Edge Computing, AI Security Among Top 10 Technology Trends for 2020

Hyperautomation. The hyperautomation trend enlists technologies such as artificial intelligence (AI) and machine learning (ML) to automate manual tasks once performed by humans but are now executed by machines. Hyperautomation extends traditional automation beyond repeatable tasks to encompass more sophisticated AI-based automation and technologies such as digital twins. Rental car companies, for example, are exploring how to use automated systems to allow customers to rent a car or get other customer service tasks completed.

Multiexperience. Today’s digital experiences are far more immersive, extending beyond the confines of the computer. That’s because technologies such as augmented reality (AR), virtual reality and conversational interfaces extend the digital experience beyond the confines of a device and into the real world. Through 2028, Gartner says, user experience of digital “inputs – from voice-based commands to schedule meetings to AR-based apps to design a new kitchen – will shift significantly in terms of how users perceive and interact with the digital world.

Democratization of expertise. Users now have easy access to technology through simplified interfaces, which makes technology accessible to novices as well as experts. This trend takes shape in many forms, from making it easier for business users to consume data and analytics to citizen development, which enables no-code or low-code environments to develop applications quickly.

Human augmentation. This trend enables people to use technology to enhance cognitive or physical experiences. Technology implants and wearable devices can augment human physical capabilities, while technologies such as AR and VR can promote cognitive augmentation.

Transparency and traceability. Digital ethics and data privacy are critical issues as data-driven processes become more essential to business activity. Data needs to be handled ethically and secured from malicious attacks. Transparency and traceability thus address a range of practices to ensure that companies comply regulatory requirements, apply ethics as they bring emerging technologies into their operations and incorporate practices to address the lack of trust in companies. Recent Deloitte data indicates that while three-quarters of executives see promise with AI, one-third are concerned about ethical risks associated with AI.

The empowered edge. Edge computing architecture brings data closer to the devices that process that data, compared with centralized models (read: cloud computing, on-premises data centers). While cloud computing often requires a round-trip for data transport – from the device to the cloud and back again – localizing data at the edge can minimize traffic latency and empower users with actionable data without delays. Industries that require millisecond-fast response time benefit from edge models, but as internet of things (IoT) devices proliferate, the edge model will become beneficial for myriad users .5G cellular technologies will also boost capabilities at with the edge. As providers built out 5G connectivity at the edge, it can enable greater volume and speed.

Distributed cloud computing. While historically, cloud computing has been centralized, new distributed cloud models are taking hold, delivering services to different locations. A centralized public cloud provider, in turn, assumes responsibility for operating, governing and updating services as they evolve. Distributed architectures can circumvent some of the regulatory, security and latency issues of a centralized model. They also work in concert with edge computing and micro-data center architectures.

Autonomous things. Autonomous things are physical devices that enlist AI to automate functions previously performed by humans, such as self-driving cars. So too, as the number of autonomous things grows exponentially, it will create new environment, with “things” that work in tandem. “As autonomous things proliferate, we expect a shift from standalone intelligent things to a swarm of collaborative intelligent things,” said Brian Burke, research vice president at Gartner.

Practical blockchain. A blockchain is a shared, distributed, secure ledger to enable transactions that don’t rely on central authorities, such as banks. A blockchain mitigates the risk of identity theft during transactions because each transaction is verified over a network of nodes responsible for validating transactions in each block. But this security comes with costs, including scalability and interoperability of systems, Cearley said. Shipping manifests and other documentation are being used in the maritime industry to document and trace goods and services.

AI security. AI security has three components: (1) securing data through AI-based tools, (2) using AI to identify cyberthreats in an environment, and (3) nefarious use of AI to attack enterprises. According to a recent CapGemini report, 69% of organizations surveyed said that AI will be necessary to respond to cybersecurity threats.