There has been a recent surge in the quality of natural language processing technology, and much of this has been driven by a new class of neural networks, based on the concept of "attention." Attention networks localize (pay attention to) a portion of input text, when performing a task
Like many fields, neuroscience is experiencing a data deluge. Machine learning techniques are being used to learn better biomarkers, make sense of the brain, and automate tasks.
Facial recognition is currently one of the most visible applications of artificial intelligence. With a promising range of highly beneficial uses, it can also be deployed as a very effective tool of political repression.
Recent technological advancements make it possible to closely and continuously monitor patients on multiple scales, both inside and outside of the clinic.
Despite significant recent advances in molecular genetics and neuroscience, behavioral ratings based on clinical observations are still the gold standard for screening, diagnosing, and assessing outcomes in neurodevelopmental disorders, including autism spectrum disorder, the core of this talk.
The convolutional neural network (CNN) represents the current state-of-the-art for image and video analysis, and is increasingly used for analyzing time series and other data with spatial or sequential structure.
The basic concepts of neural networks are introduced, with a focus on intuition. The simpler and widely used logistic regression model is introduced first, and from this the neural network is introduced as a generalization.
The ability to translate between mediums is important for both practical and creative purposes. In this IPLE we will discuss AttnGAN, a model that can produce images from input text, and Im2Text, a model that produces text descriptions from images.
Achieving machine learning’s significant potential to promote innovative, trustworthy outcomes will require that relevant legal levers, including intellectual property, liability, privacy, and agency-specific regulation, be calibrated appropriately.
In machine learning, models are developed to represent and make predictions based on data. The model starts with random parameters and must “learn” these parameters by using historical data.