#APLS

[eclecticelectriceccentric]

btw the aphobia on this site is very much still alive. in so many of our tags, you'll find people mocking our terms and our very queerness and identies.

aros and aces and apls are here and queer. aspecs, i love you <3. i love our terms and concepts. they're beautiful. we're beautiful.

[q8q]

Alpine meadows of the Teberdinsky Reserve

[glasratz]

Lake Kochel and mt. Herzogstand on a cold spring day with no tourists around.

[joutlaw60]

New Project OTW 9/28/87

🎥Teaser For First Visual Release off the project

[govindhtech]

Optimizing LLM Cascades with Prompt Design

LLM Cascades

The following business outcomes must be achieved by these strategies.

Delivering high-quality answers to a greater number of users initially.

Higher levels of user support should be offered while protecting data privacy.

Enhancing cost and operational effectiveness through timely economization.

Eduardo outlined three methods that developers could use:

Different prompt techniques are used in prompt engineering to produce answers of a higher caliber.

By adding more context to the prompt, retrieval-augmented generation makes it better and less taxing on end users.

The GenAI pipeline moves data more efficiently when prompt economization techniques are used.

Quickly improving the quality of the results while cutting down on the number of model inferencing and associated costs is possible with effective prompting.

Quick engineering: Enhancing model output

Let’s begin by discussing the Learning Based, Creative Prompting, and Hybrid prompt engineering framework for LLM Cascade.

The one shot and few shot prompts are included in the learning-based technique. This method provides the model with context and teaches it through the use of examples in the learning prompt. A zero-shot prompt is one that queries the model using data from its prior training. Context is provided by a one-shot or few-shot prompting, which teaches the model new information so that the LLM produces more accurate results.

More accurate responses can be obtained by using strategies like iterative prompting or negative prompting, which fall under the Creative Prompting category. While iterative prompting offers follow-up prompts that enable the model to learn over the course of the series of prompts, negative prompting sets a boundary for the model’s response.

The above methods can be combined, or not, in the Hybrid Prompting approach.

These approaches have benefits, but there is a catch: in order to generate high-quality prompts, users must possess the necessary knowledge to apply these strategies and provide the necessary context.

LLM 7B

Typically, LLM Cascade are trained using a broad corpus of internet data rather than data unique to your company. Results from the LLM workflow prompt that incorporate enterprise data with retrieval augmented generation (RAG) will be more pertinent. In order to retrieve the prompt context, this workflow entails embedding enterprise data into a vector database. The prompt and the retrieved context are subsequently sent to the LLM, which produces the response. Your data stays private and you avoid paying extra for compute training since RAG allows you to use your data in the LLM without retraining the model.

Early economization: Saving costs and delivering value

The final technique focuses on various prompt strategies to reduce the amount of inferencing needed for the model.

Token summarization lowers costs for APIs that charge by the token by using local models to reduce the number of tokens per user prompt sent to the LLM service.

Answers to frequently asked questions are cached by completion caching, saving inference resources from having to be generated each time the question is posed.

Query concatenation reduces overhead that accumulates per-query, such as pipeline overhead and prefill processing, by combining multiple queries into a single LLM submission.

LLM cascades are designed to execute queries on more basic LLMs initially, rating them according to quality, and only moving on to more expensive, larger models when necessary. By using this method, the average compute requirements per query are decreased.

7B LLM Model

Ultimately, the amount of compute memory and power determines the model throughput. However, accuracy and efficiency are just as important in influencing the outcomes of generative AI as throughput. The above strategies can be combined to create an LLM Cascade prompt architecture that is specific to your company’s requirements.

Although large language models (LLMs) are immensely potent instruments, they can be optimized to function more effectively just like any other tool. Prompt engineering can help in this situation.

Prompt engineering

The skill of crafting input for an LLM Cascade to produce the most accurate and desired result is known as prompt engineering. It basically provides the LLM Cascade with precise instructions and background information for the current task. Prompts with thoughtful design can greatly enhance the following:

Accuracy

A well-crafted prompt can guide the LLM Cascade away from unrelated data and toward the information that will be most helpful for the given task.

Efficiency

The LLM Cascade can determine the solution more quickly and with less computation time and energy if it is given the appropriate context.

Specificity

By giving precise instructions, you can make sure the LLM produces outputs that are suited to your requirements, saving you time from having to sort through pointless data.

Prompt Engineering Technique Examples

Here are two intriguing methods that use prompts to enhance LLM performance

Retrieval-Augmented Generation

This method augments the prompt itself with pertinent background knowledge or data. This can be especially useful for assignments that call for the LLM to retrieve and process outside data.

Emotional Persuasion Prompting

Research indicates that employing persuasive prompts and emotive language can enhance LLM concentration and performance on creative or problem-solving tasks.

You can greatly improve the efficacy and efficiency of LLMs for a variety of applications by combining these strategies and experimenting with various prompt structures.

Read more on govindhtech.com

[saffigon]

not having sex is morally neutral. having sex is morally neutral

not engaging in romance is morally neutral. engaging in romance is morally neutral

not having friends is morally neutral. having friends is morally neutral

[thetisming]

people who exclude straight trans people and straight aspec people are my worst enemies. btw

[zephyr-heart]

[bloomshroomz]

Imagine

[aromantic-spinda]

A podcast run by an asexual, an aromantic, and an aplatonic called "AAA" and every time an episode starts, one of them welcomes the audience by screaming into the mic

"hello and welcome to AAA!"

[infiniteorangethethird]

sure "romantic" isn't the only type of love but also "love" isn't the only type of positive feeling. So maybe stop insisting everyone needs love to be happy and accept that loveless ppl exist? Pretty please?

[love-me-love-my-weirdness]

Cishet aspecs are queer.

Cishet aromantics are queer. Cishet asexuals are queer. Cishet aplatonics are queer. Cishet afamilials are queer. Cishet anattractionals are queer.

Aspecs are queer as hell and excluding them only isolates queer people from their community.

[thermodynamic-comedian]

reflecting on it all, i really think one issue that the aspec community refuses to actually talk about (or, at least, those of us who aren't affected by it refuse to talk about) is that acceptence of aromanticism is still entirely conditional.

i'm not aplatonic myself, but even i can see how the aspec community excludes them. like, yeah, sure, being aromantic is cool!...as long as you still experience platonic attraction and have platonic relationships and replace romance with friendship at every turn.

and if you're aromantic, you also have to be asexual. because sex without romance is immoral and dirty and abusive. and every aroallo is an invader who's trying to destroy your perfect, pure, sex-negative aspec community. if an aromantic is not asexual, they are not a valid aromantic.

if you've ever found yourself wondering why aplatonics and aroallos alike have their own small communities instead of just being a part of the wider aspec community, this is why. you drove us away.

and your acceptence of aromanticism is still entirely conditional.

[dabouse]

happy pride to

male aspecs - your existence isn't sad, and you aren't an incel.

female aspecs - you aren't a prude

non-binary aspecs - you guys are real, seen, and valid

aspecs who are loveless - you aren't any less human

aspecs who are very loving - you aren't faking it

aroallos - you aren't just a whore

alloaces - you aren't just celibate

aplatonics - you're not any less valid than other aspecs

happy pride to all aspecs!

[loveless-arobee]

The answer to aspecs asking you to stop assuming [thing] about all aspecs is not to start assuming [opposite of thing] about all aspecs btw.

"Stop assuming all AlloAros have a lot of sex (or "are sluts")" does not mean "Start assuming no AlloAro has lots of sex" and also not "No AlloAro ever feels comfortable calling themselves a slut (or whore or w/e)" and vice-versa.

"Stop assuming all aros are loveless and non-partnering" does not mean "start assuming all aros do love ("in non-romantic ways") and are always partnering" and vice-versa.

"Stop assuming all aces are sex-repulsed" does not mean "start assuming all aces are sex-favourable", and vice-versa.

"Stop assuming all aplatonic people want to make friends" does not mean "start assuming no aplatonic people want to make friends" and vice-versa.

"Stop assuming all [aros or aces, mostly*] experience no [romantic or sexual, mostly*] attraction" does not mean "start assuming all [aros or aces] experience some form of [romantic or sexual attraction]" and vice-versa.

[Continue ad infinitum; these are just some examples and listing all things like that would be impossible.]

Just stop making assumptions about people based on one part of their identity. If they decide they want you to know, they'll tell you. If you want to know, you can ask, and maybe they'll give an answer (don't act like you're owed one, tho).

Accept that all people are different and even people under the same queer identity are going to have a vastly different experience; especially vast umbrellas like the aspec-identities. Instead of taking what one aspec person says about their identity as true for everyone under that same identity and then taking everything else as a "contradiction" to that label, or as something that needs another or a different label, simply accept that different people are going to have a different experience even if they use the same words to describe them.

It's really not that hard.

[*I think this may also apply to other aspec-identities (aplatonic, afamilial, atertriary, etc), right? I see these takes mostly inside of and directed at aro- and ace-spaces; but it also seems like it just applies across the board, non-aro and non-ace aspec-identities are just lesser known and thus not discussed as often.]

[govindhtech]

NVIDIA Expands Omniverse Cloud with APIs for Digital Twins

The world’s top platform for developing industrial digital twin applications and processes will now be accessible via APIs, according to NVIDIA, enabling its use throughout the whole software developer community.

It is now possible for developers to effortlessly incorporate core Omniverse technologies into their simulation workflows for testing and validating autonomous machines such as robots or self-driving cars, or into their design and automation software applications for digital twins, thanks to the five new APIs for the Omniverse Cloud.

Ansys, Cadence, Dassault Systèmes with its 3DEXCITE brand, Hexagon, Microsoft, Siemens, Rockwell Automation, and Trimble are a few of the biggest industrial software manufacturers in the world that are using Omniverse Cloud APIs into their software offerings.

Nvidia Omniverse cloud

NVIDIA founder and CEO Jensen Huang predicted that “everything manufactured will have digital twins.” The operating system called Omniverse is used to create and manage digital twins that are physically accurate. Digitalization of the $50 trillion heavy industries industry will be based on generative AI and omniverse.

Fresh Omniverse Cloud APIs

The five new Omniverse Cloud APIs are as follows, and they may be used alone or together:

USD Render – Produces completely ray-traced NVIDIA RTX representations of OpenUSD data.

USD Write – Allows users to alter and interact with OpenUSD data.

USD Query – Allows scene inquiries and interactive situations.

USD Notify – Monitors USD fluctuations and offers information.

Omniverse Channel  – Links individuals, tools, and environments to facilitate cooperation across scenes.

Omniverse cloud Nvidia

With Teamcenter X, the premier cloud-based product lifecycle management (PLM) software in the market, Siemens, a leading technology firm for automation, digitalization, and sustainability, is implementing Omniverse Cloud APIs inside its Siemens Xcelerator Platform.

Huang demonstrated Teamcenter X’s integration with Omniverse APIs during his GTC presentation. This allows the program to leverage Omniverse RTX rendering inside the app and link design data to NVIDIA generative AI APIs.

“Siemens provides customers with generative AI to enhance the immersion of their physics-based digital twins through the NVIDIA Omniverse API,” said Roland Busch, CEO and president of Siemens AG. This will make it easier for everyone to conceptualize, create, and test factories, production techniques, and next-generation items digitally before they are really constructed. Siemens digital twin technology helps firms worldwide become more competitive, resilient, and sustainable by connecting the physical and digital worlds.

Leader in engineering simulation software, Ansys is using Omniverse Cloud APIs to provide RTX visualization and data exchange in products including Ansys AVxcelerate for autonomous cars, Ansys Perceive EM for 6G simulation, and NVIDIA accelerated solvers like Ansys Fluent and other solutions.

Enterprises can design, model, and optimize data centers in a digital twin before they are physically built out thanks to Cadence, a prominent developer of computational software, integrating Omniverse Cloud APIs into its Cadence Reality Digital Twin Platform.

To enable generative storytelling in its 3DEXCITE content creation apps, Dassault Systèmes, a pioneer in virtual worlds for sustainable innovation, is integrating Shutterstock 3D AI Services and Omniverse Cloud APIs.

Other instances consist of:

Leading provider of construction and geographic technologies Trimble intends to utilize the APIs to make it possible to integrate Trimble model data with interactive NVIDIA Omniverse RTX viewers.

Hexagon, a pioneer in reality technology worldwide, will use USD interoperability to connect its reality capture sensors and digital reality platforms with the NVIDIA Omniverse Cloud APIs, giving users access to very lifelike simulation and visualization tools.

Rockwell Automation, a provider of industrial automation and digital transformation solutions, will use Omniverse Cloud APIs to allow RTX-enabled visualization.

Microsoft and NVIDIA exhibit these achievements in early collaboration with Hexagon and Rockwell Automation in a demo that was unveiled at GTC.

Quickening the Development of Autonomous Robots

Developers are trying to speed up their end-to-end processes as demand rises for robots, autonomous vehicles (AVs), and AI-based monitoring systems.

Sensor data is essential for full-stack autonomy training, testing, and validation, including perception, planning, and control.

Through the Omniverse Cloud APIs, full-stack training and testing with high-fidelity, physically based sensor simulation is made possible. These applications and simulation tools include Foretellix’s Foretify Platform, CARLA, MathWorks, and leading sensor solution providers like FORVIA HELLA, Luminar, SICK AG, and Sony Semiconductor Solutions.

Later this year, developers will be able to utilize Omniverse Cloud APIs on self-hosted and managed NVIDIA accelerated systems, first made available on Microsoft Azure.

“The next phase of digitalization in industry has begun,” said Andy Pratt, corporate vice president of Microsoft Emerging Technologies. Organizations worldwide and in all sectors can connect, work together, and improve their current tools using NVIDIA Omniverse APIs on Microsoft Azure to develop the next generation of AI-enabled digital twins.

Digital Twin technology

Using Omniverse Digital Twins to Transform Industries

The new cloud APIs are an addition to Omniverse’s widespread use by a number of international leaders in many sectors, such as:

The biggest marketing and communications services firm in the world, WPP, has revealed a new phase of its Omniverse Cloud-based generative AI content production engine, which will enable the AI-driven solution to be used in the retail and consumer packaged goods industries.

Media.In order to achieve scalability and hyper-personalization throughout any client experience, Monks announced the deployment of Omniverse to construct a generative AI and OpenUSD-enabled content production pipeline.

To streamline production processes and shorten time-to-market, Continental, a significant automotive supplier, is creating a digital twin platform.

NVIDIA Omniverse Cloud APIs

Create and launch the next generation of 3D apps and services

Using the NVIDIA Omniverse platform of APIs, SDKs, and services, developers may quickly incorporate RTX rendering technologies and Universal Scene Description (OpenUSD) into their current software tools and simulation processes for creating artificial intelligence (AI) systems.

NVIDIA Omniverse Cloud APIs Advantages

All 3D Developments, No Matter How Big or Small

Easily Modify and Expand

With low- and no-code example applications and easily-modifiable extensions made possible by Omniverse SDKs, you may create new tools and processes from start.

Boost Your 3D Software

Using OpenUSD, RTX, accelerated computing, and generative AI technologies via Omniverse Cloud APIs, you may optimize your current software tools and apps.

Install Anywhere

Create, host, and stream your own application from Omniverse Cloud, or develop and deploy it on virtual or RTX-capable workstations.

NVIDIA Omniverse Cloud APIs Qualities

Link and Boost Three Dimensional Processes

Build 3D tools and apps that provide enhanced graphics and interoperability for digital twin use cases by using OpenUSD, RTX, and generative AI technologies.

Software Development Kit (SDK)

Create and Implement New Applications. Omniverse Kit SDK for local and virtual workstations allows you to start creating unique tools and apps from scratch. You may choose to stream and deploy via the Omniverse Cloud platform-as-a-service or through your own channels.

Cloud-Based APIs

Boost the Software Portfolio. Utilize Omniverse Cloud APIs to effortlessly include OpenUSD data interoperability and physically based, real-time rendering powered by NVIDIA RTX into your workflows, apps, and services.

Generative AI

Link 3D Workflows with Generative AI. Applications developed on Omniverse SDKs or driven by Omniverse Cloud APIs may effortlessly link to generative AI agents for the creation of language- or image-based content, including models developed on the NVIDIA Picasso foundry service, because of OpenUSD’s universal data exchange characteristics.

Read more on govindhtech.com