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Machine Learning

Machine Learning is beginning to have the impact on our world that has been anticipated since the early days of Artificial Intelligence and the computer itself. Thanks to the vast amount of data that is now available on the internet and being collected by the world's information systems and the ever expanding computational power to analyze this data, Machine Learning is finally coming into its own. Companies like Google and Facebook are placing Machine Learning at the center of their operations. Machine Learning powers Google's search, Facebook's timeline, targeted advertising that drives the bottom line at both companies, as well as products where intelligent behavior is more apparent such as voice recognition, automatic translation, and intelligent assistants such as Siri and Google Now.

This however, is only the beginning. In the coming years, Machine Learning will play an ever larger role in every area of business and transform business and society. While companies like Google and Facebook are reaping the rewards of years of research in these areas, it is not so easy for other businesses that are newer to this game to leverage this technology achieve similar rewards. This is where a company like Intelligent Systems can help companies large or small play in this game and use this technology to drive increased sales and profits, reduce costs, and gain a strategic edge on their competition.

Intelligent Systems has been doing Machine Learning research and applying its techniques to real world problems for 30 years. Founded in 1997 to leverage the Artificial Intelligence research its founder was conducting for the Defense Department and Intelligence Community, Machine Learning and other AI technologies, and their application to real world business problems, has been at the core of Intelligent Systems since its inception. Research areas included Neural Networks, Bayesian Networks, Decision Trees, Conceptual Clustering, and the application of these technqiques to Natural Language Processing.

Some of the real world areas where Intelligent Systems has applied these Machine Learning techniques include:

    • Learning rules to automatically extract and transform content at a fraction of the cost and time in large complex content and website migrations
    • Automatically classifying documents, emails, and other unstructured text data
    • Automatically building and updating taxonomies via Conceptual Clustering and automatic text classification
    • Automatically learning keywords and related metadata by discovering related words and phrases from existing content based upon context
    • Creating significantly more accurate and precise search engines by analyzing search history data to learn what customers and users really mean by their queries and which products and content they are trying to find via these queries
    • Learning product affinities from order data to automatically generate product recommendations
    • Learning auto-complete rules based upon word and letter ngram statistics
    • Discovering product issues and customer needs by analyzing call center logs
    • Financial Modeling - Intelligent Systems was applying Neural Networks and Machine Learning to analyze financial markets long before the term High Frequency Trading became a household word