MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE:
essential guide to understanding how ML and AI can be applied in practice and be compatible with human behaviour in modern times.
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Introduction
First things first: why should you even care? Why AI instead of genetic engineering or nanotechnology? Why crazy futuristic technologies at all rather than tax codes or gardening tips? The short answer is that AI is important. What used to be only relevant to computer researchers and wayward philosophy majors is now a topic that needs to be understood by everyone.
Already, AI has infiltrated every corner of our lives. Every time you interact with a smart device or an internet service, you are most likely triggering an AI program. Behind the scenes, algorithms work tirelessly to make just about everything a little cheaper, a little safer, and a little more personalized. As we move forward, what we call AI today will only be a tip of the iceberg.
Its unlikely that getting familiar with AI will help you pay the bills any time soon. But this book, and the field in general, is about thinking for the long term. Maybe youll one day find yourself in the middle of a totally overturned job market. Maybe youll soon tune in to discover a new wave of tech-driven political upheavals. In exponential times, flexibility and knowledge is the most valuable currency. As we transition into this new world, no technology, no movement, no product of the human mind will carry as much weight as artificial intelligence.
Enormous changes are hatching in the showcasing scene, and these movements are, to a great extent, down to the power ML. Such is its effect that 97% of pioneers accept the eventual fate of promoting will comprise of keen advertisers working in a joint effort with AI-based mechanization elements.
Machine learning, artificial intelligence (AI), big data, and cognitive computing dominate discussions about how emerging advanced analytics can give businesses a competitive advantage for businesses. There is no debate that existing entrepreneurs face new and unexpected competitors. These companies are looking for new strategies that can prepare them for the future. Although a company can try different strategies, they all come back to a fundamental truth: you have to follow the data. In this chapter, we will explore the value of machine learning for your business strategy. How should you think about machine learning?
Machine learning is currently one of the most important topics in development organizations looking for innovative ways to leverage data resources to help businesses reach a new level of understanding. Many online and offline courses on machine learning have been considered by Harvard Business Reviews (HBR) as the sexiest course of the twenty-first century.
ML methods are utilized to tackle a large group of different issues, and organizations continue to profit a lot as we veer towards a universe of hyper-joined information, channels, substance, and setting. For the advanced advertising group, ML is tied in with discovering bits of prescient information in the influxes of organized and unstructured data and utilizing them to further your potential benefit.
The capacity to react rapidly and precisely to changes in client conduct is basic in this day and age, and AI. Read on to get an inner understanding!
Chapter 1 Correlation Between Machine Learning and Artificial Learning
These two can easily confuse you into thinking they are the same; they are not. Artificial intelligence is usually a broad concept of machines being smart. In other words, it is the science behind intelligent computers. On the other hand, machine learning is the technology used to teach machines how to access data and learn for themselves.
Machine Learning & Artificial Intelligence systems are among the most popular catchwords in the business world; in many instances, they are put into use interchangeably. There is plenty of confusion when it comes to understanding and differentiating the two. The most common questions include; what is Artificial Intelligence? What is Machine Learning? Are the two related in any way? In most businesses however, marketing tends to ignore their differences for sales and advertising.
Machines and Artificial Intelligence have become an integral part in everyday living. This doesnt mean that the two are well understood. If youre hoping to implement and make use of ML or AI in your business, its of importance to find out which one you plan to make use of. Artificial Intelligence and Machine Learning are related, but this doesnt mean that they are similar. Choosing either AL or ML can be the determinant in moving your business to the next level.
Artificial Intelligence basically means that machines are equipped to carry out tasks in intelligent ways. These machines are programmed to do various tasks by adjusting to various situations.
Machine Learning can be viewed as an Artificial Intelligence branch; the main difference is that it is more precise than the conclusive concept. The ML concept is founded on the idea that machines that dont require constant human supervision are created. Artificial intelligence has two major subcategories namely applied as well as generalized AI. In this case, the applied AI is pretty more relevant in that it covers various programs including driverless cars in order to import stock trading programs. While on the other side, generalized AI is not that common in practice since its complicated to create. This type of AI has therefore, the ability to handle various tasks in the same way human would. Machine learning has been explored due to specific breakthroughs in Artificial Intelligence. Therefore, such breakthroughs are specific in assisting humans to handle their tasks pretty more sensibly. Another breakthrough that led to the invention of ML was the internet. The internet has since facilitated extensive storage of information. This has never happened in the past. Machines can also be used in viewing data amounts that have not been accessed over the years in many ways. This is because of the past limitations in storage. As such, the volumes of data formed in excess for human processing makes it easier to perform complex tasks in shorter frames.
