Analysis: Machine Intelligence and Its Four Major Advantages Over Human Intelligence

The essence of logistics intelligence is machine intelligence.

01    The essence of logistics intelligence is machine intelligence. MI)

A smart logistics system is a machine-based system.

In the overarching smart logistics system, the logistics “brain” that performs decision-making and analysis is a computational machine; the communication and sensing devices handle information transmission—meaning the smart logistics neural network is also composed of machines; and logistics operations are carried out by mechanized and automated equipment, which remains machinery. In essence, smart logistics is an integrated system built entirely from machines.

At present, instant logistics is developing rapidly. Systems such as instant logistics and instant delivery hinge on a core scheduling‑decision management system—essentially the “brain” of instant delivery—which remains composed of computing devices and terminal units. Although human operators carry out the actual deliveries, they must follow and execute the machine’s instructions. In summary, an intelligent logistics system is, at its essence, a machine‑based system or a large-scale system centered around machines. 2. The intelligence of smart logistics is machine intelligence: A smart logistics system is a large-scale machine system, and its intelligence should naturally be machine intelligence. MI) 3. The four essential characteristics of Machine Intelligence (MI): According to my research, the essential characteristics of machine intelligence can be summarized as follows: state perception, real-time analysis, machine decision-making, and precise execution.

02   Machine intelligence ( Definition and Analysis of MI)
 

Not only is the smart logistics system a machine-based system; systems such as intelligent manufacturing, intelligent supply chains, and intelligent services are, at their core, also machine systems, and the intelligence of such machine systems is, of course, machine intelligence. 1. Definition of Machine Intelligence Based on my research, I propose machine intelligence ( MI) is defined as follows: I believe Machine intelligence is the unique capability of machine systems—encompassing perception, computation, learning, and analysis—that, through ubiquitous environmental sensing, seamless information interconnection, and the processing and analysis of big data and cloud computing, enables these systems to learn autonomously, analyze independently, make decisions on their own, and continuously iterate and upgrade their capabilities. This is what we call machine intelligence. 2. Intelligent Machine Logic Let machines become intelligent machines rather than becoming “Robotics” embodies the fundamental logic of machine intelligence and constitutes a key distinction from the concept of artificial intelligence. Grounded in this logic, we should enable machines to leverage their unique strengths to generate machine intelligence (MI), rather than striving to replicate human intelligence in order to create AI. Building upon this framework, establishing the academic discipline of machine intelligence (MI) will better facilitate the advancement and transformation of smart logistics, intelligent manufacturing, and even the broader smart society. 3. Machine Intelligence and Thinking Machine intelligence ( MI is an extension and evolution of machine automation. We humans must recognize that machines surpass us in certain capabilities, encourage them to think in their own ways, and enable machine systems to generate powerful intelligence that neither humans nor animals possess—allowing machines to accomplish tasks beyond human reach. This is the quintessential mindset of machine intelligence. 4. Machine Intelligence Consciousness Machines can possess autonomous consciousness, learn knowledge on their own, and perform tasks automatically. However, we do not wish for machine‑based cognitive systems to develop self‑awareness, human‑like emotional intelligence, or the capacity to form personal preferences, aversions, and adversarial modes of thought. This should be the fundamental ethical principle guiding our research into artificial intelligence. Once machines acquire self‑awareness—at even a rudimentary level—humanity would inevitably find itself subjugated by them, reduced to pets or playthings within their systems. As long as machine intelligence remains devoid of self‑awareness, no matter how advanced or sophisticated it becomes, far surpassing human capabilities, it will always serve humanity. Just as cars outrun us and airplanes fly higher than we can, yet we do not regard these developments as frightening, we should not fear machine intelligence’s surpassing human capacities; rather, we should welcome such progress, seeing it as an extension of human intelligence that serves our collective well‑being.

03    Machine intelligence A Conceptual Distinction Between MI and Artificial Intelligence (AI)
1. Artificial intelligence is about making machines human-like. Artificial intelligence is a cutting-edge scientific discipline that studies and develops theories, methods, technologies, and applications aimed at simulating, extending, and augmenting human intelligence. AI seeks to uncover the essence of human intelligence and to create robots capable of responding in ways akin to human cognition. In other words, AI focuses on modeling and replicating the information-processing mechanisms underlying human consciousness and thought, enabling machines to emulate human intelligence and think much like humans—essentially, to make machines “human-like.” 2. Machine intelligence is the process of turning machines into intelligent machines. Machine intelligence ( MI is an extension and evolution of machine automation. It recognizes that machines can outperform humans in certain domains and encourages them to think in ways that leverage their unique strengths. While machines can learn from and harness artificial intelligence, they should instead capitalize on their own distinctive advantages to surpass human intelligence and help us accomplish tasks that humans cannot. These two points constitute the fundamental distinction between machine intelligence and artificial intelligence. 3. The Integration of Artificial Intelligence and Machine Intelligence Of course, research on machine intelligence can draw on and learn from artificial intelligence, integrating the two. In other words, we can digitize human intelligence and combine it with the digital intelligence generated by machines through digital means, thereby achieving a fusion of digital intelligence and intelligent digitization—this is the concept of “digital intelligence.” From another perspective, artificial intelligence is itself a subfield of machine intelligence, focusing on enabling machines to emulate human learning and thus generate machine intelligence.

     04     Current machine intelligence MI’s Four Capabilities That Surpass Human Intelligence
 

1. Comprehensive sensing capability Human perception is profoundly limited. Our sensory apparatus relies primarily on the eyes, ears, nose, tongue, and skin, with vision being the most dominant sense. Yet even this sense has significant constraints, confining our perceptual range to a narrow band. For instance, we have only two eyes, and they can see only forward; the so‑called “third eye,” or the ability to perceive beyond ordinary sight, would qualify as a paranormal phenomenon. These limitations in visual perception give rise to the symbolic and visual nature of human thought—often, we find it easier to grasp abstract ideas by translating numbers into graphical representations, yet we lack any truly efficient capacity for big‑data processing. Similarly, our auditory and olfactory ranges are even more restricted, while tactile perception via the tongue and skin demands close physical contact. Such fundamental limits on human perception profoundly shape our modes of thinking and cognitive abilities. By contrast, machine systems can achieve fully distributed, networked sensing, with capabilities far surpassing those of humans. Intelligent robotic units can be equipped with sensors on all sides—front, back, top, bottom, left, and right. “Eyes,” “ears,” “noses,” and various “tactile sensing systems” can also leverage IoT technology to transmit tactile perceptions across vast distances, enabling the detection of diverse information even in the absence of light and capturing countless types of data that humans cannot perceive. With the aid of IoT networks, machines can further integrate and process distributed sensory data spanning multiple regions and entire networks. At present, machine perception is advancing at a rapid pace, with capabilities that have already far surpassed those of human beings.
2. Networked Swarm Intelligence

Machine intelligence is networked. Machines built on the Internet achieve interconnected sensing, which not only makes their perception ubiquitous but also enables their sensory data to be uploaded to the cloud—another layer of infrastructure resting atop the pervasive network. In this networked machine intelligence, both cognitive processing and information handling are distributed, allowing for cloud‑based analytics performed in the Internet’s cloud environment. In other words, Machine intelligence can give rise to networked swarm intelligence. It is possible to build an intelligent “brain” shared by numerous devices on the Internet, which can command and control smart or automated equipment distributed across the globe, thereby realizing networked intelligence. Alternatively, each smart hardware device can be equipped with its own individual intelligent terminal, forming standalone smart hardware that operates independently of the network-wide machine brain. Together, these smart devices constitute a larger intelligent system, thus giving rise to collective intelligence. And human intelligence is, by and large, personalized. Each of us is an intelligent individual, yet our individual brains are not yet capable of being continuously interconnected to form a brain‑network that gives rise to collective intelligence. At present, brain–machine interfaces and direct brain–brain communication remain beyond our reach. We acquire knowledge, process information, and analyze data as independent individuals; even when we leverage the internet, we treat it merely as a technological conduit for information transmission. Consequently, humans remain, by and large, fundamentally individual beings. Our cognitive systems and modes of thinking cannot be fully “internetized.” “Brain‑internet” technology is still in the exploratory research phase and lacks practical value; human intelligence does not yet exhibit the characteristics of a brain‑based internet. If we compare the human brain to an internet‑enabled artificial brain system, perhaps only the omnipresent “God‑like brain” of our imagination could possess such pervasive, networked intelligent capabilities.

3. Digital Capabilities

Machine intelligence is digital, Its capabilities are far beyond those of humans; the machine’s computational and reasoning processes are realized through digitization, and the machine recognizes only 0 and 1—machines’ digital processing capabilities far surpass those of humans. With the advancement of big data and cloud computing, machine systems now outstrip human beings in memory capacity, search efficiency, and knowledge storage. Human intelligence is analog. Our ability to perceive visual patterns is more intuitive and rooted in symbolic thinking. To enable machines to understand our thought processes, we often need to attach labels to objects and define encoding schemes—transforming symbols into sequences of numbers—to make it easier for machines to “read” our ideas and learn from our intelligence. Similarly, machine systems frequently convert complex data into visual representations—such as curves and various types of charts—to facilitate human comprehension of the decisions made by artificial intelligence. The computational advantages of machine intelligence in digital processing far surpass those of analog and symbolic approaches; as big-data processing technologies advance at breakneck speed, humanity finds itself increasingly out of reach. 4. Knowledge dissemination and the ability to rapidly iterate and enhance intelligence Machine intelligence can be iteratively improved at an accelerated pace. Machine knowledge and intelligence are evolving at an unprecedented pace, enabling the rapid transfer of accumulated expertise to new systems in mere seconds, continuous self‑training around the clock, relentless ongoing learning and improvement, and the ability to store and recall an infinite wealth of information. Human intelligence improves through slow, incremental learning. As knowledge accumulates, the time required for human learning continues to grow; it often takes an individual more than two decades—from elementary school through postdoctoral studies—far outstripping the learning efficiency of machine intelligence. As machine systems’ capabilities for storing and processing information, as well as for imparting intelligent knowledge, continue to accelerate, the intelligence of machines will become increasingly astonishing.