If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Some industries are quicker to adapt to technological advancement. The insurance industry may have a mixed record on how well it has used the Internet and various communications platforms but it does not appear to be hesitating on deploying tools that rely on Artificial Intelligence (AI). In fact, it almost looks like every significant player in the insurance industry is picking up efficiencies with AI; especially the providers involved in claims processing. The management of insurance claims is being improved mostly by product leaders within various companies, and their jobs are never simple. In virtually every field, a product leader is tasked with keeping an eye on tectonic shifts in markets and technology, finding resources to address those changes, and manage the day-to-day of the customer experience and engagement.
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. This is a big deal if we are to start addressing some of the serious ethical issues in developing Artificial Intelligence systems that can't easily be made transparent. 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. A few months ago I submitted a response to the UK's Science and Technology Committee consultation on automated decision making. This consultation asked specifically how transparency could be empoyed to allow more scrutiny of algorithmic 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. An online summary of the report is available, and a PDF of the report is accessible here (22 pp., PDF, free, no opt-in). The survey is based on interviews with more than 3,000 business executives, managers, and analysts in 112 countries and 21 industries. For additional details regarding the methodology, please see page 4. 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? How often is it referenced by other sites that have themselves been determined to be reputable? Previous search engines had used brute force matching of the words contained in a web page with the words that the user was looking for.) Most people would find the FBI to be an authoritative source.
Natural Language Processing, or NLP, is a subfield of machine learning concerned with understanding speech and text data. 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 free and I encourage you to make use of this excellent resource. The course is taught by Chris Manning and Richard Socher.
Smartwatches range from simple fitness tracking wristbands to devices like the Apple Watch, which has a surprising range of functionality comparable even to smartphones. They have become a commonplace tool for business professionals, students, athletes, and so many more. But, as concerns continue to rise about privacy in an era of lightning-fast progress, are smartwatches getting too smart? Scientists at the University of Sussex have developed an algorithm that takes smartwatches to the next level. It enables the device to both detect and record everything that you do, without instruction.
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. But how did this entrepreneur go from Egypt to snagging such overwhelming corporate interest in the heart of the Big Apple? An interview with Mohamed AlTantawy - Egyptian, AUC graduate, and co-founder of Agolo - reveals how the startup progressed from ideation to actualization. AlTantawy's AI-powered startup provides machine summarisation software that gathers documents from around the web and breaks down key points for the user, eliminating the need to spend hours sifting through information to find the most important details, and works through natural language processing technology. "This is the area of computer science where algorithms try to make sense of human language," the entrepreneur explains.
Successful companies address customer needs better than the competition. However before you can address customer needs, you must first identify them. "What are my customers' needs? And how can I address them better than the competition?" A close attention to user experience (UX) will help answer the first question, and a clever use of artificial intelligence (AI) will help answer the second.