Steps to Build RAG Pipeline with Cohere For AI’s Aya LLM

February 14, 2024

Aya 101 is a state-of-the-art, open-source, massively multilingual large language model (LLM) developed by Cohere for AI. It has the remarkable capability of operating in 101 different languages, including over 50 that are considered underserved by most advanced AI models.

In this article, we will go through a step-by-step process of deploying and using the Aya model. We will also build a FAISS powered RAG pipeline using Aya, and showcase how enterprises can use this for building AI applications.

The Aya 101 Model by Cohere for AI

Aya 101 Model by Cohere for AI project is part of an open science endeavor, and is a collaborative effort involving contributions from people across the globe.

Aya's goal is to address the imbalance in language representation within AI by developing a model that understands and generates multiple languages, not just the ones that are predominantly represented online.

Key Facts about Aya

Massively Multilingual: The model supports 101 languages. It also includes over 50 languages rarely seen in AI models.
Open Source: The model, training process, and datasets are all open source.
Groundbreaking Dataset: Aya comes with the largest multilingual instruction dataset released till date, comprising 513 million data points across 114 languages.

Source: Cohere for AI

The need for such a project arises from the fact that while a significant portion of internet content is in English, there are approximately 7,000 languages spoken worldwide. However, many AI models do not support the majority of these languages, which can lead to a lack of access to technology for speakers of underrepresented languages. Aya seeks to change this by improving AI's multilingual capabilities, making it more inclusive.

Cohere for AI’s Aya initiative has contributions from everyday citizens, educators, linguists, and anyone interested in language technology. By participating, individuals helped democratize access to language technology and ensure broader language representation in the AI space.

For more detailed information, you can read about Cohere's Aya on their website.

Understanding RAG Pipeline

The Retrieval-Augmented Generation (RAG) pipeline has become a powerful tool in the field of LLMs. At its core, the RAG pipeline combines two crucial steps:

Retrieval step: Retrieving relevant stored information using Vector Search or Knowledge Graph or simple search.

Generation step: Generating coherent text using a combination of contextual knowledge and natural language generation capabilities of LLMs.

This combination allows the system to pull in essential details from a database and then use them to construct detailed and informative responses to user queries.

This helps ‘ground’ the LLMs in facts, and helps it with the context or knowledge it needs to respond to user queries.

This is very powerful for enterprise applications for a variety of reasons. Imagine you're asking a complex question that requires specific knowledge. The RAG pipeline first searches through a large collection of documents to find the pieces of information most related to your question.

Then, using a language model, it takes that information and crafts a reply that feels both precise and human-like. The beauty of the RAG pipeline lies in its ability to provide answers that aren't just generic; they are customized and informed by the retrieved data, making the responses more accurate and trustworthy.
This makes RAG pipelines incredibly important for building intelligent chatbots, search engines, and help desks that can assist users with detailed and contextually relevant information.

FAISS As Vector Store

FAISS, which stands for Facebook AI Similarity Search, is a library developed by Facebook AI that enables efficient similarity search. It provides algorithms to quickly search and cluster embedding vectors, making it suitable for tasks such as semantic search and similarity matching.

FAISS can handle large databases efficiently and is designed to work with high-dimensional vectors, allowing for fast and memory-efficient similarity search.

In this article, we will use FAISS as our Vector Store, which will provide context to the Aya LLM. We will also use LangChain for building the pipeline.

Step-by-Step Guide to Building a RAG Pipeline with Aya

Choosing a GPU node

The code in this article was hosted on a V100 GPU node provided by E2E Networks. E2E Networks offers a variety of cloud GPU nodes designed to cater to different computational needs during AI model training and inference.

Our offerings also include powerful servers such as the HGX 8xH100 and HGX 4xH100, which integrates H100 GPUs with high-speed interconnects, ideal for demanding tasks like high-performance computing and machine learning.

The best part is, all our cloud GPUs come with optimized and integrated software stacks, including TensorFlow, GPU drivers, and CUDA, to facilitate a wide range of applications and workloads efficiently.

To start with, sign up for an account here. After that, you can launch a V100 node from the ‘Compute’ tab on the sidebar.

To set up Aya, you need to first import the required modules.


from torch import cuda, bfloat16
import torch
import transformers
from transformers import AutoTokenizer
from langchain.llms import HuggingFacePipeline

Then set up the quantization config.


bnb_config = transformers.BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type='nf4',
    bnb_4bit_use_double_quant=True,
    bnb_4bit_compute_dtype=bfloat16
)

Load the model and the tokenizer.


from transformers import AutoModelForSeq2SeqLM, AutoTokenizer


checkpoint = "CohereForAI/aya-101"


tokenizer = AutoTokenizer.from_pretrained(checkpoint)
aya_model = AutoModelForSeq2SeqLM.from_pretrained(checkpoint, quantization_config=bnb_config,)

Create a query pipeline.


query_pipeline = transformers.pipeline(
        "text2text-generation",
        model=aya_model,
        tokenizer=tokenizer,
        torch_dtype=torch.float16,
        device_map="auto",
        max_length = 512,
        early_stopping=True,
        num_return_sequences=1,
        no_repeat_ngram_size=2,


)

Now let’s try to generate responses from Aya in different languages.


query_pipeline("Describe the state of Rajasthan in Hindi")

[{'generated_text': 'Rajasthan एक राज्य है जो भारत के उत्तर-पश्चिम में स्थित है। यह राजस्थान राज्य के दक्षिण में है और राजसमंद जिले के पूर्व में। राज्य में राजधानी अजमेर है, जो राजशाही शहर है जिसे राजा जयप्रकाश मौर्य ने स्थापित किया था। राजस् थान में लगभग तीन करोड़ लोग रहते हैं।'}]

Translation: ‘Rajasthan is a state located in the north-west of India. It is situated to the south of the Rajasthan state and to the east of the Rajasmand district. The capital of the state is Ajmer, which is a royal city established by King Jayaprakash Maurya. Approximately three crore people live in the state.’


query_pipeline("How to make Baklava? Give me the recipe in Turkish")

[{'generated_text': 'Baklava, tatlı olarak da bilinen çikolata soslu bir tatlıdır. İçine fıstık ezmesi, tarçın, şeker ve tarçını karıştırarak yapılır. Ayrıca yumurta, süt ve şekeri de karıştırabilirsiniz.'}]

Translation: ‘Baklava, also known as a dessert called sweet, is a dessert with chocolate sauce. It is made by mixing peanut butter, cinnamon, sugar, and cinnamon. You can also mix eggs, milk, and sugar.’


query_pipeline("How to make an igloo? Answer in Icelandic")

[{'generated_text': 'Hér er leiðbeiningar um hvernig á að búa til iglú: Fyrsta. Veldu efni: Veltu eitthvað sem þú vilt nota til að hýsa ígluna þína. Þetta gæti verið t.d. ís, snjó eða vatn. 2. Að hafa gott samband. Búðu til stað: Búið til stóran garð nálægt vatni ūar sem iglunin verđur. Setjið þar ýmsar plötur og greinar, svo sem tré, tré og tré. 3. Að búið er að setja bygginguna: Setja upp allar nauðsynlegar búnaður og setjað þá aftur efst án þess að þurfa að fjarlægja neitt. 4. Búa um ytri hluta íglúunnar: Þú getur búist við að nota ýmis tæki og verkfæri til þessa. Til dæmis, það er hægt að stilla hitastig gróðurs, hita olíu og hita lofts. Fimm. Fylgstu með henni: Fylgist vel með því að fylgjast með þörfum iðgunnar þinnar og fylgist með þeim breytingum sem þarf að gera. 6. Búđu til innri rýmið: Byrjađ ađ búđa inn nũjan herbergi hjá ūríđum. Notaðu mismunandi tegundir af dýrum, eins og dúk, dúkur og rúm. 7. Búða til útsýnið: Notađiđ útvarpstæki til ad horfa beint ur húsiđ. 8. Búðiđ til loftið og veggina: notađir rýmiđ sem er ekki mjög heitt og lofti. 9. Búðið til gluggana: Ýttu fingrum og tækjum gegn gluggunum og gluggum. Tíu. Bæta við lýsingu: Bætið við ljósi og ljósum til hússins til viðbótar við þær breytingar sem eru gerðar strax. 11 ára. Settu skjól: Settiđ svefnherbergiđ og svæđin hvort annađ undir skýliđ, til dæmi'}]

Translation: Here are instructions on how to build an igloo:

1. Choose material: Choose something to use to house your igloo. This could be ice, snow, or water, for example.

2. Have good communication.

3. Create a location: Create a large yard near water where the igloo will be. Place various plates and branches there, such as wood, trees, and trees.

4. Building the structure: Set up all necessary equipment and put them back on top without having to remove anything.

5. Build the outer part of the igloo: You can expect to use various tools and equipment for this. For example, it is possible to adjust the temperature of the greenhouse, heat oil, and heat air.

6. Follow it: Monitor the needs of your igloo and follow the changes that need to be made.

7. Create the inner space: Start by creating a cozy room with different types of bedding, such as canvas, cloth, and carpet.

8. Create the view: Use radio equipment to watch directly from the house.

9. Build the ceiling and walls: Use space that is not very hot and airy.

10. Make windows: Push fingers and tools against the windows and windows.

11. Add lighting: Add lights and lamps to the house in addition to the changes made immediately.

12. Set up shelter: Set up the bedroom and space either under the shelter, for example


query_pipeline("Write me a poem in Maltese")

[{'generated_text': "Ħej, ħbieb, I hawn biex ngħinuk b'kull mod li nista'. Jien l- ewwel u jien aħdar, Għalhekk jekk jogħġbok għidli x'tixtieq. I se jkun qed iħossu dwar dan, U huwa żmien tajjeb biżżejjed biża' bieb. Għandi t-tendenza li nibqa' lura, Jitlob grazzi, Allura ejja nikbru fuq dawn iż-żewġ affarijiet."}]

Translation: ‘Hi, friends, I'm here to help you in any way I can. I am the first and I am green, So please tell me what you want. He will be feeling this way, And it's a very good time with a closed door. I tend to stay back, Ask thanks, So let's grow on these two things.’

As we can see from the above responses, Aya, even though it can generate responses in multiple languages, is at a nascent stage as far as the quality of the responses are concerned.

Setting Up a RAG Pipeline with Gradio

Import the necessary modules.


from langchain.document_loaders import TextLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.chains import RetrievalQA
from langchain_community.vectorstores import FAISS

Define a text splitter to break down the uploaded documents into smaller chunks.


# Simulate some document processing delay
text_splitter = RecursiveCharacterTextSplitter(
chunk_size=1000,
chunk_overlap=20,
length_function=len,
is_separator_regex=False,
)

Load an embedding model to vectorize the text in the document.


model_name = "sentence-transformers/all-mpnet-base-v2"
model_kwargs = {"device": "cuda"}


embeddings = HuggingFaceEmbeddings(model_name=model_name, model_kwargs=model_kwargs)

Define a function to create a question-answering chain from the uploaded documents.


import gradio as gr
import os
from shutil import copyfile


def create_retrieval_chain(files):


    docs = []


    for file_path in files:


        if file_path.lower().endswith('.pdf'):  # Check if the file is a PDF
            loader_temp = PyPDFLoader(file_path)
            docs_temp = loader_temp.load_and_split(text_splitter=text_splitter)
            docs += docs_temp


        else:
            return (f"Please upload PDF files only")


    for doc in docs:
        doc.page_content = doc.page_content.replace('\n', ' ')


    vectordb = FAISS.from_documents(documents=docs, embedding=embeddings)
    retriever = vectordb.as_retriever()


    global qa


    qa = RetrievalQA.from_chain_type(
    llm=llm,
    chain_type="stuff",
    retriever=retriever,
    verbose=True
    )


    return f"Process PDF files. They can be queried now"

Define another function to answer the queries based on context retrieved from the documents.


def process_query(query):
    response = qa.invoke(query)
    return response

Now launch a Gradio interface. Make sure you set the host to be 0.0.0.0 and open the port at 7865, so that the application can be accessed externally.

You can do so by running the following on your server’s debian terminal.


sudo iptables -A INPUT -p tcp --dport 7865 -j ACCEPT


sudo iptables-save | sudo tee /etc/iptables/rules.v4

Then launch Gradio.


# Define the Gradio interface
iface_save_pdf = gr.Interface(fn=create_retrieval_chain,
                     inputs=gr.Files(label="Upload Files", type='filepath'),
                     outputs="text",
                     title="PDF Uploader",
                     description="Upload multiple files. Only PDF files will be saved to disk.")


iface_process_query = gr.Interface(fn=process_query,
                                   inputs=gr.Textbox(label="Enter your query"),
                                   outputs="text",
                                   title="Query Processor",
                                   description="Enter queries to get responses.")


iface_combined = gr.TabbedInterface([iface_save_pdf, iface_process_query], ["PDF Upload", "Query Processor"])


# Launch the combined interface
if __name__ == "__main__":
    iface_combined.launch(server_name='0.0.0.0', server_port=7865, share=True)

The interface has two tabs. One tab is for uploading the pdf documents and the other is for querying it. I’m going to upload a document titled ‘Why are E2E Cloud Solutions Lower in Pricing Than Competitors?’. I downloaded a pdf version of this article from here.

‍

Now let’s query the document using the other tab.

‍

Conclusion

Aya model presents a groundbreaking new capability in LLMs to handle multilingual queries. In the coming future, we believe that LLMs like Aya will transform how we communicate, and how enterprise applications build customer experiences.

If you want to learn more about how to deploy and use Aya model, reach out to us at sales@e2enetworks.com.

Sign up for Free Trial

Latest Blogs

December 20, 2024

A Complete Guide To Customer Acquisition For Startups

Any business is enlivened by its customers. Therefore, a strategy to constantly bring in new clients is an ongoing requirement. In this regard, having a proper customer acquisition strategy can be of great importance.

So, if you are just starting your business, or planning to expand it, read on to learn more about this concept.

The problem with customer acquisition

As an organization, when working in a diverse and competitive market like India, you need to have a well-defined customer acquisition strategy to attain success. However, this is where most startups struggle. Now, you may have a great product or service, but if you are not in the right place targeting the right demographic, you are not likely to get the results you want.

To resolve this, typically, companies invest, but if that is not channelized properly, it will be futile.

So, the best way out of this dilemma is to have a clear customer acquisition strategy in place.

How can you create the ideal customer acquisition strategy for your business?

Define what your goals are

You need to define your goals so that you can meet the revenue expectations you have for the current fiscal year. You need to find a value for the metrics –

MRR – Monthly recurring revenue, which tells you all the income that can be generated from all your income channels.
CLV – Customer lifetime value tells you how much a customer is willing to spend on your business during your mutual relationship duration.
CAC – Customer acquisition costs, which tells how much your organization needs to spend to acquire customers constantly.
Churn rate – It tells you the rate at which customers stop doing business.

All these metrics tell you how well you will be able to grow your business and revenue.

Identify your ideal customers

You need to understand who your current customers are and who your target customers are. Once you are aware of your customer base, you can focus your energies in that direction and get the maximum sale of your products or services. You can also understand what your customers require through various analytics and markers and address them to leverage your products/services towards them.

Choose your channels for customer acquisition

How will you acquire customers who will eventually tell at what scale and at what rate you need to expand your business? You could market and sell your products on social media channels like Instagram, Facebook and YouTube, or invest in paid marketing like Google Ads. You need to develop a unique strategy for each of these channels.

Communicate with your customers

If you know exactly what your customers have in mind, then you will be able to develop your customer strategy with a clear perspective in mind. You can do it through surveys or customer opinion forms, email contact forms, blog posts and social media posts. After that, you just need to measure the analytics, clearly understand the insights, and improve your strategy accordingly.

Combining these strategies with your long-term business plan will bring results. However, there will be challenges on the way, where you need to adapt as per the requirements to make the most of it. At the same time, introducing new technologies like AI and ML can also solve such issues easily. To learn more about the use of AI and ML and how they are transforming businesses, keep referring to the blog section of E2E Networks.

‍

Reference Links

https://www.helpscout.com/customer-acquisition/

https://www.cloudways.com/blog/customer-acquisition-strategy-for-startups/

https://blog.hubspot.com/service/customer-acquisition

This is a decorative image for: Constructing 3D objects through Deep Learning

October 18, 2022

Image-based 3D Object Reconstruction State-of-the-Art and trends in the Deep Learning Era

3D reconstruction is one of the most complex issues of deep learning systems. There have been multiple types of research in this field, and almost everything has been tried on it — computer vision, computer graphics and machine learning, but to no avail. However, that has resulted in CNN or convolutional neural networks foraying into this field, which has yielded some success.

The Main Objective of the 3D Object Reconstruction

Developing this deep learning technology aims to infer the shape of 3D objects from 2D images. So, to conduct the experiment, you need the following:

Highly calibrated cameras that take a photograph of the image from various angles.
Large training datasets can predict the geometry of the object whose 3D image reconstruction needs to be done. These datasets can be collected from a database of images, or they can be collected and sampled from a video.

By using the apparatus and datasets, you will be able to proceed with the 3D reconstruction from 2D datasets.

State-of-the-art Technology Used by the Datasets for the Reconstruction of 3D Objects

The technology used for this purpose needs to stick to the following parameters:

Input

Training with the help of one or multiple RGB images, where the segmentation of the 3D ground truth needs to be done. It could be one image, multiple images or even a video stream.

The testing will also be done on the same parameters, which will also help to create a uniform, cluttered background, or both.

Output

The volumetric output will be done in both high and low resolution, and the surface output will be generated through parameterisation, template deformation and point cloud. Moreover, the direct and intermediate outputs will be calculated this way.

Network architecture used

The architecture used in training is 3D-VAE-GAN, which has an encoder and a decoder, with TL-Net and conditional GAN. At the same time, the testing architecture is 3D-VAE, which has an encoder and a decoder.

Training used

The degree of supervision used in 2D vs 3D supervision, weak supervision along with loss functions have to be included in this system. The training procedure is adversarial training with joint 2D and 3D embeddings. Also, the network architecture is extremely important for the speed and processing quality of the output images.

Practical applications and use cases

Volumetric representations and surface representations can do the reconstruction. Powerful computer systems need to be used for reconstruction.

Given below are some of the places where 3D Object Reconstruction Deep Learning Systems are used:

3D reconstruction technology can be used in the Police Department for drawing the faces of criminals whose images have been procured from a crime site where their faces are not completely revealed.
It can be used for re-modelling ruins at ancient architectural sites. The rubble or the debris stubs of structures can be used to recreate the entire building structure and get an idea of how it looked in the past.
They can be used in plastic surgery where the organs, face, limbs or any other portion of the body has been damaged and needs to be rebuilt.
It can be used in airport security, where concealed shapes can be used for guessing whether a person is armed or is carrying explosives or not.
It can also help in completing DNA sequences.

So, if you are planning to implement this technology, then you can rent the required infrastructure from E2E Networks and avoid investing in it. And if you plan to learn more about such topics, then keep a tab on the blog section of the website.

‍

Reference Links

https://tongtianta.site/paper/68922

https://github.com/natowi/3D-Reconstruction-with-Deep-Learning-Methods

This is a decorative image for: Comprehensive Guide to Deep Q-Learning for Data Science Enthusiasts

October 18, 2022

A Comprehensive Guide To Deep Q-Learning For Data Science Enthusiasts

For all data science enthusiasts who would love to dig deep, we have composed a write-up about Q-Learning specifically for you all. Deep Q-Learning and Reinforcement learning (RL) are extremely popular these days. These two data science methodologies use Python libraries like TensorFlow 2 and openAI’s Gym environment.

So, read on to know more.

What is Deep Q-Learning?

Deep Q-Learning utilizes the principles of Q-learning, but instead of using the Q-table, it uses the neural network. The algorithm of deep Q-Learning uses the states as input and the optimal Q-value of every action possible as the output. The agent gathers and stores all the previous experiences in the memory of the trained tuple in the following order:

State> Next state> Action> Reward

The neural network training stability increases using a random batch of previous data by using the experience replay. Experience replay also means the previous experiences stocking, and the target network uses it for training and calculation of the Q-network and the predicted Q-Value. This neural network uses openAI Gym, which is provided by taxi-v3 environments.

Now, any understanding of Deep Q-Learning is incomplete without talking about Reinforcement Learning.

What is Reinforcement Learning?

Reinforcement is a subsection of ML. This part of ML is related to the action in which an environmental agent participates in a reward-based system and uses Reinforcement Learning to maximize the rewards. Reinforcement Learning is a different technique from unsupervised learning or supervised learning because it does not require a supervised input/output pair. The number of corrections is also less, so it is a highly efficient technique.

Now, the understanding of reinforcement learning is incomplete without knowing about Markov Decision Process (MDP). MDP is involved with each state that has been presented in the results of the environment, derived from the state previously there. The information which composes both states is gathered and transferred to the decision process. The task of the chosen agent is to maximize the awards. The MDP optimizes the actions and helps construct the optimal policy.

For developing the MDP, you need to follow the Q-Learning Algorithm, which is an extremely important part of data science and machine learning.

What is Q-Learning Algorithm?

The process of Q-Learning is important for understanding the data from scratch. It involves defining the parameters, choosing the actions from the current state and also choosing the actions from the previous state and then developing a Q-table for maximizing the results or output rewards.

The 4 steps that are involved in Q-Learning:

Initializing parameters – The RL (reinforcement learning) model learns the set of actions that the agent requires in the state, environment and time.
Identifying current state – The model stores the prior records for optimal action definition for maximizing the results. For acting in the present state, the state needs to be identified and perform an action combination for it.
Choosing the optimal action set and gaining the relevant experience – A Q-table is generated from the data with a set of specific states and actions, and the weight of this data is calculated for updating the Q-Table to the following step.
Updating Q-table rewards and next state determination – After the relevant experience is gained and agents start getting environmental records. The reward amplitude helps to present the subsequent step.

In case the Q-table size is huge, then the generation of the model is a time-consuming process. This situation requires Deep Q-learning.

Hopefully, this write-up has provided an outline of Deep Q-Learning and its related concepts. If you wish to learn more about such topics, then keep a tab on the blog section of the E2E Networks website.

Reference Links

https://analyticsindiamag.com/comprehensive-guide-to-deep-q-learning-for-data-science-enthusiasts/

https://medium.com/@jereminuerofficial/a-comprehensive-guide-to-deep-q-learning-8aeed632f52f

October 13, 2022

GAUDI: A Neural Architect for Immersive 3D Scene Generation

The evolution of artificial intelligence in the past decade has been staggering, and now the focus is shifting towards AI and ML systems to understand and generate 3D spaces. As a result, there has been extensive research on manipulating 3D generative models. In this regard, Apple’s AI and ML scientists have developed GAUDI, a method specifically for this job.

An introduction to GAUDI

The GAUDI 3D immersive technique founders named it after the famous architect Antoni Gaudi. This AI model takes the help of a camera pose decoder, which enables it to guess the possible camera angles of a scene. Hence, the decoder then makes it possible to predict the 3D canvas from almost every angle.

What does GAUDI do?

GAUDI can perform multiple functions –

The extensions of these generative models have a tremendous effect on ML and computer vision. Pragmatically, such models are highly useful. They are applied in model-based reinforcement learning and planning world models, SLAM is s, or 3D content creation.
Generative modelling for 3D objects has been used for generating scenes using graf, pigan, and gsn, which incorporate a GAN (Generative Adversarial Network). The generator codes radiance fields exclusively. Using the 3D space in the scene along with the camera pose generates the 3D image from that point. This point has a density scalar and RGB value for that specific point in 3D space. This can be done from a 2D camera view. It does this by imposing 3D datasets on those 2D shots. It isolates various objects and scenes and combines them to render a new scene altogether.
GAUDI also removes GANs pathologies like mode collapse and improved GAN.
GAUDI also uses this to train data on a canonical coordinate system. You can compare it by looking at the trajectory of the scenes.

How is GAUDI applied to the content?

The steps of application for GAUDI have been given below:

Each trajectory is created, which consists of a sequence of posed images (These images are from a 3D scene) encoded into a latent representation. This representation which has a radiance field or what we refer to as the 3D scene and the camera path is created in a disentangled way. The results are interpreted as free parameters. The problem is optimized by and formulation of a reconstruction objective.
This simple training process is then scaled to trajectories, thousands of them creating a large number of views. The model samples the radiance fields totally from the previous distribution that the model has learned.
The scenes are thus synthesized by interpolation within the hidden space.
The scaling of 3D scenes generates many scenes that contain thousands of images. During training, there is no issue related to canonical orientation or mode collapse.
A novel de-noising optimization technique is used to find hidden representations that collaborate in modelling the camera poses and the radiance field to create multiple datasets with state-of-the-art performance in generating 3D scenes by building a setup that uses images and text.

To conclude, GAUDI has more capabilities and can also be used for sampling various images and video datasets. Furthermore, this will make a foray into AR (augmented reality) and VR (virtual reality). With GAUDI in hand, the sky is only the limit in the field of media creation. So, if you enjoy reading about the latest development in the field of AI and ML, then keep a tab on the blog section of the E2E Networks website.

‍

Reference Links

https://www.researchgate.net/publication/362323995_GAUDI_A_Neural_Architect_for_Immersive_3D_Scene_Generation

https://www.technology.org/2022/07/31/gaudi-a-neural-architect-for-immersive-3d-scene-generation/

https://www.patentlyapple.com/2022/08/apple-has-unveiled-gaudi-a-neural-architect-for-immersive-3d-scene-generation.html