Artificial Intelligence & Machine Learning

Artificial Intelligence is the simulation of human intelligence processes by machines, especially computer systems. Specific applications of AI includes expert systems, natural language processing, and speech recognition and machine vision. AI refers to the simulation of human intelligence processes by machines that are programmed to think like humans and mimic their actions. The term may also be applied to any machine that exhibits traits associated with a human mind such as learning and problem- solving. The ideal characteristics of artificial intelligence is its ability to rationalize and take actions that have the best chance of achieving a specific goal. A subset of artificial intelligence is machine learning, which refers to the concept that computer programs can automatically learn from and adapt to new data without being assisted by humans. Deep learning techniques enables this automatic learning through the absorption of huge amounts of unstructured data such as text, images or video.

The goals of artificial intelligence include mimicking human cognovits activity. Researchers and developers in the field are making surprisingly rapid strides in mimicking activities such as learning, reasoning and perception, to the extent that these can be concretely defined. AI is continuously evolving to benefit many different industries. Machines are wired using a cross- disciplinary approach based on mathematics, computer science, linguistics, psychology and more. As machines becomes increasingly capable, tasks considered to required “intelligence” are often removed from the definition of AI, a phenomenon known as AI affect. For instance, optical character recognition is frequently excluded from things considered to be AI, having become a routine technology.

The various sub- fields of AI researches are centered on particular goals and the use of particular tools. The traditional goals of AI research include reasoning, knowledge representation, planning, learning, natural language processing, perception and the ability to move and manipulate objects. To solve these problems, AI researchers have adapted and integrated a wide- range of problem- solving techniques including search and mathematical optimization, formal logic, artificial neural networks, and methods based on statistics, probability and economics. AI also draws upon computer science, psychology, linguistics, philosophy and many other fields.

AI is important because it can give enterprises insights into their operations that they may not have been aware of previously and because, in some cases, AI can perform better than humans. While the huge volume of data being created on a daily basis would bury a human researcher, AI applications that use machine learning can take that data and quickly turn it into actionable information. The primary disadvantages of using AI is that it is expensive to process the large amounts of data that AI programming requires.

  • Advantages
  • 1. Good at detail- oriented jobs.
  • 2. Reduced time for data- heavy tasks.
  • 3. Delivers consistent results.
  • 4. AI- powered virtual agents are always available.
  • Disadvantages
  • 1. Expensive.
  • 2. Requires deep technical expertise.
  • 3. Only knows what it’s been shown.
  • 4. Lack of ability to generalize from one task to another.

Machine Learning is a type of artificial intelligence that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so. Machine learning algorithms use historical data as input to predict new output values. It is important because it gives enterprises a view of trends in customers’ behavior and business operational pattern, as well as supports the development of new products. Many of today’s leading companies, such as Facebook, Google, Uber, makes machine learning a central part of their operations. Machine learning has become a significantly competitive differentiator for many companies.

A subset of machine learning is closely related to computational statistics, which focuses on making predictions using computers, but not all machine learning is statistical learning. Some implementations of machine learning use data and neural networks in a way that mimics the working of a biological brain. In its applications across business problems, machine learning is also referred as predictive analytics. The discipline of machine learning employs various approaches to teach computers to accomplish tasks where no fully satisfactory algorithms available. In cases where vast numbers of potential answers exists, one approach is to label some of the correct answers as valid. This can then be used as training data for the computer to improve the algorithms it uses to determine correct answers. For example, to train a system for the task of digital character recognition, the MNIST dataset of handwritten digits has often been used.

Machine Learning is a technology that has witnessed an exponential rise in its usage and popularity in the last couple of years. A huge number of aspirants from around the world are quickly learning this technology and putting the knowledge to various uses. Machine Learning also has some advantages as well as disadvantages.

  • Advantages:
  • 1. Continuous Improvement
  • 2. Automation for everything
  • 3. Trends and patterns identifications
  • 4. Wide range of applications
  • Disadvantages:
  • 1. Data acquisition
  • 2. Highly error- prone
  • 3. Algorithm selection
  • 4. Time consuming

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