Robots... the future of work without pause
Published: 04:05 PM,May 21,2025 | EDITED : 08:05 PM,May 21,2025
A journalist touches a humanoid robot by UniX AI, at the robotics exhibition centre Robot World, in Beijing Economic-Technological Development Area, also known as Beijing E-Town, in China. - Reuters file photo
According to published reports and statistics, the global stock of industrial robots reached 3.4 million units by 2024, giving an average of roughly one robot for every seven human beings. These machines have already assumed about 14 per cent of the jobs once carried out by people and 90 per cent of businesses — of every size and type — now plan to incorporate robots into their operations.
These figures come from data released by “Coolest-Gadgets” in a study entitled “Robots Statistics 2024 By Revenue, Usage and Companies”. It is worth recalling that robotics actually predates what we today call artificial intelligence: the first recognisably “mechanical” robot appeared in 1495, when Leonardo da Vinci sketched a knight capable of performing limited, gear-driven motions, while in 1785 Edmund Cartwright patented a water-powered loom that, for its age, was treated as a form of automation.
Such milestones remind us that we are riding a steep scientific curve. The coming decades promise flourishing industry and commerce, yet they also threaten heightened human unemployment. We therefore face twin imperatives: to manage the steady, beneficial flow of technologies — robots foremost among them — and to safeguard every person’s right to meaningful work.
Achieving both aims demands fresh educational strategies and new specialisations so that forthcoming generations can reposition themselves in an altered labour market. Before proposing solutions, however, we must clarify what we mean by robots, how their workings have evolved, what roles they serve now and may serve tomorrow; and what risks their march into factories and offices might entail.
There is a recognised discipline called “robotics”, an interdisciplinary science concerned with designing, building and operating robots. Technically speaking, a robot is a machine that performs traditional tasks by imitating human action. The earliest examples were simple mechanical systems created for repetitive industrial chores — welding, assembly, sorting — inside automated production lines.
Such early devices were tightly constrained by classical mechanics: they could not deliberate, nor could they adapt to a changing workspace. Their core components were actuators that supplied motion, rudimentary sensors that offered minimal environmental feedback and a mechanical frame — most often a jointed arm — whose range of movement was fixed by geometric limits suited to a single repetitive duty. Control systems were primitive, relying on rigid physical logic with no capacity to interpret or process external data. Consequently, these machines were confined to static, predictable environments and could not cope with complexity or dynamism.
Over the years robotics has advanced dramatically. Contemporary robots integrate cutting-edge technologies, micro-electronics, high-fidelity sensors and above all artificial-intelligence software. Thanks to these developments, robots have migrated from mere automation to genuine interaction: they now perceive their surroundings with LiDAR, radar and high-resolution cameras, respond in real time and make swift, context-sensitive decisions. Marrying robotics to machine-learning algorithms allows them to absorb past experience and refine future performance, enabling intricate tasks such as autonomous navigation or conversational engagement. Modern robots already excel in health care, agriculture and logistics: they perform delicate surgery, harvest fruit and run warehouses with minimal human oversight, eloquent proof of the field’s maturity.
Intelligent digital robots function through a triad of sensing, processing and acting. Sensors collect environmental data; algorithms — frequently AI-driven — analyse that data and select an appropriate response; actuators convert the chosen response into physical movement. A familiar illustration is the smart vacuum cleaner: packed with proximity sensors, it detects dirt and obstacles, maps the floor plan and continuously recalibrates its route as new information arrives. In a larger industrial example, warehouse robots scan shelves, recognise individual items and ferry them to dispatch bays with remarkable efficiency. Each case shows a loop of perception, computation and execution far beyond the capabilities of the early mechanical arms.
The conversation, of course, is far from over. The spread of robots demands careful governance to balance economic gain with social equity, to ensure skills programmes keep pace with shifting demand and to frame ethical guidelines for machines whose autonomy grows by the month. Those questions will be explored in a subsequent instalment.
These figures come from data released by “Coolest-Gadgets” in a study entitled “Robots Statistics 2024 By Revenue, Usage and Companies”. It is worth recalling that robotics actually predates what we today call artificial intelligence: the first recognisably “mechanical” robot appeared in 1495, when Leonardo da Vinci sketched a knight capable of performing limited, gear-driven motions, while in 1785 Edmund Cartwright patented a water-powered loom that, for its age, was treated as a form of automation.
Such milestones remind us that we are riding a steep scientific curve. The coming decades promise flourishing industry and commerce, yet they also threaten heightened human unemployment. We therefore face twin imperatives: to manage the steady, beneficial flow of technologies — robots foremost among them — and to safeguard every person’s right to meaningful work.
Achieving both aims demands fresh educational strategies and new specialisations so that forthcoming generations can reposition themselves in an altered labour market. Before proposing solutions, however, we must clarify what we mean by robots, how their workings have evolved, what roles they serve now and may serve tomorrow; and what risks their march into factories and offices might entail.
There is a recognised discipline called “robotics”, an interdisciplinary science concerned with designing, building and operating robots. Technically speaking, a robot is a machine that performs traditional tasks by imitating human action. The earliest examples were simple mechanical systems created for repetitive industrial chores — welding, assembly, sorting — inside automated production lines.
Such early devices were tightly constrained by classical mechanics: they could not deliberate, nor could they adapt to a changing workspace. Their core components were actuators that supplied motion, rudimentary sensors that offered minimal environmental feedback and a mechanical frame — most often a jointed arm — whose range of movement was fixed by geometric limits suited to a single repetitive duty. Control systems were primitive, relying on rigid physical logic with no capacity to interpret or process external data. Consequently, these machines were confined to static, predictable environments and could not cope with complexity or dynamism.
Over the years robotics has advanced dramatically. Contemporary robots integrate cutting-edge technologies, micro-electronics, high-fidelity sensors and above all artificial-intelligence software. Thanks to these developments, robots have migrated from mere automation to genuine interaction: they now perceive their surroundings with LiDAR, radar and high-resolution cameras, respond in real time and make swift, context-sensitive decisions. Marrying robotics to machine-learning algorithms allows them to absorb past experience and refine future performance, enabling intricate tasks such as autonomous navigation or conversational engagement. Modern robots already excel in health care, agriculture and logistics: they perform delicate surgery, harvest fruit and run warehouses with minimal human oversight, eloquent proof of the field’s maturity.
Intelligent digital robots function through a triad of sensing, processing and acting. Sensors collect environmental data; algorithms — frequently AI-driven — analyse that data and select an appropriate response; actuators convert the chosen response into physical movement. A familiar illustration is the smart vacuum cleaner: packed with proximity sensors, it detects dirt and obstacles, maps the floor plan and continuously recalibrates its route as new information arrives. In a larger industrial example, warehouse robots scan shelves, recognise individual items and ferry them to dispatch bays with remarkable efficiency. Each case shows a loop of perception, computation and execution far beyond the capabilities of the early mechanical arms.
The conversation, of course, is far from over. The spread of robots demands careful governance to balance economic gain with social equity, to ensure skills programmes keep pace with shifting demand and to frame ethical guidelines for machines whose autonomy grows by the month. Those questions will be explored in a subsequent instalment.