#Natural language processing (NLP)

[neotechnomagick]

The Intersection of NLP Eye Movement Integration and the Lesser Banishing Ritual of the Pentagram: A Comparative Analysis

Introduction

Neuro-Linguistic Programming (NLP) has long been associated with cognitive restructuring and psychotherapeutic interventions. One particularly compelling technique within NLP is Eye Movement Integration (EMI), which utilizes guided eye movements to access and integrate fragmented or traumatic memories. Simultaneously, the Lesser Banishing Ritual of the Pentagram (LBRP), a foundational ceremonial magick practice from the Western esoteric tradition, employs ritualized gestures and visualizations of pentagrams to clear and harmonize psychological and spiritual space. This essay explores the striking structural similarities between EMI and the LBRP and considers the possibility that both methods engage hemispheric synchronization and cognitive integration in analogous ways.

The Structure of EMI and LBRP

Eye Movement Integration (EMI) involves tracing figure-eight (∞) or infinity-loop movements with the eyes while engaging in conscious recall of emotionally charged experiences. According to NLP theories, this process activates both hemispheres of the brain, allowing for greater coherence in how memories are processed and reintegrated (Bandler & Grinder, 1982). EMI techniques suggest that deliberate movement across specific spatial axes stimulates neural pathways responsible for sensory and emotional integration (Ward, 2002).

Similarly, the LBRP involves a structured sequence of visualized pentagrams drawn in the cardinal directions, accompanied by divine names and ritual gestures. This sequence is designed to invoke protective forces and create a harmonized psychic field. According to the Golden Dawn tradition (Cicero, 1998), the act of tracing the pentagram is intended to engage multiple layers of cognition: visual-spatial processing, linguistic invocation, and kinesthetic anchoring.

Shared Cognitive and Psychological Mechanisms

Bilateral Stimulation and Neural Integration

Both EMI and LBRP involve movements across spatial dimensions that engage both brain hemispheres.

EMI’s horizontal and diagonal eye movements mimic the process of following the pentagram’s path in ritual, possibly facilitating left-right hemisphere synchronization (Bandler & Grinder, 1982).

Symbolic Encoding and Cognitive Anchoring

EMI often integrates positive resource states during the eye-tracing process, allowing new neurological connections to be formed. The LBRP similarly encodes protective and stabilizing forces into the practitioner’s consciousness through repeated use of divine names and pentagram tracings (Cicero, 1998).

The act of drawing a pentagram in ritual space may serve as an ‘anchor’ to a specific neurological or psychological state, much like NLP anchoring techniques (Hine, 1995).

Emotional and Energetic Reset

EMI is used to defragment and neutralize distressing memories, reducing their disruptive impact. The LBRP, in an esoteric context, serves to “banish” intrusive or unwanted energies, clearing space for more intentional psychological and spiritual work (Cicero, 1998).

Practitioners of both techniques report a sense of clarity, release, and heightened awareness following their use (Hine, 1995).

Implications for Technomagick and NLP Applications

The intersection of NLP and ceremonial magick suggests that structured, repetitive movement combined with intentional focus has profound cognitive and psychological effects. In a Neo-Technomagickal framework, this insight could lead to further experimentation with custom sigils designed for EMI-style integration, or AI-assisted visualization tools for ritual practice.

Future research could examine:

Whether specific geometries (e.g., pentagrams, hexagrams) in ritual movement impact cognitive processing similarly to NLP techniques.

The effectiveness of LBRP-derived rituals in clinical or self-development contexts, particularly for trauma resolution.

The potential for EEG and neurofeedback studies comparing EMI and ritualized eye-tracing methods.

Conclusion

While originating from vastly different paradigms, NLP’s EMI technique and the LBRP share fundamental principles of hemispheric integration, cognitive anchoring, and structured movement through symbolic space. Whether consciously designed or stumbled upon through esoteric practice, these methodologies hint at deep underlying mechanisms of the human mind’s capacity for self-regulation and transformation. Understanding their similarities provides an opportunity to bridge the domains of magick, psychology, and neuroscience, opening new avenues for exploration in both mystical and therapeutic contexts.

G/E/M (2025)

References

Bandler, R., & Grinder, J. (1982). Reframing: Neuro-Linguistic Programming and the Transformation of Meaning. Real People Press.

Cicero, C. & Cicero, S. T. (1998). Self-Initiation into the Golden Dawn Tradition. Llewellyn Publications.

Hine, P. (1995). Condensed Chaos: An Introduction to Chaos Magic. New Falcon Publications.

Ward, K. (2002). Mind Change Techniques to Keep the Change. NLP Resources.

[futuretiative]

Tom and Robotic Mouse | @futuretiative

Tom's job security takes a hit with the arrival of a new, robotic mouse catcher.

TomAndJerry #AIJobLoss #CartoonHumor #ClassicAnimation #RobotMouse #ArtificialIntelligence #CatAndMouse #TechTakesOver #FunnyCartoons #TomTheCat

Keywords: Tom and Jerry, cartoon, animation, cat, mouse, robot, artificial intelligence, job loss, humor, classic, Machine Learning Deep Learning Natural Language Processing (NLP) Generative AI AI Chatbots AI Ethics Computer Vision Robotics AI Applications Neural Networks

Tom was the first guy who lost his job because of AI

(and what you can do instead)

⤵

"AI took my job" isn't a story anymore.

It's reality.

But here's the plot twist:

While Tom was complaining,

others were adapting.

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Here's the truth:

The future doesn't care about your comfort zone.

It rewards those who embrace change and innovate.

Stop viewing AI as your replacement.

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Because in 2025:

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It goes even further - now AI has been trained to create consistent.

//

Repost this ⇄

//

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[pencilrecords]

Growing in a tall man's shadow

There is a debate happening in the halls of linguistics and the implications are not insignificant.

At question is the idea of recursion: since the 1950s, linguists have held that recursion is a defining characteristics of human language.

What happens then, when a human language is found to be non-recursive?

Here, Noam Chomsky, who first placed the idea of recursion on the table, is the tall man.

And, Daniel Everett, a former missionary to the Piraha tribe in the Amazon forest, is the upstart.

At stake is one of the most important ideas in modern linguistics: recursion.

Does a human language have to be recursive? That's the question Everett poses; and advances the argument that recursion is not inherent to being human.

From the Youtube description of the documentary:

Deep in the Amazon rainforest, the Pirahã people speak a language that defies everything we thought we knew about human communication. No words for colors. No numbers. No past. No future. Their unique way of speaking has ignited one of the most heated debates in linguistic history. For 30 years, one man tried to decode their near-indecipherable language—described by The New Yorker as “a profusion of songbirds” and “barely discernible as speech”. In the process, he shook the very foundations of modern linguistics and challenged one of the most dominant theories of the last 50 years: Noam Chomsky’s Universal Grammar. According to this theory, all human languages share a deep, innate structure—something we are born with rather than learn. But if the Pirahã language truly exists outside these rules, does it mean that everything we believed about language was wrong? If so, one of the most powerful ideas in linguistics could crumble.

Documentary: The Amazon Code

Directed by: Randal Wood, Michael O’Neill

Production : Essential Media, Entertainment Production, ABC Australia, Smithsonian Networks & Arte France

=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-==-=-=-=-=-=-=-

I think that there is more to come with this story, so here is a running list of info by and from people who interact with the idea on a regular basis and actually know what they're talking about:

The Battle of the Linguists - Piraha Part 2 by K. Klein

[aionlinemoney]

Perplexity AI: A Game-changer for Accurate Information

Artificial Intelligence has revolutionized how we access and process information, making tools that simplify searches and answer questions incredibly valuable. Perplexity AI is one such tool that stands out for its ability to quickly answer queries using AI technology. Designed to function as a smart search engine and question-answering tool, it leverages advanced natural language processing (NLP) to give accurate, easy-to-understand responses. In this blog will explore Perplexity’s features, its benefits, and alternatives for those considering this tool.

What is Perplexity AI?

Perplexity AI is a unique artificial intelligence tool that provides direct answers to user questions. Unlike traditional search engines, which display a list of relevant web pages, This tool explains user queries and delivers clear answers. It gathers information from multiple sources to provide users with the most accurate and useful responses.

Using natural language processing, This tool allows users to ask questions in a conversational style, making it more natural than traditional search engines. Whether you’re conducting research or need quick answers on a topic, This tool simplifies the search process, offering direct responses without analyzing through numerous links or websites. This tool was founded by Aravind Srinivas, Johnny Ho, Denis Yarats, and Andy Konwinski in 2022. This tool has around 10 million monthly active users and 50 million visitors per month.

Features of Perplexity AI

Advanced Natural Language Processing (NLP):

Perplexity AI uses NLP, which enables it to understand and explain human language accurately. This allows users to phrase their questions naturally, as they would ask a person, and receive relevant answers. NLP helps the tool analyze the condition of the query to deliver accurate and meaningful responses.

Question-Answering System:

Instead of presenting a list of web results like traditional search engines, Perplexity AI provides a clear and short answer to your question. This feature is particularly helpful when users need immediate information without the difficulty of navigating through multiple sources.

Real-Time Data:

Perplexity AI uses real-time information, ensuring that users receive the most current and relevant answers. This is essential for queries that require up-to-date information, such as news events or trends.

Mobile and Desktop Availability:

This tool can be accessible on both desktop and mobile devices, making it suitable for users to get answers whether they’re at their computer or on their mobile. Artificial intelligence plays an important role in the tool.

Benefits of  using Perplexity AI:

Time-Saving

One of the biggest advantages of using Perplexity AI is the time it saves. Traditional search engines often require users to browse through many web pages before finding the right information. This tool eliminates this by providing direct answers, reducing the time spent on searching and reading through multiple results.

User-Friendly Interface

With its conversational and automatic format, the Perplexity machine learning tool is incredibly easy to use. Whether you are a tech expert or new to artificial intelligence-powered tools, its simple design allows users of all experience levels to navigate the platform easily. This is the main benefit of this tool.

Accurate Information

With the ability to pull data from multiple sources, Perplexity artificial intelligence provides all-round, accurate answers. This makes it a valuable tool for research purposes, as it reduces the chances of misinformation or incomplete responses.

Versatile ( Adaptable )

Perplexity AI is versatile enough to be used by a variety of individuals, from students looking for quick answers for their studies to professionals who need honest data for decision-making. Its adaptability makes it suitable for different fields, including education, business, and research.

Alternatives to Perplexity AI:

ChatGPT

ChatGPT is a tool developed by OpenAI, This is an advanced language model capable of generating human-like responses. While it does not always provide direct answers to accurate questions as Perplexity artificial intelligence does, ChatGPT is great for engaging in more detailed, conversational-style interactions.

Google Bard

Google Bard focuses on providing real-time data and generating accurate responses. This tool translates into more than 100 languages. Like Perplexity AI, it aims to give users a more direct answer to their questions. This is also a great artificial intelligence tool and alternative to Perplexity AI.

Microsoft Copilot

This tool generates automated content and creates drafts in email and Word based on our prompt. Microsoft Copilot has many features like data analysis, content generation, intelligent email management, idea creation, and many more. Microsoft Copilot streamlines complex data analysis by simplifying the process for users to manage extensive datasets and extract valuable insights.

Conclusion:

Perplexity AI is a powerful and user-friendly tool that simplifies the search process by providing direct answers to queries. Its utilization of natural language processing, source citation, and real-time data leading tool among AI-driven search platforms. Staying updated on the latest AI trends is crucial, especially as the technology evolves rapidly. Read AI informative blogs and news to keep up-to-date. Schedule time regularly to absorb new information and practice with the latest AI innovations! Whether you’re looking to save time, get accurate information, or improve your understanding of a topic, Perplexity AI delivers an efficient solution.

[softmaxai]

NLP, an acronym for Natural Language Processing, is the computer’s ability to acknowledge human speech and its meaning. NLP solutions providers in India helps Businesses using NLP solutions to improve the website flow and enhance conversions, chatbots for customer support and it saves time and money.

[algoworks]

Transform the way we interact with machines and elevate your business with the power of Natural Language Processing! 🤖🗣️🚀

[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.

[anonymousdormhacks]

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[alignminds]

Role of Natural Language Processing in AI in 2025

In 2025, Natural Language Processing (NLP) plays a pivotal role in AI, enabling machines to understand, interpret, and generate human language. NLP powers smarter chatbots, real-time translation, sentiment analysis, and voice assistants, transforming user interactions across industries.

[kickrtechnology1]

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[olivergisttv]

Using AI to Predict SEO Trends for Blog Content

Introduction Staying ahead in SEO isn’t about guessing—it’s about knowing. In 2025, with search engines getting smarter and competition stiffer, content creators need more than instinct to thrive. That’s where Artificial Intelligence (AI) steps in. Imagine having a digital crystal ball that can read search patterns, predict Google’s next algorithm update, and even tell you what your readers will…

[usaii]

Comprehending AI-Powered Product Specification Document Assistant | USAII®

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PSD Management, AI-powered PSD assistant, AI assistant, Natural Language Processing (NLP), AI model, natural language processing

[thoratketan]

Veterinary Services Strategies In The Global Market: Key Insights From The 2025 Report

The global Natural Language Processing (Nlp) In Healthcare And Life Sciences Market, valued at USD 4.94 billion in 2023, is projected to reach USD 62.7 billion by 2032, growing at a remarkable compound annual growth rate (CAGR) of 32.6% from 2024 to 2032. This surge is driven by the increasing need for advanced technologies to manage large volumes of healthcare data, the growing adoption of artificial intelligence (AI) solutions, and the need to streamline healthcare processes through automated systems.

Get Free Sample Report on Natural Language Processing (NLP) in Healthcare and Life Sciences Market

Natural Language Processing, a subset of AI, enables computers to understand, interpret, and manipulate human language. In healthcare and life sciences, NLP plays a vital role in extracting valuable insights from unstructured data sources such as medical records, clinical notes, research papers, and patient interactions. As the healthcare sector continues to expand and generate vast amounts of data, NLP has become a pivotal technology in helping healthcare professionals make more informed decisions, improve patient outcomes, and enhance operational efficiencies.

Key Drivers of Market Growth

Explosion of Healthcare Data: Healthcare data is growing at an unprecedented rate due to the increasing digitization of healthcare systems, electronic health records (EHR), and the rise of telemedicine and remote patient monitoring. A large portion of this data is unstructured, such as doctors' notes, medical transcriptions, and patient conversations. NLP provides a powerful tool to analyze and extract meaningful information from this unstructured data, enabling healthcare providers to make quicker, data-driven decisions.

Rising Demand for Personalized Healthcare: Personalized medicine is gaining significant traction in healthcare as treatments are increasingly tailored to individual patients. NLP plays a key role in this shift by enabling the analysis of diverse data sources, including genomic data, medical records, and patient-reported outcomes. By leveraging NLP, healthcare providers can gain deeper insights into a patient's medical history, genetics, and lifestyle factors, helping to create more effective personalized treatment plans and improve patient outcomes.

Growth in AI and Machine Learning Adoption: The integration of AI and machine learning (ML) with NLP technologies is one of the major factors propelling the market. These technologies allow healthcare providers to automate routine tasks, such as processing clinical notes, coding diagnoses, and identifying patterns in patient data. The combination of NLP and ML can also help with predictive analytics, improving decision-making and enabling proactive care management. The increasing adoption of AI across healthcare sectors, from hospitals to pharmaceutical research, is accelerating the use of NLP solutions.

Improved Operational Efficiencies: NLP has proven to be highly effective in improving operational efficiencies within healthcare organizations. By automating administrative tasks, such as billing, coding, and data entry, NLP helps healthcare providers reduce the administrative burden on staff, streamline workflows, and improve resource allocation. This efficiency not only saves time and costs but also enhances the quality of care delivered to patients.

Increased Focus on Healthcare Research and Drug Discovery: NLP is playing an increasingly important role in accelerating research and drug discovery processes. The ability to quickly analyze vast amounts of scientific literature, clinical trials data, and electronic health records is enhancing the pace of discovery. NLP allows researchers to extract valuable insights from medical literature, identify emerging trends, and analyze clinical trial results in real-time. As the need for faster drug development and research increases, NLP’s capabilities are being harnessed to bring new treatments to market more efficiently.

Regulatory Support and Government Initiatives: Governments around the world are taking steps to support the integration of AI technologies in healthcare. In the U.S., the FDA has been exploring the use of AI and NLP to improve drug development, while various global healthcare agencies are increasingly focusing on digital health and data-driven healthcare solutions. This regulatory support has spurred innovation in the NLP field, leading to more robust, safe, and effective NLP solutions tailored to the healthcare and life sciences sectors.

Key Segments:

By Technique

Smart Assistance

Optical Character Recognition

Auto Coding

Text Analytics

Speech Analytics

Classification and Categorization

By End-use

Providers

Payers

Life Science Companies

Others

Make Enquiry about Natural Language Processing (NLP) in Healthcare and Life Sciences Market

Key Players

Key Service Providers/Manufacturers

IBM Watson Health

Merative

Linguamatics

Haptik

Deepset

Microsoft

Amazon Web Services (AWS)

Google Health

Oracle

Nabla

Corti

Tortus

Grove AI

Infinitus Systems

Regard

Lumeris

Heidi

CitiusTech

Owkin

Insilico Medicine

Conclusion

The Natural Language Processing (NLP) in healthcare and life sciences market is poised for explosive growth over the next decade, fueled by the increasing volume of healthcare data, the rising demand for personalized care, and advancements in AI technology. With a projected market size of USD 62.7 billion by 2032, NLP is set to play a crucial role in transforming healthcare operations, improving patient outcomes, and accelerating research and drug development. As more healthcare providers, pharmaceutical companies, and research institutions adopt NLP technologies, the market will continue to expand, offering innovative solutions to some of the healthcare industry's most pressing challenges.

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