Artificial intelligence (AI) and machine learning (ML) are terms that have created a lot of buzz in the technology world, and for good reason. They’re helping organizations streamline processes and uncover data to make better business decisions.

These technologies are responsible for capabilities like facial recognition features on smartphones, personalized online shopping experiences, virtual assistants in homes, and even the medical diagnosis of diseases.

What Is Artificial Intelligence?

Artificial intelligence is a poorly defined term, which contributes to the confusion between it and machine learning, says Bethany Edmunds, associate dean and lead faculty for Northeastern’s computer science master’s program.

“Artificial intelligence is essentially a system that seems smart. That’s not a very good definition, though, because it’s like saying that something is ‘healthy’. What exactly does that mean?” she says. “On a basic level, artificial intelligence is where a machine seems human-like and can imitate human behavior.”

These behaviors include problem-solving, learning, and planning, for example, which are achieved through analyzing data and identifying patterns within it in order to replicate those behaviors.

Artificial Intelligence Skills

  • Algorithms, and techniques for analyzing them
  • Machine learning and how to apply techniques to draw inferences from data
  • The ethical concerns in developing responsible AI technologies
  • Data science
  • Robotics
  • Java programming
  • Programming design
  • Data mining
  • Problem-solving

What Is Machine Learning?

Machine learning, on the other hand, is a type of artificial intelligence, Edmunds says. “Where artificial intelligence is the overall appearance of being smart, machine learning is where machines are taking in data and learning things about the world that would be difficult for humans to do,” she says. “ML can go beyond human intelligence.”

ML is primarily used to process large quantities of data very quickly using algorithms that change over time and get better at what they’re intended to do. A manufacturing plant might collect data from machines and sensors on its network in quantities far beyond what any human is capable of processing. ML is then used to spot patterns and identify anomalies, which may indicate a problem that humans can then address.

“Machine learning is a technique that allows machines to get information that humans can’t,” she says. “We don’t really know how our vision or language systems work—it’s difficult to articulate in an easy way. For this reason, we’re relying on data and feeding it to computers so they can simulate what they think we’re doing. That’s what machine learning does.”

Machine Learning Skills

  • Applied mathematics
  • Neural network architectures
  • Physics
  • Data modeling and evaluation
  • Natural language processing
  • Programming languages
  • Probability and statistics
  • Algorithms

Machine learning and deep learning are subfields of AI

As a whole, artificial intelligence contains many subfields, including:

Machine learning automates analytical model building. It uses methods from neural networks, statistics, operations research and physics to find hidden insights in data without being explicitly programmed where to look or what to conclude.

A neural network is a kind of machine learning inspired by the workings of the human brain. It’s a computing system made up of interconnected units (like neurons) that processes information by responding to external inputs, relaying information between each unit. The process requires multiple passes at the data to find connections and derive meaning from undefined data.

Deep learning uses huge neural networks with many layers of processing units, taking advantage of advances in computing power and improved training techniques to learn complex patterns in large amounts of data. Common applications include image and speech recognition.

Computer vision relies on pattern recognition and deep learning to recognize what’s in a picture or video. When machines can process, analyze and understand images, they can capture images or videos in real time and interpret their surroundings.

Natural language processing is the ability of computers to analyze, understand and generate human language, including speech. The next stage of NLP is natural language interaction, which allows humans to communicate with computers using normal, everyday language to perform tasks.

Key differences between Artificial Intelligence and Machine learning

Artificial Intelligence Machine learning
Artificial intelligence is a technology which enables a machine to simulate human behavior. Machine learning is a subset of AI which allows a machine to automatically learn from past data without programming explicitly.
The goal of AI is to make a smart computer system like humans to solve complex problems. The goal of ML is to allow machines to learn from data so that they can give accurate output.
In AI, we make intelligent systems to perform any task like a human. In ML, we teach machines with data to perform a particular task and give an accurate result.
Machine learning and deep learning are the two main subsets of AI. Deep learning is a main subset of machine learning.
AI has a very wide range of scope. Machine learning has a limited scope.
AI is working to create an intelligent system which can perform various complex tasks. Machine learning is working to create machines that can perform only those specific tasks for which they are trained.
AI system is concerned about maximizing the chances of success. Machine learning is mainly concerned about accuracy and patterns.
The main applications of AI are Siri, customer support using catboats, Expert System, Online game playing, intelligent humanoid robot, etc. The main applications of machine learning are Online recommender system, Google search algorithms, Facebook auto friend tagging suggestions, etc.
On the basis of capabilities, AI can be divided into three types, which are, Weak AI, General AI, and Strong AI. Machine learning can also be divided into mainly three types that are Supervised learning, Unsupervised learning, and Reinforcement learning.
It includes learning, reasoning, and self-correction. It includes learning and self-correction when introduced with new data.
AI completely deals with Structured, semi-structured, and unstructured data. Machine learning deals with Structured and semi-structured data.

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