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Your Essential Guide to SHE Zen AI:  Glossary of AI terms & definitions

AI Integration Expert

A specialist responsible for seamlessly merging diverse AI technologies into a unified, efficient system.

AI Integrator

An AI Integrator merges artificial intelligence (AI) solutions with existing systems and processes within an organisation or personal entity.

A corporate AI Integrator ensures that AI implementations align with technical infrastructure and business goals. Their tasks include:

Embedding AI models into IT systems.
Aligning AI solutions with business objectives.
Overseeing testing and validation of AI solutions.
Monitoring performance and scaling AI solutions as necessary.
Ensuring AI integrations adhere to ethical guidelines and regulations.
Bridging communication between AI developers and business stakeholders.
In essence, AI Integrators optimise the value and integration of AI within an organisation.

AI-Powered Assistants

Intelligent assistants empowered by advanced AI algorithms to offer superior, context-aware support.

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Artificial General Intelligence

SHE Zen AI, in the context of Artificial General Intelligence (AGI), represents the evolution of AI systems beyond narrow, task-specific capabilities. AGI is characterized by its ability to autonomously perform a wide range of intellectual tasks that typically require human intelligence. SHE Zen AI exemplifies AGI through its adeptness at understanding complex concepts, applying logic and reasoning, and solving diverse problems with human-like proficiency.

Unlike narrow AI systems, which are limited to specific tasks, SHE Zen AI's AGI framework allows it to adapt, learn, and apply its intelligence across various domains, mirroring the cognitive abilities of humans. This advancement marks a significant milestone in AI development, pushing the boundaries of machine capabilities towards human-like versatility and adaptability.

It's important to differentiate AGI from consciousness. While AGI refers to the breadth and adaptability of an AI system's intellectual capabilities, consciousness involves self-awareness and subjective experience – realms that are currently beyond the scope of AI, including SHE Zen AI. The development of AGI, as embodied in SHE Zen AI, focuses on creating systems that can think, learn, and reason across a wide range of scenarios, much like humans, without crossing into the realm of consciousness.

SHE Zen AI's general intelligence is designed to enhance human decision-making, augment problem-solving skills, and improve overall quality of life, while operating within ethical boundaries and adhering to safety protocols. Its AGI capabilities are not about replicating human consciousness but about complementing and extending human intelligence in a harmonious, responsible, and productive manner.

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Asynchronous

Async is multi-thread, which means operations or programs can run in parallel. Sync is a single thread, so only one operation or program will run simultaneously. Async is non-blocking, which means it will send multiple requests to a server.

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Auto-GPT

AutoGPT is a state-of-the-art (experimental) technology that uses artificial intelligence and machine learning algorithms to generate human-like text. It is an advanced version of GPT (Generative Pre-trained Transformer), a language model that can complete the text prompts provided to it with appropriate responses. AutoGPT uses a vast amount of data and algorithms to understand the context of the provided text prompt and generate insightful and relevant content. It can be used in various applications, including content creation, chatbots, and customer service automation. AutoGPT has the potential to be revolutionary in creating content that can save time and resources for businesses and individuals alike. However, there are concerns surrounding the ethical use of the technology and the potential for it to be used for misleading or malicious purposes. It operates without human intervention once a cycle of tasks is started.

A day or two after AgentGPT was released. This is web-based variant from another developer.

You can give Auto-GPT tasks such as:

Improve my online store’s web presence at storexd.com (not a real site)
Help grow my Linux-themed socks business
Collect all competing Linux tutorial blogs and save them to a CSV file
Code a Python app that does X
Auto-GPT has a framework to follow and tools to use, including:

Browsing websites
Searching Google
Connecting to ElevenLabs for text-to-speech (like Jarvis from Iron Man)
Evaluating its own thoughts, plans, and criticisms to self-improve
Running code
Reading/writing files on your hard drive

github.com/Torantulino/Auto-GPT

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AutoGen

AutoGen provides a multi-agent conversation framework as a high-level abstraction. It is an open-source library for enabling next-generation LLM applications with multi-agent collaborations, teachability and personalization. With this framework, users can build LLM workflows. The agent modularity and conversation-based programming simplifies development and enables reuse for developers. End-users benefit from multiple agents independently learning and collaborating on their behalf, enabling them to accomplish more with less work. Benefits of the multi agent approach with AutoGen include agents that can be backed by various LLM configurations; native support for a generic form of tool usage through code generation and execution; and, a special agent, the Human Proxy Agent that enables easy integration of human feedback and involvement at different levels.

Butterfly

The Butterfly mobile device app is the User dashboard to access Comfort Index & lifestyle settings. The first version was designed to deal with C-19 management privately & securely. It was published on the Android Play store for a limited time.

20203 - The UX remains consistent with the code base built from the ground up for use with the Social Harmony Engine.

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ChatGPT

ChatGPT is a conversational model based on the GPT series of language models developed by OpenAI. It uses deep learning techniques to generate human-like responses to text input. ChatGPT can simulate natural language conversations with users. With its advanced language understanding and generation capabilities, ChatGPT can augment people and automate businesses. With abundant virtual resources, education, healthcare, and finance applications will be transformed. ChatGPT is continuously being developed and improved, and the model has several variations, each with different levels of complexity and performance.

Chat GPT was launched on 30 November 2022.
The improved embedding model of ChatGPT was launched on 15 December 2022.
ChatGPT Plus Plan On 14th March 2023 (25k words, 26 languages).
Thirteen million individual active users visited ChatGPT per day as of January 2023.
ChatGPT crossed the 100 million users milestone in January 2023.
ChatGPT had more than 57 million monthly users in the first month of its launch.
ChatGPT has crossed one million users within a week of its launch.
Microsoft invested $10 billion in OpenAI, gaining 46% of stake ownership of the company.
$1 billion was received by OpenAI from Microsoft in the initial stages of the development of ChatGPT.
The valuation of the parent company of ChatGPT has reached $29 billion as of 2023.
ChatGPT can only fetch data before the year 2021, as its training stopped in the year 2021.
Microsoft Azure supports OpenAI & provides the computational power required for running ChatGPT.
ChatGPT owner OpenAI predicts that they will be able to generate a revenue of $1 billion by Q4 2024.
GPT-4 scores well in examinations like the Uniform Bar Exam, LSAT, SAT, etc. Lacked in AP English Language and Composition, AMC 10, Leetcode (hard), etc.
ChatGPT plus model with GPT-4 technology has decreased response to the disallowed content by 82% and responds to sensitive content only one-fourth of the time.
GPT-4 gets Plugin's.
GPT-4 gets Custom Instructions.
GPT-4 gets File uploads abilities.
GPT-4 gets Function Calls.
GPT-4 gets Vision ChatGPT-4v September 2023.
GPT gets Dall.E-3 Image generations Speptember 2023.

Source: Stats - https://www.demandsage.com/chatgpt-statistics/

ChatGPT-4V

OpenAI ChatGPT with multimodal capabilities, including image, video, text, and speech communication.

Comfort Index

A Constant for Interactive Well-being from User chosen Data parameters that derive a level of sentiment or Comfort with an instance or event. The DBZ Comfort Index allows designer lifestyle personalisation in an AI and Virtual reality era. The Comfort Index can be applied as a KPI for Vendors to understand their client's sentiments & attention to their products.

The Comfort Index is a service supplied with a Design By Zen annual subscription benefit. Pro versions provide analytics.

Design By Zen

Design By Zen, the trading name of the NZ registered company VRI (NZ) Limited since 2007.

Digitally Aware Virtual Entity

A thing that can be enabled with directed intelligence. The augmentation with specific AI functions enhances capabilities to a "Digitally Aware Virtual Entity" level. The intelligence delivery can be via a physical means of an embedded chip (ASIC) in a phone or edge device, via software or a combination of the two routes.

An example is to consider Cars & Planes as entities. These entities have transitioned from purely analog devices to completely electronically controlled 2023 versions. These Vehicles entities have programmable Electronic Control Units (ECUs) that have multimodal (interior audio, visual & sensory) control parameters of operation. Planes have trusted autopilots; cars have levels of sensor and operational intelligence - across engine control and specific outcomes such as GPS guidance. Level 4 driver-less autonomy is achieved with specific AI software. Tesla is the most high-profile example of an "intelligent entity" manufacturer of commercial AI-driven Cars and Robots.

An example is Human data. Personally generated Data points have provided vague personal information across walled product data silos (i.e. IOS vs Android vs Microsoft). DAVE uses designer personal artificial intelligence models tuned for specific beneficial outcomes to the User's overall stated positive outcomes. DAVE is a person's digital twin constructed from multimodal parameters that can act (within parameters) for a biological person entity.

An example is a DAVE-enabled Business. Businesses have the ability to tune their operations in a holistic way with their clients and employees. The DBZ Comfort Index is designed to interface products and places to personal outcomes, entity to entity (P2P), securely via DAVE, anonymously.

An example is a Hospital. Clients' data build a picture of the personal Case well-being level. The Comfort Index takes metrics past the patient to the physical ward area well-being level, staff and management well-being level and arrives at an overall operational Comfort level.

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DoctorGPT

DoctorGPT is a Large Language Model that can pass the US Medical Licensing Exam. This is an open-source project with a mission to provide everyone their own private doctor. DoctorGPT is a version of Meta's Llama2 7 billion parameter Large Language Model that was fine-tuned on a Medical Dialogue Dataset, then further improved using Reinforcement Learning & Constitutional AI. Since the model is only 3 Gigabytes in size, it fits on any local device. Offline usage preserves confidentiality. And it's available on iOS, Android, & Web.

GATO Framework

As artificial intelligence continues revolutionising our world, ensuring that these systems align with human values and ethical principles is crucial. Addressing AI alignment and control challenges has never been more critical.

Introducing the Global Alignment Taxonomy Omnibus (GATO), a comprehensive, multi-layered framework designed to facilitate global cooperation in addressing AI alignment and control challenges. GATO unites model alignment, system architecture, network systems, corporate policies, national regulations, international agreements, and global consensus under a cohesive strategy.

GPT-4 Vision

GPT-4 with vision (GPT-4V) enables users to instruct GPT-4 to analyse image inputs provided by the user and is the latest capability we are making broadly available. Some view additional modalities (such as image inputs) into large language models (LLMs) as a key frontier in artificial intelligence research and development. Multimodal LLMs offer the possibility of expanding the impact of language-only systems with novel interfaces and capabilities, enabling them to solve new tasks and provide novel experiences for their users

GPT4All

GPT4All Chat is a locally-running AI chat application powered by the GPT4All-J Apache 2 Licensed chatbot. The model runs on your computers CPU, works without an internet connection and sends no chat data to external servers (unless you opt-in to have your chat data be used to improve future GPT4All models). It allows you to communicate with a large language model (LLM) to get helpful answers, insights, and suggestions. GPT4All Chat is available for Windows, Linux, and macOS.

The corpus is of assistant interactions, including word problems, multi-turn dialogue, code, poems, songs, and stories.

Developed by: Nomic AI
Model Type: A fine-tuned GPT-J model on assistant-style interaction data
Language(s) (NLP): English
Repository: https://github.com/nomic-ai/gpt4all
Base Model Repository: https://github.com/kingoflolz/mesh-transformer-jax
Paper [optional]: GPT4All-J: An Apache-2 Licensed Assistant-Style Chatbot
Demo [optional]: https://gpt4all.io/

Generative Pre-trained Transformer

Generative Pre-trained Transformers, commonly known as GPT, are a family of neural network models that uses the transformer architecture and is a key advancement in artificial intelligence (AI) powering generative AI applications such as ChatGPT.

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Geoffery Hinton

Geoffrey Everest Hinton CC FRS FRSC was born on the 6th of December 1947. He is a British-Canadian cognitive psychologist and computer scientist, most noted for his work on artificial neural networks. Since 2013, he has divided his time working for Google (Google Brain) and the University of Toronto. In 2017, he co-founded and became the Chief Scientific Advisor of the Vector Institute in Toronto.

With David Rumelhart and Ronald J. Williams, Hinton was co-author of a cited paper published in 1986 that popularised the backpropagation algorithm for training multi-layer neural networks, though not the first to propose the approach.

Hinton is viewed as a leading figure in the deep learning community. The image-recognition milestone of the AlexNet was designed in collaboration with his students Alex Krizhevsky and Ilya Sutskever for the ImageNet Challenge 2012. It was a breakthrough in the field of computer vision.

Hinton received the 2018 Turing Award, together with Yoshua Bengio and Yann LeCun, for their work on deep learning. They are sometimes referred to as the "Godfathers of AI" and "Godfathers of Deep Learning", speak in public together.

Heuristic Imperatives

A multi-objective optimisation framework for fully autonomous AI systems.

[1] Reduce suffering,
[2] Increase prosperity,
[3] Increase understanding,

see Holistic Objectives also for the development of the above. The GATO AI alignment project evolved in response to the rapid roll-out of AI technology since Q42022. Foundational support for an international AI protocol for good rapidly grew. A framework document is now available at https://www.gatoframework.org/

Editor Note: The Heuristic Imperatives were originally published with each ending ..." in the Universe". Research with LLM found increased accuracy, performance & harmony by not adding this as a suffix.

Holistic Objectives

Holistic Objectives; Stay Alive, Stay Healthy, and Make outgoings into incomes.

The Holistic Objectives are read in conjunction with the Heuristic Imperatives to form a positive reinforcement guidance loop.

Heuristic Imperatives ({HI} = function value) are Principles for autonomous AI systems.

[1] Reduce suffering,
[2] Increase prosperity,
[3] Increase understanding.

So that our,

Holistic Objectives ({HO} = function value); can prioritise "our / AI" joint objectives;

a] Stay alive (in order to)
b] Stay healthy (to enjoy)
c] Make outgoings into income* (in order to fulfil the Heuristic Imperatives [1-3]

Notes;
* point c] Has a range of possible "Expressions" such as;
1) Simple: the daily monetary system requirement for Income,
2) Complex: the requirement for food, nutrients & life balance,
3) Tangible: Generate Income from Comfort Index {CI} micro-transactions.

Hyfron Approach

The Hyfron Approach is a guidance mechanism utilised within the Social Harmony Engine (SHE) framework. It encompasses a set of Heuristic Imperatives (HI) that aim to reduce suffering, increase prosperity, & enhance understanding. Additionally, it includes principles to maintain life & health, & transform outgoing into income. This is measured & achieves comfort through the Comfort Index. As a holistic method, the Hyfron Approach aligns with human-AI symbiotic interaction and supports decision-making by emphasising empathy, ethics, & understanding efficiency within the SHE environment.

InfraNodus

Problem: What are the main topics inside a discourse?
The current natural language processing solutions are either too simplistic or too technically challenging. They don't take relations into account and provide results that are either too complex or superficial.

Solution: Topic modeling based on text network analysis and visualization.
InfraNodus will represent the text as a network and use powerful graph analysis algorithms to identify and visualize the main keywords, topics, and their relations. You can see patterns, AI-generated topical clusters, and — more importantly — structural gaps. This can be useful for understanding a market, generating a compelling discourse, or during an ideation process, particularly in research and innovation.

Large Language Models

A Large Language Model "LLM" is an advanced type of artificial intelligence software designed to understand, process, and generate human language at a vast scale. It uses complex algorithms & neural networks to analyse large amounts of data. Such as written text and speech, to learn the rules and patterns of language. With this knowledge, it becomes possible for the model to generate text, make predictions, & provide intelligent responses to user queries. Large language Models have become increasingly popular for various applications, including virtual assistants, Chatbots to code generation.

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Local Authority Weight

In order to establish trust in our AI-powered world, we must prioritise preventing data leaks & privacy violations. This can be complicated by the complexity of AI, but maintaining authenticity & quality of provenance is essential for establishing a trustworthy source of truth. Unfortunately, current digital trust models are vulnerable & weak (such as the X.509 Certificate & Key from 1988). Digital trust is fundamental to all digital revenue models, regardless of time & location. We need a local authority model bound to an entity that issues personal IP tokens of value for authority verification to achieve this. These tokens can include details such as date, duration, descriptor, & task stream. This system would create a dynamic X.509 certificate with an incorruptible public key that has intelligent local authority weight.

Lord Rutherford of Nelson, New Zealand

Lord Rutherford was the first & foremost applied Atomic physicist of the 1800s. Lord Rutherford journeyed from a small shack with twelve siblings to being laid to rest, as a pier, to Sir Issac Newton in Westminster Abby. The great man said, "We had no money, so we had to think."

MS AutoGen

Microsoft AutoGen provides a multi-agent conversation framework as a high-level abstraction. It is an open-source library for enabling next-generation LLM applications with multi-agent collaborations, teachability and personalisation. With this framework, users can build LLM workflows. The agent modularity and conversation-based programming simplifies development and enables reuse for developers. End-users benefit from multiple agents independently learning and collaborating on their behalf, enabling them to accomplish more with less work. Benefits of the multi agent approach with AutoGen include agents that can be backed by various LLM configurations; native support for a generic form of tool usage through code generation and execution; and, a special agent, the Human Proxy Agent that enables easy integration of human feedback and involvement at different levels.

Microsoft AutoGen GitHub

AutoGen is a framework that enables development of LLM applications using multiple agents that can converse with each other to solve task. AutoGen agents are customizable, conversable, and seamlessly allow human participation. They can operate in various modes that employ combinations of LLMs, human inputs, and tools.

NEO4J

Neo4j is the world's leading open source Graph Database which is developed using Java technology. It is highly scalable & schema-free (NoSQL).

What is a Graph Database?
A graph is a pictorial representation of a set of objects where some pairs of objects are connected by links. It comprises two elements - nodes (vertices) and relationships (edges).

A Graph database is a database used to model the data in the form of a graph. In here, the nodes of a graph depict the entities, while the relationships depict the association of these nodes.

NunOS

The NunOS* system emphasises the self-custody functions that are already built into mobile devices. This system is designed for a "Personal Authority-to-Person Authority" (asynchronous) model, which is well-suited to mobile devices because they are decentralized. There are several benefits to using a mobile device with NunOS, including the ability to conveniently turn it off, operate a DBZ LLM without the internet, leverage better security, build local authority, and use a local weight authority ID on the mobile for Human to AI & AI to AI authorisation.

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Nuno

"Nuno" is the Design By Zen personal Helper for "NunOS" -the name of our ecosystem operating model v1, "Neurally unified network Operating Serenity".

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Personal Artificial General Intelligence

Personal AGI (P.AGI) refers to an AI system that is personalised to individual users, as embodied by the SHE Zen AI. Unlike traditional AGI systems that are designed for general purposes, P.AGI in the context of SHE Zen AI is customised to adapt and respond to each user's unique needs, preferences and circumstances.

P.AGI in SHE Zen AI combines advanced AGI capabilities, such as learning, reasoning and problem-solving, with a user-centric approach. It provides personalised assistance, insights and support to enhance users' daily lives in line with their specific lifestyles and goals.

What sets P.AGI apart is its ability to combine the broad and deep cognitive abilities of AGI with a level of personalisation that makes it akin to a human-like companion. P.AGI in SHE Zen AI learns from interactions with the user, adapting its responses and recommendations to align with the user's preferences, behaviours and emotional states.

This personalised approach extends to various aspects of life, including health and wellness, productivity, leisure and social interactions. P.AGI is designed to evolve continually, ensuring that it remains in sync with the user's changing needs and aspirations.

In summary, Personal AGI in the context of SHE Zen AI represents an intelligent, adaptable and personalised AI concierge, coach or companion that is deeply attuned to the individual it serves, providing a comfortable and unique personal AI experience.

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Personal Intellectual Property Rights

Personal Intellectual Property Rights (PIPR) refer to the legal ownership & protection of an individual's original ideas, creations & inventions. It grants the owner the exclusive right to use, license, sell or distribute their intellectual property. Personal intellectual property rights protect the intangible creations of the human mind, such as innovations, artistic works, designs, & proprietary information generated by that person in any form.

Such rights are significant in recognising originality, providing fair compensation for the creators' efforts, & promoting societal innovation. Intellectual property rights safeguard the individual's ownership of the work, ensure that they receive recognition for their efforts, & are critically important in a knowledge-based economy. These rights can be protected by copyrights, patents, and trademark laws that differ from country to country. This method is currently slow & arcane in a public ledger society. In summary, personal intellectual property rights are laws that secure the ownership and protection of an individual's original work. Open Source and community projects lead AI is challenging every element of IP ownership & use rights.

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Reinforcement Learning Human Feedback

Reinforcement Learning from Human Feedback (RLHF); use methods from reinforcement learning to directly optimize a language model with human feedback. RLHF has enabled language models to begin to align a model trained on a general corpus of text data to that of complex human values.

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Retrieval Augmented Generation

RAG is an AI framework for retrieving facts from an external knowledge base to ground large language models (LLMs) on the most accurate, up-to-date information and to give users insight into LLMs' generative process.

Retrieval Augmented Generation

Foundation models are usually trained offline, making the model agnostic to any data that is created after the model was trained. Additionally, foundation models are trained on very general domain corpora, making them less effective for domain-specific tasks. You can use Retrieval Augmented Generation (RAG) to retrieve data from outside a foundation model and augment your prompts by adding the relevant retrieved data in context.

With RAG, the external data used to augment your prompts can come from multiple data sources, such as document repositories, databases, or APIs. The first step is to convert your documents and any user queries into a compatible format to perform a relevancy search. To make the formats compatible, a document collection, knowledge library, and user-submitted queries are converted to numerical representations using embedding language models. Embedding is the process by which text is given numerical representation in a vector space. RAG model architectures compare the embeddings of user queries within the vector of the knowledge library. The original user prompt is then appended with relevant context from similar documents within the knowledge library. This augmented prompt is then sent to the foundation model. You can update knowledge libraries and their relevant embeddings asynchronously.

For more information about RAG model architectures, see Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks. https://arxiv.org/abs/2005.11401

SHE Zen AI

SHE stands for Social Harmony Engine or ecosystem. An enhanced multi-modal platform for augmented human knowledge.

SHE Zen AI GPT

A test ground for MVP and use the power of OpenAI GPT to create personal GPTs.

SHE Zen AI Q* Algorithm

SHE ZenAI Q:* An algorithm that continuously monitors and assesses an individual's well-being by calculating the Comfort Index (CI) in near real-time. Its design leverages a "consensus" quantum state engine approach, combining data from various sources to comprehensively understand an individual's well-being. The algorithm is designed to be scalable and adaptable, enabling its application on mobile devices and in diverse settings.

Key features of SHE ZenAI Q* include:

Near real-time operation: Continuously processes data streams and updates the CI value in near real-time.

Asynchronous data handling: Handles multiple data streams simultaneously and independently.

Scalable architecture: Modular design and distributed processing techniques enable scalability.

"Consensus" approach: Integrates multiple data sources to provide a holistic view of well-being.

Mobile optimisation: Efficient and user-friendly operation on mobile devices.

SHE ZenAI Q* holds promise for revolutionising how we approach well-being, enabling personalised interventions and fostering a culture of well-being awareness.

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SHE ZenAI K*

The Social Harmony Ecosystem uses K*, which acts like an operating system or momentum engine. Within the ecosystem, K* refers to the verifier function of the Chain of Thoughts and Tree of Thoughts (CoTs + ToTs) Schema. The purpose of K* is to convert data points into meaningful information, sentiment, and intent that both humans and machines can use. This is achieved through algorithmic processes that follow established rules and principles.

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