#NLP in AI applications

[harshathusm]

Role of natural language processing in Artificial Intelligence

The role of natural language processing (NLP) in artificial intelligence is crucial for enabling machines to understand and respond to human language. NLP powers applications like chatbots, virtual assistants, and language translation, making AI more interactive and user-friendly. By analyzing and interpreting text or speech, NLP enhances customer support, content creation, and data insights. Its integration with AI offers businesses tools for better communication and decision-making.

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[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.

The math is simple:

➝ AI isn't slowing down

➝ Skills gap is widening

➝ Opportunities are multiplying

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.

Start seeing it as your rocket fuel.

Because in 2025:

➝ Learners will lead

➝ Adapters will advance

➝ Complainers will vanish

The choice?

It's always been yours.

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

//

Repost this ⇄

//

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[govindhtech]

Mastering Language Models: AI Conversation Building Block

What are language Models?

One kind of machine learning model that has been taught to perform a probability distribution over words is called a language models. In short, a model uses the context of the provided text to forecast the next best word to fill in a blank space in a sentence or phrase.

Since language models enable computers to comprehend, produce, and analyze human language, they are an essential part of natural language processing (NLP). A large text dataset, such a library of books or articles, is mostly used to train them. The next word in a phrase or the creation of fresh, grammatically and semantically coherent material are then predicted by models using the patterns they have learned from this training data.

The Capabilities of language models

Have you ever noticed how the Microsoft SwiftKey and Google Gboard keyboards have clever capabilities that automatically suggest whole phrases while you’re composing text messages? Among the many applications of language models is this one.

Many NLP activities, including text summarization, machine translation, and voice recognition, require it.

Creation of content: Content creation is one of the domains where language models excel. This involves using the information and terms supplied by people to generate whole texts or portions of them. Press releases, blog entries, product descriptions for online stores, poetry, and guitar tabs are just a few examples of the kind of content that may be found there.

POS (part-of-speech) labeling: World-class POS tagging performance is achieved by extensive use of this model. POS tagging assigns a noun, verb, adjective, or other part of speech to each document word. The models can estimate a word’s POS based on its context and the words that surround it in a phrase since they have been trained on vast volumes of annotated text data.

Addressing questions: It is possible to train language models to comprehend and respond to queries both with and without the provided context. They may respond in a variety of ways, such by selecting from a list of possibilities, paraphrasing the response, or extracting certain words.

Summary of a text: Documents, articles, podcasts, movies, and more may all be automatically condensed into their most essential chunks using language models. Models may be used to either summarize the material without using the original language or to extract the most significant information from the original text.

Examination of sentiment: Because it can capture the tone of voice and semantic orientation of texts, the language modeling technique is a strong choice for sentiment analysis applications.

AI that can converse: Voice-enabled apps that need to translate voice to text and voice versa inevitably include language models. This may respond to inputs with relevant text as part of conversational AI systems.

Translation by machine: Machine translation has been improved by ML-powered language models’ capacity to generalize well to lengthy contexts. It may learn the representations of input and output sequences and provide reliable results rather than translating text word for word.

Finishing the code: The capacity of recent large-scale language models to produce, modify, and explain code has been outstanding. They can only, however, translate instructions into code and verify it for mistakes to finish basic programming jobs.

Key aspects of language models

1. Natural Language Processing (NLP)

Language models use NLP approaches to analyze human language and extract meaningful components from words, phrases, and paragraphs.

2. Training

The models are trained on large datasets of text sources such as books, webpages, papers, and more. Training helps them anticipate the next word in a phrase and write like humans by teaching grammar, context, and word connections.

3. Deep Learning

Most recent language models, such as GPT and BERT, rely on deep learning, particularly transformer topologies, to effectively interpret language patterns. Transformers are effective at addressing long-term text dependencies.

4. Applications

It may be used for different activities, such as:

Text creation: Writing tales, poetry, and essays.

Translation: Language translation.

Natural discussion with chatbots.

Summarization: Shortening lengthy articles.

Answering questions with knowledge.

5. Examples

Famous language models include:

GPT-3 by OpenAI generates human-like writing and powers AI apps.

BERT by Google: Useful for search and sentiment analysis, optimized for linguistic context.

The Text-to-Text Transfer Transformer (T5) treats all NLP issues as text production problems and is used for many purposes.

It can allow robots to read, write, and speak like humans.

The Future of language models

Historically, AI business applications concentrated on predictive activities including forecasting, fraud detection, click-through rates, conversions, and low-skill job automation. These restricted uses took great effort to execute and interpret, and were only practical at large scale. However, massive language models altered this.

Large language models like GPT-3 and generative models like Midjouney and DALL-E are transforming the sector, and AI will likely touch practically every part of business in the next years.

Top language model trends are listed below.

Scale and intricacy: The quantity of data and parameters learned on language models will certainly scale.

Multimodality: Integration of language models with visuals, video, and music is intended to enhance their worldview and allow new applications.

Explaining and showing: With more AI in decision-making, ML models must be explainable and transparent. Researchers are trying to make language models more understandable and explain their predictions.

Conversation: It will be utilized increasingly in chatbots, virtual assistants, and customer service to interpret and react to user inputs more naturally.

Language models are projected to improve and be utilized in more applications across fields.

Read more on govindhtech.com

[meelsport]

How AI is Revolutionizing Voice Search Technology

The Hidden Link Between AI Voice Search and SEO: What You Need to Know AI-powered voice search is revolutionizing how users interact with technology, turning searches into seamless, conversational experiences.

Voice search is transforming how we interact with technology, turning searches into effortless conversations. No more typing—just speak to your device, and AI does the rest. In this blog, we’ll explore the evolution of voice search. We’ll discuss how AI powers it and why businesses must adapt to stay competitive. The Evolution of AI Voice Search Technology AI voice search technology has come a…

[neturbizenterprises]

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[newspatron]

Chat GPT-4o: The AI Revolution Unveiled

What do you think of Chat GPT-4o? Share your thoughts and experiences in the comments!

Close all those open tabs in your browser (and mobile apps!), because things are about to get seriously interesting in the world of AI. OpenAI has just unveiled GPT-4o, and it’s not just an upgrade – it’s a game-changer. Picture this: an AI that understands not only your words but also your voice, your photos, and even your videos. It’s like stepping into the future, and it’s all happening right…

[enterprise-cloud-services]

From mundane to mesmerizing: AI writing redefines conversation landscapes. Dive into the realm of innovation and engagement.

[rubylogan15]

Unleash the power of AI writing in transforming conversations. Dive deep into the realms of innovation and engagement.

[paulcook159-blog]

Explore the transformative impact of AI writing on conversation, driving innovation and enhancing communication.

[kasparlavik]

Explore the transformative impact of AI writing on conversation, driving innovation and enhancing communication.

[dieterziegler159]

Explore the transformative impact of AI writing on conversation, driving innovation and enhancing communication.

[public-cloud-computing]

Explore the transformative impact of AI writing on conversation, driving innovation and enhancing communication.

[spearheadtechnology]

Digital Transformation Consultant Services Company in Dallas

Spearhead Technology initiates and fast-tracks change for 360° digital transformations, driving long-lasting value and smart outcomes for enterprises and people.​

[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.

[pankukaushal]

𝐅𝐮𝐭𝐮𝐫𝐞 𝐨𝐟 𝐀𝐈-:

𝐖𝐡𝐚𝐭 𝐢𝐬 𝐀𝐫𝐭𝐢𝐟𝐢𝐜𝐢𝐚𝐥 𝐈𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞 ?

Artificial intelligence (AI) refers to computer systems capable of performing complex tasks that historically only a human could do, such as reasoning, making decisions, or solving problems.

𝐂𝐮𝐫𝐫𝐞𝐧𝐭 𝐀𝐈 𝐂𝐚𝐩𝐚𝐛𝐢𝐥𝐢𝐭𝐢𝐞𝐬-:

AI today exhibits a wide range of capabilities, including natural language processing (NLP), machine learning (ML), computer vision, and generative AI. These capabilities are used in various applications like virtual assistants, recommendation systems, fraud detection, autonomous vehicles, and image generation. AI is also transforming industries like healthcare, finance, transportation, and creative domains. 

𝐀𝐈 𝐀𝐩𝐩𝐬/𝐓𝐨𝐨𝐥𝐬-:

ChatGpt, Gemini, Duolingo etc are the major tools/apps of using AI.

𝐑𝐢𝐬𝐤𝐬 𝐨𝐟 𝐀𝐈-:

1. Bias and Discrimination: AI algorithms can be trained on biased data, leading to discriminatory outcomes in areas like hiring, lending, and even criminal justice. 

2. Security Vulnerabilities: AI systems can be exploited through cybersecurity attacks, potentially leading to data breaches, system disruptions, or even the misuse of AI in malicious ways. 

3. Privacy Violations: AI systems often rely on vast amounts of personal data, raising concerns about privacy and the potential for misuse of that data. 

4. Job Displacement: Automation driven by AI can lead to job losses in various sectors, potentially causing economic and social disruption. 

5. Misuse and Weaponization: AI can be used for malicious purposes, such as developing autonomous weapons systems, spreading disinformation, or manipulating public opinion. 

6. Loss of Human Control: Advanced AI systems could potentially surpass human intelligence and become uncontrollable, raising concerns about the safety and well-being of humanity. 

𝐅𝐮𝐭𝐮𝐫𝐞 𝐨𝐟 𝐀𝐈:-

Healthcare:AI will revolutionize medical diagnostics, personalize treatment plans, and assist in complex surgical procedures. 

Workplace:AI will automate routine tasks, freeing up human workers for more strategic and creative roles. 

Transportation:Autonomous vehicles and intelligent traffic management systems will enhance mobility and safety. 

Finance:AI will reshape algorithmic trading, fraud detection, and economic forecasting. 

Education:AI will personalize learning experiences and offer intelligent tutoring systems. 

Manufacturing:AI will enable predictive maintenance, process optimization, and quality control. 

Agriculture:AI will support precision farming, crop monitoring, and yield prediction.