White Paper: What is Artificial Intelligence (for Consumer Technology Association)

White Paper: What is Artificial Intelligence (for Consumer Technology Association)

Cognilytica is excited to present the “What is Artificial Intelligence” white paper we wrote in collaboration with the Consumer Technology Association (CTA).

One way to describe artificial intelligence (AI) is the ability of machines to exhibit the intelligence of humans. People have been working on the development of AI systems for over half a century. What has brought AI to the attention of many of late are the voice-enabled digital assistants that people are using to help them with their daily routines. The fact that people speak to these devices, and the devices speak back, helps us think of them as exhibiting human-like qualities. But AI is much more than the digital assistant you might have in your home today.

What is enabling AI to finally come into its own after decades of research and development is the processing power of modern computers coupled with massive amounts of data we have accumulated about so many things. We have data about the cars traveling on our roads, about mobile phone usage, about the weather, about who buys what where and so many other things. AI systems can take vast quantities of data and look for patterns that can help us make predictions and generally better understand the world.

AI systems are built using various core technologies and building blocks like machine learning (including emergent deep learning architectures), natural language understanding/generation and computer vision. This whitepaper will touch on each of them. Depending on the nature of data, AI systems leverage three major approaches to learn – supervised learning, unsupervised learning, and reinforcement learning. These approaches are realized using various types of algorithms from regression techniques to convolutional neural networks. AI systems are being deployed to perform various workloads/tasks like recognition, classification, pattern matching, and natural language processing, and more specific applications like digital assistants, chatbots, and self-driving vehicles. This whitepaper will touch on these and other applications, too.

AI systems also raise a number of issues. Do we need them to be able to explain why they do what they do? Perhaps, in some situations. How will AI systems impact jobs? Like all technological developments they will create new opportunities and make some existing jobs obsolete. How can biases be kept out of AI systems and when could inserting bias be beneficial to the objective? By making sure data used to train them is accurate and comprehensive and objectives are defined clearly. And will an AI “super intelligence” try to take over the world? Not in our lifetimes, and perhaps not ever. This whitepaper will cover each of these topics in more detail.

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One way to describe artificial intelligence (AI) is the ability of machines to exhibit the intelligence of humans. People have been working on the development of AI systems for over half a century. What has brought AI to the attention of many of late are the voice-enabled digital assistants that people are using to help them with their daily routines. The fact that people speak to these devices, and the devices speak back, helps us think of them as exhibiting human-like qualities. But AI is much more than the digital assistant you might have in your home today. What is enabling AI to finally come into its own after decades of research and development is the processing power of modern computers coupled with massive amounts of data we have accumulated about so many things. We have data about the cars traveling on our roads, about mobile phone usage, about the weather, about who buys what where and so many other things. AI systems can take vast quantities of data and look for patterns that can help us make predictions and generally better understand the world. AI systems are built using various core technologies and building blocks like machine learning (including emergent deep learning architectures), natural language understanding/generation and computer vision. This whitepaper will touch on each of them. Depending on the nature of data, AI systems leverage three major approaches to learn – supervised learning, unsupervised learning, and reinforcement learning. These approaches are realized using various types of algorithms from regression techniques to convolutional neural networks. AI systems are being deployed to perform various workloads/tasks like recognition, classification, pattern matching, and natural language processing, and more specific applications like digital assistants, chatbots, and self-driving vehicles. This whitepaper will touch on these and other applications, too. AI systems also raise a number of issues. Do we need them to be able to explain why they do what they do? Perhaps, in some situations. How will AI systems impact jobs? Like all technological developments they will create new opportunities and make some existing jobs obsolete. How can biases be kept out of AI systems and when could inserting bias be beneficial to the objective? By making sure data used to train them is accurate and comprehensive and objectives are defined clearly. And will an AI “super intelligence” try to take over the world? Not in our lifetimes, and perhaps not ever. This whitepaper will cover each of these topics in more detail.
White Paper PDF. Source: Copyright © Cognilytica LLC