New tools driven by machine learning and AI are improving interfaces with customers, acquisition of data on specific claims, and shortening service delivery times in an otherwise traditional business domain. Inspection-as-a-service requires machine intelligence for capacity and efficiency, and an acquisition of this nature in the business process management space is an affirmation of the value of disruption in an historically stable and conservative industry like insurance. The agreement will add artificial intelligence capabilities to Symbility's claims technology. Their goal is to use an artificial intelligence application to optimize and create self-service claims management.
Given the speed of innovation in the digital realm, it's exciting to see our partner Cloudera continue to stay ahead of the game with their recent acquisition of Fast Forward Labs (now known as Cloudera Fast Forward Labs), a top-tier applied research and advisory services company. Cloudera Fast Forward Labs, will concentrate on practical research into new approaches to data science and applying research to business problems that are broadly applicable to a variety of industries and applications.
Automated decision making and the difficulty of ensuring accountability for algorithmic decisions have been in the news. I'm breaking out of a concentrated book-writing space to offer my voice – and to outline some of the directions I think we should be taking to address the wicked problems of ethics, algorithms and accountability – and hoping also to be standing up to be counted as one of the people opening out discussions in this space, so that it can be more diverse. Automated decision making using computational methods is not new: predictive techniques including example-based or taught learning systems, which can make predictions based on examples and generalize to unseen data, were developed in the 1960s and refined in the following decades. This is a very big problem for a society that wants to expand automated decision making to many more areas, with the expansion of more generalized AI systems.
These and many other fascinating insights are from the Boston Consulting Group and MIT Sloan Management Review study published this week, Reshaping Business With Artificial Intelligence. The survey is based on interviews with more than 3,000 business executives, managers, and analysts in 112 countries and 21 industries. The research found significant gaps between companies who have already adopted and understand Artificial Intelligence (AI) and those lagging. AI early adopters invest heavily in analytics expertise and ensuring the quality of algorithms and data can scale across their enterprise-wide information and knowledge needs.
In evaluating search quality over the years, Google has used many techniques. How long has the site been around? We don't think about the tacit knowledge that lets us make that determination, and might be surprised that an algorithm lacking that knowledge might still be able to come to the same conclusion by other means. Yet billions of people have come to rely on Google's algorithms to do just that.
Statistical methods and statistical machine learning dominate the field and more recently deep learning methods have proven very effective in challenging NLP problems like speech recognition and text translation. In this post, you will discover the Stanford course on the topic of Natural Language Processing with Deep Learning methods. This course is focused on teaching statistical natural language processing with deep learning methods. Importantly, students must submit a final project report using deep learning on a natural language processing problem.
Smartwatches range from simple fitness tracking wristbands to devices like the Apple Watch, which has a surprising range of functionality comparable even to smartphones. Hristijan Gjoreski of the University of Sussex said in a press release, "Current activity-recognition systems usually fail because they are limited to recognizing a predefined set of activities, whereas of course human activities are not limited and change with time." He continued in stating that, "Here we present a new machine-learning approach that detects new human activities as they happen in real time, and which outperforms competing approaches." By eliminating the limits of defined activity as older models do, smartwatches would be able to better track and record human activity.
In early May, New York-based tech startup Agolo completed its first seed round of funding, pulling in over $3.5 million in investments from Microsoft Ventures and CRV, with participation from Point72 Ventures and Franklin Templeton. Even though the tech at the time was too primitive for Agolo, AlTantawy and Wohns saw its potential and decided to start developing on Microsoft's Azure cloud platform. By getting involved and tailoring their products to Microsoft before there was a real market for them, Agolo established themselves as Microsoft's go-to developers for natural language processing software. Not long after their experience in the Microsoft Acceleration Program, the Agolo founders began a dialogue with Microsoft Ventures, the tech enterprise's corporate venture fund.
With enough data, companies can use AI to remove painful decision-making from their customers, reduce friction in customer journeys, and ultimately deliver a better user experience. Unfortunately, Pandora didn't focus enough on increasing the conversion rate from "User opens Pandora" to "User listens to a song." With more people satisfying their search queries faster, Google keeps users coming back and satisfies advertisers with high-quality impressions, clicks, and conversions. Similarly, Amazon's product recommendation AI, "Destiny" is born out of this mission to make online shopping easy.