what AI – The hype around “artificial intelligence” promises everything from instant breakthroughs to human-level thinking. But a closer look at cognition, machine vision, pattern recognition, and chatbots shows a consistent reality: today’s systems mainly automate frag
In the last few years. “artificial intelligence” has become the loudest buzzword in tech—pushed onto product pages. stitched into pitch decks. and used like a universal solvent. It’s portrayed as faster. better. and all-knowing. as if the same tools powering online convenience are somehow also on a parallel track to cure cancer.
That’s the moment where the joke stops being funny. If you’ve ever heard the term used to explain everything at once, you may feel it too: the word is doing too much work. Not because technology isn’t real, but because “AI” is being used to smooth over what these systems actually do.
The question underneath all that glossy marketing is simple: what, exactly, are we calling “artificial intelligence”?
Cognition isn’t the same thing as intelligence, and the split matters. The article points back to how intelligence has been defined and tested. including the role of fluid intelligence (Gf) as a crucial sign of reasoning. It also notes that intelligence models—like expanded CHC theory of cognitive abilities—have been extended beyond core reasoning to include memory (knowledge and recall). acquired skills. and additional measures such as sensory. motor. and efficiency metrics. The catch is that these frameworks can become species-centric.
That’s where the argument about lines gets messy. Within cognitive processes, it’s hard to exclude sensory input and output via actuators like muscles. Whether intelligence could develop without both in- and output is raised as a valid question. And disagreements in the academic community about where to draw the line between intelligence and cognition don’t make the marketing problem go away.
Because once “intelligence” becomes a broad label. it becomes easy to apply it to systems that replicate fragments of human processing without the underlying reasoning. Even machine vision is used as an example of where the label can get stretched: a system may replicate parts of the visual cognitive process without the understanding that comes with human cognition.
So the article’s conclusion isn’t that the technology is fake—it’s that “smart” and “AI” systems often attempt to replicate a slice of the human cognitive process.
Machine vision is where that slice becomes very tangible. Its biggest strength, the article says, is that it can off-load a cognitive task to a computer system that never gets fatigued or distracted. That matters in quality assurance, especially on production lines.
Instead of relying on people to visually check each item that zips past for properties like alignments. a machine vision system can take over. The systems are described as targeting specific tasks using different sensors and outputs. In PCB assembly lines and food production. for example. the article says machine vision systems use visible light as well as near-infrared and other camera and sensor types to detect flaws. spoilage. and other issues. The data is then passed through the rest of the system, where programming is used to detect problems.
The workflow at a board house illustrates both the promise and the limit. Suspect PCBs are identified, taken off the conveyor, and handed to a human. That human then either confirms the issue and addresses it or bins it. or marks it as a false positive and puts it back on the conveyor. The advantage is described as reducing cognitive load on humans—who are described as being “notoriously terrible” at long stretches of boring work.
The article also connects machine vision to self-driving vehicles. where sensor blending and scene interpretation using techniques like edge detection and recognition with a convolutional neural network (CNN) are said to be paramount. It stresses that these systems require significant additional sensors, including radar and Lidar, to do their job effectively.
But it makes a point that undercuts the “fully autonomous” dream. Machine vision does not replace human cognition; it complements it. That’s why purely self-driving vehicles at Level 5 are described as still fictional. and why even comically obvious PCB assembly flaws can make it through automated QA. despite overall net wins for workers.
From factories to clinics, the same theme returns under a different name: pattern recognition.
Much of medicine involves categorizing symptoms and test results for differential diagnostics. and the article explains that there has been a push toward computer-aided diagnosis (CAD) for decades. The early version of this is described as expert systems implemented in Lisp. using a knowledge base and an inference system to reach a conclusion or solve a problem.
But keeping a knowledge base up to date is hard, which is why artificial neural network designs became more popular. Large language models are mentioned as one example. The shift changes what “expert” really means: if the expert system’s knowledge isn’t maintained by humans anymore and instead relies on statistical models. the “expert” part is effectively abandoned.
That leads to another approach: retrieval augmented generation (RAG). The article says RAG is pushed to the side of LLMs because it “grounds” provided facts in more factual reality, such as human-written documents, while leaving the language model to provide a friendly natural language interface.
Even then, the article warns that when analyzing test results like MRI scans and X-rays, these systems can save time but can also make incredibly dumb mistakes—so they cannot be left unsupervised.
Then there’s the chatbot era, where the hype feels strongest—and where the line between imitation and understanding gets blurred.
The article describes the biggest advancement in recent years as better chatbots that can keep up a conversation on a level that would put ELIZA to shame. But it frames this as at least partially “smoke-and-mirrors”: the conversation can look human. yet the system has no actual intelligence or therapist behind it. Instead. it’s built from a complex human-written chat interface that creates the query and handles all other details of using an LLM to generate the appearance of human-level interaction.
It also addresses “emotional intelligence” and why calling chatbots emotionally intelligent doesn’t fit. Emotional intelligence. the article says. refers to the ability to perceive and feel emotions—something impossible for an entity incapable of feeling and reasoning. It adds that projection of one’s own feelings onto another person or even an inanimate object is heavily susceptible to confusion. The chatbot is described as incapable of learning. requiring external session information to be stored within the context window. yet still capable of producing very convincing near-facsimiles under the right conditions.
All of this feeds into the final tension the article returns to: cognitive offloading can make life better, but only if limitations are faced honestly.
The article argues that increased use of machine vision and similar systems has been a boon for automating industries. reducing cognitive load and freeing humans to do more creative tasks rather than mindlessly repeating the same work. But it insists that this only works when people understand the risks and pitfalls—especially cognitive atrophy caused by cognitive surrender. That hazard is described as being identified in an increasing number of studies. along with an emphasis on maintaining critical thinking skills.
And even if “actual artificial intelligence happened next year,” the article says human intelligence still has to be treasured. It’s framed as the only intelligence humans will always have, and the sole reason humankind has come this far.
The word “AI” may be easy to throw around. The truth, as the article lays out, is less convenient: most of what people call intelligence today is the automation of cognition’s pieces—powerful in the right setting, limited in the wrong one, and always in need of human oversight.
AI hype artificial intelligence machine vision machine learning convolutional neural network CNN self-driving vehicles lidar radar computer-aided diagnosis CAD expert systems Lisp large language models retrieval augmented generation RAG chatbots ELIZA emotional intelligence cognitive atrophy