One goal of AI work in natural language is to enable communication between people and computers without resorting to memorization of complex commands and procedures. Automatic translation – enabling scientists, business people and just plain folks to interact easily with people around the world – is another goal. Both are just part of the broad field of AI and natural language, along with the cognitive science aspect of using computers to study how humans understand language.
Cognitive automation is adept in managing unstructured data. Cognitive automation is an extension of Robotic Process Automation (RPA). If RPA exists to simplify and automate repetitive, mundane tasks following a set of pre-defined and programmed rules and procedures, cognitive automation is responsible for knowledge-based tasks that require decision-making skills and judgment. Cognitive functions in this type of automation come from the integration of artificial intelligence (AI) technologies like machine learning (ML) and natural language process (NLP). Powered by AI technology, cognitive automation possesses the capacity to handle complex, unstructured, and data-laden tasks.
Developing policy informed by science and technology is now more complex than ever. Policymakers must address supply chains, climate change, inequality, technological breakthroughs, misinformation and more. Using artificial intelligence (AI) to mine the literature could put policymaking on a sounder footing. Advanced big-data and natural-language-processing models enable decision makers to look beyond conventional indicators and expert discussions. Millions of scientific articles, patents and market reports can be readily analysed to identify megatrends or fading topics, and to provide predictive opportunities (see go.nature.com/31snkp5). Machine learning can create maps of national competencies and centres of excellence of science and technology.
As our intrinsic nature, we humans are likely to form an opinion about a particular commodity or person even before we have shared any real-life experience with them. The same thing happens for a business brand; we tend to have developed some sort of subconscious thoughts regarding the brand prior to using its products and services. This bias severely impacts how businesses perform in the market and their sales figures. So it is safe to assume that how brands present themselves or appeal customers play a decisive role in determining their success. Now one may wonder how companies can figure out what the customers feel or react to their products; well, this is where emotional analytics comes in. Though data-driven Analytics provides a quick shorthand to businesses, without emotional insights, brands are a handicap.
Arvind Gopalakrishnan is a part of the AIM Writers Programme.… Data mining is taking turns in the industry like anything, but have you ever heard of Opinion Mining? Leveraging customer opinion as quantifiable data is a concept of future to a layman but with Natural Language Processing, the world can finally process and completely absorb customer feedback. Often data is associated with quantity-based statistics with numbers and metrics floating around, however, with natural language processing (NLP), qualitative factors like customer feedback can be processed and used as quantifiable data. For example, if a specific mobile phone models witness a higher number of sales in a given year, the manufacturers tend to incorporate features of that mobile phone to increase the sales of other models where they somehow miss to make upgrades properly basis the customer feedback.
The AI and ML deployments are well underway, but for CXOs the biggest issue will be managing these initiatives, and figuring out where the data science team fits in and what algorithms to buy versus build. Fifty-three percent of enterprises adopting artificial intelligence have spent more than $20 million over the past year on technology and talent, according to a survey by Deloitte. The State of AI in the Enterprise survey, based on 2,737 information technology and line of business executives, highlights how AI implementations are moving into production at a rapid pace. Deloitte's respondent base included 26% "seasoned adopters," 47% "skilled" adopters and 27% "starters." The respondents were classified based on AI adoption and systems launched into production.
Our world is being revolutionized by data-driven methods: access to large amounts of data has generated new insights and opened exciting new opportunities in commerce, science, and computing applications. Processing the enormous quantities of data necessary for these advances requires large clusters, making distributed computing paradigms more crucial than ever. MapReduce is a programming model for expressing distributed computations on massive datasets and an execution framework for large-scale data processing on clusters of commodity servers. The programming model provides an easy-to-understand abstraction for designing scalable algorithms, while the execution framework transparently handles many system-level details, ranging from scheduling to synchronization to fault tolerance. This book focuses on MapReduce algorithm design, with an emphasis on text processing algorithms common in natural language processing, information retrieval, and machine learning.
We're living in the age of the next industrial revolution: the very first three freed most of the humans from hard labor. This one is aiming to take us over the last domain of human dominance on this planet: our intelligence. In this article, we will put aside ethical, political and social effects of such revolution and concentrate a bit more on the technical side of it. What we see in media today looks a bit different from the real dominance of machines over humans… or not? The most rapidly growing areas of artificial intelligence in the few last years have been computer vision, natural language processing, speech processing and, of course, different customer analytics applications like recommender systems (you may not like it, but targeted advertisements are accurate enough to grow companies' revenues).
Google today announced that it has signed up Verizon as the newest customer of its Google Cloud Contact Center AI service, which aims to bring natural language recognition to the often inscrutable phone menus that many companies still use today (disclaimer: TechCrunch is part of the Verizon Media Group). For Google, that's a major win, but it's also a chance for the Google Cloud team to highlight some of the work it has done in this area. It's also worth noting that the Contact Center AI product is a good example of Google Cloud's strategy of packaging up many of its disparate technologies into products that solve specific problems. "A big part of our approach is that machine learning has enormous power but it's hard for people," Google Cloud CEO Thomas Kurian told me in an interview ahead of today's announcement. "Instead of telling people, 'well, here's our natural language processing tools, here is speech recognition, here is text-to-speech and speech-to-text -- and why don't you just write a big neural network of your own to process all that?' Very few companies can do that well. We thought that we can take the collection of these things and bring that as a solution to people to solve a business problem. And it's much easier for them when we do that and […] that it's a big part of our strategy to take our expertise in machine intelligence and artificial intelligence and build domain-specific solutions for a number of customers."
Microsoft is shedding its empathetic chatbot Xiaoice into an independent entity, the U.S. software behemoth said (in Chinese) Monday, confirming an earlier report by the Chinese news site Chuhaipost in June. The announcement came several months after Microsoft announced late last year it would close down its voice assistant app Cortana in China among other countries. Xiaoice has over the years enlisted some of the best minds in artificial intelligence and ventured beyond China into countries like Japan and Indonesia. Microsoft said it called the shots to accelerate Xiaoice's "localized innovation" and buildout of the chatbot's "commercial ecosystem." The spin-off will see the new entity license technologies from Microsoft for subsequent research and development in Xiaoice and continue to use the Xiaoice brand (and Rinna in Japanese), while Microsoft will retain its stakes in the new company.
Implementing Artificial Intelligence (AI) in an organization is a complex undertaking as it involves bringing together multiple stakeholders and different capabilities. Many companies make the mistake of treating AI as a'pure play' technology implementation project and hence end up encountering many challenges and complexities peculiar to AI. There are three big reasons for increased complexity in an AI program implementation – (1) AI is a'portfolio' based technology (example, comprising sub-categories such as Natural Language Processing (NLP), Natural Language Generation (NLG), Machine Learning) as compared to many'standalone' technology solutions (2) These sub-category technologies (example, NLP) in turn have many different products and tool vendors with their own unique strengths and maturity cycles (3) These sub-category technologies (example, NLG) are'specialists' in their functionality and can solve certain specific problems only (example, NLG technology helps create written texts similar to how a human would create it). Hence, organizations need to do three important things – 'Define Ambitious and Achievable Success Criteria', 'Develop the Right Operating Rhythm', and'Create and Celebrate Success Stories' to realize the true potential of AI. Most companies have very narrow or ambiguous'success criteria' definition of their AI program.