Types of Artificial Intelligence and machine learning and how to integrate………….

Sajjad Hussain
Feb 22 · 8 min read
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With technological advances in areas such as intelligence, data modeling, machine learning, and cloud computing solutions, the combination of artificial intelligence and smart devices has become a reality. There are roughly three types of artificial intelligence technologies that organizations can use in multiple applications.

Narrow Artificial Intelligence (NAI)

Narrow Artificial Intelligence (NAI) is used to specify systems that handle single or limited tasks. It provides an algorithm that brings machine learning capabilities to embedded platforms. For example, Google’s translation engine, Amazon Alexa or Apple’s iOS Siri.

General Artificial Intelligence (AGI)

Artificial General Intelligence (AGI) is an algorithm that helps people use the imaginary intelligence of machines to understand or learn any intelligent task that humans can complete. For example, facial recognition tools and Snapchat filters.

Super Artificial Intelligence (ASI)

Super Artificial Intelligence (ASI) is a software-based artificial intelligence system that can surpass human intelligence in a series of activities. For example, IBM Watson, Google Rankbrain or Microsoft Cortana.

Various Artificial intelligence technologies

Artificial intelligence has the ability to significantly improve user experience and is a tool for solving various problems and tasks. The following are some practical artificial intelligence technologies that can be integrated to improve the development process of smart devices.

Voice recognition technology

Voice recognition technology is one of the most popular technologies in embedded devices today. People can easily adopt the voice control system in applications. For example, Alexa, Siri or Cortana systems can observe and decode human speech and convert it into a format that the computer can understand. People can express their needs through voice commands, and this technology allow embedded devices to take responsive measures.

Natural language generation (NLG)

Natural language generation (NLG) is a software process that converts structured data into natural language. It is used to develop long-form content for organizations, automate their custom reports, and generate content for web or smart applications. This is a great benefit for customer service applications. It helps people create reports and market commentary.

Biometric technology

Biometrics technology enables people to recognize, analyze, and measure human behavior and the physical aspects of body shape and structure. This technology is suitable for images, recognition sensors, voice and gesture control. It is also used for access control; people adopt this technology in smart phones, and users can lock/unlock the phone through face recognition. This technique is also used for market research.

Text analysis and natural language processing

This technology can help people find the information they need on search engines, and then use structured plain text to generate news or stories. The most common usage of this technology is for GPS navigation applications (such as Google Maps), in this application, the user can speak the destination to get navigation. This technology understands the requirements of humans, and then further processes to provide the desired results. This technology is also used in security and fraud detection systems.

Virtual assistants and chatbots

This is also the most common technology integrated into mobile applications. This is an important tool for business applications because it can interact with users online. It can also be used to get feedback from users or online communities. Organizations use chatbots and virtual assistants to enhance the end user experience.

Image recognition technology

Image recognition technology can help process objects and functions in digital images or videos. The most common usage in mobile applications is infrared code scanner or license plate scanner. It can be used to diagnose diseases and analyze and recognize faces. It can also be used to analyze payments and images, to check credit cards and other payment options.

Emotion recognition

Nowadays, emotion recognition is one of the most interesting and commonly used techniques in smart devices. This technology allows software to observe and evaluate the emotions of human faces by using advanced image processing or audio data. It captures human sensory changes and then helps smart devices make changes in faces, gestures or sounds.

Cloud Machine learning platform

If an organization has sufficient algorithms, tools, and APIs, it can design and deploy models into production applications based on machine learning platforms and models. There are also various prodction applications for prediction or classification.

Decision management

This is one of the most important aspects of artificial intelligence. Some industry organizations are developing engines that can inject logic and rules into artificial intelligence systems, and help organizations and users make decisions through their automated decision-making capabilities.

Why use artificial intelligence in an organization

If discussing from an organizational perspective, then the following are the benefits that organizations or startups can use when implementing artificial intelligence in smart devices:

  • Promote sales and marketing: The use of artificial intelligence in an organization’s sales and marketing operations can help significantly improve statistics.

How to integrate artificial intelligence in your model

The following steps can be used to implement and integrate artificial intelligence in your project.

Recognize the problem to be solved

Artificial intelligence can be applied to multiple applications as one or more functions. However, it is recommended to implement artificial intelligence all at once to prevent complexity and make full use of all the advantages of artificial intelligence. The phased implementation of artificial intelligence may increase complexity. People should recognize the problems that need to be solved using artificial intelligence technology.

Understand the expectations of artificial intelligence

There is no doubt that artificial intelligence is an extremely influential technology, and it can undoubtedly improve existing applications and take them to a new level. However, it is important to understand the potential of artificial intelligence and the types of improvements sought from it. People can evaluate the functions of existing applications and focus on the functions or features that can be added to the proposed applications to take advantage of artificial intelligence. Organizations can conduct a thorough market analysis to understand whether similar implementations have been implemented in the market and what services can be provided to consumers. Organizations must analyze whether artificial intelligence, machine learning, image processing, and pattern recognition are really needed, as well as the expected benefits and return on investment. This is the basic step to determine the problem and its scope of implementation.

Understand the nature of data

Data is the foundation of artificial intelligence, and it is very important for organizations to understand the flow of data. Artificial intelligence operates on data, and its working mode changes as the nature of data in smart devices changes. Once the data flow is evaluated, the refinement of the data must be considered. Organizations must ensure that AI modules provide clean, informative, and non-repetitive data.

Beyond AI-based APIs

There are many AI-related APIs on the market, but using AI-based APIs does not guarantee a complete AI-based solution. Organizations should devote more resources to data modeling and other related aspects of artificial intelligence to develop truly intelligent systems.

Data integration and security

Having a definite data organization model is very important. Organizations must ensure proper management of existing data. Improper management may affect the efficiency and effectiveness of artificial intelligence deployment. Focusing on data sets and databases and their structure is essential. Well-structured, well-organized data and its integration will improve application performance and ensure higher quality results. Data security is another important aspect and should be given priority. Organizations need to adopt the right strategy to integrate current security standards, so that the application has the ability to resist cyber attacks and sufficient security to meet security challenges.

Implementation stage

Once all necessary analysis and other tasks are completed, the organization can proceed with development work. Before introducing a real-time environment, it is important to carefully test and deploy the results of these implementations. It is recommended to use an appropriate powerful analysis system before integrating artificial intelligence functions into the proposed model. This will help organizations analyze the pros and cons of artificial intelligence integration and help organizations make better decisions in the future.

Assistive technology

Organizations must choose the right technology and tool set to develop the required digital solutions. In order to keep an organization’s applications consistent, its computing services, cloud computing infrastructure, data storage, development stacks, databases, backup software, security tools, and other optimization solutions must be reliable, robust, and withstand future tests. If the appropriate technology and tool set is not selected, the applications adopted by the organization will have an adverse impact on performance.

Effectiveness of artificial intelligence integration

Evaluating the performance of artificial intelligence integration is indeed an important task, and performance can only be evaluated after the organization has appropriate metrics. Organizations can review and evaluate the performance of artificial intelligence integration based on indicators and make appropriate decisions when needed.

Data scientist

Data scientists can help organize and improve data and meet its data management requirements. Data plays the most important role in the implementation of artificial intelligence, and organizations need the right expertise to meet these requirements.

Data expansion

Data augmentation can help developers manage the diversity of data without collecting new data. Several data enhancement techniques such as padding, flipping, and trimming can be used for large neural networks that process a variety of data. Organizations must be adequately prepared in the system to ensure normal operation under various data and environments.

Most famous (AI) tools

Organizations can use the following tools in their projects to implement artificial intelligence and machine learning:

(1) IBM Watson: Help organizations develop and train artificial intelligence models, and provide the function of preparing and analyzing data in an integrated environment.

(2)TensorFlow: An open source platform for artificial intelligence and machine learning, providing a flexible resource ecosystem that can help developers build robust applications based on artificial intelligence/machine learning.

(3) Azure: The core artificial intelligence cloud computing service, which allows organizations to build and deploy artificial intelligence/machine learning models and solutions.

(4) API.ai: Use artificial intelligence/machine learning algorithms to evaluate and match user needs, and help organizations develop dynamic artificial intelligence/machine learning models accordingly.

(5) Clarifai: An enterprise platform for artificial intelligence that uses advanced machine learning to understand text, images and videos.

(6) WIT.ai: Facebook’s chat bot framework and natural language interface, developers can use its natural language processing function to convert sentences into structured data.

(7) Amazon AI: Provides a variety of artificial intelligence services to provide intelligent services for the organization’s applications. Its artificial intelligence services can be easily integrated with the organization’s applications to solve complex problems.

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