"We are probably in the second or third inning." Lo, a professor of finance at the MIT Sloan School of Management, and Ajay Agrawal of the University of Toronto's Rotman School of Management shared their perspective at the inaugural CFA Institute Alpha Summit in May. In a conversation moderated by Mary Childs, they focused on three principal concepts that they expect will shape the future of AI and big data. Lo said that applying machine learning to such areas as consumer credit risk management was certainly the first inning. But the industry is now trying to use machine learning tools to better understand human behavior.
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AI and big data are perfect companions, right? It's indisputable that access to huge volumes of data allows AI assistants to deliver better, faster, more-accurate responses. But there are downsides too. For example, is this reliance on huge amounts of data sustainable or ethical? And if you need 1,000,000 examples to create an application, do time and money become too big a barrier for many developments?
I've been keeping an eye on the use of machine learning algorithms, particularly by venture capitalists, to make investment decisions for some time now. They've been investing in machine learning companies for years, so applying their products to other businesses, once you have studied how they work, seems a reasonable proposition. After all, what is the decision to invest in a startup based on? Basically, the fruit of a set of analyses and previous experiences that can be systematized and verified in different ways, while the experience corresponds, in reality, to the imperfect distillation, with its biases and errors, of a series of previous decisions, weighted by the results obtained in each. That said, venture capitalists are not entirely objective: they usually allow multiple factors to enter the decision-making process, which include anything from the feelings generated by the company's founding team, to more or less rigorous analyses of its capacity for future development.
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How can we assess the value of data objectively, systematically and quantitatively? Pricing data, or information goods in general, has been studied and practiced in dispersed areas and principles, such as economics, marketing, electronic commerce, data management, data mining and machine learning. In this article, we present a unified, interdisciplinary and comprehensive overview of this important direction. We examine various motivations behind data pricing, understand the economics of data pricing and review the development and evolution of pricing models according to a series of fundamental principles. We discuss both digital products and data products. We also consider a series of challenges and directions for future work.
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Uday Kamath has more than 20 years of experience architecting and building analytics-based commercial solutions. He currently works as the Chief Analytics Officer at Digital Reasoning, one of the leading companies in AI for NLP and Speech Recognition, heading the Applied Machine Learning research group. Most recently, Uday served as the Chief Data Scientist at BAE Systems Applied Intelligence, building machine learning products and solutions for the financial industry, focused on fraud, compliance, and cybersecurity. Uday has previously authored many books on machine learning such as Machine Learning: End-to-End guide for Java developers: Data Analysis, Machine Learning, and Neural Networks simplified and Mastering Java Machine Learning: A Java developer's guide to implementing machine learning and big data architectures. Uday has published many academic papers in different machine learning journals and conferences.
The team here at insideBIGDATA is deeply entrenched in following the big data ecosystem of companies from around the globe. Our in-box is filled each day with new announcements, commentaries, and insights about what's driving the success of our industry so we're in a unique position to publish our quarterly IMPACT 50 List of the most important movers and shakers in our industry. These companies have proven their relevance by the way they're impacting the enterprise through leading edge products and services. We're happy to publish this evolving list of the industry's most impactful companies! The selected companies come from our massive data set of vendors and industry metrics.
Had you invested in a dedicated Artificial Intelligence ETF one year ago, you could have made close to a 40% return. Investing in individual companies that trust in AI use cases could have been even more lucrative. Consider Netflix, which uses machine learning for content recommendation, or Amazon, which uses AI not only for product recommendations but also in its fulfillment centers, AWS and Echo: both more than doubled their market capitalization over the last 12 months, though admittedly driven by coronavirus. Irrespective of the pandemic, AI is a growth business – which is rightfully reflected in share prices – with spending on AI systems projected to grow annually by 28%, according to a report by IDC. Not only established organizations have found AI to be beneficial.