Today, April 22, 2020, is Earth Day. Accenture, Intel and the Sulubaaï Environmental Foundation announced Project: CORaiL, an artificial intelligence (AI) - powered solution to monitor, characterize and analyze coral reef resilience. Since May 2019, it's been deployed to the reef surrounding Pangatalan Island, Philipines. Researchers have been using the 40,000 images collected to study the effects of climate change in the area. According to the United Nations Environment Programme, coral reefs protect coastlines from tropical storms, provide food and income for 1 billion people, and generate $9.6 billion in tourism and recreation.

This part of the series looks at the future of AI with much of the focus in the period after 2025. The leading AI researcher, Geoff Hinton, stated that it is very hard to predict what advances AI will bring beyond five years, noting that exponential progress makes the uncertainty too great. This article will therefore consider both the opportunities as well as the challenges that we will face along the way across different sectors of the economy. It is not intended to be exhaustive. Machine Learning is defined as the field of AI that applies statistical methods to enable computer systems to learn from the data towards an end goal. The term was introduced by Arthur Samuel in 1959. Deep Learning refers to the field of Neural Networks with several hidden layers. Such a neural network is often referred to as a deep neural network. Neural Networks are biologically inspired networks that extract abstract features from the data in a hierarchical fashion. Deep Reinforcement Learning will be considered in greater detail in part 3 of this series. For the purpose of this article I will consider AI to cover Machine Learning and Deep Learning. Narrow AI: the field of AI where the machine is designed to perform a single task and the machine gets very good at performing that particular task.

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If you are a major manufacturer in 2020 and you have employed the likes of Deloitte, McKinsey or PWC, it is safe to assume that they have advised you to invest big in artificial intelligence and machine learning. According to reports by Deloitte and McKinsey, machine learning improves product quality and has the potential to double cash flow. Let's take a look at three global manufacturers who are already on board. SEE ALSO: Introduction to machine learning in Node.js Siemens is the largest industrial manufacturer in Europe, and whether they are putting together planes, trains or automobiles, their goal is to solve production challenges efficiently and sustainably.

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Artificial Intelligence (AI) also known as machine learning has come a long way in the recent few years. Instead of being a subject of discussion, it has become a reality. There has been ready integration of AI across a large number of industries. This has given rise to several AI development companies across the world. These AI consulting firms offer services to their clients and help with the integration of AI in their operations.

Perhaps you've embarked on a pilot program that applies technologies and techniques such as machine learning, natural language processing and computational intelligence to solve a specific business challenge. Over time, if the pilot yields solid results, you'll want to continue the initiative. The key is to determine what's next--how to expand the value--and that requires successfully scaling the AI effort. What does it mean to scale AI? Basically, it's extending an AI capability from an initial pilot program to the widest strategic scope and impact, bringing the most value to an organization. But at too many companies, AI initiatives hit major roadblocks after the proof of concept, even as executives recognize that scaling AI is a major priority.