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Technologies can work together provide more benefits than the sum of their parts. In the “old days” when mainframes were the platform employed by major companies, the software was, for the most part, proprietary and limited to what the hardware company provided or allowed on their system. By the 1980s, minicomputers were becoming the norm and relatively open operating systems, such as Unix and Linux, the default.

A new tech segment opened up, the systems integrator. Find the hooks that allow software from various “prime providers” to interoperate was the rallying cry—and the lack of standards had many IT (information technology) department heads crying, too. While networking standards were being created to provide a wide range of options for hardware connection, software interoperability was struggling.

Then came the PC (personal computer). While not the first business-oriented microcomputer—remember the Apple III?—the IBM Personal Computer and the Microsoft DOS) distributed operating system) OS changed the landscape. Desktop computers were the rage and the API (application processing interface) provided by DOS allowed developers to create applications without asking permission of the hardware company. This led to an explosion of software developers, finding their own niches and strengths, hoping to provide the right combination of benefits and price to make enough money to allow more R&D (research and development). It became the cycle for many companies.

Connection and interoperability, adding benefits and removing risks, continues. Only now, the “computers” are likely to be smartphones, tablets, wristwatches, and embedded chips with power not dreamed of in 1982, all connected via the internet by 5G cellphone networks and Wi-Fi. We take it for granted everything will work together, regardless of the companies involved in the hardware, software, networking, and distribution systems. As new applications come up, plug them in and everything will work.

The power in your pocket would make a 1990 IT guru think of science fiction before reality. Star Trek communicators and holograms were from hundreds of years in the future so how could the same technology be only decades away? Hal in 2001: A Space Odyssey was fantasy, much like the space station where it “lived.” And yet, here we are, talking about artificial intelligence as commonplace, carrying smartphones with the computing power of a 1980s IBM 360, communicating over 5G networks with people just about anywhere in the world, storing data in terabyte quantities on a remote server called the cloud—and no one blinks.

AI Meets IoT

Two of the growing technology foundations of today are, indeed, AI (artificial intelligence) and chips that talk to one another—the IoT (Internet of Things)—and they are growing because they provide services and benefits to a wide variety of people and industries. The AI segment is currently very fragmented, however, characterized by most companies focusing on silo approaches to solutions. Longer-term, the researchers at Mind Commerce see many solutions involving multiple AI types as well as integration across other key areas such as the IoT and data analytics.

Consumer-facing apps and services supported by AI include the chatbots and VPA (virtual personal assistants) in support of customer care and lifestyle enhancement that are in the news daily. The automobile industry is another example in which AI is becoming increasingly useful, both in the near term for solutions such as the inclusion of VPAs, and longer-term uses such as support of self-driving vehicles.

Another consumer market area in which AI will be integrated is wearable technology. As wearables become more mainstream and integrate into everyday life with increasing dependency, there will be a need for integration with the big three of IT: artificial intelligence, big data, and analytics.

AI is expected to have a big impact on data management. However, the impact goes well beyond data management as these technologies will increasingly become part of every network, device, application, and service. One area important to the enterprise will be IDSS (intelligent decision support systems), which are a form of expert system that use AI to optimize decision making. IDSS will be used in many fields including agriculture, medicine, urban development, and policy making and strategy at the highest levels of enterprises, construction jobsites, and governmental organizations.

The combination of AI and the IoT has the potential to dramatically accelerate the benefits of digital transformation for consumer, enterprise, infrastructure, industrial, and government segments. Mind Commerce sees the AIoT (artificial intelligence of things) as transformational for both technologies, as AI adds value to IoT through machine learning and decision making and IoT adds value to AI through connectivity and data exchange. The AIoT market constitutes solutions, applications, and services involving AI in IoT systems and IoT support of AI solutions.

With AIoT, AI is embedded into infrastructure components, such as programs, chipsets, and edge computing, all interconnected with IoT networks. APIs are then used to extend interoperability between components at the device level, software level, and platform level. These units will focus primarily on optimizing system and network operations as well as extracting value from data.

AIoT Market Dynamics

While early AIoT market solutions are rather monolithic, it is anticipated AIoT integration within businesses and industries will ultimately lead to more sophisticated and valuable inter-business and cross-industry solutions. These solutions will focus primarily upon optimizing system and network operations as well as extracting value from industry data through dramatically improved analytics and decision-making processes.

As IoT networks proliferate throughout every major industry, there will be an increasingly large amount of unstructured machine data. The growing amount of human-oriented and machine generated data will drive substantial opportunities for AI support of unstructured data analytics solutions. Data generated from IoT supported systems will become extremely valuable, both for internal corporate needs as well as for many customer-facing functions such as product lifecycle management.

The use of machine learning and other AI tools will facilitate advanced data analytics that will derive useful information from the raw data generated by IoT devices. It is anticipated the AIoT market will foster integration within industries that will ultimately lead to more sophisticated and valuable inter-business and cross-industry solutions. These solutions will focus primarily upon optimizing system and network operations as well as extracting value from industry data through dramatically improved analytics and decisionmaking processes.

AIoT solutions improve operational effectiveness and the value of machine-generated data. Because of this, AIoT market solutions are expected to become an integral component of business operations including supply chains, sales and marketing processes, product and service delivery, and support models.

AI will add significant value to IoT network supported applications for decision making, especially in streaming data and realtime analytics associated with edge computing networks. Realtime data will be key for all segments and solutions. The ability to capture streaming data, determine valuable attributes, and make decisions in realtime will add an entirely new dimension to serviced logic.

In many cases, the data itself, and actionable information will be the product, often delivered in the cloud as a DaaS (data as a service) model. AIoT market infrastructure and services will therefore be leveraged to achieve more efficient IoT operations, improve human-machine interactions, and enhance data management and analytics, creating a foundation for IoTDaaS (IoT data as a service) and AI-based decisions as a service.

While Hal “had a mind of its own,” and could disobey a human command, current AI is more likely to be affected by external actors than to impact them. But AI is also somewhat self-healing because it is a lightweight application (it does not require all the data that comes with tracking digital signatures/code for known computer viruses), is more effective in identifying malware, and easier and less costly to maintain as there is no need to constantly identify new malware code. This is all because AI-based security is looking for malicious behaviors rather than known malicious code.

As the integration of AI and IoT shows, we’ve come a long way from proprietary software on remote computers or even networked desktop systems vying for access to the data. Now the data is everywhere, waiting for the right signal to be collected and used, the right application to provide benefits from the data, and AI to analyze the data—and the operations—for security. Maybe 2001 is still in the future but it’s not as far away as it once seemed. What will our jobsites look like in the future will certainly determine how we will build it! Want to tweet about this article? Use hashtags #construction #IoT #AI #5G #cloud #edge

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