The Artificial Intelligence in Big Data

Artificial Intelligence (AI) facilitates the efficient and effective supply of information to enterprises for optimized business decision-making. Major IT and software vendor companies are investing billions to generate revenue from AI based commercial solutions in various areas including robotics, machine translators, chat bots, voice recognizers, business intelligence systems, mobility control systems, intelligent search, and more.

The field was founded on the claim that a central property of humans, intelligence—the sapience of Homo sapiens—can be so precisely described that it can be simulated by a machine. This raises philosophical issues about the nature of the mind and limits of scientific hubris, issues which have been addressed by myth, fiction and philosophy since antiquity. Artificial intelligence has been the subject of breathtaking optimism, has suffered stunning setbacks and, today, has become an essential part of the technology industry, providing the heavy lifting for many of the most difficult problems in computer science.

Artificial Intelligence (AI)  research is highly technical and specialized, deeply divided into subfields that often fail to communicate with each other. Subfields have grown up around particular institutions, the work of individual researchers, the solution of specific problems, longstanding differences of opinion about how AI should be done and the application of widely differing tools. The central problems of AI include such traits as reasoning, knowledge, planning, learning, communication, perception and the ability to move and manipulate objects. General intelligence (or “strong AI”) is still a long-term goal of (some) research.

The AI based solution market is valued at US$ 900 million globally by year end 2013 and is expected to grow exponentially over the next five years. Some of the biggest opportunity areas are commercial applications, search in the Big Data environment, and mobility control for generation of actionable business intelligence. The entire mobile/wireless ecosystem is well-positioned for AI via the growing adoption and expanded usage of consumer and enterprise electronics devices including smartphone, tablet, portable devices and wearable technologies.

• The current mainstream business analytics research and solution development along with the convergence of AI with machine learning techniques will continue to underpin high-value, human decision support solutions.
• Cognitive systems can combine natural language processing, hypothesis generation and evaluation, and dynamic learning for a powerful, fast, and intelligent problem solving.
• To effectively apply intelligent problem solving using AI solutions, you can extend the capabilities of IBM Watson and its DeepQA technology architecture to other domains.