How Predictive Intelligence Is Used
Computer aides such as Siri and Cortana are the most visible use of natural language processing today and the use of predictive intelligence.
Some companies have poured a lot of resources into these developments as it relates to searches, allowing us to write or ask a natural question and receive a relevant response.
Several services and platforms also use natural language processing to create predictive text responses to, for example, their clients’ emails, or messages on social networks, allowing users to choose between one of three responses and Reply to an email or message with a single click.
You may even have used this processing, such as when you wanted to use the “translate” option within a social network or search service to understand a foreign language (with variable results).
Reliable machine translation has been a goal of natural language processing since the 1950s, and results have been improving over time.
Other programs are being developed and used to be able to automatically summarize long documents or extract relevant keywords in the search process. The legal system is using such applications, for example, to help lawyers rank thousands of pages of documents in any case and find relevant information.
Sellers themselves are using natural language processing for consumer sentiment analysis, combining millions of tweets and other social media messages to determine how users feel about a particular product or service.
It has the potential to turn all Twitter or Facebook into a giant focus group, at a fraction of the cost.
Another way to use this technology daily in your life is with text sorting, which is what some services and other email providers use to determine whether a message is spam or not.
This is a very simple binary classification: an email is spam or it is not. But more sophisticated forms are being used for such complex analyzes as the determination of the author of a work, comparing it with other works.
With this, predictive intelligence is made to avoid plagiarism.
Companies are predicting that chatbots, for example, will be able to take on some customer service functions in as little as five years, providing automated, real-time answers to simple questions and questions that users make.
Integrations are also being developed for particular situations and users. For example, IBM, with Watson Analytics, has developed a platform that can enable business leaders to track key performance metrics.
The power to accurately predict has helped people gain an edge in business, politics and everyday life.
Often called “predictive intelligence” or “predictive analytics”, these methodologies more accurately anticipate climate, financial markets, elections … even the construction of a sports team.
Natural language processing, coupled with predictive intelligence, is based on observing client behavior and, with each action taken, building a profile of preferences.
This information is used to anticipate the wishes and needs of customers, and predict what content would be best to deliver through any channel.
This is the rise of predictive intelligence in industries such as eCommerce, using the developments of natural language processing, something in which Watson Analytics is well ahead of its competitors and helps to democratize the data analysis.
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