#GenerativeArtificialIntelligence

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Utilize Dell Data Lakehouse To Revolutionize Data Management

Introducing the Most Recent Upgrades to the Dell Data Lakehouse. With the help of automatic schema discovery, Apache Spark, and other tools, your team can transition from regular data administration to creativity.

Dell Data Lakehouse

Businesses’ data management plans are becoming more and more important as they investigate the possibilities of generative artificial intelligence (GenAI). Data quality, timeliness, governance, and security were found to be the main obstacles to successfully implementing and expanding AI in a recent MIT Technology Review Insights survey. It’s evident that having the appropriate platform to arrange and use data is just as important as having data itself.

As part of the AI-ready Data Platform and infrastructure capabilities with the Dell AI Factory, to present the most recent improvements to the Dell Data Lakehouse in collaboration with Starburst. These improvements are intended to empower IT administrators and data engineers alike.

Dell Data Lakehouse Sparks Big Data with Apache Spark

An approach to a single platform that can streamline big data processing and speed up insights is Dell Data Lakehouse + Apache Spark.

Earlier this year, it unveiled the Dell Data Lakehouse to assist address these issues. You can now get rid of data silos, unleash performance at scale, and democratize insights with a turnkey data platform that combines Dell’s AI-optimized hardware with a full-stack software suite and is driven by Starburst and its improved Trino-based query engine.

Through the Dell AI Factory strategy, this are working with Starburst to continue pushing the boundaries with cutting-edge solutions to help you succeed with AI. In addition to those advancements, its are expanding the Dell Data Lakehouse by introducing a fully managed, deeply integrated Apache Spark engine that completely reimagines data preparation and analytics.

Spark’s industry-leading data processing capabilities are now fully integrated into the platform, marking a significant improvement. The Dell Data Lakehouse provides unmatched support for a variety of analytics and AI-driven workloads with to Spark and Trino’s collaboration. It brings speed, scale, and innovation together under one roof, allowing you to deploy the appropriate engine for the right workload and manage everything with ease from the same management console.

Best-in-Class Connectivity to Data Sources

In addition to supporting bespoke Trino connections for special and proprietary data sources, its platform now interacts with more than 50 connectors with ease. The Dell Data Lakehouse reduces data transfer by enabling ad-hoc and interactive analysis across dispersed data silos with a single point of entry to various sources. Users may now extend their access into their distributed data silos from databases like Cassandra, MariaDB, and Redis to additional sources like Google Sheets, local files, or even a bespoke application within your environment.

External Engine Access to Metadata

It have always supported Iceberg as part of its commitment to an open ecology. By allowing other engines like Spark and Flink to safely access information in the Dell Data Lakehouse, it are further furthering to commitment. With optional security features like Transport Layer Security (TLS) and Kerberos, this functionality enables better data discovery, processing, and governance.

Improved Support Experience

Administrators may now produce and download a pre-compiled bundle of full-stack system logs with ease with to it improved support capabilities. By offering a thorough evaluation of system condition, this enhances the support experience by empowering Dell support personnel to promptly identify and address problems.

Automated Schema Discovery

The most recent upgrade simplifies schema discovery, enabling you to find and add data schemas automatically with little assistance from a human. This automation lowers the possibility of human mistake in data integration while increasing efficiency. Schema discovery, for instance, finds the newly added files so that users in the Dell Data Lakehouse may query them when a logging process generates a new log file every hour, rolling over from the log file from the previous hour.

Consulting Services

Use it Professional Services to optimize your Dell Data Lakehouse for better AI results and strategic insights. The professionals will assist with catalog metadata, onboarding data sources, implementing your Data Lakehouse, and streamlining operations by optimizing data pipelines.

Start Exploring

The Dell Demo Center to discover the Dell Data Lakehouse with carefully chosen laboratories in a virtual environment. Get in touch with your Dell account executive to schedule a visit to the Customer Solution Centers in Round Rock, Texas, and Cork, Ireland, for a hands-on experience. You may work with professionals here for a technical in-depth and design session.

Looking Forward

It will be integrating with Apache Spark in early 2025. Large volumes of structured, semi-structured, and unstructured data may be processed for AI use cases in a single environment with to this integration. To encourage you to keep investigating how the Dell Data Lakehouse might satisfy your unique requirements and enable you to get the most out of your investment.

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Get ready for a sneak peek into the future of Apple's artificial intelligence (AI) ambitions! Apple CEO Tim Cook has surprised industry watchers by revealing that the company might unveil details about its generative AI plans sooner than expected. Previously anticipated for a grand debut at the upcoming Worldwide Developers Conference (WWDC) in June, glimpses of Apple's AI strategies could be on display at the upcoming "Let Loose" event scheduled for May 7th. This exciting revelation came during Apple's recent quarterly earnings call, where Cook expressed unwavering optimism about the company's position and prospects within the rapidly evolving generative AI landscape. Apple Accelerates Its AI Journey AI Integration: A Seamless Blend Within the Apple Ecosystem Cook's enthusiasm goes beyond unveiling Apple's AI roadmap – it extends to how AI will seamlessly integrate into the existing Apple ecosystem. He highlighted the company's distinct advantage in hardware innovation, particularly with processors and engines. This in-house expertise will undoubtedly play a crucial role in creating a cohesive and efficient experience when integrating AI features into Apple devices. Furthermore, Cook emphasized Apple's unwavering commitment to user privacy. He reassured users that upcoming AI functionalities will be designed to operate directly on-device, adhering to Apple's stringent privacy standards. This focus on on-device processing ensures that user data remains secure and under their control. Apple's proactive approach to AI development is undeniable. The company has been actively bolstering its AI capabilities through strategic acquisitions of AI-focused companies and by publishing insightful research papers on AI models. These strategic moves underscore Apple's serious intent to become a major player in the ever-evolving AI domain. A Glimpse into AI-Powered Features: What to Expect While details remain scarce, some potential AI-powered features have piqued user interest. One such feature is an "Intelligent Search" function within the Safari browser. This innovative feature aims to analyze and summarize articles and web pages, significantly enhancing the user experience by providing quick and readily digestible information. Another exciting concept in the pipeline is an AI-powered "web eraser" tool. This feature would empower users to remove specific elements from a web page based on their preferences. Imagine the convenience of easily eliminating distracting ads or irrelevant content from a webpage, creating a cleaner and more focused reading experience. These are just a few of the anticipated AI-powered features that might be showcased at the upcoming "Let Loose" event. The main stage at WWDC in June is expected to be the official launchpad for these functionalities, alongside the unveiling of iOS 18 and macOS 15. Apple's decision to tease its AI plans at both the "Let Loose" event and WWDC demonstrates a strategic approach. The May 7th event will provide a glimpse into the company's AI vision, sparking user curiosity and industry conversation. WWDC, traditionally a developer-focused event, will then delve deeper into technical aspects and unveil the functionalities themselves. Frequently Asked Questions Q: What is generative AI? A: Generative AI refers to a subfield of artificial intelligence focused on creating new data, such as text, images, or code. Q: How will Apple integrate AI into its devices? A: Cook emphasized that Apple's AI features will be designed to operate on-device, meaning user data remains on the device itself rather than being sent to remote servers for processing. This approach aligns with Apple's commitment to user privacy. Q: What AI-powered features might we see at the "Let Loose" event? A: While details remain under wraps, potential features include an "Intelligent Search" function within Safari and an AI-powered "web eraser" tool. Q: Where will Apple officially unveil its AI functionalities? A: The official launch of Apple's AI-powered features is expected to take place at the upcoming Worldwide Developers Conference (WWDC) in June.

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Variational AI Announces Generative AI Project With Merck

With funding from the CQDM Quantum Leap program, Variational AI, the creator of the EnkiTM generative artificial intelligence (AI) platform for drug development, has announced a joint effort with Merck Research Labs. Merck has agreed to be an early adopter of the EnkiTM Platform in order to test the platform's potential to produce new small molecules based on targets that Merck has selected.

By utilizing generative AI, the EnkiTM Platform is intended to expedite the drug discovery process. EnkiTM is a foundation model that can produce new lead-like structures that are synthesizable, selective, and unique. It generates original graphics in response to predetermined cues, much like previous generative AI models like DALL-E and Midjourney. With EnkiTM, molecules are described using the language of chemistry through a sequence of prompts based on a target product profile (TPP). Afterwards, EnkiTM quickly produces structures that satisfy the TPP, giving chemists a variety of lead-like compounds that are synthesizable, selective, and available for further analysis.

AI is being more and more used in medication discovery; many businesses are using their own proprietary models to find and create new products. By offering a platform where chemists can submit their TPPs and receive innovative structures in a matter of days, variational AI seeks to streamline the process and expedite the lead optimization process.

The CEO of Variational AI, Handol Kim, announced with delight that Merck will be using the EnkiTM Platform in early access. Through this partnership, Merck will be able to evaluate Enki's capacity to produce innovative small molecules that are customized to their particular targets. Kim underscored the value of incorporating AI into drug discovery and said that chemists who prefer not to create their own generative AI models might use EnkiTM as a substitute.

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[govindhtech]

Intel Tiber Developer Cloud, Text- to-Image Stable Diffusion

Check Out GenAI for Text-to-Image with a Stable Diffusion Intel Tiber Developer Cloud Workshop.

What is Intel Tiber Developer Cloud?

With access to state-of-the-art Intel hardware and software solutions, developers, AI/ML researchers, ecosystem partners, AI startups, and enterprise customers can build, test, run, and optimize AI and High-Performance Computing applications at a low cost and overhead thanks to the Intel Tiber Developer Cloud, a cloud-based platform. With access to AI-optimized software like oneAPI, the Intel Tiber Developer Cloud offers developers a simple way to create with small or large workloads on Intel CPUs, GPUs, and the AI PC.

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Developers and enterprise clients have the option to use free shared workspaces and Jupyter notebooks to explore the possibilities of the platform and hardware and discover what Intel can accomplish.

Text-to-Image

This article will guide you through a workshop that uses the Stable Diffusion model practically to produce visuals in response to a written challenge. You will discover how to conduct inference using the Stable Diffusion text-to-image generation model using PyTorch and Intel Gaudi AI Accelerators. Additionally, you will see how the Intel Tiber Developer Cloud can assist you in creating and implementing generative AI workloads.

Text To Image AI Generator

AI Generation and Steady Diffusion

Industry-wide, generative artificial intelligence (GenAI) is quickly taking off, revolutionizing content creation and offering fresh approaches to problem-solving and creative expression. One prominent GenAI application is text-to-image generation, which uses an understanding of the context and meaning of a user-provided description to generate images based on text prompts. To learn correlations between words and visual attributes, the model is trained on massive datasets of photos linked with associated textual descriptions.

A well-liked GenAI deep learning model called Stable Diffusion uses text-to-image synthesis to produce images. Diffusion models work by progressively transforming random noise into a visually significant result. Due to its efficiency, scalability, and open-source nature, stable diffusion is widely used in a variety of creative applications.

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The Stable Diffusion model in this training is run using PyTorch and the Intel Gaudi AI Accelerator. The Intel Extension for PyTorch, which maximizes deep learning training and inference performance on Intel CPUs for a variety of applications, including large language models (LLMs) and Generative AI (GenAI), is another option for GPU support and improved performance.

Stable Diffusion

To access the Training page once on the platform, click the Menu icon in the upper left corner.

The Intel Tiber Developer Cloud‘s Training website features a number of JupyterLab workshops that you may try out, including as those in AI, AI with Intel Gaudi 2 Accelerators, C++ SYCL, Gen AI, and the Rendering Toolkit.

Workshop on Inference Using Stable Diffusion

Thwy will look at the Inference with Stable Diffusion v2.1 workshop and browse to the AI with Intel Gaudi 2 Accelerator course in this tutorial.

Make that Python 3 (ipykernel) is selected in the upper right corner of the Jupyter notebook training window once it launches. To see an example of inference using stable diffusion and creating an image from your prompt, run the cells and adhere to the notebook’s instructions. An expanded description of the procedures listed in the training notebook can be found below.

Note: the Jupyter notebook contains the complete code; the cells shown here are merely for reference and lack important lines that are necessary for proper operation.

Configuring the Environment

Installing all the Python package prerequisites and cloning the Habana Model-References repository branch to this docker will come first. Additionally, They are going to download the Hugging Face model checkpoint.%cd ~/Gaudi-tutorials/PyTorch/Single_card_tutorials !git clone -b 1.15.1 https://github.com/habanaai/Model-References %cd Model-References/PyTorch/generative_models/stable-diffusion-v-2-1 !pip install -q -r requirements.txt !wget https://huggingface.co/stabilityai/stable-diffusion-2-1-base/resolve/main/ v2-1_512-ema-pruned.ckpt

Executing the Inference

prompt = input("Enter a prompt for image generation: ")

The prompt field is created by the aforementioned line of code, from which the model generates the image. To generate an image, you can enter any text; in this tutorial, for instance, they’ll use the prompt “cat wearing a hat.”cmd = f'python3 scripts/txt2img.py --prompt "{prompt}" 1 --ckpt v2-1_512-ema-pruned.ckpt \ --config configs/stable-diffusion/v2-inference.yaml \ --H 512 --W 512 \ --n_samples 1 \ --n_iter 2 --steps 35 \ --k_sampler dpmpp_2m \ --use_hpu_graph'

print(cmd) import os os.system(cmd)

Examining the Outcomes

Stable Diffusion will be used to produce their image, and Intel can verify the outcome. To view the created image, you can either run the cells in the notebook or navigate to the output folder using the File Browser on the left-hand panel:

/Gaudi-tutorials/PyTorch/Single_card_tutorials/Model-References /PyTorch/generative_models/stable-diffusion-v-2-1/outputs/txt2img-samples/Image Credit To Intel

Once you locate the outputs folder and locate your image, grid-0000.png, you may examine the resulting image. This is the image that resulted from the prompt in this tutorial:

You will have effectively been introduced to the capabilities of GenAI and Stable Diffusion on Intel Gaudi AI Accelerators, including PyTorch, model inference, and quick engineering, after completing the tasks in the notebook.

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