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
This article will portray how data related to diabetes can be leveraged to predict if a person has diabetes or not. More specifically, this article will focus on how machine learning can be utilized to predict diseases such as diabetes. By the end of this article series you will be able to understand concepts like data exploration, data cleansing, feature selection, model selection, model evaluation and apply them in a practical way.
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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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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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