology properly. They can slap a personality onto a character to influence the word choices. That's how it was meant to be used - the technology was made so you could talk to the robot. AI chatbot assistants can also be aided with LLMs, because if it has a fixed, small set of information to draw from, and still has the ability to transfer you to human agents, it can use the LLM technology to generate more conversational, fluid responses to questions that still draw on the knowledge it's built for. It's a combination of pre-ChatGPT automated assistants, and LLMs. That's still prone to failure but it's not like what Google did.

Google's Gemini took the premise of a robot that talks like a human and decided that it should answer questions. Despite the fact that there's literally no reason for Google to need AI overviews, despite the fact that the LLM isn't built for information retrieval or for comprehending language and the meaning of words, Google went ahead with it. This is a fundamental misapplication of the LLM technology. To make the application possible, an information retrieval AI would need to be developed to work with the LLM. Such an AI would have to be able to identify fact from fiction, satire from genuine, truth from myths, and good information from disinformation. That AI doesn't exist yet. Gemini is just an LLM with perhaps a knowledge set but not a sufficient one for all of the world's questions, and regardless is prone to mistakes because it's still just predictive text. It's just a robot that can talk like a human. It can't process information like it's being asked to. It doesn't know the meaning of the words it says. That technology isn't made for the application it's being given and it's honestly pathetic at best and careless at worst for Google to have done so anyway.

TL;DR don't just laugh at Google's AI overview and say the AI is dumb. The AI is working as intended. Google is who's dumb because they gave the task of information retrieval to an AI that's primary function is that it can talk like a person and simulate conversation.

(Yes I know there's also ethical concerns regardless but I think that's a different conversation I want to specifically talk about how Google is completely misusing LLM technology in a way it fundamentally wasn't made for)

[rajibperfection]

A Review on Merlin Lifetime deals.

It’s hard to believe AI tools help you work smarter when you’re still stuck switching between tabs to get things done. (“Just call me an AI assistant juggler.”)

With so many AI models and features on the market, you’re using way too much tech to research and generate different types of content.

What if there was a Chrome extension packed with all the AI models you need to speed up your research and content creation process?

Overview:

Merlin is a Chrome browser extension and web app that gives you access to popular AI models to research, summarize, and write content.

      Best for: 

Alternatives to:Integrations:Main Features:

Educators

Marketers

Small businesses

Copy.ai

Grammarly

Jasper

Facebook

Gmail

LinkedIn

Outlook

Twitter

GDPR-compliant

AI

Pros and cons:

Chat with leading AI models, from one browser

With Merlin, you’ll receive access to prominent AI models, like GPT-4, Claude-3, Gemini 1.5, Leonardo, and others—all from your Chrome web browser.

No more moving between browser tabs! Use Merlin’s AI Chatbot on every websites you visit.

Use complex image-generation models to develop captivating brand storylines.

Plans & features

Deal terms & conditions

Lifetime access to Merlin

All future Pro Plan updates

If Plan name changes, deal will be mapped to the new Plan name with all accompanying updates

No codes, no stacking—just choose the plan that’s right for you

You must activate your license within 60 days of purchase

Ability to upgrade between 3 license tiers while the deal is available

Ability to downgrade between 3 license tiers within 60 days of purchase

GDPR compliant

Available for new Merlin users and returning AppSumo purchasers

Previous AppSumo customers who purchased Merlin can upgrade their license to increase their feature limits

1 Merlin query = 1 Chat GPT 3.5 query

Find all other AI model Query Standards here

All purchasers subject to Merlin’s Terms & Conditions

60 day money-back guarantee. Try it out for 2 months to make sure it’s right for you!

Features included in all plans

Chat with documents

Image generation

Chatbots

Chat with web pages

YouTube summarization

Blog summarization

Twitter, Gmail, Outlook, and LinkedIn FAB bars

LinkedIn Pro connect

Create from YouTube

Post in YouTube comments

AI personas

[acuvate-updates]

Revolutionizing Enterprises: CXO’s GenAI Transformation

1. Unlocking AI’s Potential: A Strategic Overview

AI adoption, embraced by 70% of executives, promises enhanced customer experiences despite challenges. Understanding and integrating AI into business operations is essential. Explore our guide for actionable insights, ensuring businesses not only survive but thrive in the AI-driven era.

Learn more about Artificial Intelligence impact in 2025

AI Reshaping Decision-Making in 2025

Generative AI, like GPT, simplifies business processes. It transforms decision-making with its user-friendly interfaces, self-learning capabilities, and efficient sorting.

Furthermore, it’s a budget-friendly solution with no training fees, making it accessible for businesses of various sizes.

Our guide aims to offer practical insights for responsibly adopting this transformative technology. Following our roadmap allows businesses to navigate the Generative AI landscape, ensuring success in the constantly changing digital environment.

To stay informed and up to date with the latest trends, join our webinars featuring industry experts from organizations like Microsoft, Shell, and more.

C-Suite Roles Transformed by AI

Strategic AI Adoption Tips for Leaders

To successfully adopt AI, prioritize it for strategic goals, use tailored features, and embrace multilingual capabilities. Ensure secure deployment for data integrity. Offices that adopt AI enjoy streamlined processes, ongoing innovation, and secure frameworks.

2. Transforming C-Suite Roles with AI

Empowering CIOs: Innovating IT with AI

In enterprise IT, AI, particularly models like GPT, empowers CIOs to break traditional boundaries and improve operations through groundbreaking innovations.

Use Cases:

· Smart IT Helpdesk Support: AI ensures 24x7 support with human-like conversations, reducing user effort and cost.

· Smart Search: AI transforms data management, improving user engagement with easy-to-use search capabilities.

· Next-Gen Customer Support: AI automates email-based queries, crafting personalized responses for enhanced customer experiences.

To stay informed and up to date with the latest trends, join our webinars featuring industry experts from organizations like Microsoft, Shell, and more.

Implementation Tips:

· Prioritize AI for strategic goals.

· Personalized and multilingual capabilities.

· Ensure secure deployment for data integrity.

· Offices embracing AI experience streamlined helpdesks, continuous innovation, and secure frameworks.

Empowering HR with AI: From Administration to Leadership

Use Cases:

· AI-powered Talent Acquisition: AI streamlines global recruitment, automating candidate screening and optimizing interview scheduling.

· Efficient Employee Onboarding: AI redefines onboarding by using chatbots to create personalized experiences and promote communication across departments.

· Personalized Employee Engagement: AI’s learning capabilities drive adaptive engagement activities, ensuring timely interventions and integrating feedback loops.

· Data-Driven Learning and Development: AI changes learning through advanced knowledge mining, personalized modules, and interactive interfaces.

Implementation Tips:

· Align AI integration with strategic HR goals.

· Leverage AI’s personalization and multilingual features.

· Uphold data integrity and fortify security during deployment.

· Offices leveraging AI experience streamlined recruitment, efficient onboarding, personalized engagement, and reimagined L&D.

Also, read more about How GPT-powered Chatbots Can Help HR Leaders Drive Engagement and Retention

AI-Powered Marketing: A CMO’s Secret Weapon

Use Cases:

· AI-Powered Brand Engagement Solutions: AI revolutionizes brand engagement with personalized content, human-like communication, and timely identification of upsell opportunities.

· Smartly allocate ad spending: AI enables CMOs to allocate budgets wisely by analyzing real-time market trends predictively.

Implementation Tips:

· Prioritize AI Integration aligned with core marketing goals.

· Leverage Multilingual Features for global brand reach.

· Strategize Deployment with a focus on data integrity and customer privacy.

· Offices with AI experience tailored brand engagement, proactive ad spend decisions, and seamless multilingual marketing.

Explore the Power of Generative AI for enhancing CX — Marketing and Customer support/ Engagement

AI: The COO’s Catalyst for Operational Agility and Efficiency

In the realm of Operational efficiency, Chief Operating Officers (COOs) orchestrate processes to optimize resources.

Use Cases:

· Simplifying the supply chain: Artificial Intelligence (AI) provides a high-level perspective, facilitating proactive demand forecasting and prompt corrective actions for effective supply chains.

· Enhancing Operational Communication: AI-powered chatbots ensure role-specific information flow, facilitating real-time feedback and swift issue resolution.

· Driving Operational Cost Optimization: AI analyzes data for cost leakage points, recommends resource redistribution, and encourages real-time cost insights.

To stay informed and up to date with the latest trends, join our webinars featuring industry experts from organizations like Microsoft, Shell, and more.

Implementation Tips:

· Justify Integration Effort with improved operational KPIs.

· Leverage Iterative Learning for continuous process refinement.

· Prioritize Data Security, safeguarding organizational assets.

· Offices with AI experience data-driven supply insights, intelligent communication, and dynamic cost optimization.

· In the dynamic field of data management, Chief Data Officers (CDOs) use AI, including GPT and other generative AI models, as strong supporters to decode large datasets effectively.

Use Cases:

· Enhancing Data Intelligence: AI’s advanced algorithms mine data, providing insights that shape business strategies through predictive modeling and intelligent summarizing.

· Managing Unstructured Data: AI’s NLP features efficiently process and convert unstructured data into organized, clear formats, enhancing data processing efficiency.

· Enhancing Data Governance: AI simplifies data management by automating organization, ensuring compliance with regulatory policies, real-time breach detection, and maintaining data standards.

Implementation Tips:

· Start with a clear data strategy aligning AI’s abilities with major data challenges.

· Prioritize data protection in AI adoption for utility and security.

· Invest in continuous training, refining AI models for better understanding of organizational data.

· Offices with AI experience automated, intelligent data insights, streamlined data, and proactive, AI-assisted data governance.

3. AI’s Impact: Boosting Enterprise Efficiency

Discover how advanced AI, including Azure OpenAI’s GPT, is reshaping enterprise operations. Explore real-world use cases across departments, showcasing the profound impact of Generative AI on organizational efficiency.

AI Integration Across Departments

SharePoint Search Integration

Structured Data Insights & Summarization

AI enables the effortless transformation of structured data into actionable intelligence. This module analyzes tables and databases, extracting meaningful insights presented in user-friendly natural language summaries, empowering teams for informed decision-making.

R&D Assistant

In Research and Development, AI acts as a dedicated assistant, leveraging internal and external data sources for comprehensive reports and analysis.

Customer/Consumer Support

Elevate customer support with an AI-powered chatbot that delivers personalized and context-aware responses. By training the model with customer support data, this solution ensures accuracy and seamless integration with existing systems.

HR Chatbot

AI becomes an invaluable virtual assistant in HR, guiding employees through common queries with personalized responses. From leave requests to company policies, this intelligent chatbot ensures a seamless and efficient employee experience.

IT Chatbot

Revolutionize IT support by using an AI-powered chatbot. The chatbot can troubleshoot common issues, give step-by-step instructions, and escalate complex cases. Enhance user experience and streamline technical support with this essential tool.

To stay informed and up to date with the latest trends, join our webinars featuring industry experts from organizations like Microsoft, Shell, and more.

Document Comparison/RFP Validation

AI streamlines procurement and HR processes by comparing documents. Quickly analyze text documents for similarities, differences, and changes, ensuring accuracy in document validation and specifications.

Procurement Assistant

Automate and streamline the procurement process with an AI-powered assistant. Generate purchase orders, request for quotations, and vendor evaluations based on predefined templates and user inputs, ensuring efficiency and accuracy.

Search Integration with SAP JAM/ServiceNow KB/Salesforce KB

Bridge the knowledge gap by integrating AI with ERP and ITSM systems. Enable interactive conversations beyond search results, enhancing user understanding and engagement with content.

Knowledge Management Solution

Empower your workforce with a Knowledge Management Solution seamlessly merging AI with Azure Cognitive Search. Unlock information from diverse sources, fostering a culture of knowledge-sharing and collaboration.

Integrate innovative AI use cases into your strategy for streamlined processes and enhanced user experiences.

4. Unlocking AI’s Power with Acuvate: A Comprehensive Guide

As businesses embrace AI’s transformative potential, Generative Pre-trained Transformers (GPT) take center stage, enhancing productivity. Our guide delves into AI FAQs, ensuring data security and adaptability for enterprise needs.

To stay informed and up to date with the latest trends, join our webinars featuring industry experts from organizations like Microsoft, Shell, and more.

Acuvate Advantage

Experience the Org Brain GPT framework, combining analytics and enterprise security. Acuvate’s expertise, spanning 16 years, ensures customized AI solutions for streamlined processes.

Explore our AI trends guide to boost your organization’s capabilities. Request a demo or insight into Acuvate’s transformative AI solutions for enhanced performance.

Also, read our other blogs on the AI revolution on Medium

9 Must-Watch Webinars of 2025 for Tech Enthusiasts | Medium

- AI-Driven Transformation: A CXO's Guide to Generative AI Success | Medium

GPT Revolution in AI - A Strategic Guide for CXO | Medium

Emerging Energy Technologies: Data, AI & Digital Solutions in 2025 | Medium