If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Human intelligence has been creating and maintaining complex systems since the beginnings of civilizations. In modern times, digital twins have emerged to aid operations of complex systems, as well as improve design and production. Artificial intelligence (AI) and extended reality (XR) – including augmented reality (AR) and virtual reality (VR) – have emerged as tools that can help manage operations for complex systems. Digital twins can be enhanced with AI and emerging user interface (UI) technologies like XR can improve people's abilities to manage complex systems via digital twins. Digital twins can marry human and AI to produce something far greater by creating a usable representation of complex systems. End users do not need to worry about the formulas that go into machine learning (ML), predictive modeling and artificially intelligent systems, but also can capitalize on their power as an extension of their own knowledge and abilities. Digital twins combined with AR, VR and related technologies provide a framework to overlay intelligent decision making into day-to-day operations, as shown in Figure 1. Figure 1: A digital twin can be enhanced with artificial intelligence (AI) and intelligent realities user interfaces, such as extended reality (XR), which includes augmented reality (AR) and virtual reality (VR). The operations of a physical twin can be digitized by sensors, cameras and other such devices, but those digital streams are not the only sources of data that can feed the digital twin. In addition to streaming data, accumulated historical data can inform a digital twin. Relevant data could include data not generated from the asset itself, such as weather and business cycle data. Also, computer-aided design (CAD) drawings and other documentation can help the digital twin provide context.
Begin with a single use case, linking just a few data sets and reports, and add data and links to it organically so that it's a dynamic structure. Once you have a use case, identify the content you'll need and classify it according to a taxonomy. While you can refer to industry standard taxonomies for ideas, invest the time to make the taxonomy meaningful for your organization and understand how users organize their information. Buying taxonomies out of the box or contracting a consultant to do it for you is bound to lead to problems. The organizing structure becomes even more powerful -- an ontology -- when you use semantic indexing to replace users' own words with synonyms to better understand what they mean.
This article touches upon the building blocks that are necessary to enable machine learning in the data received from IoT, and how cloud infrastructure can help if we use the power of open source tools effectively. The protocol usually runs over TCP/IP; however, any network protocol that provides ordered, lossless, bi-directional connections can support MQTT. It is designed for connections with remote locations where a'small code footprint' is required or the network bandwidth is limited (Source: https://en.wikipedia.org/wiki/MQTT). Figure 1 explains how to connect the device to the cloud. Let's discuss the components in detail in the sections below.
As of last year, four out of every five enterprises had adopted machine learning and other types of artificial intelligence (AI) within their core business models. That statistic is made all the more remarkable by the fact that five years ago, this was the case at only 1 in 10 large organizations. However, this rapid adoption comes with a flipside: Too many businesses today -- in the race to adapt their customer experiences for the age of automation -- have powered AI programs that have gone nowhere. This is especially the case in the realm of natural language understanding (NLU), where overly simplistic executions fail to capture the nuance needed to serve real human needs. At its core, good NLU solutions do more than digest words and derive possible responses.
Artificial Intelligence has become a nearly ubiquitous term nowadays. A Deloitte survey revealed that most companies -- a staggering 90 percent of those approached by the research and consultancy firm -- consider cognitive technologies to be of crucial strategic importance, and over 80 percent of those were either already using it on some level or planning to implement it in the near future. That is hardly surprising considering the dramatic efficiency savings that adoption can bring. According to Bill Eggers, executive director of Deloitte's Center for Government Insights, in the U.S. alone, federal employees spend about 4.3 billion hours per year on a variety of mundane tasks such as recording information and handling. He estimates that currently available AI and robotic process automation could free up about 1.3 billion of those hours by automating such tasks, effectively enabling quantum leaps in productivity as AI allows institutions to anticipate rather than merely react to problems after they occur.
As an industry, we've gotten exceptionally good at building large, complex software systems. We're now starting to see the rise of massive, complex systems built around data – where the primary business value of the system comes from the analysis of data, rather than the software directly. In fact, many of today's fastest growing infrastructure startups build products to manage data. These systems enable data-driven decision making (analytic systems) and drive data-powered products, including with machine learning (operational systems). They range from the pipes that carry data, to storage solutions that house data, to SQL engines that analyze data, to dashboards that make data easy to understand – from data science and machine learning libraries, to automated data pipelines, to data catalogs, and beyond.
For some, it is self-evident to use AI in their business and digitalization in the company. For others, it seems more like a trend. And yet we can observe how more and more AI platforms are in use. Nevertheless, and perhaps precisely because of this, it is challenging to determine how to implement this technology. If misused, misunderstood, or wrongly applied, artificial intelligence can damage businesses.
A few years ago, everyone was trying to figure out how to get started with artificial intelligence and one of its components, machine learning. But today many organizations have put together pilot programs, identified promising use cases, and even turned around some value for their organizations. After you've won those initial successes, it's time to expand that value to other use cases and other parts of the organization. But with each of your initial use cases you learned something. You developed some technology that you may want to use again.
Daniel Fallmann is Founder and CEO of Mindbreeze, a leader in enterprise search, applied artificial intelligence and knowledge management. CEOs and line-of-business leaders are smart people. But they don't know what their enterprise knows. As a result, their organizations miss out on a multitude of valuable opportunities. Organizations possess a wealth of data. However, the data is all over the place, held in siloed systems and various formats.
What are the most successful AI use cases you know in your business, product or field? And why do you define them as successful? They've likely made a significant impact on a business metric by taking a process or customer proposition and improving them. Take radically optimised search, much cheaper logistics or highly relevant question answering, for example. While these applications and their impact are impressive and important, they often do not fundamentally change or future-proof a business, product or company.