Z by HP Data Science & AI Community Webinar
Hi DS Community! Reminder that there's less than 24 hours until the next AI Studio webinar event.ready to collaborate and hear questions. The team will have subject matter experts and experience managers ready to field questions and ideas. If you're not already using AI Studio but are interested in trying it out for yourself, you can contact the team after the event to get you onboarded. Can't wait to see you there!https://bit.ly/3MFISxkRegister Today!
Thank you for joining our recent webinar, “Skip the Cloud: Train AI Models Using Local Resources with AI Studio”, and hope you found the session both engaging and informative!If you’d like to revisit the webinar or explore additional resources, you can find the recording and related materials below. Additionally, if you're interested in learning more about AI Studio, feel free to schedule a one-on-one session with us here: https://calendly.com/sothan-thach-hp/ 🎥Webinar Recording We're looking forward to connecting with you at our next webinar on October 4th! Register here Also get ready for an exclusive look at the latest innovations from HP and the Z by HP team, coming your way at HP Imagine 2024 on September 24th. Stay tuned for all the exciting updates! Resources🖥️ Z by HP AI Studio 📝 AI Studio EAP Sign-up Form Webinar Q&AsQ: Are there any specific requirements for data preparation before uploading an asset from local storage or S3? A: That's a great question. So, this isn’t
OpenAI has announced the release of its latest AI model, codenamed Strawberry, now officially named OpenAI o1. This new model is designed to improve how AI handles complex tasks in math, science, and coding by evaluating its steps before providing a response.Key Updates: Advanced Reasoning: Unlike previous models, OpenAI o1 takes more time to think through problems, emulating a more human-like reasoning process. This approach helps the model better handle difficult questions and adhere more closely to safety guidelines. New Naming and Features: OpenAI o1 introduces a new naming system and will be available alongside existing models like GPT-4o. There’s also a lightweight variant, o1-mini, focused on code generation. Rolling Out Access: The new model is being introduced in phases. Starting today, ChatGPT Plus and Team users will get limited access to both o1-preview and o1-mini, with educational and enterprise users gaining access next week. Current Limitations: While promising, O
Hello everyone!I just created a blog post about exploring current Medical LLMs. If you are interested about AI in Healthcare and GenAI, and would like to learn how to create demos with Python and Gradio, I invite you to read it! https://medium.com/@miracfence/exploring-fine-tuned-medical-llms-cbb42dd4911f
According to Gartner, by 2027, 40% of generative AI (GenAI) solutions will feature multimodal capabilities—encompassing text, images, audio, and video—up from just 1% in 2023. This transition from single-modal to multimodal models promises to improve human-AI interactions and create unique opportunities for differentiated GenAI-powered products and services.The Rise of Multimodal GenAIMultimodal Generative AI is set to revolutionize enterprise applications by introducing innovative features and capabilities previously unattainable. Its influence spans across various industries and applications, enhancing interactions at every point where AI meets human engagement. Currently, many multimodal models incorporate only two or three modalities, but this is expected to expand significantly in the coming years.Open-Source LLMsOpen-source large language models (LLMs) are transforming enterprise value by democratizing access to advanced generative AI. They enable businesses to tailor models for
Ready to dive into the future of AI workflow? Join us for "Skip the Cloud: Train AI models using local resources with AI Studio"! This engaging webinar will give you the lowdown on Z by HP’s latest AI tool that’s set to transform how you work. We’ll explore standout features, reveal how it can streamline your tasks, and answer all your questions in real-time.What to ExpectWelcome & Introduction Live Demo: AI Studio Interactive Q&A Session Next Steps and ResourcesSecure Your Spot Now—Register Here!
As it is already well-known, for the last couple of years companies have been using AI in films in order to optimize multiple processes, from computer graphics, to animation, etc. Today, however, we can see it being hugely involved in the process, even in the whole automation of creating a film. For some artists, this may be a huge concern, for others it MAY be an opportunity to explore their creativity. In posts and programs like this https://runwayml.com/news/runway-partners-with-tribeca-festival we can see the potential in GenAI applications in the film industry, as long as the latest research https://runwayml.com/research/introducing-gen-3-alpha What do you think that are the actual limits of this? Do you think it will eventually ceil and just become one more tool for humans to explore their own creativity? Or, do you think it will become something like that one Black Mirror’s episode “Joan is Awful” where content is being automatically generated without any restrictions, just for
Some years ago, one of the things that was hyped in the AI Community but that I still think has huge relevance for the future in AI is Federated Learning and Privacy Preserving Machine Learning (PPML). This comes from an idea that future devices will have more than enough power to run the majority of our biggest models, or that models will be efficient enough to run on them. This without losing the aspect of keeping our data private and secure.What do you think about this? Do you think this is where AI Development would head eventually? What others directions could be the most feasible and with the most usage our future? A couple of those that come to my mind are Embodied AI and Causal Machine Learning.
Google researchers have unveiled GameNGen, a groundbreaking game engine entirely driven by AI that can simulate intricate video games in real-time. This innovative technology was showcased by recreating the iconic first-person shooter, Doom.GameNGen employs a neural network to generate game frames at more than 20 frames per second, creating visuals that closely mirror the original game. The AI model underwent a two-phase training process: initially, an AI agent was taught to play Doom, followed by training a separate model to predict the next frame based on previous actions and frames.“We introduce GameNGen, the first game engine entirely powered by a neural network, capable of real-time interaction within a complex environment over extended periods and at high quality,” the researchers detailed in their paper.This technology holds the potential to revolutionize game development by enabling the creation and modification of games through text descriptions or example images, moving away
As we wrap up August 2024, we’re excited to highlight some of the most insightful articles and posts shared by our vibrant community. Dive into these expert opinions, groundbreaking research, and practical guides to stay ahead in the AI and data science fields.AI Trends in Data ScienceDelve into the "AI Trends in Data Science" report, brought to you by NVIDIA and our community of 800 data scientists. This report covers the latest trends, key adoption barriers, and emerging opportunities. Enhance your AI strategy by exploring these unique insights. Check out the full report here. Optimizing Language Models with NVIDIANVIDIA's research team has developed an innovative approach to creating smaller, highly accurate language models using structured weight pruning and knowledge distillation. This breakthrough reduces training time, costs, and improves model performance. Learn about how this technique was applied to the Llama-3.1-Minitron 4B model here.Webinar: AI Studio Live Demo RecapOur fi
Explore what 800 data scientists have to say about the future of AI with our “AI Trends in Data Science” report, developed in partnership with NVIDIA. This in-depth report offers unique insights into the latest trends, highlights key barriers to adoption, and emerging opportunities in AI. Unlock these key insights and supercharge your AI strategy today: https://bit.ly/4cUmVGcWhich findings from the report stand out to you, and how do you think these trends will shape the future of AI?
NVIDIA's research team has introduced an innovative approach to developing smaller, yet highly accurate language models by leveraging structured weight pruning and knowledge distillation. These techniques are designed to streamline the model without compromising its performance, offering several key benefits for developers and organizations focused on AI development.Key Benefits and Innovations: Performance Improvement: The refined models achieve a 16% improvement in MMLU (Massively Multilingual Language Understanding) scores. MMLU is a benchmark used to evaluate a model's understanding and processing of multilingual content, making this improvement significant for applications that require high linguistic accuracy across different languages. Training Efficiency: The method reduces the number of tokens required to train new models by a factor of 40. Tokens are the building blocks of language models, and reducing their number drastically cuts down on computational resources and time n
Just wanted to say a quick thank you for joining our first Webinar “AI Studio Live Demo: Empowering Early Access Program Participants”. It was great to have you with us, and I hope you found it useful!If you missed anything or want to check it out again, we've got the recording and some extra resources available below.Resources🎥 Webinar Recording AI Studio EAP Sign-up Form AI Studio Frequently Asked QuestionsFeel free to reach out if you have any questions or just want to chat more about what we covered. We're always happy to help!Thanks again and stay tuned for more details about next webinar in a few weeks.
HP Data Science Ambassador Akash James uses a zero-shot approach to create Veronica to be aware of her spatial surroundings through visual, audio, and text interactions to describe what she sees. Check out his LinkedIn post to see a demo of how it works…
OpenAI announced the launch of fine-tuning for GPT-4o, addressing one of the most frequently requested features from developers. This new capability allows developers to fine-tune the GPT-4o model with custom datasets, enhancing performance while reducing costs for specific applications.To support the rollout, OpenAI is offering 1 million free training tokens per day for every organization through September 23. This incentive is designed to help developers explore fine-tuning without immediate cost concerns.Developers can begin fine-tuning by visiting the fine-tuning dashboard. The process involves:Accessing the dashboard. Clicking "Create" to start a new project. Selecting gpt-4o-2024-08-06 from the base model options.GPT-4o Mini Also AvailableOpenAI also offers GPT-4o Mini for fine-tuning. This smaller model is available with 2 million free training tokens per day until September 23. To use it, developers should select gpt-4o-mini-2024-07-18 on the fine-tuning dashboard.Data Privacy
Data StorageAI Studio does not store any data, it simply identifies where the data exists and provide tools for data processing. Users have complete control of their own data governance and systems. Data Source IngestionLinking to the Data Source: Users can link AI Studio to a data source like an S3 bucket by providing the necessary pointers or connection details. This step establishes a connection between AI Studio and the data stored in the cloud (in this case, AWS). Downloading Data: Depending on specific project needs and the setup of AI Studio, users can download the required data from the source (like the S3 bucket) to their local device. Data Stays on the User's Device: Once downloaded, the data resides on the user’s local device. Data Governance and Security: users are responsible for managing the access, security, and compliance aspects of the data used with AI Studio. AI Studio AccessLocal: AI Studio allows users to access and manage data stored directly on their local devic
Model Training BasicsWhen training models using AI Studio, compute runs locally on the user's laptop. Once users have access to AI Studio, models can be trained through the notebooks section. Users can write code, collaborate with team members, run the code and track model training experiments through MLFlow and TensorBoard dashboards. AI Studio Effect on Model AccuracyArtificial Intelligence and Machine Learning solutions can only ever be as accurate as the data the solution is trained on. AI Studio will not independently alter or fact check the data provided. However, many of the tools included in AI Studio do promote good data hygiene practices which can have a significant effect on the reliability of the model created. NLP/LLM libraries included with Large ImageLarge images include Tensorflow and Pytorch, common packages for running Deep Learning experiments as well as Scikit-learn for basic NLP. AI Studio does not have an NLP/LLM base image at this time; however, the current versi
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