What’s the Difference between Artificial Intelligence (AI), Machine Learning and Deep Learning?

Artificial Intelligence

What is the difference between artificial intelligence (AI), machine learning and deep learning?

AI is the ability for a machine to make a decision. The simple explanation is that AI works is that it takes data and then makes a prediction using that data. It learns to make a prediction using three ways: by learning from previous answers, by applying a mathmatical formula to the answer, or typically by a combination of the two.

There are multiple types of AI:
  • Computer Vision

    The ability to see and categorize images.

  • Natural Language Processing

    The ability to categorize text-based data and predict a response.

  • Natural Language Understanding

    The ability to understand and respond.

  • Detection and Prediction

    The ability to identify patterns in data and use this data to make decisions.

  • Robotic Process Automation

    The ability to automate digital tasks (This isn’t exactly AI but can be used to integrate into AI processes).

Computer vision

Computer vision detects information from an image or video. Today computer vision is used in self driving vehicles to detect option objects cars stoplights etc. Other computer vision use cases include gender detection, age detection, object detection and categorization. Companies are starting to use computer vision to identify what ads should be displayed based on your appearance.

Natural Language Processing

Another type of AI is natural language processing so natural language processing. This is the ability to understand Text-based information. Natural language processing is used to analyze information in document format and categorize information from those documents.This could be used to summarize information and is usually used to categorize a comment or statement and predict sentiment.

Natural Language Understanding

Natural Language Understanding is the ability for a computer to understand language and respond. Common use cases for natural language Understanding are customer service bots or sales bots in which company will try to engage Or help you using automated processes. One solution that we like is X.AI Which uses full natural language processing and natural language understanding to schedules Meeting appointments. To use X.AI you CC your virtual assistant on the meeting request with some generic information such as “Can you schedule a meeting for next Thursday” and your AI powered assistant will contact the other party to negotiate times and automatically schedule it on your calendar.

Machine Learning & Deep Learning (Detection & Prediction)

Machine learning and deep learning detect patterns in your data to predict the result. Machine learning is more math focused to predict an answer while deep learning is a little more abstract and is modeled on the way the human brain identifies patterns to make a prediction.

Both machine learning and deep learning can be used to solve the same types of problems but they solve them in different ways. Usually deep learning is used for computer vision problems and potentially for natural language problems.

An example of machine learning include predictive analytics or predicting Churn - if a customer is going to cancel or is not going to cancel. Another example of machine learning is commonly used for customer segmentation. Customers are grouped into segments based on their attributes and similarities.

Deep learning is technically a subset of machine learning but the difference is when you push in the data, each of those data points is given a weight that determines what the prediction value is.

It’s often harder to interpret the learning results because it is not transparent how the decision was made. The Deep Learning algorithm identified a pattern hundreds, thousands, or millions of record and then created algorithm behind the scenes.

Deep learning and machine learning are used for all areas of computer vision, natural language processing, natural language understanding, and detection and prediction.

Robotic Process Automation

Robotic Process Automation is a script that automates tasks that are typically performed by a person. Today, these are generally repeatable and can be integrated into a Machine Learning or Deep Learning algorithm in order to make your bot smarter. Robotic Process Automation is heavily used in the insurance industry to process claims and is heavily used for IT operational tasks such as account creation, password reset requests or security audits. Today, robotic process automation is the most flexible way to automate digital tasks without redesigning processes and systems. They are designed to be a low code way to automate tasks.

Levels of AI

There are three levels of AI: Narrow AI, General AI, and Super AI. All solutions today are considered Narrow AI solutions. They solve one specific task that they are given. Narrow AI is also called “week” AI because they use predetermined data and algorithms to make a prediction. Narrow AI is commonly used to automate a number routine tasks or tasks that require huge computational intelligence in order to respond. Even advanced technologies such as self-driving cars are considered Narrow AI because they chain together multiple Narrow AI solutions.

Artificial General Intelligence

The next level of AI is Artificial General Intelligence. AGI focuses on the ability to perform at the level of human intelligence and to chain together a complex series of actions. AGI is expected to be able to reason, solve problems, make judgments under uncertainty, plan, and learn over time.

Artificial Super Intelligence

Artificial Super Intelligence is expected to surpass human intelligence in all regards. Think HAL, The Terminator, The Matrix. This is the AI that people like Elon Musk believe will lead to the extinction of the human race.

While we are years away from Artificial Super Intelligence, people such as Ray Kurzweil and Elon Musk are focused on creating solutions where we can create superhuman intelligence using AI-powered brain implants.

I, for one, would love to get my super-intelligent brain implant (my husband will never be right again), but today, those technologies are in the not-too-distant future.

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