#natural language processing

[from-my-linguistics-textbook]

sheep?!

[rislas]

Termovision HUD from The Terminator (1984) A head-up display (HUD) is a transparent display that presents data over a visual screen. A Termovision refers to HUD used by Terminators to display analyses and decision options.

[inziladun]

When ChatGPT Broke an Entire Field: An Oral History | Quanta Magazine

Researchers in “natural language processing” tried to tame human language. Then came the transformer.

Asking scientists to identify a paradigm shift, especially in real time, can be tricky. After all, truly ground-shifting updates in knowledge may take decades to unfold. But you don’t necessarily have to invoke the P-word to acknowledge that one field in particular — natural language processing, or NLP — has changed. A lot. The goal of natural language processing is right there on the tin: making the unruliness of human language (the “natural” part) tractable by computers (the “processing” part). A blend of engineering and science that dates back to the 1940s, NLP gave Stephen Hawking a voice, Siri a brain and social media companies another way to target us with ads. It was also ground zero for the emergence of large language models — a technology that NLP helped to invent but whose explosive growth and transformative power still managed to take many people in the field entirely by surprise. To put it another way: In 2019, Quanta reported on a then-groundbreaking NLP system called BERT without once using the phrase “large language model.” A mere five and a half years later, LLMs are everywhere, igniting discovery, disruption and debate in whatever scientific community they touch. But the one they touched first — for better, worse and everything in between — was natural language processing. What did that impact feel like to the people experiencing it firsthand? Quanta interviewed 19 current and former NLP researchers to tell that story. From experts to students, tenured academics to startup founders, they describe a series of moments — dawning realizations, elated encounters and at least one “existential crisis” — that changed their world. And ours.

[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.

[sharks-pear-shake-shake]

I am very enthusiastic in NLP and LLMs and am always excited to learn something new about it! Plus since I happened to be a junior student major in AI, and regard NLP as my interest of research, I'm taking this is as a serious career choice.

I'll keep learning about innovations and philosophy in this field, and will always be happy communicate with fellas. But I found no NLP community in this platform (maybe I was blocked though, this app design looks weird), so I built a community to talk about technologies for natural language processing. Hope you'll like it and welcome to join the community.

New here btw, I am not sure if there are any convenient feature to share the community. I just put the link here.

https://www.tumblr.com/communities/nlp-pathways

[automatticdata]

AI-powered Typo Hunting: Trust Your Docs, Readers Will

Our documentation has a trust problem, and I just found 142 reasons why. It started with a silly typo I noticed on one of the pages – something like “cotnact” instead of “contact”.  It was quick to fix, but it got me thinking: are there more?Third‑party writing assistants are available as browser extensions, and we also have a spelling mistake checker available within Jetpack. With such tools,…

AI-powered Typo Hunting: Trust Your Docs, Readers Will

[anonymousdormhacks]

Google says alexander "cheated on his wife" hamilton rights ig

[frank-olivier]

Bayesian Active Exploration: A New Frontier in Artificial Intelligence

The field of artificial intelligence has seen tremendous growth and advancements in recent years, with various techniques and paradigms emerging to tackle complex problems in the field of machine learning, computer vision, and natural language processing. Two of these concepts that have attracted a lot of attention are active inference and Bayesian mechanics. Although both techniques have been researched separately, their synergy has the potential to revolutionize AI by creating more efficient, accurate, and effective systems.

Traditional machine learning algorithms rely on a passive approach, where the system receives data and updates its parameters without actively influencing the data collection process. However, this approach can have limitations, especially in complex and dynamic environments. Active interference, on the other hand, allows AI systems to take an active role in selecting the most informative data points or actions to collect more relevant information. In this way, active inference allows systems to adapt to changing environments, reducing the need for labeled data and improving the efficiency of learning and decision-making.

One of the first milestones in active inference was the development of the "query by committee" algorithm by Freund et al. in 1997. This algorithm used a committee of models to determine the most meaningful data points to capture, laying the foundation for future active learning techniques. Another important milestone was the introduction of "uncertainty sampling" by Lewis and Gale in 1994, which selected data points with the highest uncertainty or ambiguity to capture more information.

Bayesian mechanics, on the other hand, provides a probabilistic framework for reasoning and decision-making under uncertainty. By modeling complex systems using probability distributions, Bayesian mechanics enables AI systems to quantify uncertainty and ambiguity, thereby making more informed decisions when faced with incomplete or noisy data. Bayesian inference, the process of updating the prior distribution using new data, is a powerful tool for learning and decision-making.

One of the first milestones in Bayesian mechanics was the development of Bayes' theorem by Thomas Bayes in 1763. This theorem provided a mathematical framework for updating the probability of a hypothesis based on new evidence. Another important milestone was the introduction of Bayesian networks by Pearl in 1988, which provided a structured approach to modeling complex systems using probability distributions.

While active inference and Bayesian mechanics each have their strengths, combining them has the potential to create a new generation of AI systems that can actively collect informative data and update their probabilistic models to make more informed decisions. The combination of active inference and Bayesian mechanics has numerous applications in AI, including robotics, computer vision, and natural language processing. In robotics, for example, active inference can be used to actively explore the environment, collect more informative data, and improve navigation and decision-making. In computer vision, active inference can be used to actively select the most informative images or viewpoints, improving object recognition or scene understanding.

Timeline:

1763: Bayes' theorem

1988: Bayesian networks

1994: Uncertainty Sampling

1997: Query by Committee algorithm

2017: Deep Bayesian Active Learning

2019: Bayesian Active Exploration

2020: Active Bayesian Inference for Deep Learning

2020: Bayesian Active Learning for Computer Vision

The synergy of active inference and Bayesian mechanics is expected to play a crucial role in shaping the next generation of AI systems. Some possible future developments in this area include:

- Combining active inference and Bayesian mechanics with other AI techniques, such as reinforcement learning and transfer learning, to create more powerful and flexible AI systems.

- Applying the synergy of active inference and Bayesian mechanics to new areas, such as healthcare, finance, and education, to improve decision-making and outcomes.

- Developing new algorithms and techniques that integrate active inference and Bayesian mechanics, such as Bayesian active learning for deep learning and Bayesian active exploration for robotics.

Dr. Sanjeev Namjosh: The Hidden Math Behind All Living Systems - On Active Inference, the Free Energy Principle, and Bayesian Mechanics (Machine Learning Street Talk, October 2024)

Saturday, October 26, 2024