Join CSIAC Wednesday, August 12th, at 1100 EDT for our next webinar presentation titled "A Fistful of Data, or the Good, Bad and Ugly of Adversarial Machine Learning." Please register in advance for the webinar at: https://www.anymeeting.com/PIID=EF52D880844A3F This webinar provides an overview of Adversarial Machine Learning (AML), its relationship to
Topic: Machine Learning (ML)
This webinar provides an overview of Adversarial Machine Learning (AML), its relationship to Generative (Deep) Learning, and ways to view AML as a potential enabler for deploying more comprehensive system-level Machine Learning capabilities. The basic ideas driving AML and the system-level architecture needs of an effective integrated ML capability are
If AI is really going to make a difference to patients we need to know how it works when real humans get their hands on it, in real situations.
Machine Learning (ML) appears to be the ubiquitous go-to solution for a great many modern problems across many domains. But what is really under the hood of a typical ML solution? And, why are so many problems suddenly becoming good ML candidates? This webinar explores non-mathematically the foundational aspects of ML and how they add up to a satisfactory
The Trump administration is proposing new rules to guide future federal regulation of artificial intelligence used in medicine, transportation and other industries.
Not too long ago, it was considered state of the art research to make a computer distinguish cats vs dogs. Now image classification is 'Hello World' of Machine Learning (ML), something one can implement in just a few lines of code using TensorFlow.
Google's search engine will now better understand your confusing search queries, the company said Friday. Google said it's updating the tool to improve analysis of natural language. The idea is to let people type in queries that reflect how they speak in real life, instead of entering a string of keywords they think the software is more likely to understand.
Researchers have demonstrated a new algorithm for detecting so-called deepfake images-those altered imperceptibly by AI systems, potentially for nefarious purposes. Initial tests of the algorithm picked out phony from undoctored images down to the individual pixel level with between 71 and 95 percent accuracy, depending on the sample data set used. The
The Federal Bureau of Investigation (FBI) and Immigration and Customs Enforcement (ICE) are exploiting state DMV records for facial recognition data without the knowledge or permission of drivers.
The U.S. intelligence community's research arm wants to train algorithms to track people across sprawling video surveillance networks, and it needs more data to do it.