Top 10 AI That Will Dominate In 2020

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The growth in Artificial Intelligence (AI) is increasing day by day. AI products are gradually entering houses and workplaces. However, there is a concern that it will take many jobs. While these are all real discussions. AI should not be only smart home gadgets or used in automation. Instead, if done correctly AI can solve many problems of the world. There is a vast advancement in the artificial intelligence field in the year 2019, and it will continue to do in 2020.

Let’s look at the top 10 AI technologies that will dominate in 2020.

1. Virtual Agents

Virtual Agents are computer made artificial intelligence with a virtual character. It works as an online customer service representative. It can have an intelligent conversation with its customers. It can also answer the questions of the users. We use virtual agents in the online market, e-commerce and smart home. Some of the companies that deliver virtual agents are Amazon, Google, Microsoft, IBM, etc. A virtual agent can play the role of a customer care representative to handle hundreds of call inquiries. For example, in healthcare, AI agents will take customer surveys and will give suggestions to improve the system.

2. Machine Learning

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Computers are becoming intelligent day by day. Machine learning is a branch of AI. The main machine learning is to produce technologies that help the computer system, to learn. ML provides training tools, algorithms, applications of big data, and some other features. The machine learning system is getting grip day by day. Today they are generally used for classification and prediction. Some of the companies that use ML are Google, Amazon, Microsoft, etc.

3. Natural Language Generation

Natural language generation comes under AI. It converts data into writing that allows a computer to generate ideas with flawless accuracy. Many companies use this technology in customer services. It can create reports and summaries, and many companies use the system such as Lucid works, Cambridge semantics, etc. NLG helps the company to understand customer’s needs, and find out if they are satisfied or not. Industries also have virtual agents and chatbots to solve the problems if the users.

4. Biometrics

Biometrics technology can identify, analyze, and measure the human body form and structure. It allows for more normal communications between machines and humans. It includes interactions such as speech, body, and touch. It can also recognize body language. It will be a huge milestone in the field of security.

5. Cyber Defense

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Cyber defence is a computer defence device that prevents, detects, and responds to the cyber-attacks and threats. Artificial intelligence and machine are learning are used in cyber-defence. Cyber-threat is increasing. There are many examples of cyber-attacks such as an attack on the ATMs of various banks that possessed harm to the data of many customers. The neural networks along with ML can help prevent the attacks. They can create learning technologies that can discover suspicious activities by the users. It will be helpful as it can detect up to eighty-five per cent of the cyber-attacks.

6. Marketing Automation

Marketing sector industries have benefitted from AI technology the most. 55 per cent of marketers think that AI will have a bigger impact in this field than social media. Marketing automation lets industries to improve appointment and increase effectiveness to grow income faster. It uses software to systematize customer division, customer data incorporation, and campaign organization. For example, the software can automate all the procedure of operation management and can make more than five hundred daily adjustments to optimize the campaign.

7. Deep Learning

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The scope of deep learning is massive in the future. Encouraged by the technique the human brain works, the computer programmers have advanced in the ML field. The deep learning solutions also help the digital healthcare service. They have flawlessly adopted big data analyses. AI applications can provide ePrescription, precise medicine to patients, etc. Deep learning uses an exclusive form of ML that includes artificial neural circuits.

8. Robotic Automation

Robotic processes automation uses techniques that imitate and mechanize human tasks to back up corporate processes. It is useful for conditions when signing humans for a particular job or task that is too costly or inefficient. It can make work time efficient. Robots can work more efficiently and faster. There are many responsibilities that are difficult for a man to complete, but with this automation that would not be a drawback in the future.

9. Digital Twins

A digital twin is a software that decreases the gap between a digital world and the physical system. General Electric is a company is building artificial intelligence to screen the aeroplane engines. The digital twins are mostly software code, but there are elaborate versions that look like a 3-D model. A digital twin is also used to protect significant infrastructure.

10. Compliance

It is the authorization that a person or association meets the necessities of practices, regulations, rules, standards, and there is an important industry that sustains it. There are compliance solutions that use AI. It provides efficiency through automation. There are many examples such as NLP can scan supervisory text and match the outlines with keywords to recognize the changes.

Further reading:

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arushasuyal
Engineer by education, writer by heart.

1 Comment

  1. Thanks for sharing such an informational blog which will, surely be a big help to the small medium enterprise so that they can choose the best suited tool for their business.

    One such enterprise is toolowl. The company is powered by intelligence of few experienced, inexperienced, trained, untrained, laymen, domain experts, and business/technology acquainted people or experts. They all work as a team to identify key performance indicators for a particular tool from Individual’s as well as Business’ point of view. This brainstorming is then translated into a distilled methodology for review framework. Teams do hands-on, research on the tool/domain and record their inferences in the form of reviews that help you make your choice!

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