Many machine learning models operate on images, but ignore the fact that images are 2D projections formed by 3D geometry interacting with light, in a process called rendering. Enabling ML models to understand image formation might be key for generalization. However, due to an essential rasterization step involving discrete assignment operations, rendering pipelines are non-differentiable and thus largely inaccessible to gradient-based ML techniques. In this paper, we present DIB-R, a differentiable rendering framework which allows gradients to be analytically computed for all pixels in an image. Key to our approach is to view foreground rasterization as a weighted interpolation of local properties and background rasterization as an distance-based aggregation of global geometry. Our approach allows for accurate optimization over vertex positions, colors, normals, light directions and texture coordinates through a variety of lighting models. We showcase our approach in two ML applications: single-image 3D object prediction, and 3D textured object generation, both trained using exclusively using 2D supervision. Our project website is: https://nv-tlabs.github.io/DIB-R/ Read More
Daily Archives: December 9, 2019
Failure Modes in Machine Learning
In the last two years, more than 200 papers have been written on how Machine Learning (ML) can fail because of adversarial attacks on the algorithms and data; this number balloons if we were to incorporate non-adversarial failure modes. The spate of papers has made it difficult for ML practitioners, let alone engineers, lawyers and policymakers, to keep up with the attacks against and defenses of ML systems. However, as these systems become more pervasive, the need to understand how they fail, whether by the hand of an adversary or due to the inherent design of a system, will only become more pressing. The purpose of this document is to jointly tabulate both the of these failure modes in a single place.
— Intentional failures wherein the failure is caused by an active adversary attempting to subvert the system to attain her goals – either to misclassify the result, infer private training data, or to steal the underlying algorithm.
— Unintentional failures wherein the failure is because an ML system produces a formally correct but completely unsafe outcome.
Read More
A DARPA Perspective on Artificial Intelligence
DARPA envisions a future in which machines are more than just tools that execute human-programmed rules or generalize from human-curated data sets. Rather, the machines DARPA envisions will function more as colleagues than as tools.
DARPA sees three waves of AI:
— Handcrafted Knowledge
— Statistical Learning
— Contextual Adaptation
Read More
Using Artificial Intelligence To Analyze Markets: An Interview With Ainstein AI CEO Suzanne Cook
To learn more about the use of artificial intelligence at it may be applied to analyzing stocks and markets, I asked the CEO and originator of Ainstein AI about her work in this area.
Suzanne Cook is a Wharton School graduate and a seven-time Institutional Investor All Star Analyst. Read More
Rick's Cafe AI 