The Next AI Blog

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Synthetic and Real-World Data in Facial Landmark Detection Models

Can synthetic data lead to better performance on the computer vision task of facial landmark detection? This is the main question we set out to answer in our new research paper, Facial Landmark Detection Using Synthetic Data. Not only is synthetic data privacy-preserving, it is also much more scalable than real-world data. Synthetic data technology...
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Review of Synthetic Data Generation using Imitation Training Paper

Aman Kishore, et al. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops, October 2021 A Review of Synthetic Data Generation using Imitation Training Paper Problem Statement: One of the central challenges of supervised learning methods is training models that are robust enough to handle examples not adequately represented in the training...
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Five Top Papers from NeurIPS 2021

The Conference on Neural Information Processing Systems, NeurIPS, is one of the most anticipated AI research conferences. Apart from neuroscience and machine learning, NeurIPS features a dazzling array of topics like cognitive science, psychology, information theory, and computer vision. NeurIPS 2021 featured an exciting lineup of keynote speakers, paper presentations, and panel discussions. One of...
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Five Takeaways from GTC 2021

Nvidia gave us a glimpse into the future of AI in its GPU Technology Conference (GTC) 2021. Here, we summarize five key takeaways from the conference. 1. Omniverses are interconnected virtual worlds Talks of metaverse dominated headlines recently – and for good reasons. In the future, we’ll be able to instantaneously teleport across virtual worlds...
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Microsoft’s Face Analysis in the Wild Using Synthetic Data Alone – Summarized

From unlocking your iPhone with your face to making payments with a glance at a camera, facial recognition technology has afforded us convenience unimaginable just years ago. Yet, collecting the necessary data for facial recognition tasks is not always a walk in the park. Not only does collecting and labeling face data require significant effort...
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Embedding Synthetic Assets to Train AI Models

Dr. Jonathan Laserson, Head of AI Research at Datagen Technologies, is an expert in the field of photorealistic synthetic images. He shares how Neural Radiance Fields (NeRF) can be used to generate a nearly infinite number of synthetic assets to train AI models. Dr. Laserson also explains how synthetic objects can be represented in a...
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Why I Joined Datagen

Today Gil dives into the life of Datagen’s head researcher Dr. Jonathan Laserson. Dr. Jonathan Laserson is the head of research at Datagen and an early adopter of deep learning algorithms. He has a great list of credentials. He has a bachelor’s and master’s at Technion- the Israel Institute of Technology- and earned his Ph.D....
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The Danger of Hand Labeling Your Data

“Paradoxically, data is the most under-valued and de-glamorized aspect of AI,” lamented Google researchers in a 2021 paper Data Cascades in High Stakes AI. The paper also shrewdly pinpointed poor data quality as the accomplice in causing a series of compounding events causing negative downstream effects. This phenomenon of data cascade served as an impetus...
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ML System Development Takes More Than ML Modeling

As artificial intelligence (AI) becomes increasingly ubiquitous, companies are flocking to implement AI in their products and services. From providing recommendations to discovering drugs, the potential of AI is vast. Yet, many organizations fail to deploy AI at scale and realize such potential.  In fact, 69.5% of companies named artificial intelligence AI the most impactful...
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