The desire to create machines similar to itself, which mankind has dreamed of for centuries, began to take concrete form along with technological advances. After the software that can think, artificial intelligence systems are now being developed that can move in the physical world and interact with the environment. Physical artificial intelligence stands out as one of the most remarkable areas of artificial intelligence technologies that have stepped from the digital world to the physical world.
Physical AI is an interdisciplinary field that combines artificial intelligence algorithms and systems with robotic systems that can perform actions in the physical world. According to the definition of the Massachusetts Institute of Technology (MIT), physical artificial intelligence is described as “artificial intelligence systems capable of interacting with physical objects found in the real world, sensing their environment, and providing adaptive responses to that environment.”
Physical artificial intelligence, unlike classic software-based artificial intelligence systems, has the capabilities to collect data through sensors, move in physical environments, manipulate objects, and perform actions in the real world. These systems combine artificial intelligence technologies such as deep learning, reinforcement learning, computer vision, natural language processing with mechanical engineering, materials science and electronics.
Among the systems covered by physical artificial intelligence are:
There are applications in different areas such as.
Physical artificial intelligence has its roots in the development of both robotics and artificial intelligence fields. The first word “robot” was used in the 1920 play “Universal Robots of Rossum” by Czech writer Karel Čapek. The word is derived from the word “robota”, which means “forced labor” in Czech.
Alan Turing's work on thinking machines in the 1950s and the emergence of the term “artificial intelligence” at the Dartmouth Conference in 1956 formed the theoretical foundations of this field. In 1961, the first industrial robot, the Unimate, began to be used in General Motors factories, which can be considered one of the first practical applications of physical artificial intelligence.
Developed in the 1970s at Stanford University, the robot “Shakey” went down in history as the first mobile robot capable of sensing its environment and performing simple tasks. In the 1980s, Rodney Brooks' work on “artificial life” and his behavior-based robotic approach made significant contributions to the field of physical artificial intelligence.
In the 2000s, the Grand Challenge, organized by DARPA (Defense Advanced Research Projects Agency), gave great impetus to the development of autonomous vehicles. In 2011, IBM's Watson system Jeopardy! Defeating human champions in the competition has been an important milestone in the interaction of artificial intelligence with the physical world.
As noted in the article “Physical Artificial Intelligence” published in the journal Science Robotics, with the development of deep learning techniques in the late 2010s and the strengthening of computer hardware, physical artificial intelligence systems have become capable of much more complex tasks.
Physical artificial intelligence systems require the integration of multiple technologies. The basic components are:
Physical artificial intelligence systems use a variety of sensors to detect their environment:
The data collected through these sensors allows the system to understand its environment and give appropriate responses.
Powerful processors are required to process the collected data:
According to a 2023 study by Stanford University researchers, the use of energy-efficient special processors is increasing so that physical artificial intelligence systems can react in real time.
Software components that make up the “brain” part of physical artificial intelligence systems:
Components that enable the system to perform actions in the physical world:
Components that provide the energy necessary for the operation of physical artificial intelligence systems:
According to Oxford University's “Energy Constraints in Physical AI Systems” report published in early 2024, energy efficiency is one of the most critical factors in the adoption of physical AI systems.
Physical artificial intelligence systems operate in a perception-think-to-act cycle. This process consists of the following steps:
The system collects data from its surroundings through its sensors. These data can be images, sounds, sensation of touch, temperature, pressure and other physical characteristics. Using computerized vision and sensor fusion techniques, this raw data is transformed into meaningful information.
The data collected and processed are analyzed by artificial intelligence algorithms. The system tries to understand its environment, assess the situation and determine the appropriate actions to achieve its goals. In this step, deep learning, reinforcement learning and other machine learning techniques are used.
The system forms an action plan to achieve the set goals. This includes such steps as determining the path of movement, getting around obstacles, calculating how to manipulate objects.
Planned actions are carried out in the physical world through motors and actuators. The system monitors their movements in real time and makes corrections as needed.
The system evaluates the results of its actions and learns from their experience to improve its future performance. This happens through reinforcement learning techniques, online learning, and adaptive control algorithms.
According to the research “RT-X: Design for Real-Time Embodied AI” published by DeepMind and Google Robotics in 2023, physical AI systems need to have millisecond response times to work effectively in the real world.
Physical artificial intelligence systems can automate tasks that are dangerous, monotonous or physically demanding for humans. This covers a wide range of applications from industrial production to search-and-rescue operations.
Unlike traditional robotic systems, physical AI systems can learn from their experiences and adapt to changing conditions. This allows them to produce effective solutions even in unplanned situations.
Physical artificial intelligence systems can work with humans to create collaborative systems that can take advantage of the strengths of both parties. According to the World Economic Forum's “The Future of Jobs 2023" report, human-machine cooperation could increase workforce productivity by up to 40% in the future.
Physical artificial intelligence systems can collect large amounts of data from the physical world and turn that data into meaningful insights. This brings benefits in many areas, from the optimization of production processes to the improvement of health care.
Physical artificial intelligence systems are extremely complex, both in terms of hardware and software. The integration of different disciplines creates great challenges in system design and production.
The safety of systems moving in the physical world is critical. Systems need to work safely and not harm people, even in unexpected situations.
Physical artificial intelligence systems often have high energy consumption. For long-term autonomous work, energy efficiency poses a major challenge.
Advanced sensors, processors and mechanical components increase the cost of physical AI systems. This can be a barrier, especially for small businesses and emerging economies.
The use of physical artificial intelligence systems raises ethical concerns such as job loss, privacy violations, and security risks. In addition, the development of legal frameworks regulating the use of these systems is also a significant challenge.
The field of physical artificial intelligence continues to develop rapidly and reveal new applications. Important developments in recent years include:
Robots made from elastic and soft materials rather than traditional hard materials offer great potential for safer human-robot interaction and tasks that require precise manipulation. According to a study published in the journal Science Robotics, publications in the field of soft robotics have increased by more than 300% in the past five years.
Physical artificial intelligence systems are able to develop a more comprehensive understanding of the world by combining visual, auditory, tactile and other modes of perception. This allows for more natural and intuitive human-machine interactions.
Access to real-world data for training physical robots can be limited and costly. Sim2Real techniques allow robots to train in virtual environments and transfer these skills to the real world. According to a 2023 study by Nvidia, advanced Sim2Real techniques can reduce training time by up to 90%.
Physical AI systems can continue to learn and evolve even after they are deployed. This allows systems to become more capable over time and can adapt to new tasks.
Advanced tactile sensors and artificial skin give robots human-like sensory capabilities. MIT's “e-skin” project is developing technologies that allow robots to hold even sensitive objects without damaging them.
According to the article “Future Prospects of Physical AI” published in the journal Nature Machine Intelligence at the beginning of 2024, the following developments are expected in the field of physical artificial intelligence in the next decade:
This integration of artificial intelligence with the physical world marks an important milestone in the future of technology. In the coming years, it is expected that physical artificial intelligence systems will become more and more part of our daily lives.
The transformation of AI from being merely a digital entity to systems capable of performing concrete actions in our physical world has the potential to radically change humanity's relationship with technology. But for this potential to be fully realized, it is necessary to overcome technical challenges, address ethical concerns, and achieve societal acceptance.
Physical artificial intelligence is taking important steps towards making robots in science fiction films a reality. Keeping a close eye on these developments and contributing to the responsible development of these technologies offers great opportunities for those who want to play a role in shaping the future. To learn more about the latest developments in the field of physical artificial intelligence and keep abreast of innovations in this field, you can follow industry conferences, take advantage of online resources and contact experts in the field.
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