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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

Maikel Ronnau et al will publish Automatic segmentation and classification of Papanicolaou-stained cells and dataset for oral cancer detection in next month’s Computers in Biology and Medicine.Their research uses a CNN U-Net architecture with ResNet encoder to achieve expert level performance by segmenting and classifying a trained on UFSC OCPap dataset of oral cytology images. The team uses transfer learning for image segmentation to train the model which is used to evaluate more than 1500 images from 52 patients labeled by specialists resulting in an average Dice score of 0.66 and Intersection over Union (IoU) of 0.65. “The CNN model architecture for encoding layers are based on DenseNet-169 while the decoding layers are LinkNet but to replace the regular softmax layer with a temperature scaling softmax with a temperature parameter value of 0.1 to increase the confidence of the predictions and avoid the bias towards the prediction of background pixels. The model’s prediction is furth

GPU availability and job interruption can be an issue for me, so this paper caught my attention. The paper is titled “Mirage: Towards Low-interruption Services on Batch GPU Clusters with Reinforcement Learning” and it proposes a method for reducing service interruptions on GPU clusters for deep learning jobs.The tool is called Mirage. Mirage is a Slurm-compatible foundational model that uses statistical and reinforcement learning (RL) techniques to proactively provision resources to mitigate interruptions. To accomplish this, researchers alter the decoder transformer architecture to create a Mixture of Experts (MoE) of neural networks. The neural networks share the same network architecture to support scaling up of model capacity and determine ‘best-fit experts’ that optimize job trace analysis outputs for resource allocations. What’s really cool about this paper is how it investigates the use of Deep Q-Learning RL techniques to reenforce rewards to improve GPU queue wait times for a f

2024Galileo's Hallucination Index report is a critical resource for AI developers, offering detailed insights into the performance and cost-effectiveness of various large language models (LLMs). This enables developers to make informed decisions to enhance the reliability and accuracy of GenAI applications. The report highlights the current landscape of LLMs and sets the stage for future advancements.Key FindingsModels Evaluated: The report covers 22 models, including 10 closed-source and 12 open-source models from providers like OpenAI, Anthropic, Meta, Google, and Mistral. Models were assessed based on context length capabilities and source type.Major TrendsOpen Source Improvement: Open-source models are rapidly closing the performance gap with closed-source models. Model Size: Smaller models sometimes outperform larger ones, challenging the belief that bigger is always better. Context Length Performance: Many models maintain high performance even with extended context lengths. Anthr

At last month's Figma annual conference, Config, several new features were unveiled, including Figma AI. Designers like me have been eagerly awaiting updates from the collaborative design tool giant, especially after their deal with Adobe fell through. The question now is: how will Figma lead the way for creatives to design with AI as a supportive tool? How will it be different from what we’ve seen from existing tools?The new features are impressive. They include a text translation feature to visualize product designs in different languages and Figma Slides, a natural business and feature decision for Figma as designers are already presenting in Figma or creating slide decks rather than migrating to Google Slides or PowerPoint, as can be seen from the Figma Community templates available. Additionally, a redesign of Dev Mode allows developers to focus on details like new published updates.The most significant feature is Figma AI, where users can click a button to "Make a design," enter

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

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.

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.