#Generative AI Industrialization

[cozylittleartblog]

"content creator" is a corporate word.

we are artists.

[kariniai]

Generative AI: Reshaping Industrial Landscapes

Hype of Generative AI

Generative AI is not just a fleeting trend; it's a transformative force that's been captivating global interest. Comparable in significance to the dawn of the internet, its influence extends across various domains, altering the way we search, communicate, and leverage data. From enhancing business processes to serving as an academic guide or a tool for crafting articulate emails, its applications are vast. Developers have even begun to favor it over traditional resources for coding assistance. The term Retrieval Augmented Generation (RAG), introduced by Meta in 2020 (1), is now familiar in the corporate world. However, the deployment of such technologies at an enterprise level often encounters hurdles like task-specificity, accuracy, and the need for robust controls.

Why enterprises struggle with Industrializing Generative AI

Despite the enthusiasm, enterprises are grappling with the practicalities of adopting Generative AI.

According to survey by MLInsider,

62% of AI professionals continue to say it is difficult to execute successful AI projects. The larger the company, the more difficult it is to execute a successful AI project.

Lack of expertise, budget, and finding AI talent are the top challenges organizations are facing when it comes to executing ML programs.

Only 25% of organizations have deployed Generative AI models to production in the past year.

Of those who have deployed Generative AI models in the past year, several benefits have been realized. About half said they have seen improved customer experiences (58%) and improved efficiency (53%).

In summary, Generative AI offers massive opportunities to enterprise but due to skills, requirements for enterprise security and governance, they are still behind in the adoption curve.

Industrialization of Generative AI applications

The quest for enterprise-grade Generative AI applications is now easier, thanks to SaaS-based model APIs and packages like Langchain and Llama Index. Yet, scaling these initiatives across an enterprise remains challenging. Historical trends show that companies thrive when utilizing a centralized platform that promotes reusability and governance, a practice seen in the formation of AI and ML platform teams.

Enterprises should think about Gen AI platforms with the above four layered cake,

Infrastructure - Most companies have a primary cloud infrastructure and typically utilize Gen AI building blocks offered by the cloud.

Capabilities - These are set of foundational building block services offered by cloud native (e.g. Opensearch, Azure OpenAI) or 3rd party SAAS products(e.g. Milvus Vector search)

Reusable services - Central Gen AI teams typically have to build a RAG (Retrieval Augmented Generation), Fine Tuning or Model Hub Services that can be readily consumed with enterprise guard-rails

Use cases - Using the reusable services, use cases can be deployed and integrated with a variety of applications such as Customer support bot, summarizing customer reviews and more.

Many Data, ML and AI vendors are snapping these capabilities on top of their existing platform. ML Platforms that start with supervised labels and depend on model building & deployment aspect of MLOps, Generative AI platforms begin with a pre-trained Open source model(e.g. Llama2) or proprietary SAAS model(GPT4), focuses on capabilities to contextualize Large Language models and deploy capabilities to enable smarts in applications such as Copilots or Agents. Hence we propose a radically different approach to fulfill the promise of industrialized Gen AI that focuses on LLMOps development loop ( Connect to Model Hub -> Contextualize Model for Data -> Human Evaluation )

Introducing Generative AI Platform for all

Karini AI presents "Generative AI platform", designed to revolutionize enterprise operations by integrating proprietary data with advanced language models, effectively creating a digital co-pilot for every user. Karini simplifies the process, offering intuitive Gen AI templates that allow rapid application development. The platform offers an array of data processing tools and adheres to LLMOps practices for deploying Models, Data, and Copilots. It also provides customization options and incorporates continuous feedback mechanisms to enhance the quality of RAG implementations.

Conclusion

Karini AI accelerates experimentation, expedite market delivery, and bridge the generative AI adoption gap, enabling businesses to harness the full potential of this groundbreaking technology.

About Karini AI:

Fueled by innovation, we're making the dream of robust Generative AI systems a reality. No longer confined to specialists, Karini.ai empowers non-experts to participate actively in building/testing/deploying Generative AI applications. As the world's first GenAIOps platform, we've democratized GenAI, empowering people to bring their ideas to life – all in one evolutionary platform. 

Contact:

Jerome Mendell

(404) 891-0255

sales@karini.ai

https://www.karini.ai/

[ayeforscotland]

NVIDIA announced their 5000 series graphics card which are able to do something they’re calling Neural Faces. Neural Faces is a feature that uses Generative AI to create ‘photorealistic’ faces based off an initial 3D model.

Initial model on the left. ‘Upscaled’ model on the right.

To be clear, that’s supposed to be the same character. And Neural Faces has yassified the hell out of it - it’s smoothed out everything, changes the eyes, nose, lips into something distinctly more vaguely European.

I hate the term, but this is pretty problematic for a number of reasons.

Also NVIDIA have basically created the incel filter.

[10001gecs]

one 100 word email written with ai costs roughly one bottle of water to produce. the discussion of whether or not using ai for work is lazy becomes a non issue when you understand there is no ethical way to use it regardless of your intentions or your personal capabilities for the task at hand

with all due respect, this isnt true. *training* generative ai takes a ton of power, but actually using it takes about as much energy as a google search (with image generation being slightly more expensive). we can talk about resource costs when averaged over the amount of work that any model does, but its unhelpful to put a smokescreen over that fact. when you approach it like an issue of scale (i.e. "training ai is bad for the environment, we should think better about where we deploy it/boycott it/otherwise organize abt this) it has power as a movement. but otherwise it becomes a personal choice, moralizing "you personally are harming the environment by using chatgpt" which is not really effective messaging. and that in turn drives the sort of "you are stupid/evil for using ai" rhetoric that i hate. my point is not whether or not using ai is immoral (i mean, i dont think it is, but beyond that). its that the most common arguments against it from ostensible progressives end up just being reactionary

i like this quote a little more- its perfectly fine to have reservations about the current state of gen ai, but its not just going to go away.

[twelveskidneys]

steven moffat really said alright how many aspects of modern society can i criticise in the one (1) episode i’m writing for this season

[tea-knight]

[creampuffqueen]

“don’t blame peoples’ individual choices of using ai for climate change, blame the big corporations!!”

i’m gonna hold your hand when i say this. generative ai is the big corporation, and this product only continues to exist because of peoples’ individual choices to use it

[gideonisms]

I think we could all stand to unpack our feelings about societal standards of Intelligence and why it's important to us that someone else is less intelligent & capable

[bombersoldier]

[ayeforscotland]

Certain people in the Games industry have been talking up Generative AI for a while now, promising advancements like never before and how AI can do the role of multiple team members.

But when it's actually deployed in a commercial setting, nobody wants to take responsibility for it.

This is the trailer.

Here is the response.

[cozylittleartblog]

The animation strike is looking more and more likely, please be ready to support creatives this summer and boycott Hollywood again if you care about animation or the human artists who make it happen.

+ indie animation youtube playlist

[catgoesboom]

I sincerelly don't think Markiplier understands how him being openly and unapogetically ok with using chat gpt like a fucking alexa undermines all his projects and his contribution to them as a writer, specially the big ones that are still down the line like Iron Lung. Like sorry dude, there are people who are now losing all respect for you as a filmaker since we don't know how far and how you used chatGPT in the inception of your projects because despite being a big fact that one of the issues with chatgpt is that it makes up data and fake information on the fly, you use it like it's google. Like, how the fuck can we even trust your creative decisions and writting aren't stolen from a someone else since now you clearly showed you constantly put yourself in a position that there's a chance of you doing that "by accident"?

[imaginal-ai]

"Dark Romance" (0002)

(More of this series)

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[tea-knight]

[tropicalcontinental]

What if he was human chat (he is human)

[unrealityliminal]