STANFORD, Calif. — At the dawn of the modern computer era, two Pentagon-financed laboratories bracketed Stanford University. At one laboratory, a small group of scientists and engineers worked to replace the human mind, while at the other, a similar group worked to augment it.

In 1963 the mathematician-turned-computer scientist John McCarthy started the Stanford Artificial Intelligence Laboratory. The researchers believed that it would take only a decade to create a thinking machine.

Also that year the computer scientist Douglas Engelbart formed what would become the Augmentation Research Center to pursue a radically different goal — designing a computing system that would instead “bootstrap” the human intelligence of small groups of scientists and engineers.

For the past four decades that basic tension between artificial intelligence and intelligence augmentation — A.I. versus I.A. — has been at the heart of progress in computing science as the field has produced a series of ever more powerful technologies that are transforming the world.

Now, as the pace of technological change continues to accelerate, it has become increasingly possible to design computing systems that enhance the human experience, or now — in a growing number of cases — completely dispense with it.

The implications of progress in A.I. are being brought into sharp relief now by the broadcasting of a recorded competition pitting the I.B.M. computing system named Watson against the two best human Jeopardy players, Ken Jennings and Brad Rutter.

Watson is an effort by I.B.M. researchers to advance a set of techniques used to process human language. It provides striking evidence that computing systems will no longer be limited to responding to simple commands. Machines will increasingly be able to pick apart jargon, nuance and even riddles. In attacking the problem of the ambiguity of human language, computer science is now closing in on what researchers refer to as the “Paris Hilton problem” — the ability, for example, to determine whether a query is being made by someone who is trying to reserve a hotel in France, or simply to pass time surfing the Internet.

If, as many predict, Watson defeats its human opponents on Wednesday, much will be made of the philosophical consequences of the machine’s achievement. Moreover, the I.B.M. demonstration also foretells profound sociological and economic changes.

Traditionally, economists have argued that while new forms of automation may displace jobs in the short run, over longer periods of time economic growth and job creation have continued to outpace any job-killing technologies. For example, over the past century and a half the shift from being a largely agrarian society to one in which less than 1 percent of the United States labor force is in agriculture is frequently cited as evidence of the economy’s ability to reinvent itself.

That, however, was before machines began to “understand” human language. Rapid progress in natural language processing is beginning to lead to a new wave of automation that promises to transform areas of the economy that have until now been untouched by technological change.

“As designers of tools and products and technologies we should think more about these issues,” said Pattie Maes, a computer scientist at theM.I.T. Media Lab. Not only do designers face ethical issues, she argues, but increasingly as skills that were once exclusively human are simulated by machines, their designers are faced with the challenge of rethinking what it means to be human.

I.B.M.’s executives have said they intend to commercialize Watson to provide a new class of question-answering systems in business, education and medicine. The repercussions of such technology are unknown, but it is possible, for example, to envision systems that replace not only human experts, but hundreds of thousands of well-paying jobs throughout the economy and around the globe. Virtually any job that now involves answering questions and conducting commercial transactions by telephone will soon be at risk. It is only necessary to consider how quickly A.T.M.’s displaced human bank tellers to have an idea of what could happen.

To be sure, anyone who has spent time waiting on hold for technical support, or trying to change an airline reservation, may welcome that day. However, there is also a growing unease about the advances in natural language understanding that are being heralded in systems like Watson. As rapidly as A.I.-based systems are proliferating, there are equally compelling examples of the power of I.A. — systems that extend the capability of the human mind.

Google itself is perhaps the most significant example of using software to mine the collective intelligence of humans and then making it freely available in the form of a digital library. The search engine was originally based on a software algorithm called PageRank that mined human choices in picking Web pages that contained answers to a particular typed query and then quickly ranked the matches by relevance.

The Internet is widely used for applications that employ a range of human capabilities. For example, experiments in Web-based gamesdesigned to harness the human ability to recognize patterns — which still greatly exceeds what is possible by computer — are generating a new set of scientific tools. Games like FoldIt, EteRNA and Galaxy Zoo make it possible for individuals to compete and collaborate in fields like astronomy to biology, medicine and possibly even material science.

Personal computing was the first step toward intelligence augmentation that reached a broad audience. It created a generation of “information workers,” and equipped them with a set of tools for gathering, producing and sharing information. Now there is a cyborg quality to the changes that are taking place as personal computing has evolved from desktop to laptop and now to the smartphones that have quickly become ubiquitous.

The smartphone is not just a navigation and communication tool. It has rapidly become an almost seamless extension of almost all of our senses. It is not only a reference tool but is quickly evolving to be an “information concierge” that can respond to typed or spoken queries or simply volunteer advice.

Further advances in both A.I. and I.A. will increasingly confront the engineers and computer scientists with clear choices about how technology is used. “There needs to be an explicit social contract between the engineers and society to create not just jobs but better jobs,” said Jaron Lanier, a computer scientist and author of “You are not a Gadget: A Manifesto.”

The consequences of human design decisions can be clearly seen in the competing online news systems developed here in Silicon Valley.

Each day Katherine Ho sits at a computer and observes which news articles millions of Yahoo users are reading.

Her computer monitor displays the results of a cluster of software programs giving her almost instant updates on precisely how popular each of the news articles on the company’s home page is, based on her readers’ tastes and interests.

Ms. Ho is a 21st-century version of a traditional newspaper wire editor. Instead of gut and instinct, her decisions on which articles to put on the Yahoo home page are based on the cues generated by the software algorithms.

Throughout the day she constantly reorders the news articles that are displayed for dozens of demographic subgroups that make up the Yahoo readership. An article that isn’t drawing much interest may last only minutes before she “spikes” it electronically. Popular articles stay online for days and sometimes draw tens of millions of readers.

Just five miles north at Yahoo’s rival Google, however, the news is produced in an entirely different manner. Spotlight, a popular feature on Google’s news site, is run entirely by a software algorithm which performs essentially the same duties as Ms. Ho does.

Google’s software prowls the Web looking for articles deemed interesting, employing a process that is similar to the company’s PageRank search engine ranking system to make decisions on which articles to present to readers.

In one case, software-based technologies are being used to extend the skills of a human worker, in another case technology replaces her entirely.

Similar design decisions about how machines are used and whether they will enhance or replace human qualities are now being played out in a multitude of ways, and the real value of Watson may ultimately be in forcing society to consider where the line between human and machine should be drawn.

Indeed, for the computer scientist John Seely Brown, machines that are facile at answering questions only serve to obscure what remains fundamentally human.

“The essence of being human involves asking questions, not answering them,” he said.