AI Articles and Reports about Data management
Explore articles and reports related to artificial intelligence for data management, including applications in data auditing, knowledge management, data collection, and more.
Episode Summary: Crowdsourcing is a relatively common term in technical vernacular today. Even if you're not a self-identified "techie", you may very may well have leveraged crowdsourcing in journalism, the sciences, public policy, or elsewhere. One area in which this concept hasn’t really taken off is in finance and hedge funds. In this episode, we speak with Numerai Founder Richard Craib, whose company is crowdsourcing a machine learning hedge fund. Their model is based on pooling data science talent from all over the world and using "anonymous" models to train financial data. These models compete against one another, and the winning models' creators are rewarded in bitcoin - a process based entirely on encryption and anonymity. Craib speaks about his overarching vision for the company, and also delves into his thoughts on the past, present, and future of AI applications in finance.
While there have been and continue to be innovative and significant machine learning applications in healthcare, the industry has been slower to come to and embrace the big data movement than other industries. But a snail's pace hasn't kept the data from mounting, and the underlying value in the data now available to health care providers and related service providers is a veritable goldmine. In this editorial, we provide an overview of where healthcare's big data actually comes from, and why providing robust data analytics services in this sector matters.
Episode Summary: This episode's guest is Uri Sarid, PhD, CTO for MuleSoft, Inc. Sarid speaks about where he believes the future of machine learning (ML) applications in industry might go - he thinks applications might stay small and niche-based, and will develop based on how well each serves its individual purposes. He also gives his perspective on how companies may adapt to deal with these disparate ML technologies, and expands on his belief that finding ways to connect technologies will be an important path in the development of machine learning applications and platforms across industries.
Episode Summary: Predictive analytics and machine learning are all the rage in Silicon Valley, but how do companies actually derive value by leveraging these technologies? We asked this question to Dr. Ronen Meiri, CTO and Founder of DMWay, a predictive analytics and machine learning platform company based in Israel. In this episode, Ronen speaks about what his company does and how smart executives are starting to make decisions on how to choose and decide on a smart, user-friendly platform that fits their business' needs.
Episode Summary: One of the most memorable moments from this interview is when our guest mentioned that Larry Page hired him to solve intelligence; very few people can say this, and this says something about today’s guest, Dr. Nando de Freitas - a senior researcher at Google and professor at Oxford - as well as the gravity of his present work. Today, I speak with Nando about a topic well known through his research at Google, deep learning. de Freitas gives his perspective on the basics of deep learning, the applications in conversational interfaces and recognizing images and videos, and what the future of this technology might look like in the nearer future.
At the recent KDD2016 (knowledge discovery and data mining) conference in San Francisco, Managing Director at Amazon Development Center Germany GmbH and Director of Amazon Machine Learning Ralf Herbrich discussed three lessons that he’s learned while working with sparse machine learning models at scale.
Episode Summary: In this week's episode, we feature an in-person interview from Facebook's headquarters with Hussein Mehanna, director of engineering of the Core Machine Learning group. Mehanna and I talk in-depth about the topic of personalization, touching on the pros and cons, how it works at Facebook, and how his team is working to overcome technological barriers to implement personalization in a way that improves the customer experience. This interview was recorded in their famous "Building 20," which houses thousands of Facebook artificial intelligence employees and developers.
Episode Summary: When you go to Harvard Business School and then to McKinsey company to work in private equity, there’s really only one thing left to do - go to Silicon Valley and launch an AI startup. At least, this is exactly what CEO Praful Krishna did when he moved to San Francisco to start Coseer, an AI company focused on understanding natural language and unstructured data. In this week’s episode, we speak about where unstructured data lives in a business, and how a business can be changed if the right data is unlocked. Krishna also discusses his experience in how executives are making decisions around how, or how not to, leverage AI in their companies.