When we think about a swarm, we perhaps instinctively think about bees, ants, or maybe birds, but the fact is most creatures in the animal kingdom have figured out that by banding together, they have a better chance of surviving, giving credence to the adage—it’s an eat or be eaten world. Swarming affords all kinds of creatures, including organisms as diverse as bacteria, insects, fish, birds, and mammals—a better chance of finding food while also providing protection in numbers so they do not become food themselves.
Swarming animals benefit from their collective senses. As individuals, single animals may struggle to fend off predators, find shelter and water, or navigate to their winter grounds. However, when they swarm, their ears and eyes act in unison, exponentially increasing their ability to sense danger or corral prey. This collective behavior also offers protection through sheer numbers, making it difficult for predators to single out any one individual.
To some extent, humans also exhibit swarming behavior. For example, crowds of party revelers gather in New York City’s Times Square each year to witness the symbolic ball drop event marking the end of one year and the beginning of another. In Tokyo, the Shibuya Crossing boasts the title of the busiest pedestrian crossing in the world, with countless visitors and locals stopping to watch the giant human wave spectacle. Meanwhile, the Kumbh Mela pilgrimage and festival in India, and the Arba'een Walk in Iraq, are the largest public human gatherings globally, attracting millions of people to celebrate and participate in spiritual and cultural traditions.
Animal swarms embody the collective behavior of individuals acting as one to find optimal solutions for survival, food, and shelter. Artificial intelligence (AI) seeks to replicate and harness this collective intelligence by simulating how individuals collaborate to solve complex problems.
AI swarm intelligence refers to the use of AI algorithms to mimic the collective behavior of swarms of animals, such as bees, ants, or birds, to solve complex problems. These algorithms are inspired by the way that these swarms work together to find optimal solutions to tasks such as foraging, nest-building, or migration.
Swarm intelligence algorithms typically involve many individual agents, each with a limited set of capabilities and knowledge. These agents interact with one another and with their environment, exchanging information and adjusting their behavior based on the collective feedback of the swarm.
One common example of AI swarm intelligence is the ant colony optimization algorithm, which is used to solve optimization problems such as finding the shortest path between two points. In this algorithm, individual ants leave pheromone trails as they explore the problem space, and these trails are reinforced by other ants that follow them. Over time, the most efficient path emerges as the pheromone trails are reinforced by more and more ants.
Other examples of AI swarm intelligence include particle swarm optimization (PSO), which is used to optimize complex functions, and artificial bee colony (ABC) optimization, which is used to solve scheduling problems. Additionally, some swarm intelligence algorithms are inspired by human social systems, such as voting or consensus-building. Researchers can also use these swarms to study swarm behavior in real-world settings, providing valuable insights into collective decision-making and collaboration. Overall, swarm intelligence draws on a wide range of sources and disciplines, including biology, mathematics, physics, computer science, and engineering. By combining insights from these different areas, researchers are developing new and innovative approaches to problem-solving and decision-making.
There is growing interest and investment in AI swarm intelligence across a wide range of industries, and many companies and research institutions are actively exploring its potential applications.
Massachusetts Institute of Technology (MIT): According to the MIT Center for Collective Intelligence, groups have historically made greater progress than individuals. They believe the definition of intelligence is achieving goals and refer to a "supermind" as a group with a high degree of collective intelligence. This includes not just human minds but also computer technology that can harness collective intelligence for innovation and problem-solving.
NASA: NASA's Sabrina Thompson and UMBC's Jose Vanderlei Martins are developing AI and machine learning algorithms for small satellites, such as those in the Hyper-Angular Rainbow Polarimeter (HARP) CubeSat and HARP2 projects. Their goal is to enable swarms of these satellites to communicate and coordinate their positions, capturing data on weather patterns from different angles and orbits in real time. This could revolutionize weather forecasting, disaster reporting, and climate modeling, providing new insights into global weather and climate change.
U.S. Army: The U.S. Army, in partnership with the Defense Advanced Research Projects Agency (DARPA), recently conducted its largest-ever military drone swarm test. Over 30 small drones were deployed from various sources and coordinated to gather intelligence on rival positions using infrared sensors and electronic warfare payloads. The drones carried armaments and could detect electromagnetic signals from foe communications systems, radars, and electronic jammers. The collected data was then transmitted back to manned aircraft and command posts.
Other major players in the field of AI swarm intelligence include Google with its Brain project, Alibaba for logistics and supply chain management, Siemens for manufacturing and industrial automation, Honda for robotics, and Intel for autonomous vehicles.
Stampedes have caused the loss of human life in various instances throughout history. The science of swarming could offer valuable insights into crowd management, for instance, optimizing the flow of people in and out of large venues like concerts and sporting events or reducing congestion at airports. AI swarm intelligence holds great promise in transforming numerous industries and offering solutions to intricate problems. Its potential applications span various fields, such as:
This week's New Tech Tuesday features two cutting-edge products from Seeed Studio and ADLINK Technology. These tools are designed to enhance deep learning AI applications and AI vision applications in harsh environments, representing a significant step forward in next-level design.
Seeed Studio NVIDIA® Jetson AGX Orin™ Developer Kit
The NVIDIA Jetson AGX Orin Developer Kit enables next-level AI performance for next-gen robotics! With up to 275 TOPS of AI performance, this compact and powerful developer kit is perfect for prototyping advanced AI-powered robots and autonomous machines. Featuring the latest NVIDIA GPU technology and advanced deep learning software stack, you can develop solutions using your largest and most complex AI models to solve problems such as natural language understanding, 3D perception, and multisensor fusion. The kit includes the Jetson AGX Orin module, reference carrier board, power adapter, USB cords, and a quick start guide. Get started with the Jetson ecosystem partners, who offer additional AI and system software, developer tools, and custom software development.
ADLINK Technology NEON-2000-JT2-X Starter Kits
Looking for AI smart camera kits designed for harsh environments? Check out the NEON-2000-JT2-X Series Starter Kits from ADLINK. With their IP67 certification, pre-installed software, and validated accessories, the kits are optimized for vision applications and support leading AI inference engines. ADLINK's AI Vision Solutions are ideal for labor-intensive industries, including food sorting, logistics, packaging, and farming. Improve your product sorting, classification, and quality assurance tasks with ADLINK's innovative technology. With autonomous production equipment, you can enjoy seamless operation processes, identify and analyze abnormalities, and receive real-time notifications, making smart manufacturing easier than ever before.
Swarming is a behavior common to most animals, enabling them to thrive in challenging environments. This collective behavior inspired the development of AI swarm intelligence, mimicking the collective behavior of swarms to solve complex problems. With their exceptional features and advantages, the NVIDIA Jetson AGX Orin Developer Kit and NEON 200 JT2-X AI smart camera kits can significantly expedite the development of swarm intelligence. These cutting-edge products offer immense potential, and as researchers continue to develop new algorithms and technologies, we are likely to see more and more practical applications of swarm intelligence in the years to come.
Rudy Ramos brings 35+ years of expertise in advanced electromechanical systems, robotics, pneumatics, vacuum systems, high voltage, semiconductor manufacturing, military hardware, and project management. Rudy has authored technical articles appearing in engineering websites and holds a BS in Technical Management and an MBA with a concentration in Project Management. Prior to Mouser, Rudy worked for National Semiconductor and Texas Instruments..