Artificial intelligence (AI) vs ML vs deep learning

Artificial intelligence

Artificial intelligence is the process of transferring data and information to machines to function in the same way as human intelligence. The main objective is to develop self-sufficient machines that can think and act like humans. These machines can mimic human behavior and accomplish tasks through learning and problem-solving. Most systems simulate natural intelligence to solve complex questions.

Artificial intelligence focuses on performing 3 cognitive skills such as human learning, reasoning, and self-correction. It can be classified into two broad categories. I am:

Type 1: Skill-based

There are three types of skill-based AI.

  • Limited AI: Also known as ‘Poor AI’, which can program machines to perform specific tasks, but in a much better way than a human.
  • General Artificial Intelligence: AI that can perform a variety of intellectual/intelligent tasks with the same precision as a human.
  • Artificial superintelligence: This is the most advanced form, also known as ‘active AI’. It can overcome specific tasks in no time with better accuracy and speed.

Type 2: Functionality Based

It is of 4 types based on the working principle of the machines.

  • Reactive machines are systems that only react. These systems don’t form memories and don’t use past experiences to make new decisions.
  • Limited memory: These systems refer to the past and information is added over time. The information referred to is short-lived.
  • Theory of mind: includes systems that can understand human emotions and how they influence decision-making. They are trained to adapt their behavior accordingly.
  • Self-awareness: These systems are designed and built to be self-aware. They have the ability to understand their own inner circumstances, anticipate the feelings of others, and act accordingly.

Currently, artificial intelligence is used in many ways. Some of them include:

  • Chatbots that answer questions based on user input
  • Machine translation like Google Translate
  • Autonomous vehicles like Google’s Waymo
  • AI robots such as Sophia and Aibo
  • Voice recognition programs such as Apple’s Siri, Google Assistant, Alexa, and Cortana
  • Various facial recognition systems

Machine learning

AI and ML are closely related as the latter is a subset of the former. ML is a computer science discipline that uses algorithms and computer analytics to build predictive models or make decisions based on data or experience, without being explicitly programmed and useful for solving business problems. Machine learning uses a large amount of structured and semi-structured data so that the ML model can generate appropriate results or allow data-based predictions. ML is widely used in the following places:

  • Sales forecasts for different products
  • Bank fraud analysis
  • Product recommendations
  • Stock price forecast

Deep learning

Deep Learning is a subset of ML, which deals with algorithms inspired by the structure and function of the human brain. Deep learning algorithms can work with a large amount of structured and unstructured data. The central concept resides in artificial neural networks (ANNs) that allow machines to make decisions.

The main difference between deep learning and ML is the way data is presented to the machine. ML algorithms require structured data, while deep learning networks operate at various levels of ANN. The concept of deep learning is mainly used in the following places:

  • Caption bot to approve an image
  • Detection of cancerous tumor
  • Music Generation
  • Image color
  • Object detection

Wrapping Up

Many AI systems are powered by ML and deep learning algorithms. The ultimate goal of the three is the same: to make machines smarter. Knowing these three and understanding the differences can help the individual get better results.

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