The traditional model of computing is being disrupted,
Traditionally operating systems would mainly deliver the foundation for your apps, the handling of hardware, files, security and how your interaction with systems would work. It is with the continued growing capabilities in AI that are currently leading us to systems where AI becomes a central part of computing.
This discussion into the implications of AI-first operating systems raises one key question.
What happens when the infrastructure itself is built around AI rather than just having AI available as a feature/application?
From Application-Centric to Intent-Driven Computing
A standard approach for computing currently is for an application to be opened and for tasks to be performed throughout an application.
With an AI-first approach to computing, this can be simplified for the end user; allowing them to think about what the required outcome should be and communicate this need, then it could be AI's role to handle moving between application spaces, find necessary information and perform repetitive tasks that may be encountered. However this does not mean the death of traditional applications, they will still form a crucial part in systems like these but with an AI layer positioned on top connecting it all.
Why Edge Computing Is Critical
AI also brings with it an important infrastructure question: where should processing happen?
Cloud computing resources are immense and can handle the most demanding computations, however sending all data to be analyzed and processed in remote data centers cannot always be the most efficient model.
Edge computing is about moving computation into the location where the data is being generated.
This approach is particularly effective for;
IoT devices
Industrial systems
Smart infrastructure
Real-time analysis of data
Connected environments.
Processing at source makes efficient response to events extremely valuable.
AI + IoT = AIoT
The convergence of artificial intelligence and the Internet of Things brings with it another core computing technology: AIoT.
IoT systems are systems that have sensors sending continuous streams of data with devices actively making some form of decision from this data. The beauty of the connection with AI is that AI analysis of these inputs can make systems much more proactive by detecting patterns or anomalous situations rather than having simple decision making implemented. This transformational value for data stems from taking raw sensors information and being able to derive meaning and insights which can then be implemented into a real-world scenario. An industrial system for example can monitor and react to abnormal operating behaviors of its devices as and when they are detected.
AIoT, Beyond Industrial Processes
These benefits of AIoT are not reserved for factories, but can also be found in smart buildings, logistic systems, environments monitoring systems, power grid systems, and much more across many connected environments. Typical systems will contain:
IoT Sensors Data Collection Edge Processing AI Analysis Insights Action
Sensors would be providing the inputs required, Edge computing would be collecting them while processing where possible locally, with an intelligent AI model sitting and making decisions about this information, followed by an Action taking place based on analysis results, all the while the information is transmitted to wherever the business requires the analysis to sit, usually a cloud-based solution if long-term archiving of all data is required along with further higher-level AI processes.
Why the Challenge Isn't the Technology Itself
Many organizations today already own many databases, ERP systems, cloud infrastructure, IoT devices and many operational systems, but the difficulty facing businesses here will often not be gaining access to either AI or IoT but to integrate it all. A successful AIoT system is more than just having an AI model you also need,
A well-defined business case
Appropriate data infrastructure and integrations strategy
Security measures and policies
An understanding of the problem your trying to solve
What developers need to keep watch on
Developers will also have their own area of development to keep their eyes on in these converging industries:
Edge AI
AI-powered IoT devices
Real-time data analysis
Intelligent Automation
Sensor Data analysis
Distributed AI systems
Cloud Edge Interaction Models
This developing landscape will continue to evolve and the way in which a developer is approaching the development of these applications will surely continue to do so.
The Bigger Picture
The concept of AI-first computing need not merely focus on operating systems but rather more broadly on the future of connected computing systems. Intelligence is provided by AI, sensors by IoT and real-world connectivity by edge computing with the processing and storage provided by a cloud solution forming the core architecture of an intelligent digitally controlled future. Businesses seeking innovation within these fields of computing can benefit from research within companies such as aperture venture studios, http://aperture-venture.com/.
Conclusion
The computing environment of the future is likely to move away from individual applications towards being part of intelligent environments linking together devices, users, applications and data. As AI plays an increasing part in the computing infrastructure that businesses use and IoT and edge continue to mature, we can only predict that these two paradigms will move from having simple connections and interactions towards becoming one fully intelligent integrated environment where the gap between digital and physical is all but invisible, developers and businesses of all levels should keep these trends in consideration in order for it to benefit their systems of tomorrow.
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