How they describe themselves: Actionable analytics is the backbone of NYSE-listed Enova International, a global online lending company. In the past 14 years, the analytics team has applied predictive and prescriptive analytics to fraud detection, credit risk management, and customer retention and built the Colossus Digital Decisioning Platform to automate and optimize many of Enova's operational decisions. As a result, Enova has extended over $20 billion in credit to over 5 million customers worldwide. Enova Decisions was launched in 2016 to help businesses in financial services, insurance, healthcare, telecommunications, and higher education achieve similar outcomes by leveraging the same analytics expertise and decisioning technology. How they describe their product/innovation: Enova Decisions Cloud is a complete decision management suite where clients can integrate 1st and 3rd party data, deploy machine learning models, manage business rules, monitor performance, and continuously optimize performance.
Sounds a lot like the road that led to an AI winter historically... I think we're well past the point where that's a genuine risk of AI interest globally cooling down at all (it's already very practical and profitable in many arenas just with what we have) but openAI themselves? If historical trends are any indication, that kind of talk will buy them at most 5 years of normal investor questions, 5 years of severe questions, then bankruptcy. They've very generously got a decade to figure something actually practical out, and realistically the clock might only have five years on it or less. Wonder if they'll invent a thing that'll teach them to make money before then, haha.
This model learns low dimensional vectors to represent vertices appearing in a graph and, unlike existing work, integrates global structural information of the graph into the learning process. We also formally analyze the connections between our work and several previous research efforts, including the DeepWalk model of Perozzi et al. as well as the skip-gram model with negative sampling of Mikolov et al. We conduct experiments on a language network, a social network as well as a citation network and show that our learned global representations can be effectively used as features in tasks such as clustering, classification and visualization. Empirical results demonstrate that our representation significantly outperforms other state-of-the-art methods in such tasks.
The deployment of machine learning models is the process for making your models available in production environments, where they can provide predictions to other software systems. It is only once models are deployed to production that they start adding value, making deployment a crucial step. However, there is complexity in the deployment of machine learning models. This post aims to at the very least make you aware of where this complexity comes from, and I'm also hoping it will provide you with useful tools and heuristics to combat this complexity. If it's code, step-by-step tutorials and example projects you are looking for, you might be interested in the Udemy Course "Deployment of Machine Learning Models".
"AI is being fed directly into the bloodstream of society, and in many cases without sufficient checks and balances," says Kate Crawford, a professor and cofounder of New York University's AI Now, the world's first academic research institute dedicated to the social impact of artificial intelligence. Last year, Crawford partnered with data-viz guru Vladan Joler to create "Anatomy of an AI System," a map and research paper demonstrating the real-world consequences of developing and manufacturing the Amazon Echo. The paper highlights the radical differences in income distribution between Amazon executives and the workers who enable its vast infrastructure, as well as its devastating environmental impacts. The project has been exhibited at museums around the world, and Crawford has presented it to leaders in France, Germany, Spain, and Argentina.
This week Amazon CEO Jeff Bezos got the tech sector's attention with emerging reports of his "fascination" with the rapidly developing world of autonomous autos. "If you think about the auto industry right now, there's so many things going on with Uber-ization, electrification, the connected car -- so it's a fascinating industry," Bezos said. "It's going to be something very interesting to watch and participate in, and I'm very excited about that whole industry." Amazon has made some sizable investments to accompany that interest -- most notably in automation and electrification start-up Rivian and self-driving startup Aurora. And fascination aside, Amazon has a race for the consumer's whole paycheck to vie in with Walmart -- and there is little doubt that auto automation plays like Rivian and Aurora could put some octane, so to speak, behind that effort.
Within the next decade, healthcare will see emerging technologies including artificial intelligence, cloud computing, predictive analytics and blockchain spurring billions of dollars in value increases, according to a new McKinsey & Company report on this tech-driven "era of exponential growth." For these innovations to impact areas like clinical productivity, care delivery and waste reduction, though, certain value pools will need to be disrupted across the entire industry. Here are four possible disruptive changes that could transform healthcare in the coming years, according to McKinsey. More articles about AI: How AI can enhance clinical productivity IBM Research using self-driving car tech to promote seniors' wellbeing Bill calls for $2.2B in federal AI funding