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Artificial Intelligence : What it is and why it matters

Artificial intelligence (AI) is an area of computer science that emphasizes the creation of intelligent machines that work and react like humans. Some of the activities of computers with artificial intelligence are designed to include:

  1. Speech recognition
  2. Learning
  3. Planning
  4. Problem solving 

Artificial intelligence (AI) makes it possible for machines to learn from experience, adjust to new inputs and perform human-like tasks. Most AI examples that you hear about today – from chess-playing computers to self-driving cars – rely heavily on deep learning and natural language processing. Using these technologies, computers can be trained to accomplish specific tasks by processing large amounts of data and recognizing patterns in the data.


Why is Artificial Intelligence important?

  1. AI automates repetitive learning and discovery through data.
  2. AI adds intelligence to existing products.
  3. AI adapts through progressive learning algorithms to let the data do the programming.
  4. AI analyzes more and deeper data using neural networks that have many hidden layers.
  5. AI achieves incredible accuracy though deep neural networks – which was previously impossible. For example, your interactions with Alexa, Google Search and Google Photos are all based on deep learning – and they keep getting more accurate the more we use them.
  6. AI gets the most out of data. When algorithms are self-learning, the data itself can become intellectual property.


Knowledge engineering is a core part of AI research. Machines can often act and react like humans only if they have abundant information relating to the world. Artificial intelligence must have access to objects, categories, properties and relations between all of them to implement knowledge engineering. Initiating common sense, reasoning and problem-solving power in machines is a difficult and tedious approach.


Machine learning is another core part of AI. Learning without any kind of supervision requires an ability to identify patterns in streams of inputs, whereas learning with adequate supervision involves classification and numerical regressions. Classification determines the category an object belongs to and regression deals with obtaining a set of numerical input or output examples, thereby discovering functions enabling the generation of suitable outputs from respective inputs.


Machine perception deals with the capability to use sensory inputs to deduce the different aspects of the world, while computer vision is the power to analyze visual inputs with a few sub-problems such as facial, object and gesture recognition.

Robotics is also a major field related to AI. Robots require intelligence to handle tasks such as object manipulation and navigation, along with sub-problems of localization, motion planning and mapping.


Artificial Intelligence in Today's World


Every industry has a high demand for AI capabilities – especially question answering systems that can be used for legal assistance, patent searches, risk notification and medical research. Other uses of AI include:

  • Health Care
  • Retail
  • Manufacturing
  • Sports


Applications of Artificial Intelligence In Use Today


Beyond our quantum-computing conundrum, today's so-called A.I. systems are merely advanced machine learning software with extensive behavioral algorithms that adapt themselves to our likes and dislikes.These are some of the most popular examples of artificial intelligence that's being used today.

  • Siri - Everyone is familiar with Apple's personal assistant, Siri.
  • Alexa - Alexa's rise to become the smart home's hub, has been somewhat meteoric.
  • Amazon.com - Amazon's transactional A.I. is something that's been in existence for quite some time, allowing it to make astronomical amounts of money online.
  • Cortana - Cortana is a voice-controlled virtual assistant for Microsoft Windows Phone 8.1.


What are the challenges of using artificial intelligence?

Artificial intelligence is going to change every industry, but we have to understand its limits.

The principle limitation of AI is that it learns from the data. There is no other way in which knowledge can be incorporated. That means any inaccuracies in the data will be reflected in the results. And any additional layers of prediction or analysis have to be added separately.


Today’s AI systems are trained to do a clearly defined task. The system that plays poker cannot play solitaire or chess. The system that detects fraud cannot drive a car or give you legal advice. In fact, an AI system that detects health care fraud cannot accurately detect tax fraud or warranty claims fraud.

In other words, these systems are very, very specialized. They are focused on a single task and are far from behaving like humans.

Likewise, self-learning systems are not autonomous systems. The imagined AI technologies that you see in movies and TV are still science fiction. But computers that can probe complex data to learn and perfect specific tasks are becoming quite common.


If AI is seen to contribute to business success via enabling a better understanding of customers, along with a more rapid response to their needs, then its uptake within the world of work is likely to continue.  In the future, many tasks will have the opportunity of input from AI.  However, rather than replacing humans, it is the combination of AI and humans that is likely to bring the greatest benefits to the working world.  Therefore, we might conclude that it will be how AI ‘interacts’ with humans that will influence its role in the future world of work.  If human values are carefully articulated and embedded into AI systems then socially unacceptable outcomes might be prevented.


Opinions on this are divided, and the reality is likely to be somewhere in between the two extremes.  AI will continue to change the world of work, and workers will need to engage in life-long learning, developing their skills and changing jobs more often than they did in the past.

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