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Not only are machines rapidly catching up to — and exceeding — humans in terms of raw computing power, they are also starting to do things that we used to consider inherently human

Quite simply, humans are amazing pattern-recognition machines. They have the ability to recognize many different types of patterns - and then transform these "recursive probabalistic fractals" into concrete, actionable steps.

Intelligence, then, is really just a matter of being able to store more patterns than anyone else

Although pattern recognition is commonly associated with computer science and engineering, it also applies to nature, people and social systems. In fact, even animals and babies are born with the ability to recognize patterns. Sharpening our pattern recognition ability helps us cultivate vision, which is crucial for gaining an edge in a rapidly changing world.

(Unfortunately, technology also allows powerful interests to recognize patterns in big data to manipulate voters and consumers, but that's another story.)

We can become slaves to patterns. Extrapolate this tendency broadly and you can see how a society becomes fixed in its ways.

The science of pattern recognition has been explored for hundreds of years, with the primary goal of optimally extracting patterns from data or situations, and effectively separating one pattern from another. Applications of pattern recognition are found everywhere, whether it’s categorizing disease, predicting outbreaks of disease, identifying individuals (through face or speech recognition), or classifying data. In fact, pattern recognition is so ingrained in many things we do, we often forget that it’s a unique discipline which must be treated as such if we want to really benefit from it.

When you walk or drive in the UK, you’re being nudged by dozens of hidden messages embedded in the roads and pavements.

He suffers from a rare inherited condition that leaves him only able to make out vague colour contrasts around him. Yet he is able to safely pick his way through the hectic city streets, thanks to dozens of hidden messages embedded in our roads and pavements that few of us even notice are there.

This subtle form of communication is not just confined to the pavement, either: increasingly, motorists and cyclists are also unknowingly being told what to do.

the pace of improvement in technology would become a runaway phenomenon that would transform all aspects of human civilization.

the structure and functioning of the human brain is actually quite simple, a basic unit of cognition repeated millions of times. Therefore, creating an artificial brain will not require simulating the human brain at every level of detail. It will only require reverse engineering this basic repeating unit.

Face-matching today could enable mass surveillance, “basically robbing everyone of their anonymity,” he says, and inhibit people’s normal behavior outside their homes.

Dr. Atick says the technology he helped cultivate requires some special safeguards. Unlike fingerprinting or other biometric techniques, face recognition can be used at a distance, without people’s awareness; it could then link their faces and identities to the many pictures they have put online. But in the United States, no specific federal law governs face recognition.

some casinos faceprint visitors, seeking to identify repeat big-spending customers for special treatment. In Japan, a few grocery stores use face-matching to classify some shoppers as shoplifters or even “complainers” and blacklist them.

There are many different kinds of intelligence, each reliant on its own set of skills and abilities. One such ability is pattern recognition, without which we’d have trouble recognizing faces, learning languages, or reading other people’s emotions. Because it’s so central to our cognitive and social functioning, pattern recognition is sometimes used by researchers as a shorthand for overall intelligence.

Finding that higher pattern detection ability puts people at greater risk to detect and apply stereotypes, but also to reverse them, implicates this ability as a cognitive mechanism underlying stereotyping,” co-author Jonathan Freeman said in the statement.

much of this angst can be interpreted as part of a noisy but inexorable endgame: the end of white supremacy. From this vantage point, Americanness and whiteness are fitfully, achingly, but finally becoming delinked—and like it or not, over the course of this generation, Americans are all going to have to learn a new way to be American.

What is the story of “us” when “us” is no longer by default “white”? The answer, of course, will depend on how aware Americans are of what they are, of what their culture already (and always) has been.

The thing about the list, though, was that it was—by design—heavy on the deeds and words of the “dead white males” who had formed the foundations of American culture but who had by then begun to fall out of academic fashion.

GPT-4, in contrast, is a state-of-the-art system capable of creating not just words but describing images in response to a person’s simple written commands.

When shown a photo of a boxing glove hanging over a wooden seesaw with a ball on one side, for instance, a person can ask what will happen if the glove drops, and GPT-4 will respond that it would hit the seesaw and cause the ball to fly up.

an AI program, known as a large language model, that early testers had claimed was remarkably advanced in its ability to reason and learn new things

Using an artificial intelligence technique inspired by theories about how the brain recognizes patterns, technology companies are reporting startling gains in fields as diverse as computer vision, speech recognition and the identification of promising new molecules for designing drugs.

They offer the promise of machines that converse with humans and perform tasks like driving cars and working in factories, raising the specter of automated robots that could replace human workers.

what is new in recent months is the growing speed and accuracy of deep-learning programs, often called artificial neural networks or just “neural nets” for their resemblance to the neural connections in the brain.

Not just any old green, of course: Pantone 15-0343, colloquially known as greenery, which is to say a “yellow-green shade that evokes the first days of spring.”

That is, the Color of the Year for 2017.

“This is the color of hopefulness, and of our connection to nature. It speaks to what we call the ‘re’ words: regenerate, refresh, revitalize, renew. Every spring we enter a new cycle and new shoots come from the ground. It is something life affirming to look forward to.”

If you’ve taken to the United States streets anytime lately, you may have noticed that most public school buses are a very particular shade of yellow. That shade is called National School Bus Glossy Yellow in Canada and the US, and it was specially designed by Dr. Frank Cyr.

Yellow is easy to see in the dim lights of early morning or late evening, and because it's seen across both the green and red cones in the eye, it pops in our vision faster than other colors.

What’s more, while the majority of those who are color blind have trouble distinguishing red from green, they can still see yellow just fine.

In the study of TOK in DP-1 (11th grade), I have learnt about the ways of knowing, and two of the areas of knowledge - natural sciences and human sciences. Of all the discussions and explorations done into these areas of knowledge, there can be patterns observed in the methods of experimentation, data collection, data analysis and also assumptions being made when generalizing situations. In some of the above points, human sciences is similar to natural sciences, while in the others, the ideologies and working is different.

Natural sciences and human sciences for instance both use experiments as a form of data collection but the methods and outputs are different. Natural scientists have lesser moral values to take care of compared to human scientists such as psychologists. Psychologists are directly dealing with humans and hence need to take more ethical implications into consideration. Secondly, human scientists conduct experiments on a large set of people - not limited to one race or gender unless it is required. Natural scientists do not require survey type data. Here, a single scientist conducts the experiments. Both the types of experiments are empirical based though, direct experimentation is more in natural sciences while human sciences are more observation based. But experiments in human sciences have more unpredictable results due to what is known as the Hawthorne effect. This effect explains that when a particular group comes to know that they are under observation for a experiment or an analysis, they tend to be more productive. These cause errors in results of human science experiments especially the ones of psychology.

Psychologists who study how the human mind responds to randomness call this the gambler’s fallacy — the belief that on some cosmic plane a run of bad luck creates an imbalance that must ultimately be corrected, a pressure that must be relieved

The opposite of that is the hot-hand fallacy — the belief that winning streaks, whether in basketball or coin tossing, have a tendency to continue

Both misconceptions are reflections of the brain’s wired-in rejection of the power that randomness holds over our lives. Look deep enough, we instinctively believe, and we may uncover a hidden order.

Tokyo University, known as Todai, is Japan’s best. Its exacting entry test requires years of cramming to pass and can defeat even the most erudite. Most current computers, trained in data crunching, fail to understand its natural language tasks altogether. Ms. Arai has set researchers at Japan’s National Institute of Informatics, where she works, the task of developing a machine that can jump the lofty Todai bar by 2021. If they succeed, she said, such a machine should be capable, with appropriate programming, of doing many — perhaps most — jobs now done by university graduates.

There is a significant danger, Ms. Arai says, that the widespread adoption of artificial intelligence, if not well managed, could lead to a radical restructuring of economic activity and the job market, outpacing the ability of social and education systems to adjust.

Intelligent machines could be used to replace expensive human resources, potentially undermining the economic value of much vocational education, Ms. Arai said.