Source: AI Scientist
Summary: As hierarchical control accumulates more wealth, the rise of automation is leading to fewer jobs for the working class. How will the working class survive in the future? A university professor had a brilliant idea; his logic is clear: players with RMB are the financial backers of gaming companies, and to keep them happy, gaming companies must support a group of poor players.
Every time you ask Siri for directions, a complex string of code is activated, enabling “her” to understand your question, search for the information you want, and respond in a language you can understand. This has become a reality over the past few years. Even now, we still interact with computers in a language they can understand, rather than in our own language. We have learned their language.
But now, they are learning our language.
The technology that supports this transformation in human-machine relationships is Natural Language Processing (NLP). It is changing business intelligence in ways that go beyond simple interaction. Soon, the business world will transform, and life-changing information will be available simply by talking to a chatbot.
This future is not far off. In fact, it can be said that it is right around the corner.
What Is Natural Language Processing?
Natural Language Processing, also known as computational linguistics, combines machine learning, artificial intelligence, and linguistics that allows us to converse with humanoid machines.
Think back a few years when we achieved effective Google searches using Boolean search terms combined with keywords like “and,” “or,” and “not.” To get the answers you wanted from Google, you had to know its language.
Then, Google introduced semantic search. Its algorithms learned the relationships between words, allowing you to ask questions of Google as if it were a friend. Internally, it translates your question into a Boolean structured search that the computer can understand—but this process is invisible.
This is the same technology used when you ask Siri, “How’s the weather today?” or “Which flight to Beijing is the cheapest tomorrow?” without modifying your English into logical constructs.
You just need to ask Siri: “Which flight to Beijing is the cheapest tomorrow?” “She” will respond: “Understood,” and then start searching for flights from your location to Beijing, comparing costs to find the cheapest flight.Siri understands “tomorrow” and “cheapest” without needing you to specify a particular date or define “cheapest” as the lowest price.
These examples are still relatively elementary. Although impressive, they still make mistakes. When they do, it indicates that your question requires a highly integrated data response. The goal of Natural Language Processing is to eliminate user graphical interfaces—even user interfaces—making interaction with machines as simple as talking to a person.
This will be a significant branch of business intelligence applications.
NLP Will Democratize Data
Overall, the biggest impact will be to lower or completely remove the barriers to accessing business intelligence and big data. Many companies in the business intelligence field have already noticed this trend and made significant progress, ensuring that data becomes easily accessible and retrievable for users. However, there is still a long way to go.
Imagine a future where you can ask a question and receive an answer anytime, anywhere. Transforming business intelligence into a conversation with a chatbot will make understanding information as simple as asking, “How was the revenue over the past three quarters?” without needing years of experience, familiarity with software, or worrying about whether the machine will understand your question.
With the demand for user graphical interfaces declining, Natural Language Processing will make access easier. Users can query via text or voice commands on their smartphones, with the processing occurring in the cloud.
Google may currently tell you what the weather will be tomorrow, but soon you will be able to ask your personal data chatbot how your customers feel today, how they will view your brand next week, and other subjective questions.
NLP Will Make Business Intelligence More Insightful
Currently, Natural Language Processing tends to be based on converting natural language into machine language. However, as this technology matures—especially the artificial intelligence component—computers will better “understand” needs and provide answers without searching for results.
This is a step forward from asking questions in natural language. Initially, data chatbots might respond to a question like, “How has the revenue changed over the past three quarters?” by displaying several pages of data for you to analyze.
Once it learns semantic relationships and question inference, it will be able to automatically filter and organize responses into intelligent answers rather than just presenting data.
You will no longer ask questions in natural language.
NLP Will Navigate Unstructured Data
Natural Language Processing expands the range of possible answers by enabling machines to understand unstructured data.
Early attempts at sentiment analysis have far exceeded expectations. For instance, it can analyze surrounding text from a tweet about your business and determine whether the sentiment conveyed is positive, negative, or neutral. As language recognition technology improves, audio and video will also become more readily available resources.
This technology is still in its infancy; the current level of sentiment analysis can be glimpsed from the accuracy of using Google Translate on a German news article (a process that relies on various aspects of Natural Language Processing). IBM’s Watson, at the forefront of semantic analysis, can currently only detect emotions like joy, fear, sadness, disgust, and anger, while humans can feel a broader range of emotions.
As Watson’s tasks become more refined, Natural Language Processing opens up a wealth of public multimedia for extensive machine analysis, obtaining data that previously required manual analysis, and providing quantitative answers, natural language responses, or both.
Hire a Personal Data Assistant
This upcoming interface will resemble the service-oriented chatbots you see online today. It will drive various applications, integrating your business intelligence analysis into all aspects of your business, providing data-driven processing at any moment.
Imagine your business intelligence chatbot in the near future will be like the old paperclip assistant, always ready to answer questions from Slack chats, Skype meetings, or Microsoft calendar events.
Who is your company’s best salesperson this year? Getting the answer to this question will no longer require asking yourself but simply clicking on your data. You will be able to ask the chatbot any question and receive an answer, as easy as asking your all-knowing friends.
Original link:https://dzone.com/articles/why-natural-language-processing-is-the-future-of-b
Author:Gur Tirosh