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Complete guide How small businesses can take advantage of AI Download this guide 1 Definition By September 2024 By: George Lawton George Lawton By: Sebastian Klovig Skelton Sebastian Klovig Skelton By: Andy Patrizio Andy Patrizio By: Katie Terrell Hanna Katie Terrell Hanna –Equinix –Solventum –IEX Latest TechTarget resources resources Search Business Analytics Search Business Analytics Solving specific problems driving enterprise adoption of AI 7 data storytelling examples: How to turn data into stories 9 best practices for self-service analytics Search CIO Search CIO New FTC rules unlikely with limited funds, policy shifts An essential guide to 6 IT executive roles Enterprise AI strategy surges in importance for IT buyers Search Data Management Search Data Management Qdrant update adds security measures for AI development Alation unveils AI agents plus SDK for agentic development Teradata unveils vector store to fuel AI development Search ERP Search ERP What to include in an RFP for an OMS, with template 8 use cases for generative AI in finance Goldberg: Practical value key for AI in NetSuite ERP Copyright 2018 - 2025, TechTarget Privacy Policy Cookie Preferences Cookie Preferences Do Not Sell or Share My Personal Information X Free Download A guide to artificial intelligence in the enterprise Embodied AI refers to artificial intelligence systems that can interact with and learn from their environments using a suite of technologies that include sensors, motors, machine learning and natural language processing. Some prominent examples of embodied artificial intelligence are autonomous vehicles, humanoid robots and drones. Although the term embodied AI or embodied intelligence is relatively new, it's related to mechanisms like adaptive control systems, cybernetics and autonomous systems, which have been around for centuries. For example, an autonomous security system using cameras and motion detectors does not have much of a physical body, but it can learn from how well its decisions mitigate security events on physical routers, hard drives and compute infrastructure, and adapt its monitoring strategies accordingly. Embodied AI's ability to learn from its experience in the physical world sets it apart from cognitive AI, which learns from what people and data sources say about the world. Human cognitive intelligence characterizes how we summarize, abstract and synthesize stories about our experience of interacting in the physical world and with other humans, animals and machines. The stories we compose summarizing our understanding of these things are what cognitive AI processes. Embodied AI's type of intelligence goes in a different direction that is more akin to a reflex than a concept: It learns to match its output to its sensory inputs. This article is part of Some kinds of embodied intelligence in the physical world span multiple bodies, such as swarms, flocks and herds of animals that synchronize their efforts. In embodied artificial intelligence, this kind of intelligence could apply to a swarm of drones, a fleet of vehicles in a warehouse or a collection of industrial control systems coordinating their efforts. Embodied AI can respond to different kinds of sensory input, similar to how the classic five senses in humans do. It can, however, also use a multitude of senses outside our human sensory experience. These abilities include detecting X-rays, ultraviolet and infrared light, and magnetic fields; knowing where things are using GPS; and understanding the performance of various enterprise systems or the inventory levels throughout a supply chain. It's also important to clarify that many embodied AI systems, such as robots or autonomous cars, move, but movement is not required. For example, an autonomous IT or security system might learn from the physical interactions of agents running on networking, storage and computing infrastructure that rests in place. Embodied AI applies to any AI system that learns from interacting with its environment. More capable systems typically include many elements across the different dimensions of embodiment. Here are some of the most important ones: As noted above, there is a spectrum of AI embodiment across different dimensions. At one end is something akin to humans with five senses that can move, effect complex changes, reason and adapt to the environment. On the other hand, there is an autonomous building control system that might just be designed to learn to optimize heating and cooling settings to keep occupants happy and reduce energy consumption. Here are some dimensions of embodiment to consider: Here are some of the potential use cases of embodied AI: The term embodied AI is relatively new, but the fundamental concept of adaptive control systems dates back centuries. In the early days, it was all about designing analog control systems that could learn from their decisions. These days, the focus is on how neural networks can create better representations of the physical world. Adaptive control. James Watt invented a centrifugal governor that employed a feedback system to adjust fuel flow into a steam engine, igniting the Industrial Revolution. Analog feedback. Norbert Wiener integrated a novel analog feedback system to improve the control of anti-aircraft guns in response to external stimuli. Cybernetics. Wiener and associates coined the term cybernetics to characterize the science of control in humans and machines. The term was a nod to the Greek word for "steersman." Cybernetic tortoise. U.K. researchers developed a turtlelike robot to study and improve how a robot could move around its environment. Adaptive business. Stafford Beer persuaded management at United Steel to fund a management cybernetics computer. Shakey the robot. Stanford Research Institute researchers developed a new robot capable of learning to deduce the consequences of its actions. Scaling cybernetics. Beer helped Chile build a cybernetic system for managing the country's economy. The project was scrapped after a coup. Humanoids. Japanese researchers developed WABOT-1, the first humanoid robot. Autonomous vehicles. The autonomous vehicle ALVINN used neural networks to learn to drive from coast to coast in the U.S. Intelligence without representation. Rodney Brooks published a paper on a new "behavior-based robotics" approach to AI that suggested training AI systems independently. DARPA Grand Challenge. The U.S. Defense Advanced Research Projects Agency hosted a competition to develop autonomous systems that could drive around the desert. This renewed interest in autonomous systems. Semantic segmentation. Researchers developed SegNet, an image analysis technique that used neural networks to decipher the meaning of visual data to improve autonomous systems. Simulation. Wayve researchers developed a new approach to help autonomous cars learn from simulation. Autonomous driving informed by feedback. Wayve researchers developed a new AI approach to learn from real-world experience with less reliance on pretrained models. Vision-language-action models. Wayve researchers developed new models that help cars communicate their interpretation of the world to humans. The first vision-language-action model that could simultaneously drive a car and converse in language opened up many new controllability and interpretability opportunities. Embodied AI is a work in progress. What will its future hold? It will certainly be informed by improvements in generative AI, which can help interpret the stories humans tell about the world. However, embodied AI will also benefit from improvements to the sensors it uses to directly interpret the world and understand the impact of its decisions on the environment and itself. The costs of sensors and compute are all coming down. Also, researchers are developing better algorithms for interpreting and adapting to the impact of embodied AI's decisions. To be clear, we are still early in the development of creating adaptable AI systems that learn from their environment. 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