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We Will Never Fully Understand How AI Works — But That Shouldn't Stop You From Using It

To unlock the true potential of artificial intelligence, investors need to accept the black box. ....

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Explained: How to tell if artificial intelligence is working the way we want it to

Deep-learning models have become very powerful, but that has come at the expense of transparency. As these models are used more widely, a new area of research has risen that focuses on creating and testing explanation methods that may shed some light on the inner-workings of these black-box models. ....

Yilun Zhou , Marzyeh Ghassemi , Artificial Intelligence Laboratory , Healthy Ml Group , Interactive Robotics Group Of The Computer Science , Interactive Robotics Group , Computer Science , Marzyeh Ghassemi , Explainable Ai , Deep Learning , Black Box Models , Eature Attribution Methods , Explainable Artificial Intelligence ,

Calculating the "fingerprints" of molecules with artificial intelligence

With conventional methods, it is extremely time-consuming to calculate the spectral fingerprint of larger molecules. But this is a prerequisite for correctly interpreting experimentally obtained d . ....

Ulf Leser , Kanishka Singh , Annika Bande , Humboldt University Berlin , Explainable Artificial Intelligence , X Ray Absorption , Quantum Dots , Artificial Intelligence , Neural Networks , Absorption Spectroscopy , X Ray Absorption Spectroscopy ,

When should someone trust an AI assistant's predictions? | MIT News | Massachusetts Institute of Technology

MIT researchers created a method that helps humans develop a more accurate mental model of an artificial intelligence teammate, so they have a better understanding of when they should trust the AI agent’s algorithmic predictions. ....

Arvind Satyanarayan , David Sontag , Hussein Mozannar , Artificial Intelligence Laboratory , Clinical Machine Learning Group , Institute For Medical Engineering , Visualization Group , National Science Foundation , Computer Science , Medical Engineering , Clinical Machine Learning , Artificial Intelligence , National Science , Hussein Mozannar , Clinical Machine Learning Group , Explainable Artificial Intelligence , I Assisted Decision Making ,

How well do explanation methods for machine-learning models work?

Feature-attribution methods are used to determine if a neural network is working correctly when completing a task like image classification. MIT researchers developed a way to evaluate whether these feature-attribution methods are correctly identifying the features of an image that are important to a neural network’s prediction. ....

Yilun Zhou , Julie Shah , Marco Tulio Ribeiro , Microsoft Research , Artificial Intelligence Laboratory , Interactive Robotics Group , National Science Foundation , Computer Science , Serena Booth , National Science , Explainable Artificial Intelligence , Eature Attribution , Image Classification ,