Types and Use Cases of Artificial Intelligence

types and use cases of artificial ointelligence

Artificial Intelligence , or AI, is the simulation if human intelligence processes by machines, especially by computer systems. AI includes the following processes:

  •  learning, the acquisition of information and rules for using the information
  •  reasoning, using the rules to reach approximate or definite conclusions
  •  self-correction

There are two main types of Artificial Intelligence: weak and strong.

Weak AI, or narrow AI, is designed to fulfil a particular task; whereas strong AI, or general AI, is a system with generalised cognitive abilities, when presented with unfamiliar task, it is intelligent enough to find a solution.

Another classification, created by Arend Hintze, an assistant professor of integrative biology and computer science and engineering at Michigan State University, categorizes AI into four types, from the kind of AI systems that exist today to sentient systems, which do not yet exist.

  1. Reactive machines. An example is Deep Blue, the IBM chess program that beat Garry Kasparov in the 1990s. Deep Blue and Google’s AlphaGO were designed for narrow purposes and cannot easily be applied to another situation.
  2. Limited memory. These AI systems can use past experiences to inform future decisions. Some of the decision-making functions in autonomous vehicles have been designed this way.
  3. Theory of mind. This is a psychology term. It refers to the understanding that others have their own beliefs, desires and intentions that impact the decisions they make. This kind of AI does not yet exist.
  4. Self-awareness. In this category, AI systems have a sense of self, have consciousness. Machines with self-awareness understand their current state and can use the information to infer what others are feeling. This type of AI does not yet exist.

The difference between Artificial Intelligence, Machine Learning, and Deep Learning

Machine Learning, or ML, is a practice to use algorithmes to parse data, learn from it, and then make determinations or predictions about something. In other words, the machine is trained to use large amount of data and algorithmes that give it the ability to learn how to perform the task.

To be short, Machine Learning is an approach to achieve Artificial Intelligence, whereas Deep Learning is a technique for implementing Machine learning. Deep learning teaches computers to do what comes naturally to humans: learn by example. It is a key technology behind driveless cars, enabling them to recognize a stop sign, for example. In DL a computer model learns to perform classification tasks directly from images, text, or sound.

Here is an interesting podcast about Deep Learning from Will Ramey, NVIDIA Senior Manager for GPU Computing:

Use Cases of Artificial Intelligence

  • AI in healthcare. The biggest bets are on improving patient outcomes and reducing costs. Companies are applying machine learning to make better and faster diagnoses than humans. One of the best known healthcare technologies is IBM Watson. It understands natural language and is capable of responding to questions asked of it. The system mines patient data and other available data sources to form a hypothesis, which it then presents with a confidence scoring schema. Other AI applications include chatbots, a computer program used online to answer questions and assist customers, to help schedule follow-up appointments or aiding patients through the billing process, and virtual health assistants that provide basic medical feedback.
  • AI in business. Robotic process automation is being applied to highly repetitive tasks normally performed by humans. Machine learning algorithms are being integrated into analytics and CRM platforms to uncover information on how to better serve customers. Chatbots have been incorporated into websites to provide immediate service to customers. Automation of job positions has also become a talking point among academics and IT consultancies such as Gartner and Forrester.
  • AI in education. AI can automate grading, giving educators more time. AI can assess students and adapt to their needs, helping them work at their own pace. AI tutors can provide additional support to students, ensuring they stay on track. AI could change where and how students learn, perhaps even replacing some teachers.
  • AI in finance. AI applied to personal finance applications, such as Mint or Turbo Tax, is upending financial institutions. Applications such as these could collect personal data and provide financial advice. Other programs, IBM Watson being one, have been applied to the process of buying a home. Today, software performs much of the trading on Wall Street.
  • AI in law. The discovery process, sifting through of documents, in law is often overwhelming for humans. Automating this process is a better use of time and a more efficient process. Startups are also building question-and-answer computer assistants that can sift programmed-to-answer questions by examining the taxonomy and ontology associated with a database.
  • AI in manufacturing. This is an area that has been at the forefront of incorporating robots into the workflow. Industrial robots used to perform single tasks and were separated from human workers, but as the technology advanced that changed.

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