When I purchased a Furby, the must-have toy of the year in 1998, little did I know that it was an early precursor to an autonomous thing! This furry robot toy’s main claim to fame was the ability to learn a language and gradually switch from “Furbish” to the language it was learning (English or 23 other languages that Furby was preprogrammed to speak). Sensors paired with a simple electromechanical system also enabled Furbys to move and respond to certain stimuli, giving the impression of learned intelligence. The Computer History Museum later added Furby to its permanent “The Robots Around Us” collection.[i]
My next robotic purchase was more like an autonomous thing. The iRobot Roomba relies on sensors and a few simple algorithms to navigate the floor area of a home and vacuum it. For example, it can detect the presence of obstacles, dirty spots on the floor and avoid falling down the stairs. Later Roomba models included additional intelligent features such as the ability to communicate wirelessly with other iRobot products to conduct a series of cleaning tasks in sequence.[ii]
The iRobot Roomba, an early generation autonomous thing, relies on sensors and a few simple algorithms to navigate the floor area of a home and vacuum it.
At its most basic level, an autonomous thing (AuT) is a physical entity (robot, drone, vehicle, etc.) capable of operating in its environment without human direction, freely moving and interacting with humans and other objects.[iii] AuTs can range in size and sophistication from small self-navigating drones to autonomous vehicles and ships, and can operate across many different environments. [iv] Several definitions also assume that artificial intelligence (AI) is a core element of a machine’s autonomy. In that sense, Furby was not a true AuT but Roomba, particularly the later models, can be considered rudimentary AuT. And as discussed in a previous blog, “The Future of Robotics is Bi-Directional Learning,” true autonomy depends on a device’s ability to learn and adapt to its environment over time. That requires an AI-learning platform where AuTs can interconnect with digital ecosystems for private data exchange, AI data processing, analytics and model building.
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Autonomous Things vs Hyperautomation
AuTs and hyperautomation were both named by Gartner in their “Top 10 Strategic Technology Trends for 2020.”iv Like AuTs, hyperautomation combines traditional automation tools and technologies with AI and machine learning (ML) to automate a wider variety of increasingly sophisticated tasks. And while AuTs operate in the physical world, hyperautomation can be physical or virtual. Take, for example, a chatbot or software automation of a business process such as customer service or talent recruitment. AuTs and hyperautomation provide benefits such as:
- Offloading repetitive, time-consuming and/or physically intensive tasks so humans can focus on more strategic areas.
- Operating under hazardous conditions such as chemical leaks, fires or infectious disease and performing maintenance on hard-to-reach areas such as bridges or turbines.
- Improving the quality of life for people such as companion robots in social care facilities.
The COVID-19 pandemic may be accelerating automation across a broad swath of industries from healthcare to manufacturing to hospitality and more. KPMG predicts an acceleration of enterprise investment in intelligent automation, with overall spending expected to reach $232 billion by 2025 compared with an estimated $12.4 billion in 2018.[v] Notable examples include:[vi]
- Healthcare and public health: Maccabi, a healthcare provider in Israel, automated updating patient records with COVID-19 test results from the Ministry of Health. Robots are also being deployed across many regions for patient monitoring and care, as well as disinfecting hospitals, cities and more. The diagram below illustrates a wide range of ways that automation/robots are being used during the crisis.
Image source: IEEE Spectrum (R. Murphy, V. Gandudi/Texas A&M; J.Adams/Center for Robot-Assisted Search and Rescue)
Food services: Autonomous delivery robots and drones from Starship Technologies, Kiwibot, Amazon and others are expanding around the world. For instance, Starship saw demand double overnight in one of its existing delivery areas due to COVID-19 social distancing measures. These AuTs rely on cameras, sensors and ML algorithms to navigate, cross streets and avoid obstacles to complete deliveries. The image below shows an example of the 3D map of the physical world that a delivery robot uses to navigate.
Interconnection is essential for full autonomy
Automation is not new. We have been applying tools and technologies to reshape the world since long before the emergence of AI. Automating software and machines to augment human ability depends on a range of algorithm intelligence and compute power. At the lower end of the spectrum, basic automation of pre-selected simple tasks can be handled by built-in algorithms. However, moving to the higher end of the spectrum toward greater autonomy requires the ability to learn, adapt and respond as situations change. For example, a new sidewalk or vending machine that doesn’t exist in a delivery robot’s current 3D map.
These more human aspects of autonomy require a distributed IT infrastructure capable of removing the distance between AuTs and the cloud ecosystems that house the apps, data and core algorithms with which they interact. Equinix Cloud Exchange Fabric™ (ECX Fabric™) and other global interconnection solutions available on Platform Equinix® can connect distributed AuTs with these digital ecosystems using low-latency, secure connection to privately exchange data, insights and AI models.