AI News, Explainable Artificial Intelligence (XAI)

Explainable Artificial Intelligence (XAI)

The Explainable AI (XAI) program aims to create a suite of machine learning techniques that: New machine-learning systems will have the ability to explain their rationale, characterize their strengths and weaknesses, and convey an understanding of how they will behave in the future.

XAI Concept XAI is one of a handful of current DARPA programs expected to enable “third-wave AI systems”, where machines understand the context and environment in which they operate, and over time build underlying explanatory models that allow them to characterize real world phenomena.

The XAI program is focused on the development of multiple systems by addressing challenge problems in two areas: (1) machine learning problems to classify events of interest in heterogeneous, multimedia data;

These two challenge problem areas were chosen to represent the intersection of two important machine learning approaches (classification and reinforcement learning) and two important operational problem areas for the DoD (intelligence analysis and autonomous systems).

Building Explainable Machine Learning Systems: The Good, the Bad, and the Ugly

This meetup was held in New York City on 30th April. Abstract: The good news is building fair, accountable, and transparent machine learning systems is ...

Explainable Artificial Intelligence (XAI): Why, When, and How? by Mridul Mishra at #ODSC_India

Machine learning models are rapidly conquering uncharted grounds with new solutions by proving themselves to be better than the existing manual or software ...

A DARPA Perspective on Artificial Intelligence

What's the ground truth on artificial intelligence (AI)? In this video, John Launchbury, the Director of DARPA's Information Innovation Office (I2O), attempts to ...

Responsible AI: Why we need Explainable AI

What if decisions impacting personal concerns (e.g., work performance, lending, education, safety) are made by AI that doesn't explain itself and how can that ...

Practical Tips for Interpreting Machine Learning Models - Patrick Hall, H2O.ai

This talk was recorded at H2O World 2018 NYC on June 7th, 2018. The slides from the talk can be viewed here: ...

Interpretable Machine Learning Meetup

This meetup was recorded in New York City on September 10th, 2018. Slides from the meetup can be found here: ...

Explainable Artificial Intelligence

Oralia Arjona Villarreal David Ensminger Engl1301 Explainable Artificial Intelligence: Creating A Transparent Artificial Intelligence Imagine a person holding a ...

Engineering By Design: Raytheon is Working on a Way to Make A.I. Explain Itself

We Need More Moon Dust In May 2017, the European Space Agency announced that it was using heat from the sun to 3D print bricks made out of simulated ...

The Deep Learning Revolution in Automatic Speech Recognition by Dr Ananth Sankar at #ODSC_India

In the last decade, deep neural networks have created a major paradigm shift in speech recognition. This has resulted in dramatic and previously unseen ...

Socio algorithmic processes and the Everyday-Part 02

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