Introduction to AI technology

 Future of Artificial Intelligence

As humans, we have always been fascinated by technological changes and fiction, right now, we are living amidst the greatest advancements in our history. Artificial Intelligence has emerged to be the next big thing in the field of technology. Organizations across the world are coming up with breakthrough innovations in artificial intelligence and machine learning. Artificial intelligence is not only impacting the future of every industry and every human being but has also acted as the main driver of emerging technologies like big data, robotics and IoT. Considering its growth rate, it will continue to act as a technological innovator for the foreseeable future. Hence, there are immense opportunities for trained and certified professionals to enter a rewarding career. As these technologies continue to grow, they will have more and more impact on the social setting and quality of life.

Getting certified in AI will give you an edge over the other aspirants in this industry. With advancements such as Facial Recognition, AI in Healthcare, Chat-bots, and more, now is the time to build a path to a successful career in Artificial Intelligence. Virtual assistants have already made their way into everyday life, helping us save time and energy. Self-driving cars by Tech giants like Tesla have already shown us the first step to the future. AI can help reduce and predict the risks of climate change, allowing us to make a difference before it’s too late. And all of these advancements are only the beginning, there’s so much more to come. 133 million new Artificial Intelligence jobs are said to be created by Artificial Intelligence by the year 2022.

Introduction

Introduction to AI Technology is a starting point for business decision-makers who would like to get a high-level overview of AI. This module will discuss how the AI technologies are transforming organizations by giving them a competitive advantage; improving customer experiences; and enhancing efficiencies in their internal processes.

    Microsoft has made tremendous AI advancements from our global research and data scientist teams. These AI innovations have enabled Microsoft to produced products to empower businesses, developers, and data scientists to build and deploy models faster. Using Cognitive Services, developers can easily apply Microsoft pre-trained AI models in their own applications. They can implement vision, speech, language, search, and decision-making functionality without requiring machine-learning expertise.

Introduction to AI technology

What is AI?

The term AI tends to be thrown around a lot. Artificial Intelligence (AI), machine learning or deep learning are common terms that confuse many people. So, what are they anyway? In this unit, we will clarify these methods so you can understand how it applies to your business problem.

Artificial Intelligence (AI) is an ability of computer program or machine exhibited or mimic human-like behavior (for example, visual senses, speech recognition, decision-making, natural language understanding, and so on.

Machine learning is a subset of AI. Machine learning is a technique where a machine sifts through numerous of data to find patterns over time. Machine learning uses algorithms that train a machine how to learn patterns based on differentiating features about the data. The more the training data, the more accurate the predictions. Here are some examples:

Email spam detection. Machine learning could look at patterns where email has words like "free" or "guarantee". Email address domain is on a blocked-list. A link displayed in text doesn't match the URL behind it.

Credit card fraud detection. Machine learning could look at patterns like the spending in a zip code the owner doesn't usually visit; buying a big-ticket item; or a sudden shopping spree.

Deep learning is a subset of machine learning. Deep learning is imitating how a human brain processes information, as a connected artificial neural network. Unlike machine learning, deep learning can discover complex patterns and differentiating features about the data on its own. It normally works with unstructured data like images, text, and audio. That’s why it requires enormous amounts of data for better analysis and massive computing power for speed. For instance:

Detecting cancerous cells in medical images. Deep learning scans every pixel in the image as input to the neural nodes. The nodes analyze each pixel to filter out features that look cancerous. Each layer of nodes pushes its findings of potential cancerous cells to the next layer of node to repeat the process and eventually aggregate all its finding to classify what the image is. For example, a healthy image versus an image with cancerous features found.

            Expanding on the primary concepts of AI, where machines show capabilities that are usually associated with human capabilities, you can see how learning over time, interpreting data, and reasoning with data works. To achieve this, we need to feed the machine a lot of data before it can learn. Additionally, machine learning creates algorithms varying from simple linear functions to extremely complex ones, like an artificial neural network.