What are the new AI Technologies?

Artificial intelligence (AI) describes the simulation of human intelligence in devices that are built to understand, learn, and solve problems similar to people. It is a fast-developing field that incorporates a number of fields, including computer science, machine learning, robotics, natural language processing, and more. AI’s main objective is to build intelligent machines that are capable of carrying out tasks that traditionally require human intelligence, such as comprehending and analyzing complex data, spotting patterns, making judgment calls, and adapting to new circumstances.

The ultimate goal of AI is to create computers with thinking, learning, and problem-solving abilities on par with or better than those of humans. Large-scale data analysis, pattern recognition, and prediction or decision-making are all capabilities of artificial intelligence (AI) systems. AI algorithms can automate complex operations, find hidden links, and extract valuable insights from data by processing and interpreting it. This improves productivity, accuracy, and innovation across a range of businesses and areas.

In recent months, the introduction of this new breed of “generative” AI tools has demonstrated that the field is no longer limited to academic research.

Let’s drive through some of the new AI technologies.

1.Natural language generation

NLG, a subfield of artificial intelligence (AI), aims to produce text or speech that is similar to what a human would say or write using structured data or other information sources. The NLG system evaluates data, considers its context, and creates coherent and contextually relevant narratives, summaries, reports, and dialogues by it.

Scalable production of highly individualized and tailored information is possible with NLG systems. Depending on the user’s preferences, the target market, or particular needs, they can modify the product produced. For instance, an NLG system can provide news stories that are specifically written for each reader, personalized marketing communications, or dynamic reports that are updated in real-time.

Here are some real-time examples of Natural language generation AI used.

1. Business Intelligence and Reporting: Generates reports, data analytics, summaries, and insights automatically
2. E-commerce Product Descriptions: Generate product descriptions at scale automatically
3. Personalized Emails and Marketing Campaigns: Generate personalized emails and marketing campaigns through NLG systems
4. Virtual Assistants and Chatbots: Systems like Siri, Alexa, and Google Assistant generate responses to user requests using the NLG system

NLG is widely used across industries and provides chances to automate content creation, improve consumer experiences, and speed up information sharing. NLG has the ability to fundamentally alter how we communicate, access information, and cooperate with intelligent systems as it develops.

2. Voice recognition

Voice recognition using artificial intelligence (AI), also referred to as automatic speech recognition (ASR). Spoken language can be translated into text or other machine-readable formats. It allows for voice commands, hands-free interaction, transcription, and other features by allowing machines to comprehend and understand human speech.

In many different fields and sectors, voice recognition AI is used. Here are few examples;

1. Virtual Assistants: Apple’s Siri, Amazon’s Alexa, Google Assistant, and Microsoft’s Cortana are some of the popular trending virtual assistants.
2. Transcription Services: Automatically convert spoken audio or video content into written text here.
3. Voice-controlled Devices and Smart Homes: Using voice recognition AI, it can control household devices and other devices. Users get complete control of the devices through voice recognition AI technology.

Deep learning and neural network-based models are propelling further advancements in voice recognition AI. To enable more fluid and precise voice interactions between humans and robots, current research focuses on strengthening accuracy, handling different accents and languages, and improving natural language understanding.

3. Decision Management Systems

Decision management AI refers to the application of artificial intelligence (AI) techniques and technologies to automate and optimize decision-making processes within organizations.

Decision management systems aim to improve decision-making accuracy, consistency, and efficiency while utilizing massive amounts of data. Organizations can use it to analyze complex data, spot patterns, and trends, and produce useful insights that help decision-making at different levels.

Data collection and integration, data Analysis, decision automation and optimization, feedback and learning are some of the main components of the decision management system.

Decision management systems are used in many fields and industries all over the world to enhance their work. Here are some examples of it

1. Financial Services: Used for fraud detection, data analysis and loan approval processes
2. Healthcare sector: Used in analyzing patient’s details with a treatment panning for better treatments
3. Supply Chain Management: Supply chain operations can be made more efficient by AI through improving inventory management, demand forecasting, and route planning.
4. Human Resources: Employee data management, workplace management

Decision Management Systems have the ability to boost productivity, accuracy, and strategic decision-making across a variety of fields, ultimately improving results and the company.

4. Text Analytics systems

Text Analytics systems refer to the techniques and algorithms that are used to evaluate and glean insightful information from text data. It entails processing and comprehending written text as well as other forms of human language utilizing AI technologies including natural language processing (NLP), machine learning, and deep learning.

Natural Language Processing (NLP), text preprocessing, sentiment analysis, text classification, and text summarization are the key components in text analytics systems

Here are the real-time application where text analytics systems are used in;

1. Customer Feedback Analysis: Used to analyze customer details and feedback
2. Social Media Monitoring: Monitor social media platforms for various purposes such as to get a proper idea about customer opinions and new trends in the industries
3. Market Research: Analyze an enormous volume of textual data of customer reviews, purchase history, new trends
4. Legal Document Analysis: Analyze large volumes of legal documents, contracts, and case law

By using text analytics systems in different fields and organizations, it can gain valuable insights, automate processes, and make data-driven decisions based on large volumes of textual data to enhance the organization.

The way we handle, examine, and comprehend text data has been changed by AI technologies. AI has made it possible for us to gain useful insights, automate time-consuming procedures, and make data-driven decisions based on massive amounts of textual data thanks to the power of natural language processing, machine learning, and deep learning. Text analytics AI has evolved into an essential tool across businesses and disciplines, from customer feedback analysis to social media monitoring, market research, fraud detection, and beyond.

In the future AI will bring unimaginable technologies that were impossible earlier to a reality and will create new trends in technology.