Anticipating AI, Data Analytics, and IoT Convergence as Artificial Intelligence Evolves to become more General Purpose

Evolution from Narrow AI to General Purpose AI

Also known as Artificial General Intelligence (AGI), general purpose Artificial Intelligence (AI) represents silicon-based intelligence that mimics human-like cognition to perform a wide variety of tasks that may also leverage multiple AI technologies and functions.

Whereas most current AI solutions represent “Weak AI”, which are limited in terms of the type and variety of problems that may be solved, general purpose AI may be viewed as “Strong AI”, able to be employed to solve many different problems.

For example, when you say “Hey Alexa, what is my schedule today?”, you don’t expect this AI solution to make adjustments based on your communications or behaviors. Firstly, Alexa has a narrowly defined range of functions. It simply responds to request for information and commands such as turn off the light.

Secondly, it is not supposed to be listening to your conversations unless you say “Hey Alexa” (privacy and security issue). Thirdly, AI solutions like Alexa do not (yet) have a comprehensive view into your daily life. Therefore, it could not see your emails, text, digital calendar, social media exchanges, etc.

General Purpose AI Converges with Data Analytics and the Internet of Things

As IoT expands and evolves, there will an increasingly large amount of unstructured machine data. The growing amount of machine generated data will drive substantial opportunities for AI support of unstructured data analytics solutions. The use of AI for decision making in IoT and data analytics will be crucial for efficient and effective decision making.

AI coupled with advanced big data analytics provides the ability to make raw data meaningful and useful as information for decision-making purposes. The use of AI for decision making in IoT and data analytics will be crucial for efficient and effective decision making, especially in the area of streaming data and real-time analytics associated with edge computing networks.

Major vendors will develop AI platforms that can act as a utility AI function, offering general purpose AI functionality to virtually any other network element such IoT platforms, software programs, and/or devices via an Application Programming Interfaces (API).

This will be a great way for the general purpose AI market to get started, but longer-term, there will be a need for solutions to operate in a more flat hierarchical structure. Peer-to-peer interchanges will occur via open APIs. This will enable vendor and solution independent implementation and operation of general purpose AI systems.

General Purpose AI will provide a Utility Function, acting as a Resource to virtually any other Device or Network Element such IoT Platforms and Software Programs via APIs

The market for combined AI and IoT (e.g. the AIoT market)will be both transformational and mutually beneficial wherein AI adds value to IoT through machine learning and other AI capabilities and IoT adds value to AI through connectivity signaling, and data exchange. AIoT leverages AI technologies such as machine learning, object detection, motion recognition, facial recognition, speech recognition, and other technologies to embed with IoT infrastructure device and components.

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AIoT, coupled with advanced data analytics will facilitate future AI Decision as a Service (AIDaaS) and IoT Data as a Service (IoTDaaS) solutions. These DaaS solutions will include data sourced from a machine (such as from a jet engine) that is not “Internet-connected” and thus limited in utility without the IoT to collect, relay, and provide opportunities for feedback loops.

Accordingly, Mind Commerce sees the need to address the AIoT data as a service market opportunity by collection type, which includes IoT DaaS data and Non-IoT DaaS data. Machine Data that does not use IoT, by definition, will not be streaming data or allow for real-time analytics. 

As part of its AI research and strategic advisory practice, Mind Commerce evaluates the general purpose AI market including leading companies, services potential, technology integration, and application ecosystem. Our research analyzes the AI agent market and the relationship between general purpose AI with other technologies including edge computing, 5G networks, and blockchain.

General Purpose AI for Medical Devices alone represents a $1.4B Market Globally

The overall general purpose AI market is anticipated to reach $50.8 billion globally by 2023. Hardware remains the largest market component during this period. However, general purpose AI services and software are the fastest growing elements at 56.5% CAGR and 51.4% CAGR respectively through 2023.

AI Chipsets designed for the general purpose AI market represents a $542 million global opportunity by 2023. Key application areas represent deep learning, supervised, unsupervised, and reinforcement learning.

Healthcare is a Key Sector for the General Purpose AI Market ( Mind Commerce )

The healthcare sector will lead industry verticals at $8.2 billion by 2023 with general purpose AI supported medical devices alone representing a $1.4 billion opportunity globally. North America is anticipated to be the largest regional market for general purpose AI through 2023 at $17.2 billion.

General purpose AI will add significant value to many other industry sectors such as agriculture in which it will support precision farming, livestock monitoring, drone analytics, agriculture robots, irrigation, insect management, and many other important AgriTech functions. Mind Commerce is open to dialog about AI evolution towards general purpose functionality. Contact us to schedule a briefing.

Opinions expressed by contributors are their own.

About Gerry Christensen

Contributor ICT industry professional with extensive technical and business experience. Over 30 years in planning, engineering, product management and business development for signaling networks, intelligent networks, and wireless communications networks. Author of many technical papers about various telecommunications subjects including the published reports “Yes 2 Prepay” and “Data on SS7” as well as co-author of the books “Wireless Intelligent Networking” and "Mobile Positioning and Location Management".

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