Summary: Autonomous vehicles (AUVs) and many other systems that need to accurately perceive the world around them will be much better off when image classification moves from 2D to 3D. Here we examine the two leading approaches to 3D classification, Point Clouds and Voxel Grids.
One of the well-known problems in CNN image classification is that because the CNN classifier sees only a 2D image of the object it won’t recognize that same object if it’s rotated. The solution thus far has been to train on many different orthogonal views of the same object and that vastly expands the problem of training data and training time. Read More
Daily Archives: September 25, 2019
China’s momentum and challenges in artificial intelligence investments yield telling lessons for its worldwide observers.
China’s ambition in artificial intelligence is often framed as a tech rivalry between two important centers for digital innovation — the east coast of China and the U.S. West Coast. But this rivalry is an undercard for the main event: AI’s largest and most enduring contributions will be in non-technology sectors, as traditional companies unlock value in regions far away from Silicon Valley and the string of coastal cities that constitute China’s innovation corridor.
For non-tech sectors, our research indicates that Chinese companies’ approach to adopting AI differs from those in other regions, raising important questions. Read More
Artificial Intelligence: Why It's Essential For Digital Platforms
Companies widely recognize the potential power of artificial intelligence (AI). They instinctively understand that it feels like we’re on the cusp of something that will change our lives and our businesses in a profound way. Yet, many struggle with where to apply it. Executives can’t shake the feeling that they should have use cases for AI and use it productively today, even recognizing that AI is not mature yet and will be far more powerful tomorrow and in the future. If you’re looking for how and where your company should use AI, let me give you a perspective on a great application of AI today: your digital platforms.
A good way to understand what is happening in digital transformation is that businesses are moving from process orientation to platform orientation. Read More
MLflow: an Open Source Machine Learning Platform
Everyone who has tried to do machine learning development knows that it is complex. Beyond the usual concerns in the software development, machine learning (ML) development comes with multiple new challenges. MLFlow is an open interface, open source machine learning platform, released by DataBricks in 2018, that can be used to create an internal ML platform for tracking, packaging, and deploying ML models.
Version Control ML Model
Machine Learning operations (let’s call it mlOps under the current buzzword pattern xxOps) are quite different from traditional software development operations (devOps). One of the reasons is that ML experiments demand large dataset and model artifact besides code (small plain file).
This post presents a solution to version control machine learning models with git and dvc (Data Version Control). Read More
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