Sometime ago, the world's most affable and recognizable AI leader, Andrew Ng launched a specialization called AI for medicine through his MOOC institution, deeplearning.ai. I have always been a big fan of Andrew Ng, and it was he who had introduced me to the world of machine learning through his grainy Youtube videos of Stanford lectures back in 2012. I was very excited that finally, Andrew Ng has finally turned his attention to the critical shortage of AI experts in the medical field . Truth be told, AI in the medical world has not seen as much progress as other domains like personalized advertisements, recommendations, autonomous driving etc. There are lot of complex issues like data privacy, small sample sizes etc. which I would prefer to discuss in depth in another post.
Online Courses Udemy - Deployment of Machine Learning Models Build Machine Learning Model APIs Created by Soledad Galli, Christopher Samiullah English [Auto] Students also bought Data Science: Natural Language Processing (NLP) in Python Recommender Systems and Deep Learning in Python Artificial Intelligence: Reinforcement Learning in Python Unsupervised Machine Learning Hidden Markov Models in Python Deep Learning: Recurrent Neural Networks in Python Preview this course GET COUPON CODE Description Learn how to put your machine learning models into production. Deployment of machine learning models, or simply, putting models into production, means making your models available to your other business systems. By deploying models, other systems can send data to them and get their predictions, which are in turn populated back into the company systems. Through machine learning model deployment, you and your business can begin to take full advantage of the model you built. When we think about data science, we think about how to build machine learning models, we think about which algorithm will be more predictive, how to engineer our features and which variables to use to make the models more accurate.
TrafficGuard uses machine learning to prevent ad fraud but has faced the challenges that come along with it. Full-scale commitment and investment have eased those obstacles. In the fraud detection field, machine learning has become a necessary tool for organizations seeking an advantage. Though applying machine learning technology reads like a simple statement, mastering it requires strong companywide commitment and proper AI frameworks. One digital ad fraud detection company, TrafficGuard, has embraced the possibilities of machine learning.
Two Google executives said Friday that bias in artificial intelligence is hurting already marginalized communities in America, and that more needs to be done to ensure that this does not happen. X. Eyeé, outreach lead for responsible innovation at Google, and Angela Williams, policy manager at Google, spoke at (Not IRL) Pride Summit, an event organized by Lesbians Who Tech & Allies, the world's largest technology-focused LGBTQ organization for women, non-binary and trans people around the world. In separate talks, they addressed the ways in which machine learning technology can be used to harm the black community and other communities in America -- and more widely around the world. Bias in algorithms IS NOT JUST A DATA PROBLEM. The choice to use AI can be biased, the way the algorithm learns can be biased, and the way users are impacted/interact with/perceive a system can reinforce bias! checkout @timnitGebru's work to learn more!
Walter Bender, the Chief Learning Architect at Sorcero and the founder of Sugar Labs and One Laptop One Child, shared with IBL News how transparent AI will revolutionize online learning following his talk at the Open edX conference last month in San Diego. The main goal, he posits, is "to leverage what makes us human to become part of the learning process." His talk, "Beyond the Black Box: How Transparent AI can Transform Learning," focused on the strides that Sorcero is making with AI and online learning. With his extensive experience in academia and accessible and open online education, he says his experiences were "a case study for transparency, for providing tools and a framework." The natural extension from this was to switch gears and talk about AI, the "tool du jour in machine learning these days."
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