MicroAI Debuts to Advance AI Edge Technology Across the Digital Ecosystem
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MicroAI Debuts to Advance AI Edge Technology Across the Digital Ecosystem

One Tech Becomes MicroAI

MicroAI Debuts to Advance AI Edge Technology Across the Digital Ecosystem

MicroAI is the new name of One Tech, Inc., a supplier of edge-native artificial intelligence (AI) and machine learning (ML) products. Founded as One Tech, Inc. in 2018 by Tokyo-based Systena Corp and Dallas-based Plasma Group, MicroAI initially focused on the industrial internet of things (IIoT) market. In 2021, the company introduced MicroAI AtomML, an innovative AI technology that can help businesses in virtually every industry reduce cloud computing and connectivity costs, decrease latency, and improve security. To reflect this broader market, the company is changing its brand to MicroAI. Read the MircoAI Press Release here.

MicroAI Debuts to Advance AI Edge Technology Across the Digital Ecosystem

Analyst Take: MicroAI comes charging out of the blocks with a rebrand and a refreshed portfolio aimed at powering edge-native AI across the digital ecosystem. MicroAI’s main proposition is harvesting raw micro data and using artificial intelligence (AI) to deliver macro insights and real-time actions especially across Industrial 4.0 environments. The MicroAI portfolio focuses on four key objectives: advancing Edge AI innovation, assuring operational excellence, automating predictive maintenance, and fortifying endpoint protection.

I see MicroAI attaining a competitive edge by innovating edge-native AI capabilities including inferencing and training on the edge, personalizing device intelligence, and embedding AI throughout connected endpoint devices such as Microcontroller Units (MCUs) and Microprocessor Units (MPUs). The MicroAI Edge AI technology is critical to avoiding failures across cloud and IoT fabrics by minimizing and eliminating downtime for any connected device or mechanical system. This includes predicting failures, providing health scores, remotely scheduling preventative maintenance, and reducing connectivity and cloud compute costs.

For efficiency optimization, MicroAI harvests micro data from industrial or manufacturing operations. Through AtomML capabilities, the MicroAI technology federates a variety of machine micro data types and endpoints as well as operational information. This capability includes tags from Programmatic Logic Controllers (PLCs), raw sensor data, and other operational data inputs. As a result, I anticipate that machine and man outputs and productivity are improved and helps solve the common issue of organizations having insufficient visibility into remote assets and asset performance.

MicroAI’s endpoint protection capabilities include monitoring all connected endpoints, visualizing cyber disturbances in real-time, and detecting zero-time events. To further solidify security MicroAI automates intrusion detection and assures critical assets remain operational. The comprehensive endpoint protection is integral to pushing intelligence to the IoT edge as security intrusion has escalated recently across IoT devices such as the Mirai malware and Mozi botnet attacks. I view secure personalized intelligence as essential to solving the full range of technical and business case challenges regardless of wireless connectivity scenarios including Bluetooth, LPWA, WiFi, LTE-M, 2G/3G, and 5G.

As a result, I anticipate that the MicroAI platform is ready to fulfill a wide range of major application use cases including:

Telecommunications. Deliver network optimization for mobile network operators by receiving alerts if trend analysis detects asset health decline.

Manufacturing Equipment. Provides predictive maintenance for processing data from industrial equipment (i.e., Robotic arm/welders) to identify signs of failure and lessen unexpected downtime.

Utility Infrastructure. Through asset performance management capabilities that increase awareness of utility assets health and utilization (infrastructure, energy, water) and prognosticate asset lifecycle span.

Automotive. The localization of data processing can significantly reduce the amount of data transmitted from vehicle within the Electronic Control Unit (ECU)/Telematics Control Unit (TCU) board.

Connected Hardware/Device. By embedding MicroAI into connected assets, device OEMs gain insight into performance and security of assets.

Medical Devices. Improve data security and privacy of health data by keeping data local to device for analytics.

Oil & Gas. Process data from industrial equipment to identify signs of failure to reduce unexpected downtime.

Smart Home Devices. Enables voice training and voice authentication directly in the local environment to improve data transmission efficiency.

Why MicroAI’s Edge-Native Approach is Key to Innovation

I am bullish on MicroAI’s edge-native approach and see this as a key driver of innovation. MicroAI’s AtomML is embedded on Arm Cortex M series MCUs to assure the embedment of its AI model across endpoints on an ecosystem-wide basis. By being agnostic to sensor values and supporting time series inputs, AtomML ensures the reliable and consistent acquisition of multi-channel data. Autonomous training and inferencing on compact MCUs ensures that the embedded AI model evolves throughout inferencing processes. Users can access result visualization and report generation through mobile, on-prem, and cloud options.

MicroAI’s edge-native approach emphasizes agnosticism across sensor, data, and communications protocols to assure integration ease with IoT sensors, operational technology, and system data. These capabilities are essential to natively training AI models and processes at the edge and endpoint using multidimensional behavioral algorithms that run recursive analysis.

MicroAI’s edge-native process uses a model building phase where multivariant stochastic analysis is performed recursively on the time series data to create the model (training can be performed on the MCU). In addition, the inference phase applies self-correction to the time series data to localize the model’s prediction to complete the one step ahead calculation.

Key Takeaways on MicroAI’s Rebrand Debut

I see the dual model building and inference phase capabilities as enabling MicroAI to provide AI-powered edge-native innovation, including the extreme edge of the endpoint, that dynamically delivers personalized and contextual services across the vast spectrum of industry vertical and use cases. The edge-native innovations underpin MicroAI’s go to market products consisting of MicroAI Launchpad and MicroAI Insight.

MicroAI Launchpad supports the overall process needed to enable MicroAI technology including the account creation, device profile, connectivity, cloud interworking. MicroAI AtomML implementation, and service enablement steps. MicroAI Insight provides custom dashboard capabilities such as smart asset management, health score & predictive maintenance visualization, intelligent workflow automation, existing application/vertical templates (e.g., manufacturing, telecom), and next gen templates prepared to support additional new verticals (e.g., smart cities, smart homes).

I anticipate that the new products will prove key in broadening MicroAI’s addressable market and powering AI innovation across an expanding array of edge-native applications and industry verticals. The business relations and channel resources of top-tier parent organizations Systena ($578.5 million in global 2021 annual revenues) and U.S.-based Plasma Group will further expand MicroAI’s business opportunities. MicroAI is now ready to forge its new organizational and brand identity as the company transitions away from the One Tech brand and advances AI edge-native technology across the digital ecosystem.