Natural language and predictive intelligence, the perfect cocktail to democratize data analytics
Thanks to technologies like IBM Watson Analytics, the machines are already able to understand and process our natural language.
It is not science fiction, but a reality that any company can use to improve its business processes. Here’s how to do it.
A few years ago, if someone had asked him what his phone or computer answered about whether this day was good for a business transaction, or why the expected customers did not arrive at his restaurant this month, he might have thought: What this guy is talking about. ”
Advances in natural language processing and predictive intelligence
However, today, with advances in natural language processing and predictive intelligence, the likelihood that your mobile team will show you through a graph or graphics the odds that this day will be the best to boost your business, is Very feasible. Solutions like IBM Watson Analytics are the answer.
This IBM tool helps anyone to have access to a data analysis of your business, just follow a few steps. It is the democratization of this technology that can help you make better decisions and predict how and when it is important to make investments, or better wait a little longer to avoid generating losses to the organization.
Natural language processing is an aspect of automatic learning, large data, and artificial intelligence, which has the potential to change the way decisions are made, to know what might happen and to anticipate possible risks.
Learning Natural Language
In basic terms, natural language processing is the ability of a computer to understand human speech, as one person expresses it with another.
It’s the difference between saying, “Siri, where’s the nearest coffee shop?” And “Finding coffee shops with the cheapest prices and the best cut.”
For a long time, online searches had to be done using sequences of words combined with boolean search terms (which defines the relationship between words) that ended up not being a traditional conversation between two people. However, today various platforms can answer a question exactly as a friend would. In addition, the results are very reliable.
The same recognition of natural language is being developed for digital speech. Artificial Intelligence assistants like Siri, Cortana and Google Now are good examples of this.
Although it seems simple for a human to answer a natural language question, it is an incredibly complex task for a computer, which requires many calculations and predictions, which should occur in the cloud and in a fraction of a second.
The fascinating thing is that, while a human being inherently understands what is being said, a computer can not really say that it understands language. It can analyze the different words, the context, the grammatical use, etc. And then make a prediction about which answer will be the best, but do not really understand what we’re saying.
One of the goals of natural language processing is to eliminate programming languages such as Java, Ruby or C, and replace them with human instructions in terms of speech. Another is realistic artificial intelligence, in which the computer can react and interact with a human being without problems.
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