Our group likes to discuss and understand Cognitive Computing (CC), which "addresses complex situations that are characterized by ambiguity and uncertainty. In these dynamic, information-rich, and shifting situations, data tends to change frequently, and it is often conflicting. The goals of users evolve as they learn more and redefine their objectives. To respond to the fluid nature of users’ understanding of their problems, cognitive computing systems offer a synthesis not just of information sources but of influences, contexts, and insights. To do this, systems often need to weigh conflicting evidence and suggest an answer that is “best” rather than “right”. Cognitive systems will coexist with legacy systems into the indefinite future. Many cognitive systems will build upon today’s IT resources. But the ambition and reach of cognitive computing is fundamentally different. Leaving the model of computer-as-appliance behind, it seeks to bring computing into a closer, more fundamental partnership in human endeavors." (Wikipedia edit pages)
If all of this sounds interesting, then this group is for you! A warm welcome!
An autoencoder is a machine learning algorithm that represents unlabeled high-dimensional data as points in a low-dimensional space. A variational autoencoder (VAE) is an autoencoder that represents unlabeled high-dimensional data as low-dimensional probability distributions. In addition to data compression, the randomness of the VAE algorithm gives it a second powerful feature: the ability to generate new data similar to its training data. For example, a VAE trained on images of faces can generate a compelling image of a new "fake" face. It can also map new features onto input data, such as glasses or a mustache onto the image of a face that initially lacks these features. In this talk, we will survey VAE model designs that use deep learning, and we will implement a basic VAE in TensorFlow. We will also demonstrate the encoding and generative capabilities of VAEs and discuss their industry applications.
• 18.30: Doors Open
• 18.50: Redis Labs AI Lightning Talk
• 19.00: Main Presentation
• 19:45: Q/A
• 20:00: Mingling
Steven Flores is a machine learning engineer and researcher at Comp Three Inc. He use deep learning and other cutting-edge methods in data modeling, computer vision, and NLP to find data-driven solutions that solve our clients' most difficult business challenges. Having worked in a variety of fields, spanning from computer science and AI to physics and mathematics, Steven blends expertise from different domains to design novel and practical solutions and then deploys them at scale. He earned his Ph.D in applied math from the University of Michigan in 2012 and is published in top research journals. While at Comp Three, Steven has worked with customers in real-estate appraisal, web search, and NLP.