In the deep corners of some of the most advanced AI research labs in the world that are trying to outperform each other in the AI race, there is a delicate ballet underway between silicon and steel. Welcome to the world of Physical AI, perhaps the next biggest frontier in AI that the general populace is expected to encounter, by our predictions – between 2026-2028. Being researchers for decades and having witnessed the first-hand rise and fall of technology marvels from mainframes to the rise of Python over Malbolge, we find ourselves drawing parallels between an intricate piece of art like the Mona Lisa and the marvelous technological complexity behind the seemingly simple interfaces of Physical AI, which we present to you, dear readers, in this article.
Physical AI – The Grand Entrance
Picture this, if you will – a world where the digital and physical realms blur like watercolors on a canvas. Forming new shades entirely interdependent. Yes, physical AI has made its grand entrance in the global technology realm, where sophisticated algorithms do not just process data – they (yes, we’re thinking sci-fi movies as much as you are right now) reach out into the physical world and act intelligently with mechanical precision. We are not talking about the mere flip-flopping robot videos from Boston Dynamics here, but actual, intelligent, AI-driven intelligent robots that, quite unlike their digital counterparts, are capable of grappling with the not-so-orderly, realities of gravity, friction, and the purely inherent unpredictable nature of physical interactions.
The Basics – Before Anything
The fundamental architecture, much like that of the basis of artificial intelligence itself, is to mimic humans, both in terms of intellect such as decision-making skills and pattern recognition, and now, physically, in terms of motion, power, and harmonized actions within their limbs/appendages. The foundations of this architecture rely almost entirely on neural networks that, as usual, mimic the human brain’s capabilities to learn from experience and by recognizing patterns and in this case, actions as well.
Now, allow us to present you with the twist in the plot. While traditional neural networks, as we know them to be, process data within an intuitive computational framework, physical AI systems, instead, take these very computational-based frameworks and translate them into mechanical actions within milliseconds, and with surgical precision. Picture teaching a kid to play the guitar, for instance, except that the child in question here is a complex construct of semiconductors, motors, and actuators, and the guitar in question is a complex manufacturing process, that it will eventually master to play on its own.
Then Came the Evolution
The robotics domain has evolved far ahead of the mere automated forklifting antics of yesteryears, in what used to be a seemingly fancy automated phenomenon back then. Robotic systems of today are nearing autonomy, deploying sophisticated technologies including fusion-based algorithms, implementing what industry insiders call proprioceptive/exteroceptive integrations (for the layman- proactively perceiving external environments), which allow modern robots to maintain an accurate internal model of their physical state, while at the same time, ingesting and analyzing extra sensory information via various forms of data ingestion including computer vision, speech recognition, and more.
Information Overload? Here’s a quick breakdown:
Here’s a simple pragmatic example of Intelligence that we take for granted because of our instincts and reflexes that have been honed since we were born – picking up a delicate goblet of water from the table – something we hardly ever notice because of its sheer instinctiveness. However, let us now look at the same action from the perspective of modern robotics using physical AI. To achieve this feat (yes, it is still a feat worthy of achievement in the world of robotics and physical AI), the system must perform the following actions:
Use cognitive vision to process the visual data through multiple convolutional neural network layers
Calculate inverse kinematics in real-time (for the layman: process the properties of the object while it is in motion)
Adjust grip force based on surface friction coefficients (how tightly should the goblet be held without breaking it? Or might it fall if the appendages involved are flexed to a certain degree?)
Compensate for dynamic loading conditions (how much energy is required to lift the goblet if it is full or how much if it is empty?)
Monitor and adjust for unexpected perturbations (make sure the waiter does not trip over the table or the angle of the goblet does not suddenly cause it to spill over)
So, dear reader, what is seemingly a simple task to quench the thirst for the average human, in the realm of physical AI and next-gen robotics, is not really that simple anymore. This level of sophistication requires expertise across functions and disciplines, perhaps the primary reason why the role of AI engineers has become so critical in the modern physical world, especially in Industrie 4.0.
Primary Components of Modern Physical AI Systems:
Firstly, the disciplines:
No, we are not talking about a mechanical engineer and an AI scientist sharing a sandwich here. The development of modern physical AI systems represents a fascinating convergence of multiple engineering disciplines, each with high levels of innovation involved. A modern Robotics engineer exploring or practicing the discipline of physical AI must have a deep understanding of the following, at the bare minimum:
Advanced control theory and non-linear, non-sequential dynamics
Real-time embedded systems programming
Machine Learning and Deep Neural Network Architectures
Mechanical design and material sciences
A deep and working knowledge of sensor integration and signal processing.
Tools of the Trade and Where Physical AI Is Headed
Modern Physical AI systems require a wide array of sensing technologies, coupled with some of the most sophisticated neural networks (RNNs, CNNs, and the like) out there today. Some of these technologies include:
LiDaR – Light Detection and Ranging that must include 3d spatial mapping, coupled with computer vision technologies from the AI world
Force Torque Sensors for precise manipulation- again, coupled with IoT and edge-level computing to ensure proper calibration of force dynamics (yet another marriage of robotics and AI, all of them, as we will see)
Capacitive and resistive touch sensors for surface interactions – another parallel to IoT and sensor-based device proximity computing, and again, sensor-based technology integrated with Artificial Intelligence.
Thermal Imaging and acoustic sensors - for process monitoring and anomaly detections, again, fused with neural networks programmed to identify and remediate any anomalous phenomenon respectively.
Considering the sheer range of coverage, these sensory modalities generate terabytes of data that must be processed and integrated in real-time, which of course, requires sophisticated edge computing coupled with ensemble neural networks and advanced data fusion algorithmic architectures.
Standing at the edge of the cliff and preparing to glide into the new technological era where the virtual meets the physical, the potential and sheer breadth of possible use cases and applications of Physical AI is immense and simply cannot be overstated. We are talking autonomous surgical assistants, adaptive manufacturing systems, logistics and warehousing, and practically every industry in every sector imaginable.
The Human Element that will rule them all
Venturing into this new realm of Physical AI, the need for qualified professionals to lead this change and bring physical AI from the fringes to the fore has never been greater. As much as the rest of AI, modern robotics, and the field of physical AI are evolving at an unprecedented pace, and the industry is earnestly looking for qualified, certified, and capable professionals. If you are even remotely considering an interest in this relatively new and less crowded domain, the time to act is now – invest in your education (you have already read the roadmap in this article), pursue the relevant AI certifications, never stop learning, and even if you are a seasoned professional in the field, it is the time to upgrade and be prepared for when physical AI does go mainstream, only your knowledge and your professional certifications in Artificial Intelligence will set you apart from the herd.
For all our skeptical readers who might think we might be over the top in our predictions, here is the one and only NVidia CEO - Jensen Huang's statement at a public forum early this year " "Physical AI will revolutionize the $50T manufacturing and logistics industries. Everything that moves - from cars and trucks to factories and warehouses - will be robotic and embodied by AI"
