• AI is far more effective and efficient than humans, but AI technology still requires humans to develop the algorithms for proper execution.
  • All AI technology must be trained before it’s trusted as a reliable and accurate source of information.
  • Strong AI, known as artificial general intelligence (AGI), is still in development, but the goal of strong AI is to possess the same range of cognitive abilities as humans.

Artificial Intelligence (AI) uses a computer to mimic how humans think and perform tasks when using a computer, except AI is far more efficient than a human. It is a set of applications humans create that allows a computer to learn, reason, make decisions, and help solve problems like a human would.

Though Artificial Intelligence is the primary name used to describe AI technology and concepts, mathematical equations, statistical formulas, and computer programming are what make AI applications emulate human thinking — though AI resources work a thousand times faster and more efficiently than a human can. Here, we’ll explore different AI applications and technologies that make AI more efficient than humans.

What is Artificial Intelligence?

Artificial Intelligence is a science that uses computer systems to perform sophisticated and complex tasks that, at one time, only humans could do. Today, AI operates by using advanced algorithms that contain conditional statements, which allow AI technology to react to specific scenarios like a human would. Artificial Intelligence technology uses testing models applied against an executed algorithm to ensure the correct results are captured in a manner that a human would identify or capture an issue.

Though AI technologies and tools require humans to help construct them, the AI resources are highly efficient operationally and designed to adapt and learn when dealing with outlier information.

What are the types of AI?

Artificial Intelligence applications, technologies, and tools fall into two categories. The two types of AI are known as weak AI and strong AI, though strong AI is still in the theoretical stages. 

What is considered a Weak AI and a Strong AI?

Weak AI is considered any AI application designed to do one specific task and no more. Examples of weak AI are image and video processing technology that can detect images and recognize facial features. 

Robotics, Natural Language Processing (NLP), and transportation AI technology are all considered weak AI because they can do one thing well. Robotics uses an algorithm to manipulate movements, like grasping and recognizing a package. Natural Language Processing is designed to allow computers to understand, produce, and manipulate human languages. Transportation AI technology helps improve traffic efficiency, find the shortest route, and improve fuel consumption.

Strong AI aims to understand how humans think and interpret human needs and emotions, including the ability to reason and adapt. Currently, strong AI cannot accurately understand human feelings, emotions, or wants. Still, it has a role in cybersecurity, integration of strong AI in the Internet of Things (IoT), and language translation machines. Strong AI is a work in progress and a hypothetical ideal to match human-level thinking.

Artificial Intelligence (AI) is the broad category referred to when discussing AI. Still, underneath AI, there are additional AI technologies that are subsets of AI and the workhorses that allow a computer to perform tasks more efficiently than humans.

Machine Learning (ML)

Machine Learning is a branch of AI that uses algorithms and data to make recommendations based on evaluating the data processed in an algorithm. Machine Learning applications are taught to look for specific data in test phases. Additionally, ML can translate text from one language to another, detect fraudulent transactions, identify cancer growth by analyzing medical scans, and predict stock market changes once trained on what to look for in the test examples provided.

Natural Language Processing (NLP)

Natural Language Processing is another branch that uses multiple AI techniques (like ML, deep learning, and computational linguistics) using training methods (such as unsupervised, supervised, and reinforcement learning techniques) before computational linguistics becomes effective. Natural Language Processing also uses statistics combined with the other AI methods discussed to analyze processed text and voice data to understand what is written or spoken.

Neural Networks

Neural networks are designed to process data like the human brain processes data and makes decisions. They are trained by processing large sets of labeled and unlabeled data. Neural networks use a process called backpropagation that assigns a weighted value or number to a neural network by analyzing the error rate produced.

Backpropagation works backward from outputs to inputs to figure out how to reduce the number of errors in the produced output. The backpropagation process continues until the error rate is reduced, making the neural network more reliable.

Deep Learning

Deep learning uses the neural network concept for training and reinforcement learning to identify complex pictures, texts, and sound patterns. After deep learning has learned what it’s taught, it can predict accurate insights for better business decision-making. Deep learning is another branch of AI that must compute large amounts of data before it’s thoroughly trained to make accurate decisions. 

Deep learning helps businesses identify trends in customer buying patterns, identify specific preferences and behaviors, improve inventory planning, and optimize supply chain processes to reduce the risk of delayed deliveries. Deep learning helps doctors properly diagnose patients accurately and faster. Deep learning can improve overall business operations in multiple business processes.


Robotics are generally physical machinery in production-type environments that perform the same task repeatedly. They are controlled by complex algorithms that process information from cameras and other sensors, such as light, temperature, ultrasound distance sensors, or infrared sensors. The algorithm allows the robotic machinery to execute a movement based on the written code.

Speech Recognition Systems

Speech Recognition Systems are different from NLP. While NLP focuses on understanding the meaning of text data, speech recognition systems aim to convert speech into text. Natural language processing and speech recognition systems are complementary technologies. As speech recognition prepares the data for NLP, the NLP attempts to understand the tone and meaning of the generated text. Speech recognition systems are also known as Automatic Speech Recognition (ASR) systems. 

Speech recognition technology improves customer service by providing quicker responses to customer questions and FAQs. This subset of AI can help businesses with mundane customer service tasks, such as booking appointments and routing calls. 

Are Artificial Intelligence, Machine Learning, and Deep Learning the same?

Machine Learning and Deep Learning are both subsets of AI. Artificial intelligence is the primary category, and ML, DL, neural networks, and NLP all fall under AI as subsets of AI. Artificial Intelligence is generally associated with making computers mimic human intelligence. Still, the subsets of AI, along with mathematics and statistics, help facilitate AI technology to mimic human thinking. 

How are AI technologies and tools trained?

Before AI technology becomes an effective resource, it must be trained using large data sets that can be structured or unstructured data labeled or unlabeled. The three primary types of machine learning methods are supervised, unsupervised, and reinforcement learning.

Supervised learning

Supervised learning occurs when an AI resource is provided with labeled datasets to train an algorithm to recognize patterns and predict future outcomes. These models establish a baseline of the correct results in the generated output. Supervised learning must identify images, predict future behavior, properly categorize customer feedback, and distinguish between spam and non-spam emails.

Unsupervised learning 

Unsupervised learning presents more of a challenge because the algorithm analyzes unlabeled data without human supervision or guidance. The AI resource aims to discover hidden patterns, insights not detected by a human, and data grouping without any human guidance. Unsupervised learning helps detect anomalies — such as security breaches, fraudulent transactions, or faulty equipment — without human intervention. Unsupervised learning is successful when it accurately identifies outlier data points in a dataset.

Reinforcement learning

Reinforcement learning uses the trial-and-error learning process to achieve the most optimal result. It is behavior-based, recognizing correct actions and ignoring incorrect ones. This behavior-based training is continued until the stated results are achieved. For example, Tesla uses reinforcement learning so its vehicles learn how to avoid obstacles and other cars.

What are the Advantages and Disadvantages of AI?

When artificial intelligence is properly handled from its inception to its final implementation, it can benefit businesses across many processes and tasks. However, if an AI subset resource is not correctly vetted for a particular AI project, it can lead to bad decision-making for a business. The same principle can be applied to a dataset used by an AI resource that generates results used for decision-making.

The advantages of AI are:

  • Accuracy – When used properly, AI eliminates or reduces human error while increasing precision
  • Automation – AI automates repetitive tasks that help streamline processes
  • Decision-making – When AI is used correctly, it allows businesses to make faster decisions and data-backed recommendations
  • Digital assistance – AI-enables systems are available 24/7 to address specific tasks

The disadvantages of AI are:

  • Security risks – AI can expose a business’s proprietary information when mishandled and pose a security risk
  • Ethical issues – mishandling AI can lead to manipulation and mistakes, jeopardizing Personally Identifiable Information (PII) or sensitive business information.
  • Varying processes and procedures – can lead to compliance violations that can be costly.

Companies to watch

A featured partner is a business that does something unique that promotes the advancement of artificial intelligence. Listed are two companies focusing on some aspect of AI that innovatively improves AI technologies. 


BasicAI is an organization that develops an AI-powered data annotation platform to simplify data labeling for AI and ML models. Its platform lessens the tedious task of data labeling while increasing an algorithm’s accuracy. BasicAI provides data labeling services for all business industries.

Deci AI 

Deci AI recently became a part of NVIDIA. Deci AI’s platform is an end-to-end deep learning acceleration resource developers use to build, optimize, and deploy detailed and precise models for different IT environments, including cloud, network edge, and mobile.

Generative AI uses machine learning to create new content, and the NVIDIA AI platform can accelerate the development process by improving the entire AI workflow process. This allows businesses to reach production faster with improved infrastructure performance that lowers operational costs.

How are businesses using AI technology today?

Organizations like Open AI and Google DeepMind are constantly pushing the AI envelope to enhance AI technologies that businesses can use to their advantage for improved business performance. Any successful business must continually improve its daily operations, and any company not invested in using AI technology risks losing revenues or its competitive edge. Here are some popular AI resources that businesses use today to remain competitive:


ChatGPT helps marketing organizations generate leads by assisting marketers in creating engaging content for a target audience. ChatGPT creates product descriptions and email newsletters using the style and tone of previously written marketing material. ChatGPT improves customer service by using its natural language processing features to respond to customer requests. This AI technology also processes customer data to understand customer needs and preferences better. OpenAI created ChatGPT.


Google DeepMind developed Gemini, and it’s designed to be multimodal, meaning Gemini can process various data types concurrently, such as images, text, videos, numerical data, and speech. Multimodal AI models can take in multiple forms of sensory input, similar to humans. 

Businesses can use Gemini for data analysis to find patterns and trends in large amounts of data, automate tasks, and summarize conversations in Google Chat or documents in Google Docs. Gemini helps write and refine business documents and emails.

Anthropic’s Claude

Anthropic’s Claude is a family of AI models and chatbots that can perform text-based conversations, creative content, and cognitive tasks, such as vision analysis, code generation, and complex analysis. Claude can analyze images, debug code, and create websites using Hyper Text Markup Language (HTML) and Cascading Style Sheets (CSS).

Businesses use Claude as an AI assistant to cancel orders, provide weather updates, and access database information. Security teams use Claude to respond to attacks through automated detection and respond accordingly. Claude can also understand and draft legal documents and analyze pharmaceutical companies’ scientific data for research. Claude can also do vision analysis by transcribing static images, graphs, and photographs.

Meta Llama

Meta Llama is a group of open-source large language models developed by Meta AI. Businesses use Llama to generate educational content, summarize video calls, and provide medical information. Llama is another AI resource for customer service, improving and streamlining internal communications, and employee training and development.

Llama can be used for market research by analyzing customer reviews and social media posts. This Al resource can also improve risk management and monitor online content for potential risks, threats, or negative customer reviews.

Is an AI tool right for my business?

If you own a business, manage a team, manage projects or anything else, odds are an AI tool exists that can make your life easier. It’s not a one-size fits all approach. With a sea of newcomers and constant updates, the best tool for the job can sometimes change by the week.

Stay abreast of news surrounding the subject. Google something like “Which AI tool is best for (your task)?” This should help you locate a quality tool that, with a minimal amount of learning, can help you throughout your day.

TechnologyAdvice is able to offer our services for free because some vendors may pay us for web traffic or other sales opportunities. Our mission is to help technology buyers make better purchasing decisions, so we provide you with information for all vendors — even those that don’t pay us.

Featured partners


AI, or Artificial Intelligence, is a technology that enables machines to mimic human intelligence. It allows computers to perform tasks such as understanding language, recognizing patterns, solving problems, and making decisions.

AI can perform a wide range of tasks including recognizing speech, identifying images, making predictions, playing games, automating processes, and personalizing content. It is also used in applications like virtual assistants, recommendation systems, and autonomous vehicles.

The main purpose of AI is to enhance and automate tasks, improve efficiency, and enable new capabilities that would be difficult or impossible for humans to perform alone. It aims to replicate or augment human intelligence to solve complex problems and improve decision-making.

AI is mainly used for data analysis, natural language processing, image and speech recognition, automation, and decision-making. Industries like healthcare, finance, automotive, and customer service use AI for diagnostics, fraud detection, autonomous driving, and personalized customer interactions.

AI itself is neither good nor bad; it is a tool. Its impact depends on how it is used. AI can bring significant benefits, such as improving healthcare and efficiency, but it also poses risks like job displacement and ethical concerns if not managed responsibly.

AI is the result of contributions from many researchers and scientists over decades. Key figures include Alan Turing, who proposed the concept of a machine that could simulate any human intelligence, and John McCarthy, who coined the term “Artificial Intelligence” in 1956.

AI is needed to handle complex tasks, process large amounts of data quickly, and perform repetitive tasks efficiently. It enhances productivity, supports decision-making, and enables new technologies and innovations that improve various aspects of life and industry.