artificial intelligence technologies

As per New IDC Spending Guide, Worldwide Spending on Cognitive and Artificial Intelligence Systems Forecast will reach $77.6 Billion in 2022. Artificial intelligence is the deception of human intelligence processes by machines, especially computer systems. This umbrella term covers Robotics process automation, Machine Learning, Deep Learning, Machine Vision, Natural Language Processing, Pattern recognization, Robotics, etc. 

Introduced by John McCarthy, the American Computer Scientist at The Dartmouth Conference in 1956, it has gained prominence due to big data and its role in increasing the speed, size, and variety of data businesses. Artificial Intelligence can do the tasks such as recognizing data patterns more efficiently than humans, enabling businesses to achieve more details of their data. Artificial intelligence has gained significant importance in almost all domains like healthcare, business, education, finance, law, and manufacturing.

The overgrowing competition and trends for using AI in the applications, products, and services among the enterprises is why this sharp growth.

In this article, we will discuss the Top 15 Hot Artificial Intelligence technologies.

Related post – 5 Types of Artificial intelligence Unveiled

Following technologies will be covered in this article,

  • Natural Language Generation
  • Speech Recognition
  • Machine Learning Platforms
  • Virtual Agents
  • Decision Management
  • AI Optimized Hardware
  • Deep Learning Platforms
  • Robotic Process Automation
  • Text Analytics and Natural Language Processing (NLP)
  • Bio-metrics
  • Cyber Defense
  • Content Creation
  • Emotion Recognition
  • Image Recognition
  • Marketing Automation

So, let us get started then,

Top 15 Hot Artificial Intelligence Technologies

Natural Language Generation

Natural language generation is a subset of NLP and hard to deal with. In this process, automatic text generation happens from structured data. This way, it helps to communicate ideas and thoughts as clearly as possible. The text must be in a readable and meaningful format with a combination of phrases and sentences.

It follows three phases:

1. Text Planning – Basic content is ordered into structured data.

2. Sentence Planning – Flow of information is formed using the sentences from structured data.

3. Realization – Grammatically correct sentences are produced to represent text.

Speech Recognition

Speech Recognition converts and transforms human speech into a useful and comprehensive format that computer applications process further. Nowadays, we often witness the transcription and transformation of human language into useful formats, and it is growing rapidly.

Machine Learning Platforms

Machine learning is a sub-component of Artificial Intelligence. It can be defined as the algorithms that parse data sets and then learn from them to apply what has been learned to make informed decisions. In the case of Machine learning, the computer program learns from experience by performing some tasks and sees how those tasks’ performance improves with the experience.

The state-of-the-art field of AI is used extensively in developing tools for industry and society. The machine learning algorithms focus on solving real-world issues by automated tasks across industries. These may range from on-demand music services to data security services.

Virtual Agents

A virtual agent is a computer agent or a program that can interact effectively with humans. Many chatbots applications come under this category that serves customer service. Companies like Apple, Google, Amazon, Artificial Solutions, Assist AI, Creative Virtual, IBM, IPsoft, Microsoft, and Satisfi provide virtual agents solutions.

Decision Management

Artificially Intelligent machines are capable of introducing logic to AI systems that are further used for training, maintenance, and tuning. Organizations already use decision management to add value to the business and be profitable by incorporating it into their applications to propel and execute an automated decisions.  

AI Optimized Hardware

Thanks to better and improved graphics and central processing units, devices are being structured and used to execute AI-oriented tasks specifically. A prominent example of this is the AI-optimized silicon chip which can be inserted into any portable device. Therefore, companies and organizations are investing greatly in AI to accelerate the next generation of applications. Companies like Alluviate, Google, Cray, Intel, IB, and Nvidia offer this technical service. 

Deep Learning Platforms

Deep learning is also seen as a subset of machine learning. Deep learning is focused on achieving more power by learning how to represent the world in a hierarchy of concepts. It shows how the concept is related to more straightforward concepts and how there can be less abstract representations for more abstract ones.

It works on the model of continuous data analysis with a logical structure similar to the human brain. It works on many layers of algorithms called Artificial Neural Network (ANN). These networks are identical to the way the biological neural networks of the brain are. Hence deep learning is the field creating ANN that learns and makes intelligent decisions by itself.

In addition to that, like machine learning, it also comes under the category of Artificial Intelligence. But deep learning is related to the part of Artificial Intelligence, which was mostly like humans.

Robotic Process Automation

Robotic Process Automation refers to the functioning of corporate processes. They exclusively mimic human tasks and automate them. In this particular sphere, it is important to bear in mind that AI is not meant to replace humans but to support and complement their skills and talent. Companies like Pega systems, Automation Anywhere, Blue Prism, UiPath, and WorkFusion, focus on this process. 

Text Analytics and Natural Language Processing (NLP)

NLP shows the importance of natural language processing in artificial intelligence for these voice-activated platforms or chatbots to language translation. Besides, several applications of natural language processing in artificial intelligence clearly indicate how natural language processing in artificial intelligence will shape and improve a new era of communication technology with the power of artificial intelligence, deep learning, and machine learning.

Bio-metrics

Biometrics helps recognize physical features of the human body’s structure, form, and behavior with measurement and analysis. It is an organic interaction between machines and humans as it works with touch, image, speech, and body language. It is predominantly used for the purpose of market research.

Cyber Defense

Cyber defense is a computer defense mechanism that aims to detect, prevent and mitigate attacks and threats to data and infrastructure of systems. Neural networks capable of processing sequences of inputs can be used along with machine learning techniques to create learning technologies to reveal suspicious user activity and detect cyber threats.

Content Creation

Although content is created by people working on videos, ads, blogs, and white papers, brands like Hearst, USA Today ad CBS is using AI to generate content. Wordsmith is a popular tool created by Automated Insights, which applies NLP to generate news stories.

Emotion Recognition

This kind of AI technology enables human emotions to be read and interpreted using advanced image processing or audio data processing. Law enforcers often use this technology during interrogation. Some companies that use emotion recognition are Beyond Verbal, nViso, and Affectiva.

Image Recognition

Image Classification/recognition –

So far, we have an overview of the Computer Vision where we see it takes input as an image and provides an output that could be categorized in some specific object category.

Input: An image with a single object, such as a photograph.

Output: A class label (e.g., one or more integers mapped to class labels).

However, it is the most complicated thing in the Computer Vision part because an image can be classified into more than one category. ImageNet is the most popular dataset used in this context, composed of millions of classified images utilized in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC). Image classification assigns a class label to an image or predicts the class of one object. 

Deep learning uses the concept of photo classification datasets, which follows CIFAR. Deep learning helps to create a convolutional neural network that recognizes images with more accuracy. This network works similarly to neurons in the human brain.

However, before Deep learning is applied for image classification, we need to perform supervised learning on the computers. It is nothing but feeds object patterns, for example, more images of dogs so that the computer can build its own cognition.

Marketing Automation

Marketing and sales teams and divisions have adopted AI and benefited a lot from it in return. Methods incorporating AI through automated customer segmentation, customer data integration, and campaign management are widely used. AdextAI has grown to become a pioneer in adopting marketing automation.