World of Engineering Why are there no academic scientists on this list? There is no incentive for any of these individuals to push AI safety.

Paper: arxiv.org/abs/2311.15206 Project Page: uark-cviu.github.io/projects/insec…

Amidst the rush to build bigger models, the real game-changer for AI success is quality data. 🔍 In our latest blog, we unpack: 👉 Real-world examples of model failures due to bad data 👉 The secret to building high-performing models 👉 And more! voxel51.com/blog/data-qual… #ML

I recently learned that some companies are adding "Must have published papers in XYZ conference" where XYZ is something like CVPR, ICLR, NeurIPS, etc. PLEASE STOP. Not only is published XYZ work mostly irrelevant to the actual work they'll be doing at your company, it's not

The potential revocation of President Biden’s AI Executive Order, as I discussed in Friday's VentureBeat article by Emilia David, raises serious concerns for the future of AI regulation and innovation. While voluntary responsible AI practices are vital, I shared my thoughts about

🎉 It’s Giving Tuesday, and we’re doubling down on giving back! Here's how: Today, we’re donating to three amazing charities on behalf of our AI Meetup community: AI4ALL, Heart to Heart Int'l, and Oceana. 🌎 And because it’s Giving Tuesday, we have the chance to 2x our annual

In this paper from NeurIPS 2024, researchers challenge the growing trend of using AI-generated images to train vision models. They compared two approaches: synthetic images from Stable Diffusion versus carefully selected real photos from the same dataset used to train it. The

🎥🖐🎥🖐 New Video GenAI with Better Rendering of Hands --> Instructional Video Generation 🎥🖐🎥🖐 New Paper Alert . Instructional Video Generation – we are releasing a new method for Video Generation that explicitly focuses on fine-grained, subtle hand motions. Given a

⚠️ 📈 ⚠️ Annotation mistakes got you down? ⚠️ 📈 ⚠️ There's been a lot of hooplah about data quality recently. Erroneous labels, or mislabels, put a glass ceiling on your model performance; they are hard to find and waste a huge amount of expert MLE time; and importantly, waste

🔥 New blog post! Dive into the intricacies of data bias and discover how embeddings provide insightful perspectives on the differences between real and synthetic data. Key findings: 💡 Some synthetic images that look realistic to humans show unexpected patterns in feature

💎 Introducing ZCore: Find hidden gems in massive datasets without labels. This open source solution cuts datasets by 95%—perfect for efficient model training, fine-tuning, or eval. See how it works: voxel51.com/blog/how-to-ta… #AI #ML #ComputerVision

🏈 Hey, NFL: Meet Visual AI, Your Old Friend 🏈 1/n The NFL is a game of inches, split-second decisions, and an ever-evolving playbook of strategies. While the league has embraced modern technology in areas like player safety and fan engagement, the disastrous 4th down call

Did you hear? DeepSeek just dropped Janus-Pro 7B, an open source game-changer for multimodal AI. And here's where it gets even better... Thanks to our own harpreet, we're excited to introduce our Janus-Pro VQA Plugin for FiftyOne. 🔥 Tap into Janus-Pro’s visual question

DeepSeek AI is on fire! Just yesterday they shipped Janus-Pro, a Unified Multimodal Model for multimodal understanding and text-to-image generation. The model comes in two model sizes: • Janus-Pro-1B, which is a little over 1.5 billion parameters • Janus-Pro-7B with 7 billion

Selective Transparency and The Battle for Open Source What a great way to start a new week other than a new contributed article in VentureBeat!!! Focus: the battle for open source AI through the serious risk that selective transparency poses. venturebeat.com/ai/the-open-so…

If you’re deploying models into the wild, this new open source technique for visualizing model certainty on unlabeled data at scale could change the game for you. ⬇️ In our latest post, we dive into: ✅ A simple method for turning model outputs into actionable certainty scores

PaliGemma2-Mix is now integrated into FiftyOne! You can use this model for: • Image captioning (multiple detail levels) • Object detection • Semantic segmentation (Not perfect, but good for initial exploration) • Optical character recognition (OCR) • Visual question

Builders gonna build! Eric Hofesmann from the Voxel51 rolled out a new #FiftyOne plugin last week for interactive visualization of the hyperspectral imagery including the ability to vary the transfer function dynamically. Check out the plugin here with instructions on how to