Machine intelligence is another name for the artificial intelligence that is demonstrated by robots. It’s the product of machine learning, deep learning, natural language programming, or natural language understanding. 

Machine intelligence is now used in major industries such as customer service and manufacturing. Chatbots, or chatter robots, are helping handle customer queries from online shoppers, while cobots, or collaborative robots, are helping out in major factories. Several applications have also been developed for the healthcare industry, with some even assisting in delicate surgical procedures. Machine intelligence is expected to be used more widely as research into the technology continues.

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At its core, machine learning attempts to mimic human behavior and so has to embody several cognitive functions, along with noncognitive components. 

Said cognitive functions include:

1. Learning

If there is one component of machine intelligence that is already well established, that would be machine learning (ML). Machine intelligence-enabled systems are expected to process massive amounts of data, look for patterns, learn, and apply their knowledge without human intervention.

Learning can come from the machine’s observation of data. An example would be giving an e-commerce site the ability to come up with a list of recommended products based on what buyers usually purchase together.

Learning can also come from a machine’s experience with processing data. Google’s autocomplete search functionality is a result of this. Learning can also come from human input. Grammarly’s ability to correct verb tenses based on the rules set by its developer is one example.

2. Automated reasoning

Reasoning is one of the challenges that machine intelligence faces. It entails drawing inferences, making logical conclusions, and coming up with predictions based on facts. Like humans, machines may use different types of reasoning, which include:

  • Deductive: Using one or more of the given statements or premises to reach a logical conclusion.
  • Inductive: The opposite of deductive reasoning where premises are viewed as evidence of the truth of the conclusion.
  • Abductive: A form of logical inference that starts with an observation then seeks to find the simplest and most likely explanation for it. Unlike deductive reasoning, abductive reasoning yields a plausible conclusion but does not positively verify it.

Other types include commonsense, monotonic and nonmonotonic reasoning.

3. Problem Solving

Machine intelligence can solve different problems that deal with image recognition, time-intensive manual tasks, and spam or fraud detection. It roughly follows the same problem-solving process that human intelligence entails, more specifically:

  • Identifying the problem
  • Analyzing the problem 
  • Identifying possible solutions
  • Selecting the most logical solution
  • Implementing the chosen solution

4. Natural Language Processing (NLP)

While it’s relatively easy to create programs that show fluency in specific spoken languages, teaching them to understand how human beings communicate with one another is a different story. NLP deals with this particular challenge. It teaches computers to read, understand, and derive meaning from natural human languages. We see NLP in action when we use Google Translate or personal assistants like Siri, Alexa, and Cortana.