The Next AI Blog

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Experimenting with Real and Synthetic Data for Hands-on-Wheel Detection

This is the last part of our series outlining our benchmark on leveraging synthetic data for hands-on-wheel detection. You can read parts 1 and 2 here and here. We conducted two types of experiments to demonstrate the added value of easily reachable synthetic data. We evaluate our performance with AUC scores for each of the hands. Training & Fine Tuning...
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Body Models: Driving the Age of the Avatar

This transcript was edited for clarity and length. Listen to the full podcast. Michael J. Black is one of the founding directors of the Max Planck Institute (MPI) for intelligent systems in Tübingen, Germany. He completed his PhD in computer science at Yale university, his postdoc at the University of Toronto and has co-authored over...
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3D Common Corruptions and Data Augmentation

Introduction Data distribution shift happens when actual data distribution differs from those the machine learning model was trained on. The most common cause of data distribution shifts in the computer vision domain is data corruption/perturbation. Corrupted visual data distributions can result from illumination changes, camera movements, or weather conditions. Additionally, there are “more semantic” data...
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Data-Centric AI Development with Synthetic Data

Andrew Ng, Stanford Professor and founder of Google Brain, defined what Data-Centric AI means to him. He wrote on “Data-Centric AI is the practice of systematically engineering the data used to build AI systems.” – with the goal of iteratively improving their performance. Similar to how agile development processes and clean code best practices...
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The Opportunities and Risks of Foundation Models

Read the first blog in the series reviewing different foundation models here. The opportunities of foundation models The sheer size of these foundation models granted them an unexpected ability to perform tasks that they are not explicitly trained on. Trained on a huge corpus of unlabeled text, GPT-3 learned to respond to a task with a...
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Preparing the Data for Hands-on-Wheel Detection

In our first blog, we reviewed why we chose to focus on hands-on-wheel detection and how synthetic data impacted our outcomes. Here we will discuss the data we used in our benchmark.  What data did we use?  We chose the DMD dataset as our real dataset because it contains a large number of drivers, driving...
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Top 10 Face Datasets for Facial Recognition and Analysis

What is a Face Recognition Dataset? Most people can recognize about 5,000 faces, and it takes a human 0.2 seconds to recognize a specific one. We also interpret facial expressions and detect emotions automatically. In other words, we’re naturally good at facial recognition and analysis. But, in recent years, Computer Vision (CV) has been catching...
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Four Technological Takeaways from CVPR 2022

The annual CVPR is the gathering of the brightest minds in the world of computer vision. It represents the amalgamation of ideas–both incremental improvements and monumental discoveries –that collectively advance the state of computer vision.  Having perused the papers submitted to CVPR 2022, we distilled four key learning points from four key technologies – transformers,...
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Foundation Models – A New Paradigm of AI

This is part 1 of a series of blogs on Foundation Models and their impact on AI.  Have you seen an astronaut riding a horse in space? Probably not, but DALL·E 2–OpenAI’s AI that creates images from natural language–has an idea (Figure 1). Figure 1. DALL·E 2’s response to the prompt of “an astronaut riding...
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