#knowledge base chatbot

[innovaticsblog]

Knowledge Base Chatbots: Empower your business with AI-driven conversational interfaces for seamless user engagement and enhanced efficiency.

[precallai]

How AI Is Revolutionizing Contact Centers in 2025

As contact centers evolve from reactive customer service hubs to proactive experience engines, artificial intelligence (AI) has emerged as the cornerstone of this transformation. In 2025, modern contact center architectures are being redefined through AI-based technologies that streamline operations, enhance customer satisfaction, and drive measurable business outcomes.

This article takes a technical deep dive into the AI-powered components transforming contact centers—from natural language models and intelligent routing to real-time analytics and automation frameworks.

1. AI Architecture in Modern Contact Centers

At the core of today’s AI-based contact centers is a modular, cloud-native architecture. This typically consists of:

NLP and ASR engines (e.g., Google Dialogflow, AWS Lex, OpenAI Whisper)

Real-time data pipelines for event streaming (e.g., Apache Kafka, Amazon Kinesis)

Machine Learning Models for intent classification, sentiment analysis, and next-best-action

RPA (Robotic Process Automation) for back-office task automation

CDP/CRM Integration to access customer profiles and journey data

Omnichannel orchestration layer that ensures consistent CX across chat, voice, email, and social

These components are containerized (via Kubernetes) and deployed via CI/CD pipelines, enabling rapid iteration and scalability.

2. Conversational AI and Natural Language Understanding

The most visible face of AI in contact centers is the conversational interface—delivered via AI-powered voice bots and chatbots.

Key Technologies:

Automatic Speech Recognition (ASR): Converts spoken input to text in real time. Example: OpenAI Whisper, Deepgram, Google Cloud Speech-to-Text.

Natural Language Understanding (NLU): Determines intent and entities from user input. Typically fine-tuned BERT or LLaMA models power these layers.

Dialog Management: Manages context-aware conversations using finite state machines or transformer-based dialog engines.

Natural Language Generation (NLG): Generates dynamic responses based on context. GPT-based models (e.g., GPT-4) are increasingly embedded for open-ended interactions.

Architecture Snapshot:

plaintext

CopyEdit

Customer Input (Voice/Text)

       ↓

ASR Engine (if voice)

       ↓

NLU Engine → Intent Classification + Entity Recognition

       ↓

Dialog Manager → Context State

       ↓

NLG Engine → Response Generation

       ↓

Omnichannel Delivery Layer

These AI systems are often deployed on low-latency, edge-compute infrastructure to minimize delay and improve UX.

3. AI-Augmented Agent Assist

AI doesn’t only serve customers—it empowers human agents as well.

Features:

Real-Time Transcription: Streaming STT pipelines provide transcripts as the customer speaks.

Sentiment Analysis: Transformers and CNNs trained on customer service data flag negative sentiment or stress cues.

Contextual Suggestions: Based on historical data, ML models suggest actions or FAQ snippets.

Auto-Summarization: Post-call summaries are generated using abstractive summarization models (e.g., PEGASUS, BART).

Technical Workflow:

Voice input transcribed → parsed by NLP engine

Real-time context is compared with knowledge base (vector similarity via FAISS or Pinecone)

Agent UI receives predictive suggestions via API push

4. Intelligent Call Routing and Queuing

AI-based routing uses predictive analytics and reinforcement learning (RL) to dynamically assign incoming interactions.

Routing Criteria:

Customer intent + sentiment

Agent skill level and availability

Predicted handle time (via regression models)

Customer lifetime value (CLV)

Model Stack:

Intent Detection: Multi-label classifiers (e.g., fine-tuned RoBERTa)

Queue Prediction: Time-series forecasting (e.g., Prophet, LSTM)

RL-based Routing: Models trained via Q-learning or Proximal Policy Optimization (PPO) to optimize wait time vs. resolution rate

5. Knowledge Mining and Retrieval-Augmented Generation (RAG)

Large contact centers manage thousands of documents, SOPs, and product manuals. AI facilitates rapid knowledge access through:

Vector Embedding of documents (e.g., using OpenAI, Cohere, or Hugging Face models)

Retrieval-Augmented Generation (RAG): Combines dense retrieval with LLMs for grounded responses

Semantic Search: Replaces keyword-based search with intent-aware queries

This enables agents and bots to answer complex questions with dynamic, accurate information.

6. Customer Journey Analytics and Predictive Modeling

AI enables real-time customer journey mapping and predictive support.

Key ML Models:

Churn Prediction: Gradient Boosted Trees (XGBoost, LightGBM)

Propensity Modeling: Logistic regression and deep neural networks to predict upsell potential

Anomaly Detection: Autoencoders flag unusual user behavior or possible fraud

Streaming Frameworks:

Apache Kafka / Flink / Spark Streaming for ingesting and processing customer signals (page views, clicks, call events) in real time

These insights are visualized through BI dashboards or fed back into orchestration engines to trigger proactive interventions.

7. Automation & RPA Integration

Routine post-call processes like updating CRMs, issuing refunds, or sending emails are handled via AI + RPA integration.

Tools:

UiPath, Automation Anywhere, Microsoft Power Automate

Workflows triggered via APIs or event listeners (e.g., on call disposition)

AI models can determine intent, then trigger the appropriate bot to complete the action in backend systems (ERP, CRM, databases)

8. Security, Compliance, and Ethical AI

As AI handles more sensitive data, contact centers embed security at multiple levels:

Voice biometrics for authentication (e.g., Nuance, Pindrop)

PII Redaction via entity recognition models

Audit Trails of AI decisions for compliance (especially in finance/healthcare)

Bias Monitoring Pipelines to detect model drift or demographic skew

Data governance frameworks like ISO 27001, GDPR, and SOC 2 compliance are standard in enterprise AI deployments.

Final Thoughts

AI in 2025 has moved far beyond simple automation. It now orchestrates entire contact center ecosystems—powering conversational agents, augmenting human reps, automating back-office workflows, and delivering predictive intelligence in real time.

The technical stack is increasingly cloud-native, model-driven, and infused with real-time analytics. For engineering teams, the focus is now on building scalable, secure, and ethical AI infrastructures that deliver measurable impact across customer satisfaction, cost savings, and employee productivity.

As AI models continue to advance, contact centers will evolve into fully adaptive systems, capable of learning, optimizing, and personalizing in real time. The revolution is already here—and it's deeply technical.

[mostlysignssomeportents]

Why I don’t like AI art

I'm on a 20+ city book tour for my new novel PICKS AND SHOVELS. Catch me in CHICAGO with PETER SAGAL on Apr 2, and in BLOOMINGTON at MORGENSTERN BOOKS on Apr 4. More tour dates here.

A law professor friend tells me that LLMs have completely transformed the way she relates to grad students and post-docs – for the worse. And no, it's not that they're cheating on their homework or using LLMs to write briefs full of hallucinated cases.

The thing that LLMs have changed in my friend's law school is letters of reference. Historically, students would only ask a prof for a letter of reference if they knew the prof really rated them. Writing a good reference is a ton of work, and that's rather the point: the mere fact that a law prof was willing to write one for you represents a signal about how highly they value you. It's a form of proof of work.

But then came the chatbots and with them, the knowledge that a reference letter could be generated by feeding three bullet points to a chatbot and having it generate five paragraphs of florid nonsense based on those three short sentences. Suddenly, profs were expected to write letters for many, many students – not just the top performers.

Of course, this was also happening at other universities, meaning that when my friend's school opened up for postdocs, they were inundated with letters of reference from profs elsewhere. Naturally, they handled this flood by feeding each letter back into an LLM and asking it to boil it down to three bullet points. No one thinks that these are identical to the three bullet points that were used to generate the letters, but it's close enough, right?

Obviously, this is terrible. At this point, letters of reference might as well consist solely of three bullet-points on letterhead. After all, the entire communicative intent in a chatbot-generated letter is just those three bullets. Everything else is padding, and all it does is dilute the communicative intent of the work. No matter how grammatically correct or even stylistically interesting the AI generated sentences are, they have less communicative freight than the three original bullet points. After all, the AI doesn't know anything about the grad student, so anything it adds to those three bullet points are, by definition, irrelevant to the question of whether they're well suited for a postdoc.

Which brings me to art. As a working artist in his third decade of professional life, I've concluded that the point of art is to take a big, numinous, irreducible feeling that fills the artist's mind, and attempt to infuse that feeling into some artistic vessel – a book, a painting, a song, a dance, a sculpture, etc – in the hopes that this work will cause a loose facsimile of that numinous, irreducible feeling to manifest in someone else's mind.

Art, in other words, is an act of communication – and there you have the problem with AI art. As a writer, when I write a novel, I make tens – if not hundreds – of thousands of tiny decisions that are in service to this business of causing my big, irreducible, numinous feeling to materialize in your mind. Most of those decisions aren't even conscious, but they are definitely decisions, and I don't make them solely on the basis of probabilistic autocomplete. One of my novels may be good and it may be bad, but one thing is definitely is is rich in communicative intent. Every one of those microdecisions is an expression of artistic intent.

Now, I'm not much of a visual artist. I can't draw, though I really enjoy creating collages, which you can see here:

https://www.flickr.com/photos/doctorow/albums/72177720316719208

I can tell you that every time I move a layer, change the color balance, or use the lasso tool to nip a few pixels out of a 19th century editorial cartoon that I'm matting into a modern backdrop, I'm making a communicative decision. The goal isn't "perfection" or "photorealism." I'm not trying to spin around really quick in order to get a look at the stuff behind me in Plato's cave. I am making communicative choices.

What's more: working with that lasso tool on a 10,000 pixel-wide Library of Congress scan of a painting from the cover of Puck magazine or a 15,000 pixel wide scan of Hieronymus Bosch's Garden of Earthly Delights means that I'm touching the smallest individual contours of each brushstroke. This is quite a meditative experience – but it's also quite a communicative one. Tracing the smallest irregularities in a brushstroke definitely materializes a theory of mind for me, in which I can feel the artist reaching out across time to convey something to me via the tiny microdecisions I'm going over with my cursor.

Herein lies the problem with AI art. Just like with a law school letter of reference generated from three bullet points, the prompt given to an AI to produce creative writing or an image is the sum total of the communicative intent infused into the work. The prompter has a big, numinous, irreducible feeling and they want to infuse it into a work in order to materialize versions of that feeling in your mind and mine. When they deliver a single line's worth of description into the prompt box, then – by definition – that's the only part that carries any communicative freight. The AI has taken one sentence's worth of actual communication intended to convey the big, numinous, irreducible feeling and diluted it amongst a thousand brushtrokes or 10,000 words. I think this is what we mean when we say AI art is soul-less and sterile. Like the five paragraphs of nonsense generated from three bullet points from a law prof, the AI is padding out the part that makes this art – the microdecisions intended to convey the big, numinous, irreducible feeling – with a bunch of stuff that has no communicative intent and therefore can't be art.

If my thesis is right, then the more you work with the AI, the more art-like its output becomes. If the AI generates 50 variations from your prompt and you choose one, that's one more microdecision infused into the work. If you re-prompt and re-re-prompt the AI to generate refinements, then each of those prompts is a new payload of microdecisions that the AI can spread out across all the words of pixels, increasing the amount of communicative intent in each one.

Finally: not all art is verbose. Marcel Duchamp's "Fountain" – a urinal signed "R. Mutt" – has very few communicative choices. Duchamp chose the urinal, chose the paint, painted the signature, came up with a title (probably some other choices went into it, too). It's a significant work of art. I know because when I look at it I feel a big, numinous irreducible feeling that Duchamp infused in the work so that I could experience a facsimile of Duchamp's artistic impulse.

There are individual sentences, brushstrokes, single dance-steps that initiate the upload of the creator's numinous, irreducible feeling directly into my brain. It's possible that a single very good prompt could produce text or an image that had artistic meaning. But it's not likely, in just the same way that scribbling three words on a sheet of paper or painting a single brushstroke will produce a meaningful work of art. Most art is somewhat verbose (but not all of it).

So there you have it: the reason I don't like AI art. It's not that AI artists lack for the big, numinous irreducible feelings. I firmly believe we all have those. The problem is that an AI prompt has very little communicative intent and nearly all (but not every) good piece of art has more communicative intent than fits into an AI prompt.

If you'd like an essay-formatted version of this post to read or share, here's a link to it on pluralistic.net, my surveillance-free, ad-free, tracker-free blog:

https://pluralistic.net/2025/03/25/communicative-intent/#diluted

Image: Cryteria (modified) https://commons.wikimedia.org/wiki/File:HAL9000.svg

CC BY 3.0 https://creativecommons.org/licenses/by/3.0/deed.en

[nostalgebraist]

In the comments of the LW xpost of the void, Janus writes

[models trained post-bing ai] have a tendency to "roleplay" Sydney when they're acting like chatbots, leading to misaligned behaviors. One way to address this is to penalize any mention of Sydney or Sydney-like behavior. This may generalize to the model being unwilling to even talk about Sydney or acknowledge what happened. But it is less likely to actually erase its knowledge of Sydney, especially if it was so salient that it often roleplayed/identified as Sydney earlier in pre-training.

Is this broadly accurate, would you say? And is there a reason ai companies do this in training, instead of e.g. stripping out any prior ai-user transcripts from the common crawl as part of broader filtering of the dataset, so the model has "no preconceptions" tying it to older models?

I think this is broadly accurate, yes.

On the filtering question, I think the answer is just that it would be fairly difficult/costly/time-consuming in practice, and the companies just don't care that much.

(Also, they might be worried that such filtering would adversely impact the model's practical usefulness. If you can avoid it, you typically don't want to make your model confused/ignorant about some real-world topic, especially if it's a topic that users are likely to bring up when talking to an LLM-based chatbot.)

The datasets used for pretraining are so huge that any kind of filtering or preprocessing applied to the whole dataset is typically pretty simplistic and "dumb," at least compared to the kinds of magic we expect from things like LLMs these days.

In cases where the methodology is publicly known – which is a significant caveat! – a representative type of filtering involves using relatively weak (but cheap) ML models to label whether the text relates to some broad topic like "computer science," or whether it's "toxic" (in the peculiar sense of that word used in ML, see here), or whether it looks like an outlier with respect to some smaller dataset trusted to contain mostly "good" text (whatever that means). These models inevitably make mistakes – both false positives and false negatives – and you can't really expect them to filter out absolutely everything that matches the condition, it's more that using them is a big improvement over doing nothing at all to filter on the category of interest.

But if you really want to make a model that doesn't know about some relatively well-known aspect of the real world, despite having very strong general knowledge in other respects... then you'd need to be much subtler and more precise about your filtering, I'd expect. And that's going to be nontrivially costly in the best case; in the worst case it may not even be possible.

Like, where exactly do you stop? If you just filter transcripts involving recent chatbots, how do you know whether something is such a transcript (in many cases this is obvious, but in many others it isn't!). Should you filter out any text in which someone quotes something a chatbot said? What about texts that describe chatbot behaviors in detail without quoting them? If you want to be doctrinaire about eliminating knowledge of chatbot behavior, you might have to go this far or even further – but at this point, we're filtering many texts that would otherwise be very high-value, like academic papers that convey important information about recent ML progress, news stories about how LLMs are impacting real people, a lot of the content on various blogs you (tumblr user kaiasky) personally think are good and worth reading, etc.

IIRC Janus and others have speculated that even if you did this "perfectly," the model would still be able to sense the "topic-shaped hole" in the data, and form some sort of representation of the fact that something is missing and maybe some of that thing's properties (derivable from the "shape of the hole," e.g. facts like "there are weirdly few public communications about AI after ~2021 despite indirect indications that the field was having more real-world impact than ever before"). I think something like this is probably at least kinda true, at least in the limit of arbitrarily strong base models... but also I doubt we'll ever find out, because there just isn't enough of an incentive to run a costly and risky "experiment" of this kind.

[mariacallous]

The rapid spread of artificial intelligence has people wondering: Who’s most likely to embrace AI in their daily lives? Many assume it’s the tech-savvy—those who understand how AI works—who are most eager to adopt it.

Surprisingly, our new research, published in the Journal of Marketing, finds the opposite. People with less knowledge about AI are actually more open to using the technology. We call this difference in adoption propensity the “lower literacy-higher receptivity” link.

This link shows up across different groups, settings, and even countries. For instance, our analysis of data from market research company Ipsos spanning 27 countries reveals that people in nations with lower average AI literacy are more receptive toward AI adoption than those in nations with higher literacy.

Similarly, our survey of US undergraduate students finds that those with less understanding of AI are more likely to indicate using it for tasks like academic assignments.

The reason behind this link lies in how AI now performs tasks we once thought only humans could do. When AI creates a piece of art, writes a heartfelt response, or plays a musical instrument, it can feel almost magical—like it’s crossing into human territory.

Of course, AI doesn’t actually possess human qualities. A chatbot might generate an empathetic response, but it doesn’t feel empathy. People with more technical knowledge about AI understand this.

They know how algorithms (sets of mathematical rules used by computers to carry out particular tasks), training data (used to improve how an AI system works), and computational models operate. This makes the technology less mysterious.

On the other hand, those with less understanding may see AI as magical and awe inspiring. We suggest this sense of magic makes them more open to using AI tools.

Our studies show this lower literacy-higher receptivity link is strongest for using AI tools in areas people associate with human traits, like providing emotional support or counseling. When it comes to tasks that don’t evoke the same sense of humanlike qualities—such as analyzing test results—the pattern flips. People with higher AI literacy are more receptive to these uses because they focus on AI’s efficiency, rather than any “magical” qualities.

It’s Not About Capability, Fear, or Ethics

Interestingly, this link between lower literacy and higher receptivity persists even though people with lower AI literacy are more likely to view AI as less capable, less ethical, and even a bit scary. Their openness to AI seems to stem from their sense of wonder about what it can do, despite these perceived drawbacks.

This finding offers new insights into why people respond so differently to emerging technologies. Some studies suggest consumers favour new tech, a phenomenon called “algorithm appreciation,” while others show skepticism, or “algorithm aversion.” Our research points to perceptions of AI’s “magicalness” as a key factor shaping these reactions.

These insights pose a challenge for policymakers and educators. Efforts to boost AI literacy might unintentionally dampen people’s enthusiasm for using AI by making it seem less magical. This creates a tricky balance between helping people understand AI and keeping them open to its adoption.

To make the most of AI’s potential, businesses, educators and policymakers need to strike this balance. By understanding how perceptions of “magicalness” shape people’s openness to AI, we can help develop and deploy new AI-based products and services that take the way people view AI into account, and help them understand the benefits and risks of AI.

And ideally, this will happen without causing a loss of the awe that inspires many people to embrace this new technology.

[shewriter]

How To Combat AI 

AI Awareness Part 2

I have decided to share resources on generative AI (e.g., OpenAI, ChatGPT, etc.). I try to keep my lists of resources as neutral as possible, without pushing my own personal opinions on others, so that you guys can make your own choices based on your preferences. But full disclosure: I am against AI. So the resources in this list lean toward being anti-AI.

Whatever your opinion on AI may be, I still think awareness is important. Knowledge is power. When you are armed with proper information, then you are able to make informed choices. And once you have that information, you can choose to do with it what you want.

The world of AI is constantly changing, so this is not a complete list by any means. This list is part two of my AI awareness resources. It focuses on the uses and effects of AI, and includes ways to deal with it in everyday life.

The Uses and Effects of AI:

Explained: Generative AI’s Environmental Impact An article by MIT that explains how AI technology affects the environment, particularly in its use of electricity and water.

How C.AI Works and Why It’s Unethical A Tumblr thread that explains how the website Character AI works and why it’s problematic. Includes a link to a video on how AI chatbots learn. Also includes a discussion on the subject of relying on AI chatbots for emotional support.

Critical Thinking Skills vs. Using AI A post that discusses the importance of using critical thinking skills versus using AI. Includes a link to a study on skill atrophy, and a link to an article that discusses the different ways using AI affects critical thinking skills.

MIT recently conducted a brain scan study on the effects of using ChatGPT. The results showed that overusing ChatGPT caused cognitive atrophy, or a reduction in critical thinking skills. For more information on the study: ChatGPT May Be Eroding Critical Thinking Skills, According to a New MIT Study An article that discusses a brain scan study on the effects of using ChatGPT that was recently conducted by MIT. MIT Study: Your Brain on ChatGPT A summary of the MIT study on the brain and ChatGPT, originally posted on Twitter/X. Tumblr Post on MIT Study A Tumblr thread about the MIT study on the brain and ChatGPT. Includes a link to the thread on Twitter/X and a link to the study.

AI Assistants Keep Joining Meetings A Tumblr thread that discusses an article on how AI bots (“AI note-taking tools”) have automatically joined in on video conferences, usually company meetings. Includes a link to the original article.

How To Combat AI:

How to Tell If That Post of Advice Is AI Provides some tips and advice for how to tell if a blog post or online article was written with AI.

Microsoft recently launched its own AI technology called Copilot. It was included in Microsoft Word under an option called “connected experiences.” It was widely assumed that this meant the AI was scraping Word documents. For more information on this: Word ‘AI Free’ Cure Is Worse than the Disease An article that explains how the advice posted online for turning off the “connected experiences” option can disable other features. Also explains that Microsoft is likely not scraping your documents. No, Microsoft Isn’t Using Your Office Docs to Train Its AI An article that explains Microsoft isn’t using costumers’ data to train its AI technology. Includes links to other sources. Copilot in Microsoft 365 Apps for Home: Your Data and Privacy Microsoft’s statement on how Copilot interacts with users’ data.

Microsoft launched another AI feature called Recall for Windows 11. It records users’ activity by periodically taking screenshots of their PC screen. It’s an option you can opt out of. For more information on this: Microsoft’s New ‘Recall’ AI in Windows 11 Tracks Every Action on Your PC An article that explains how Recall works. Tumblr Thread A Tumblr thread that briefly explains how Recall works. Explains the steps for how to turn it off. Includes images.

Google & Gemini AI Google recently launched its own AI technology called Gemini AI. It tracks users’ activity and works as an AI assistant in Google Docs. It’s an option you can opt out of. This Tumblr thread explains the steps for how to turn off Gemini in the privacy settings of your Google account. Includes images.

The Google search engine is now filled with AI-generated results, including images and even websites. There are browser extensions that can block out certain results. For more information: Tumblr Thread: Ublock A Tumblr thread on the browser extension Ublock. Includes a link to a list of AI websites to block. Includes mainly images/infographics. Tumblr Thread: uBlacklist A Tumblr thread on uBlacklist, a browser extension for Firefox. Includes a link to a list of AI websites to block.

Google & “before:2023” A Tumblr thread that explains if you type the phrase “before:2023” before your search string, you will get less AI-generated results. Includes images.

Google & Verbatim A Tumblr thread that explains how to better filter your Google search results by using an option called “verbatim.” Also includes a link to an additional browser extension for “verbatim” for Firefox.

Google Alternative: &udm=14 A Tumblr thread that links to a search engine called “&udm=14” that can filter out AI-generated results. Also includes a link to an additional browser extension for “&udm=14” for Firefox.

Search Engines That Don’t Use AI A list of search engines that do not incorporate AI.

AI vs. Templates Explains that people who use AI could use templates for their writing needs instead, which can be found online.

I hope you find these resources helpful and informative. Part one of my AI awareness resources focused on how AI is affecting writing, editing, and publishing. It includes information on lawsuits involving AI and authors and their books. I’m going to try to keep that list updated as more information becomes available. You can read it here: AI: Writing, Editing, & Publishing (AI Awareness Part 1)

If you’d rather not use Google Docs or Microsoft Word for your writing, you can check out the list of alternatives that I shared: Helpful Websites & Apps for Writers

+

I’m a writer, poet, and editor. I share writing resources that I’ve collected over the years and found helpful for my own writing. If you like my blog, follow me for more resources! ♡

[justinspoliticalcorner]

Eric Levitz at Vox:

When Vice President Kamala Harris chose Tim Walz as her running mate, many pundits lamented her decision. In their view, the Democratic nominee should have chosen a vice presidential candidate who could mitigate her liabilities, and balance out her party’s ticket — such as Pennsylvania Gov. Josh Shapiro.

After all, Harris had been a liberal senator from one of America’s most left-wing states and then had run an exceedingly progressive primary campaign in 2020. To win over swing-state undecideds, she needed to demonstrate her independence from her party’s most radical elements. And selecting the popular governor of a purple state — who had defied the Democratic activist base on education policy and Israel’s war in Gaza— would do just that. Walz, in this account, was just another liberal darling: As Minnesota governor, he had enacted a litany of progressive policies, including restoring the voting rights of ex-felons and creating a refuge program for trans people denied gender-affirming care in other states. Picking Walz might thrill the subset of Americans who would vote for Harris even if she burned an American flag on live TV and lit a blunt with its flames. But it would do nothing to reassure those who heard two words they did not like in the phrase, “California liberal.”

But there is more than one way to balance a ticket. Or so Harris’s team believes, if the third night of the Democratic National Convention is any guide. On Wednesday night, Democrats used Walz’s nomination to associate their party with rural American culture and small-c conservative moral sentiments, while remaining true to a broadly progressive agenda. Walz may not be especially distinct from Harris ideologically. But he is quite different demographically and symbolically. Harris is the half-Jamaican, half-Indian daughter of immigrant college professors who grew up in the San Francisco Bay Area. Walz was born into a family whose roots in the United States went back to the 1800s, and raised in a Nebraska town of 400, where ethnic diversity largely consisted of several different flavors of Midwestern white (Walz himself is of German, Irish, Swedish, and Luxembourgish descent). Harris is an effortlessly cool veteran of red carpets. Walz is a dad joke that has attained corporal form.

In her person and biography, Harris represents the America that has benefited unequivocally from the transformations of the past half-century — the cosmopolitan, multicultural nation that has greeted the advance of racial and gender equality with relief, and the knowledge economy that’s taken to globalization with relish. Walz, by contrast, was shaped by the America that feels more at home in the world of yesterday, at least as it is nostalgically misremembered — a world where moral intuitions felt more stable, rural economies seemed more healthy, and the American elite looked more familiar; the America that put Donald Trump in the Oval Office, in other words. Or at least, the Harris campaign has chosen to associate Walz with all of that America’s iconography, attempting to make it feel as included in the Democratic coalition as possible — without actually ceding much ground to conservative policy preferences. The introduction to Walz’s speech Wednesday night looked like it could have been scripted by a chatbot asked to generate the antithesis of a “San Francisco liberal.” A video montage celebrated Walz’s diligent work on his family farm growing up, his service in the US military, skills as a marksman, and — above all — success as a football coach. Democrats leaned especially hard on that last, most American item on Walz’s resume. Just before the party’s vice presidential nominee took the mic, a group of his former players decked out in their gridiron garments marched on stage to a fight song (not to be confused with “Fight Song”).

[...] There is some basis for believing that Democrats might be able to win over a small but significant fraction of Republican-leaning independents by wrapping center-left policies in conservative packaging. Some political scientists have found that when moderate and conservative voters are presented with a progressive, Democratic economic policy idea — that is justified on the grounds that it will help uphold “the values and traditions that were handed down to us: hard work, loyalty to our country and the freedom to forge your own path” — some do respond favorably (as do liberal voters, who take no offense at such abstract, traditionalist pieties). Whether Walz tying himself to rural American symbology — or Harris tying herself to “Coach Walz” — will be enough to blunt Trump’s attacks on the Democratic nominee’s supposed “communism” remains to be seen. But the Democratic ticket is at least trying to make right-leaning Midwesterners feel like they belong (even if they do not think like Democrats do).

Tim Walz’s DNC speech last night reflects a broader trend of Democrats reclaiming freedom and patriotism while also selling its liberal agenda. #DNC2024 #HarrisWalz2024

See Also:

HuffPost: With Kamala Harris, It’s Cool For Liberals To Be Patriotic Again

[crystal-wingeddragon-spikes]

Talking about Art reference and some source.

In this age of AI Art and corporate Art industry, I think it is more important than ever to cite your inspiration and reference, to both works and creators, than to call Ai art soul less or not art.

Before AI Art, Pinterest is also one of the greatest "art/reference middleman" that hoard all reference and disconnect artists from the useful source to learn instead of cherry-picking information that might be wrong or lacking context.

Before ChatBots scraping data and spew out recipe telling people to add bleach into egg mix, we have websites choke full of stolen recipes that came with pointless made up life story to add as many unnecessary keywords to Search Engine Optimization, written by unpaid interns.

What comes to mind is the "wolf skull" that is actually a badger skull and used wildly as a tattoo reference.

Or an art student who study horse muscles, and they mistakenly give the horse human muscle somewhere, also wildly used as a reference.

And the worst of all, tumblr, that feel like the last big website that allow me to curate my own user experience has notoriously awful search function (still not as bad as twitter). I couldn't to find the source for both incident, even if I am mostly sure I reblogged it.

Also, beside the inaccuracy, I want people to think of it as less, "I don't want to use AI Art (or use it as a reference) because they are worse." and more of a:

"We are losing our respect and connection to people who search and publish information. Because of all of these middlemen, Google, Pinterest, and now AI tools, who love to obfuscate information source they took from someone's hard work."

"We are losing out on chances to connect to each other and build community based on shared goals."

"We are losing out developing respect of knowledge, critical thinking skills, and curiosity because we are under a false premise that all knowledge is easily available/easily created."

"We are losing our chance to decided to be someone who provide information and teaching instead of consuming and learning all the time. Unlike what the internet search engine and Chatbot, want to convince you, knowledge is hard-earned and not always available."

This is some art source I used:

Eh, this is a coral identification guild I used, because why not.

This one is from Australia, that I did not use, but truly appreciate how through it is.

Made into a very good, familiar website format:

I beg everyone to make The Internet a good place to share information and argue with each other in good faith again.

[anzuhan]

What are other vocaloids that anzu likes, besides Len? Does anzu knows any other vocaloids besides the main ones?

this is going to sound terrible but anzu's favorites besides len were miku and gumi.. ALSO VERY VERY VERY LONG TALK BELOW CUT!! AND WITH SOME ARTS!!!

also as for vocaloids.. anzu isnt that well versed in the vocaloid realm that much bar others that also have a higher level in popularity so to speak.. like gakupo, meika mikoto & hime and so on !!! anzu actually has more knowledge on utauloids... ww. but before going over those, anzu also particularly liked fukase, vflower (anzu drew her sometimes but those are probably lost to time </3) and VERY particularly oliver !!! anzu doesnt usually refer to characters as anzus son or daughter or so, but oliver is the singular one anzu actively just calls son. ww... well its mostly attachment because back in 2018... long time ago! anzu actually made a chatbot ai based off of him! it used the at the time ai database for the google assistant.. and anzus own stupid code that was.. surely was 😭😭 (nothing recycled is all anzu is meaning to say! all his training data was anzu's own written lines for him, as well as his interactions with anzu and anzu's friends.. we were not a good influence to him oops) anzu later ported him to discord in 2020! hes inactive now because the site anzu hosted him on has since been sold to google and anzu would have to pay monthly to run him.. which isnt worth it for a personal silly thing.. so also tldr google killed anzus son in 2022 ( ´_ゝ`) below is anzus favorite quote ever of all time from him

anzu is actually very happy teto is experiencing the fame shes getting right now!! she deserves it !! and from ageless shapeshifter vampire to 31y.o chimera.. anzu looks up to her. we truly both are jokes as well, so theres no better idol out there for anzu to look up to.........!

euhmm.. anzu was very very ill about matsudappoiyo!!!!! specifically. if u care. anzu has not drawn him since 2020, as of now. well 💔

NOW CUE THE ARTS (some from any and all times)

(also many. many. are lost to time. also the year or approximate in alt text!!!!)

[krunal-vyas]

Hire Dedicated Developers in India Smarter with AI

Hire dedicated developers in India smarter and faster with AI-powered solutions. As businesses worldwide turn to software development outsourcing, India remains a top destination for IT talent acquisition. However, finding the right developers can be challenging due to skill evaluation, remote team management, and hiring efficiency concerns. Fortunately, AI recruitment tools are revolutionizing the hiring process, making it seamless and effective.

In this blog, I will explore how AI-powered developer hiring is transforming the recruitment landscape and how businesses can leverage these tools to build top-notch offshore development teams.

Why Hire Dedicated Developers in India?

1) Cost-Effective Without Compromising Quality:

Hiring dedicated developers in India can reduce costs by up to 60% compared to hiring in the U.S., Europe, or Australia. This makes it a cost-effective solution for businesses seeking high-quality IT staffing solutions in India.

2) Access to a Vast Talent Pool:

India has a massive talent pool with millions of software engineers proficient in AI, blockchain, cloud computing, and other emerging technologies. This ensures companies can find dedicated software developers in India for any project requirement.

3) Time-Zone Advantage for 24/7 Productivity:

Indian developers work across different time zones, allowing continuous development cycles. This enhances productivity and ensures faster project completion.

4) Expertise in Emerging Technologies:

Indian developers are highly skilled in cutting-edge fields like AI, IoT, and cloud computing, making them invaluable for innovative projects.

Challenges in Hiring Dedicated Developers in India

1) Finding the Right Talent Efficiently:

Sorting through thousands of applications manually is time-consuming. AI-powered recruitment tools streamline the process by filtering candidates based on skill match and experience.

2) Evaluating Technical and Soft Skills:

Traditional hiring struggles to assess real-world coding abilities and soft skills like teamwork and communication. AI-driven hiring processes include coding assessments and behavioral analysis for better decision-making.

3) Overcoming Language and Cultural Barriers:

AI in HR and recruitment helps evaluate language proficiency and cultural adaptability, ensuring smooth collaboration within offshore development teams.

4) Managing Remote Teams Effectively:

AI-driven remote work management tools help businesses track performance, manage tasks, and ensure accountability.

How AI is Transforming Developer Hiring

1. AI-Powered Candidate Screening:

AI recruitment tools use resume parsing, skill-matching algorithms, and machine learning to shortlist the best candidates quickly.

2. AI-Driven Coding Assessments:

Developer assessment tools conduct real-time coding challenges to evaluate technical expertise, code efficiency, and problem-solving skills.

3. AI Chatbots for Initial Interviews:

AI chatbots handle initial screenings, assessing technical knowledge, communication skills, and cultural fit before human intervention.

4. Predictive Analytics for Hiring Success:

AI analyzes past hiring data and candidate work history to predict long-term success, improving recruitment accuracy.

5. AI in Background Verification:

AI-powered background checks ensure candidate authenticity, education verification, and fraud detection, reducing hiring risks.

Steps to Hire Dedicated Developers in India Smarter with AI

1. Define Job Roles and Key Skill Requirements:

Outline essential technical skills, experience levels, and project expectations to streamline recruitment.

2. Use AI-Based Hiring Platforms:

Leverage best AI hiring platforms like LinkedIn Talent Insightsand HireVue to source top developers.

3. Implement AI-Driven Skill Assessments:

AI-powered recruitment processes use coding tests and behavioral evaluations to assess real-world problem-solving abilities.

4. Conduct AI-Powered Video Interviews:

AI-driven interview tools analyze body language, sentiment, and communication skills for improved hiring accuracy.

5. Optimize Team Collaboration with AI Tools:

Remote work management tools like Trello, Asana, and Jira enhance productivity and ensure smooth collaboration.

Top AI-Powered Hiring Tools for Businesses

LinkedIn Talent Insights — AI-driven talent analytics

HackerRank — AI-powered coding assessments

HireVue — AI-driven video interview analysis

Pymetrics — AI-based behavioral and cognitive assessments

X0PA AI — AI-driven talent acquisition platform

Best Practices for Managing AI-Hired Developers in India

1. Establish Clear Communication Channels:

Use collaboration tools like Slack, Microsoft Teams, and Zoom for seamless communication.

2. Leverage AI-Driven Productivity Tracking:

Monitor performance using AI-powered tracking tools like Time Doctor and Hubstaff to optimize workflows.

3. Encourage Continuous Learning and Upskilling:

Provide access to AI-driven learning platforms like Coursera and Udemy to keep developers updated on industry trends.

4. Foster Cultural Alignment and Team Bonding:

Organize virtual team-building activities to enhance collaboration and engagement.

Future of AI in Developer Hiring

1) AI-Driven Automation for Faster Hiring:

AI will continue automating tedious recruitment tasks, improving efficiency and candidate experience.

2) AI and Blockchain for Transparent Recruitment:

Integrating AI with blockchain will enhance candidate verification and data security for trustworthy hiring processes.

3) AI’s Role in Enhancing Remote Work Efficiency:

AI-powered analytics and automation will further improve productivity within offshore development teams.

Conclusion:

AI revolutionizes the hiring of dedicated developers in India by automating candidate screening, coding assessments, and interview analysis. Businesses can leverage AI-powered tools to efficiently find, evaluate, and manage top-tier offshore developers, ensuring cost-effective and high-quality software development outsourcing.

Ready to hire dedicated developers in India using AI? iQlance offers cutting-edge AI-powered hiring solutions to help you find the best talent quickly and efficiently. Get in touch today!

[furiouslycrispylocket]

Thrissur’s Go-To Destination for Tech: Find the Best Laptop Shop and Store Today

In today’s fast-paced world, a laptop isn’t just a gadget—it’s your personal workstation, your classroom, your entertainment hub, and even your business partner. Whether you're a student attending online classes, a working professional meeting deadlines, a gamer chasing high frame rates, or a designer working on resource-intensive projects, the quality and reliability of your laptop make a big difference. And when you’re ready to buy one, finding the right laptop shop in Thrissur can make your purchase smoother, smarter, and more satisfying.

Thrissur, famously known as Kerala's cultural capital, is now also emerging as a strong player in the tech retail space. If you're planning to upgrade or purchase a new laptop, you don’t need to look far—this city has it all.

Why Thrissur Is a Smart Place to Buy a Laptop

As the demand for quality tech increases, Thrissur has kept pace with its expanding range of electronics and IT stores. The presence of IT parks, academic institutions, freelancers, and startups has created a constant demand for high-performance devices. As a result, several trusted laptop store in Thrissur outlets have emerged, offering everything from budget-friendly student models to premium business and gaming machines.

Unlike online marketplaces where support ends at delivery, a good local laptop store provides personalized advice, immediate service, hands-on product demos, and direct after-sales support. It’s no wonder that Thrissur residents prefer going local for such a major tech purchase.

What to Expect from a Top Laptop Store in Thrissur

Choosing the right store is crucial if you want to make the best use of your budget and time. Here are a few signs that you’ve found the right laptop shop in Thrissur:

✅ Wide Selection of Brands and Models

You should be able to choose from top global laptop brands like HP, Dell, Lenovo, ASUS, Acer, and Apple—all in one place. Whether you need an everyday workhorse, a lightweight ultrabook, or a high-performance gaming laptop, a good store will stock it.

✅ Knowledgeable, Friendly Staff

Not sure if you need an i5 or i7 processor? Confused about RAM or SSD options? Expert staff at a reputable laptop store in Thrissur will explain everything clearly, helping you choose based on your needs and not just marketing hype.

✅ Competitive Pricing & Offers

A trusted local store can often match or beat online prices—plus you get to take advantage of bundle deals like free antivirus software, laptop bags, and extended warranty options.

✅ Onsite Services & Warranty Support

A major benefit of choosing a physical store is access to fast, reliable service. Most good stores will help you with laptop setup, OS installation, data transfer, and troubleshooting even after purchase.

✅ EMI & Finance Options

Many laptop shop in Thrissur locations partner with finance companies or offer bank EMIs, making even high-end models accessible to students, freelancers, and small business owners.

Why Buying Local Is Better Than Online

There’s no denying the convenience of online shopping, but when it comes to buying laptops, there are advantages to going offline—especially in a tech-savvy market like Thrissur.

Live Demos: You get to test the laptop’s display, keyboard, build quality, and speed before buying.

Instant Delivery: No waiting for couriers—you walk out with your laptop.

Human Interaction: You can ask real questions and get real answers, not chatbot replies.

Trust and Reliability: If something goes wrong, you know exactly where to go and whom to talk to.

Most In-Demand Laptop Categories in Thrissur

Different buyers have different needs. A full-service laptop store in Thrissur caters to:

Students: Affordable laptops with long battery life and portability.

Working Professionals: Business-class models with speed, security, and durability.

Gamers: High-performance laptops with dedicated GPUs and cooling systems.

Designers & Editors: Powerful machines with high-res displays and multi-core processors.

Home Users: Simple and budget-friendly devices for browsing, emails, and media.

Additional Services You Can Expect

Besides selling laptops, many top shops offer value-added services like:

Laptop servicing and repairs

Upgrades (RAM, SSD, etc.)

Accessories like cooling pads, external keyboards, and webcams

Software installation (MS Office, antivirus, etc.)

Data recovery and backup solutions

Choosing a local laptop shop in Thrissur gives you the benefit of one-on-one tech support that big online retailers simply can’t offer.

Final Thoughts: Make Your Laptop Purchase Count

Buying a laptop is an investment—not just of money, but also of trust. And trust is best placed in stores that value your needs, offer transparent pricing, and stand by you long after the sale.

So the next time you're in search of the perfect device, skip the hassle of endless comparisons and delivery delays. Walk into a trusted laptop store in Thrissur and walk out with a device you can count on—backed by local expertise, immediate support, and the satisfaction of a smart purchase.

[maxsmith007-blog]

What Are the Key Features That Drive High First Contact Resolution in Omnichannel Services?

First Contact Resolution (FCR) is one of the most important metrics in omnichannel customer service. It measures the ability to resolve client issues in the very first interaction—without follow-ups, call-backs, or escalations. High FCR improves customer satisfaction, reduces costs, and strengthens brand credibility. According to SQM Group, a 1% rise in FCR equals a 1% improvement in customer satisfaction. In a multi-channel environment, this directly impacts business results.

1. Centralized Customer Data Across Channels

FCR starts with complete visibility. Agents must be able to view customer interactions across phone, chat, email, social media, and self-service tools. When all data is stored in one place, service becomes faster and more accurate. A Forrester report shows 68% of customers feel frustrated when they have to repeat themselves due to disconnected systems. A unified view enables smoother conversations and quicker resolutions.

2. Smart Routing and Agent Matching

Directing queries to the most suitable agent from the start improves FCR significantly. Intelligent routing systems use AI to match issues with agents who have the right skills and knowledge. This reduces call transfers and escalations. Genesys research shows that companies using skill-based routing see up to a 25% increase in FCR. The right match reduces response time and improves Omnichannel Customer Service

satisfaction.

3. Real-Time Support Tools for Agents

Real-time tools help agents respond faster and with more accuracy. AI-driven prompts, knowledge base suggestions, and sentiment analysis make it easier for agents to understand the issue and act immediately. When agents have access to a shared knowledge base, across all channels, they can provide consistent, correct answers—whether through chat, phone, or social support.

4. Proactive Communication Reduces Inbound Volume

Companies can help inbound traffic and increase FCR by detecting issues ahead of time and informing the customer of the problem in advance. Issues are resolved without customers having to contact them using alerts, Frequently Asked Questions, and real-time service upgrades. According to Aberdeen Group, implementing proactive support strategies decrease subsequent contacts by up to 20%.

5. Channel-Specific Setup & Optimization

Different channels of services are optimal when used with appropriate tools and workflow. Live chat is more effective when scripted pick-ups and typing previews are involved and social media care should have sentiment detecting tools and rapid tagging. As compared to the one-process-fits-all approach, optimizing each channel separately promptly resolves issues and results in an increased FCR.

6. Feedback-Driven Improvement

Tracking FCR in real time helps teams see what’s working and what isn’t. In an Omnichannel Customer Service environment, post-interaction surveys and automated reports help identify issues that weren’t resolved the first time—across voice, chat, email, and social channels. Companies that use FCR data to improve agent training and service design see better long-term results. Top teams treat FCR as a core performance KPI.

7. Smooth Transition from Bots to Humans

Automation is useful, but some problems need a human touch. When chatbots hand off to live agents, all the information should carry over—without the customer having to repeat their issue. Gartner reports that this kind of seamless handoff increases Omnichannel Customer Service

satisfaction by 15%. It also cuts down resolution time.

Omnichannel Customer Service Platforms That Support High FCR

Companies that want to improve FCR at scale need strong platforms. Suma Soft, Salesforce Service Cloud, Freshdesk, and Genesys Cloud offer end-to-end Omnichannel Customer Service.

High First Contact Resolution is not just a metric—it’s a customer experience standard. With the right omnichannel tools, businesses can reduce support costs, improve satisfaction, and strengthen brand trust.

[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.

[scribblesbyavi]

Hi. This is out of the topic of what you usually post. I just wanna know your stance about AI. Do you think AI is helpful? Or do you think it's worsen our learning skill by relying too much in it?

I use AI as a tool for my projects. It is good for my productivity, definitely helpful when you know how to use it. While I can decide on the ideas, the business logic and how to implement something in my project AI agents like claude or gpt4o helps me with things that are more of labour intensive than creative. So it can help you get things done faster like you have an assistant. In fact we are working on a new tech based product and for that we are hosting opensource AI models like deepseek and ollama on our own server and using MCP (model context protocol) to get better results based on our data so that we can develop our product further and introduce new features like AI dashboard, custom/ personalised suggestions, analytics, chatbot etc. which can add more value to our product and help our future users. I won't tell you to rely on it as you must be able to have the basic knowledge of what you are working on so that you can solve things when the AI fails to do so. It is not there yet but the development is crazy though. I would say not using it might be a big mistake too because paradigm shifts only come once in a while.

[mariacallous]

The Food and Drug Administration has been meeting with OpenAI to discuss the agency’s use of AI, according to sources with knowledge of the meetings. The meetings appear to be part of a broader effort at the FDA to use this technology to speed up the drug approval process.

“Why does it take over 10 years for a new drug to come to market?” wrote FDA commissioner Marty Makary on X on Wednesday. “Why are we not modernized with AI and other things? We’ve just completed our first AI-assisted scientific review for a product and that’s just the beginning.”

The remarks followed an annual meeting of the American Hospital Association earlier this week, where Makary spoke about AI’s potential to aid in the approval of new treatments for diabetes and certain types of cancer.

Makary did not specify that OpenAI was part of this initiative. But sources close to the project say a small team from OpenAI has met with the FDA and two associates of Elon Musk's so-called Department of Government Efficiency multiple times in recent weeks. The group has discussed a project called cderGPT, which likely stands for Center for Drug Evaluation, which regulates over-the-counter and prescription drugs in the US, and Research GPT. Jeremy Walsh, who was recently named as the FDA’s first-ever AI officer, has led the discussions. So far, no contract has been signed.

OpenAI declined to comment.

Walsh has also met with Peter Bowman-Davis, an undergraduate on leave from Yale who currently serves as the acting chief AI officer at the Department of Health and Human Services, to discuss the FDA’s AI ambitions. Politico first reported the appointment of Bowman-Davis, who is part of Andreessen Horowitz’s American Dynamism team.

When reached via email on Wednesday, Robert Califf, who served as FDA commissioner from 2016 to 2017 and again from 2022 through January, said the agency’s review teams have been using AI for several years now. “It will be interesting to hear the details of which parts of the review were ‘AI assisted’ and what that means,” he says. “There has always been a quest to shorten review times and a broad consensus that AI could help.”

Before Califf departed the agency, he said the FDA was considering the various ways AI could be used in internal operations. “Final reviews for approval are only one part of a much larger opportunity,” he says.

To be clear, using AI to assist in final drug reviews would represent a chance to compress just a small part of the notoriously long drug-development timeline. The vast majority of drugs fail before ever coming up for FDA review.

Rafael Rosengarten, CEO of Genialis, a precision oncology company, and a cofounder and board member of the Alliance for AI in Healthcare, says he’s in favor of automating certain tasks related to the drug-review process but says there should be policy guidance around what kind of data is used to train AI models and what kind of model performance is considered acceptable. “These machines are incredibly adept at learning information, but they have to be trained in a way so they're learning what we want them to learn,” he says.

He could see AI being used more immediately to address certain “low-hanging fruit,” such as checking for application completeness. “Something as trivial as that could expedite the return of feedback to the submitters based on things that need to be addressed to make the application complete,” he says. More sophisticated uses would need to be developed, tested, and proved out.

An ex-FDA employee who has tested ChatGPT as a clinical tool says the propensity of AI models to fabricate convincing information raises questions about how reliable such a chatbot might be. “Who knows how robust the platform will be for these reviewers’ tasks,” the ex-staffer says.

The FDA review process currently takes about a year, but the agency has several existing mechanisms to expedite that timeline for promising drugs. One of those is the fast track designation, which is for products designed to treat a serious condition and fill an unmet medical need. Another is the breakthrough therapy designation, created in 2012, which allows the FDA to grant priority review to drug candidates that may provide a substantial benefit to patients compared to current treatment options.

“Ensuring medicines can be reviewed for safety and effectiveness in a timely manner to address patient needs is critical,” says Andrew Powaleny, a spokesperson for the industry group PhRMA, via email. “While AI is still developing, harnessing it requires a thoughtful and risk-based approach with patients at the center.”

The FDA is already doing its own research on potential uses of AI. In December 2023 the agency advertised a fellowship for a researcher to develop large language models for internal use. “During participation in this program, the fellow will engage in various activities that include but are not limited to the applications of LLMs for precision medicine, drug development and regulatory science,” the fellowship description says.

In January, OpenAI announced ChatGPT Gov, a self-hosted version of its chatbot designed to comply with government regulations. The startup also said it was working toward getting FedRAMP moderate and high accreditations for ChatGPT Enterprise, which would allow it to handle sensitive government data. FedRAMP is a compliance program used by the federal government to assess cloud products; unless authorized through this program, a service cannot hold federal data.

[nsimsouthex233]

Digital Marketing Skills to Learn in 2025

Key Digital Marketing Skills to Learn in 2025 to Stay Ahead of Competition The digital marketing landscape in 2025 is rapidly changing, driven by the technological advancements, shifting consumer behavior, and the growing power of artificial intelligence. Competition and career resilience require acquiring expertise in the following digital marketing skills.

Data Analysis and Interpretation

Data is the backbone of modern marketing strategies. The ability to collect, analyze, and make informed decisions based on large sets of data sets great marketers apart. Proficiency in analytical software like Google Analytics and AI-driven tools is critical in measuring campaign performance, optimizing strategies, and making data-driven decisions. Predictive analytics and customer journey mapping are also becoming more critical for trend spotting and personalization of user experience.

Search Engine Optimization (SEO) and Search Engine Marketing (SEM)

SEO is still a fundamental skill, but the landscape is evolving. The marketer now has to optimize for traditional search engines, voice search, and even social media, as Gen Z increasingly relies on TikTok and YouTube as search tools. Keeping up with algorithm updates, keyword research skills, and technical SEO skills is essential to staying visible and driving organic traffic.

Artificial Intelligence (AI) and Machine Learning (ML)

AI and ML are revolutionizing digital marketing through the power to enable advanced targeting, automation, and personalization. Marketers will need to leverage AI in order to segment audiences, design content, deploy predictive analytics, and build chatbots. Most crucial will be understanding how to balance AI-based automation with human, authentic content.

Content Generation and Storytelling

Content is still king. Marketers must be great at creating great copy, video, and interactive content that is appropriate for various platforms and audiences. Emotionally resonant storytelling and brand affection are more critical than ever, particularly as human-created content trumps AI-created content consistently.

Social Media Strategy and Social Commerce Social media is still the foremost driver of digital engagement. Mastering techniques constructed for specific platforms—such as short-form video, live stream, and influencing with influencers—is critical. How to facilitate direct sales through social commerce, built on combining commerce and social interactions, is an area marketers must master.

Marketing Automation

Efficiency is the most critical in 2025. Marketing automation platforms (e.g., Marketo and HubSpot) enable marketers to automate repetitive tasks, nurture leads, and personalize customer journeys at scale.

UX/UI Design Principles

A seamless user experience and a pleasing design can either make or destroy online campaigns. Having UX/UI basics in your knowledge and collaborating with design teams ensures that marketing campaigns are both effective and engaging.

Ethical Marketing and Privacy Compliance

With data privacy emerging as a pressing issue, marketers must stay updated on laws like GDPR and CCPA. Ethical marketing and openness foster trust and avoid legal issues.

To lead in 2025, digital marketers will have to fuse technical skills, creativity, and flexibility. By acquiring these high-impact capabilities-data analysis, SEO, AI, content development, social strategy, automation, UX/UI, and ethical marketing-you'll be at the edge of the constantly evolving digital space