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The Hidden Costs Of Cloud And Where To Find Overspending (forbes.com) I know this Article is leaning towards FinOps, but that principles and strategies are on similar trajectories for us working in AI and Data Science. Finding the right mix of computing is a fine art and takes time and understanding to find the perfect mix for you. Cloud Spending: Gartner predicts that enterprise IT spending on public cloud computing will exceed 51% by 20251. However, 30% of cloud spend is currently wastedOptimization Strategies: Key strategies include right-sizing infrastructure, managing workload placement, leveraging multi-cloud environments, and automating the deletion of idle instances.What are your thoughts on a Hybrid modality?
The Stanford Institute for Human-Centered Artificial Intelligence (HAI) has recently released the 2024 Artificial Intelligence Index Report, a comprehensive 500+ page analysis detailing the global AI landscape. It covers national strategies, global usage, publications, patents, funding, technical advancements, public perceptions, projected economic impact, jobs, and education. 2024 Artificial Intelligence Index Report - Top Ten Takeaways AI beats humans on some tasks, but not on all. AI has surpassed human performance on several benchmarks, including some in image classification, visual reasoning, and English understanding. Yet, it trails behind on more complex tasks like competition-level mathematics, visual commonsense reasoning, and planning. Industry continues to dominate frontier AI research. In 2023, the industry produced 51 notable machine learning models, while academia contributed only 15. There were also 21 notable models resulting from industry-academia collaborations in 20
Post about your data science and AI stack—what tools and languages do you usually work with for your projects? Use the following format to answer 😎 Programming Languages:Data Analysis and Visualization:BI Tools:Database Warehouse: Cloud Platforms:Compute Resources:Job Title:Industry:
Artificial Intelligence Appreciation Day – July 16, 2024 Artificial Intelligence Appreciation Day – July 16, 2024
NOVA on PBS has a special titled “Secrets in Your Data” where they talk about privacy, access, use, and innovations. A fun show to watch for fellow data lovers… and if you’re also a fan of the HBO show “Silicon Valley” like I am, you’ll enjoy the discussion around a decentralized internet… will it take off?NOVA - Secrets In Your Data
Today, I had the pleasure of interviewing Andrew Kemp. Andrew is the head of product marketing for data science and AI at Z by HP. Andrew recruited me into the Z by HP global ambassador program and is responsible for its creation. In this episode, we learn about his experience teaching in North Korea for 4 years as well as how the landscape of hardware and community is changing.
Is there ever a scenario you need to keep data out of the cloud? For me it’s company data we’re looking to retain as IP. I’m curious to hear what types of data others consider sensitive.
Grok-1.5 and Grok-1.5 Vision Pro are Elon Musk’s LLMs. In less than a year, the Grok team have developed, demonstrated, and released strong LLM open source models that aim to avoid censorship. With speed and performance, the team has secured a significant Series B funding round. Grok-1.5 (announced March 28, 2024) uses multi query attention with long context capability. It is not yet available outside of early testers, but Grok-1 (released March 17, 2024) is open source, and available for commercial use like Llama, but it’s a 314B 8-bit quantized pre-trained model trained on X data from scratch that can augment responses with X data. Grok-1 performs well on logic tasks, where other models can struggle. Grok-1.5v (announced April 12, 2024) is a multi-modal model that can process visual information to reason and interpret actions because it understands images. In comparing zero-shot without chain-of-thought prompting Grok-1.5v performed well compared to other pre-trained models. AI crea
We did a quick survey to more than 200 Data Scientists and ITDMs. Sharing some interesting responses about tools used to create AI.
AI has entered the chat and is here to stay. Small to medium businesses that are not technology focused everywhere are becoming more aware that AI solutions are available and can unlock the power to streamline and automate some of their workforce’s everyday workflows. In my previous experience, we had SMB clients from various industries like medical, industrial and manufacturing, and home builders, to name a few that voiced they did not want to get left behind but, really didn’t know where to begin when it came to implementing AI and LLMs into their work. It’s unfamiliar and mysterious, but it tends to freeze them. Where do they start?For these small businesses that don’t have a Data Scientist or technical expert already on staff to lead the way, how can they start? Any advice, resources, or stories to share? I’d really like to start a discussion from the people in action on what you’ve recommended to businesses looking to adapt.
I recently saw that a Z by HP ambassador, Ekaterina Butyugina, explored building an app with a chatbot feature that was powered by 3 different LLMs, ChatGPT, Mistral, and Gemma to answer Q & As based on the text file uploads users add using the Streamlit app (an open-source Python framework). When running, Gemma generated responses in approx. 3 seconds. For others building similar Q & A chatbots leveraging different LLMs…Were times and results drastically different among your various integrated LLMs? What were your times and findings?Curious to hear! Linking Ekaterina’s Medium article with step-by-step instructions here:Commercial, Gated, and Open-Source LLMs in Your File QA Chatbot
AI is making significant strides in retail, improving everything from inventory management to customer interaction. How are you leveraging AI in your retail projects? What improvements or challenges have you noticed? I’d love to hear others thoughts around the real-world effects of AI on our retail operations!
AI is moving so fast. I wonder how AI and Design work together. How are you incorporating design as part of your AI creation process?
Companies interested in creating AI and integrating solutions into their processes and systems constantly look to credible partners for help, but how can they know what they’re getting is reliable?In an attempt to address this gap, HP teamed up with NVIDIA, Microsoft, Intel, AMD, and other industry leading AI compute solution providers to launch the industry’s first AI MasterClass for partners to better help companies address their AI needs. Learn more about the AI MasterClass powered by HP University.
GaLore: Memory-Efficient LLM Training by Gradient Low-Rank ProjectionPublished on Mar 5Featured in Daily Papers on Mar 6 Authors:Jiawei Zhao, Zhenyu Zhang, Beidi Chen, Zhangyang Wang, Anima Anandkumar, Yuandong Tian Abstract Training Large Language Models (LLMs) presents significant memory challenges, predominantly due to the growing size of weights and optimizer states. Common memory-reduction approaches, such as low-rank adaptation (LoRA), add a trainable low-rank matrix to the frozen pre-trained weight in each layer, reducing trainable parameters and optimizer states. However, such approaches typically underperform training with full-rank weights in both pre-training and fine-tuning stages since they limit the parameter search to a low-rank subspace and alter the training dynamics, and further, may require full-rank warm start. In this work, we propose Gradient Low-Rank Projection (GaLore), a training strategy that allows full-parameter learning but is more memory-efficient than com
HP and NVIDIA announce AI Workstations that are optimized for pandas dataframes. =========================================================================NVIDIA and HP Supercharge Data Science and Generative AI on Workstations Coming to Z by HP AI Studio, NVIDIA CUDA-X Data Processing Libraries Boost Python Pandas Software for Millions of Data ScientistsMarch 7, 2024HP Amplify — NVIDIA and HP Inc. today announced that NVIDIA CUDA-X™ data processing libraries will be integrated with HP AI workstation solutions to turbocharge the data preparation and processing work that forms the foundation of generative AI development.Built on the NVIDIA CUDA® compute platform, CUDA-X libraries speed data processing for a broad range of data types, including tables, text, images and video. They include the NVIDIA RAPIDS™ cuDF library, which accelerates the work of the nearly 10 million data scientists using pandas software by up to 110x using an NVIDIA RTX™ 6000 Ada Generation GPU instead of a CPU-only
Stable Baselines3 (SB3) is a set of reliable implementations of reinforcement learning algorithms in PyTorch. On January 24th, 2024 contributors released updated documentation for the DRL algos. You can read a detailed presentation of Stable Baselines3 in the v1.0 blog post or our JMLR paper. For more info on SB3, refer to the github site.
Fun video by HP Data Science Ambassador to see how ChatGPT scores on IQ tests.
New computers with powerful GPUs can bottleneck at the CPU if code is not modified to increase batch sizes sent to the GPUs. To improve processing performance of your system, update and tune hyperparameters in your code to deliver larger batch payloads to your GPU.Tuning to balance overfitting and underfitting, we found a 5x performance on model training, giving our team more time to do experiments. Adil Lheureux published a good read on the topic called “How to maximize GPU utilization by finding the right batch size” that you should check out.
How close is OpenAI to AGI? Articles about Project Q* suggest close than some might think.
Leaving this joke here and hopes that it brightens your day:There are two types of Data Scientists: 1. Those that can extrapolate from incomplete data
Hi All. I've been searching for ways to increase my AI and data science knowledge. DeepLearning.AI has great short courses for relevant AI topics. Check it out!!
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