Image classification using convolutional neural networks (CNNs) is fairly easy today, especially with the advent of powerful front-end wrappers such as Keras with a TensorFlow back-end. But what if you want to identify more than one object in an image?
Source: freecodecamp.org
Academics, economists, and AI researchers often undervalue the role of intuition in science. Here’s why they’re wrong.
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Cue the sad tuba and attorney jokes: Machines just landed the hurt on lawyers. LawGeex, an Israel-based startup focused on automating contract reviews, released a study showing its AI software pummels lawyers in document review accuracy. The AI service outperformed 20 corporate lawyers at identifying legal risks in nondisclosure agreement contracts.
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This is an example of what’s called an adversarial image: an image specifically designed to fool neural networks into making an incorrect determination about what they’re looking at. Researchers at Google Brain decided to try and figure out whether the same techniques that fool artificial neural networks can also fool the biological neural networks inside of our heads, by developing adversarial images capable of making both computers and humans think that they’re looking at something they aren’t.
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