2. New capabilities and business model expansion-
deploy data and analytics into the enterprise, it opens up new opportunities for businesses to participate in different areas. For example, autonomous vehicle companies, with the reams of data they’re collecting, could identify new revenue streams related to insurance, while an insurance company could apply AI to its vast data stores to get into fleet management.

3. Improved monitoring-
AI’s capacity to take in and process massive amounts of data in real-time means organizations can implement near-instantaneous monitoring capabilities that can alert them to issues, recommend action, and, in some cases, even initiate a response.
For example, AI can take the information gathered by devices on factory equipment to identify problems in those machines as well as predict what maintenance will be needed when thereby preventing costly and disruptive breakdowns, as well as the cost of maintenance work, performed because it’s scheduled rather than because it’s needed.

4. Better quality and reduction of human error-
Organizations can expect a reduction of errors as well as stronger adherence to established standards when they add AI technologies to processes. When AI and machine learning are integrated with technology like RPA, which automates repetitive, rules-based tasks, the combination not only speeds up processes and reduces errors but can also be trained to improve upon itself and take on broader tasks.

5. Minimize operational costs-
Errors can not only postpone the release date of your product, for instance — they can also cost your company a lot. However, you can use AI to minimize the number of errors and improve the efficiency of your company. Here is an example for you.
Most of the company solution is allows plant managers to make better use of the sensor data they already have.
Predict asset failures with enough lead time to take corrective actions before the equipment has a chance to break down while leaving assets that do not need maintenance to continue running.

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Natural language processing :

Natural language processing which has roots in the field of linguistic is the ability of a computer program to understand and analyze human language as it is spoken and written. NLP is the core of various techniques and tools we use every day such as translation software, chatbots, spam filters, and search engines. NLP analyzes several aspects of natural human language like syntax, semantics, pragmatics, and morphology. Then the data is transformed into machine learning algorithms that can solve specific problems and perform desired tasks.NLP is defined as a problem maker to computers since it’s hard for them to master in understanding the concepts and meaning of plainly spoken human language. Conceptually, it’s a fairly straightforward technology.

“Nat­ur­al Lan­guage Pro­cessing is a field that cov­ers com­puter un­der­stand­ing and ma­nip­u­la­tion of hu­man lan­guage, and it’s ripe with pos­sib­il­it­ies for news­gath­er­ing, You usu­ally hear about it in the con­text of ana­lyz­ing large pools of legis­la­tion or other doc­u­ment sets, at­tempt­ing to dis­cov­er pat­terns or root out cor­rup­tion.”Some of the basic NLP preprocessing tasks are Encoding, marking part of speech, Derivation, Remove redundant words. Once the data is processed, it’s time to build an NLP algorithm.

Speech recognition is the most important application of NLP. It uses natural language processing to convert spoken language into a machine-readable format. Speech recognition is used in virtual assistants like Alexa, transcribing calls, machine translation, Sentiment analysis, keyword detection, text extraction, etc.

Machine translation involves translating text or spoken words from one language to another. The application can be used to reach a wide potential audience and understand foreign documents. Sentiment analysis or opinion mining is to determine whether data is favorable, pessimistic, or neutral. NLP application is also extended to spam detection, topic labeling, and intent detection.

NLP applications deliver a seamless path to interact with machines. Today, we have programs that assess and synthesize text and speech in unprecedented ways.

Speech recognition:

As previously mentioned, Speech recognition is one of the most popular applications of NLP. This procedure functions as a channel that transforms pulse code modulation digital audio from a sound card into recognized speech. Speech recognition technology has been massively used in every industry.

In speech recognition, the computer accepts sound vibrations as input using an analog to digital converter. Various algorithms run on the data to recognize the speech and convert it into the result. The result depends on the goals, for example, Google voice typing converts words to text format while virtual assistants like Alexa take speech input and gives voice feedback in return.

Speech recognition technology has multiple popular applications that enable the user to perform a wide range of voice-activated tasks.

Let’s go through some of the popular applications :

• Virtual assistants use speech recognition software to accept spoken input and deliver results as voice answers or in another form like playing music, news, searching, place an order, and so on.
• Armies or militaries all over the world are researching with speech to text conversion to make the job easy for pilots at the cockpit.
• Clinicians and doctors update patient’s medical records in real-time via voice notes.
• Speech recognition is capable to impact transportation services and streamline scheduling.
• Companies such as PayPal and Venmo allow customers to perform bank transactions through voice assistants.

Numerous industries are actively investing in speech recognition technologies like marketing, tourism, Health care, etc.NLP and speech recognition are two major subfields of artificial intelligence. Ai is continuously evolving to benefit various industries. Artificial intelligence forms the basis for all computer learning and is the future of all complex decision-making. AI is for today and also for a better tomorrow.

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