What is Artificial Intelligence (AI)?

What is Artificial Intelligence (AI)? 1383 0

What is Artificial Intelligence (AI)?

The use of Artificial Intelligence (AI) has revolutionized how organizations do business. By leveraging the power of AI, organizations can become more efficient and succeed in the rapidly changing digital world. AI can automate mundane tasks, analyze large datasets quickly and accurately, and even develop new products or services that can help businesses stay competitive. This technology allows companies to make decisions faster than ever before, reducing costs and increasing efficiency. Additionally, AI helps with customer service by providing personalized experiences and marketing tailored to each individual user's needs. By using AI, businesses can save money while delivering high quality service.

AI seems to do things magically, but that belies the complexity of how it is trained to do what it is programmed to do. In this blog, we will look at how artificial intelligence learns.

The Three Types of Artificial Intelligence

When the public hears the term AI, they believe that it is a singular

Artificial Narrow Intelligence

Narrow AI refers to artificial intelligence systems designed for specific tasks such as playing chess or recognizing faces in photos. These systems are typically very good at performing their specific task but lack the ability to generalize beyond it.

Narrow AI is used in a variety of fields, from healthcare to robotics. Examples of Narrow AI include facial recognition software, which is used for tasks such as identifying individuals in images; automated customer service systems that use natural language processing to answer customer queries; and virtual assistants such as Siri or Alexa, which use voice recognition algorithms to respond to voice commands.

Currently, artificial narrow intelligence is the only type of artificial intelligence that has been created thus far.

Artificial General Intelligence

General AI refers to artificial intelligence systems with general-purpose capabilities equivalent to the human ability to reason abstractly and understand complex concepts like emotions or morality. General AI does not yet exist, but researchers are working towards creating it using techniques such as deep reinforcement learning and transfer learning.

Artificial Super Intelligence

Artificial super intelligence would be more capable than any human using any standard of measurement. Additionally, at this level, artificial super intelligence would be able to feel emotion and form relationships.


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What is Machine Learning?

Many of the technologies we take for granted are AI-based: chatbots on websites, predictive text features in text messaging, language translation services like Google Translate, suggested movies/shows on Netflix, and what your social media feeds present to you to name a few. For AI to perform these and other tasks, it needs to learn over time with the use of machine learning.

In the 1950s, AI pioneer Arthur Samuel defined machine learning as "the field of study that gives computers the ability to learn without explicitly being programmed.". In its simplest definition, machine learning teaches AI to mimic human intelligence and problem-solving abilities. Machine learning algorithms utilize large amounts of data to detect patterns which then allows it to perform certain tasks. Organizations are using machine learning for a variety of helpful purposes including:

  • Medical imaging
  • Medical diagnostics
  • Fraud detection
  • Automated helplines/chatbots
  • Recommendation algorithms
  • Image analysis and recognition
  • Self-driving cars

There are four subcategories of machine learning: supervised, semi-supervised, unsupervised, and reinforcement.

Supervised machine learning models use labeled data sets which allows them to grow more accurate with classification or outcome prediction over time. For example, an algorithm could be trained with pictures of objects that have been labeled by humans. Over time, the algorithm would learn how to identify pictures of objects without the need for them to be labeled in advance. Of the three types of machine learning, this is the most common method used today.

Unsupervised machine learning models use unlabeled data to detect trends and patterns in the data that individuals don’t explicitly look for. For example, an algorithm could look at customer data that shows buying habits, frequency of visits, products purchased, and spending patterns. With this information, organizations can create targeted marketing campaigns and provide offers that are relevant to a cluster of similar customers.

Semi-supervised machine learning uses a small sample of labeled data and a large sample of unlabeled data—blending both supervised and unsupervised learning models. Using the labeled data, the algorithm will learn and make predictions for any unlabeled data. For example, Google uses semi-supervised machine learning to annotate content on the web and classify it which is then used as a part of its ranking component and the relevance of search results to user queries.

Reinforcement machine learning models use trial and error to train machines to take the most appropriate/best action. As a part of the learning process, a reward system is established that tells the model when it made the correct decision or the wrong decision. Over time, models learn to select the best actions for a given scenario. For example, this method of learning can be used to train autonomous vehicles how to drive, like Tesla’s Autopilot.

There are many other AI subfields that machine learning is associated with including:

Natural Language Processing (NLP): is a subfield of AI focused on understanding natural language as spoken and written by humans. This type of processing is especially important for chatbots and virtual assistants like Siri or Alexa. NLP uses several techniques to analyze natural language text or speech some of which include:

Sentiment Analysis (supervised, unsupervised, semi-supervised): a form of natural language processing that looks at the emotional tone behind words to determine how people feel about a particular subject or product. For example, a sentiment analysis algorithm can analyze a set of tweets about a new product and determine whether people generally feel positively or negatively about it.

Text Classification (supervised): a form of natural language processing used to classify text into categories. For example, an algorithm can be trained with a dataset of emails labeled as either spam or not spam. Once trained, the algorithm classifies new emails as either spam or not spam.

Entity Extraction (supervised): a form of natural language processing used to identify and extract structured data from unstructured text. For example, an entity extraction algorithm can be used to analyze a website and extract people’s names, places, and other relevant information.

Topic Modeling (unsupervised): a form of natural language processing used to uncover topics hidden in text. For example, an algorithm can be used to analyze a large collection of documents and identify the themes or topics present in them.

Natural language processing is an important component of AI that teaches machines to understand human language and extract useful insights from it.

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Neural Networks

Neural networks are composed of thousands to millions of interconnected processing nodes to simulate the way a human brain’s neurons are interconnected. In a neural network, nodes are connected and each node processes input information and outputs information which is sent to other nodes. Data is labeled and moves through the nodes, each of which perform different functions. For example, neural networks can be used to look at information contained in an image and reach a determination as to what the image represents.

Deep Learning Networks

Neural networks that have many layers are known as deep learning networks. This layering allows a deep learning network to look at and process large amounts of data. Just like neural networks, deep learning networks are modeled on how the human brain processes information. A deep learning network can handle complex tasks well the more layers it has. For example, image recognition systems could use one layer to process features of a face while a different layer determines if those features actually indicates a face. One downfall to deep learning networks is the amount of power they consume to perform its various functions.

Computer Vision

Computer vision is a branch of AI focused on understanding images or videos. Computer vision algorithms use techniques such as object detection, image segmentation, facial recognition, optical character recognition (OCR), and more to identify objects in images or videos or extract information from them.

Computer vision has a wide range of applications, from recognizing objects in photos to understanding the contents of videos. For example, computer vision can be used for facial recognition, which allows software to identify individuals in an image or video by analyzing their features. Computer vision algorithms can also be used for object detection, which involves identifying and locating specific objects in an image or video. It has widespread applications in healthcare, surveillance, manufacturing, retail, and more.

Overall, AI is an incredibly powerful tool that can be used to automate mundane tasks, solve complex problems, and create new products or services. It has wide applications and continues to quickly evolve as more advances are made in the field. With its ability to learn quickly from data and make decisions based on those insights, AI will continue to drive innovation and progress in the coming years.

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