Imagine taking a college exam, and, instead of handing in a blue book and getting a grade from a professor a few weeks later, clicking the "send" button when you are done and receiving a grade back instantly, your essay scored by a software program. And then, instead of being done with that exam, imagine that the system would immediately let you rewrite the test to try to improve your grade. EdX, the nonprofit enterprise founded by Harvard and the Massachusetts Institute of Technology to offer courses on the Internet, has just introduced such a system and will make its automated software available free on the Web to any institution that wants to use it. The software uses artificial intelligence to grade student essays and short written answers, freeing professors for other tasks. The new service will bring the educational consortium into a growing conflict over the role of automation in education.

People are good at understanding one another's emotions. We realize quickly that now is not a good time to approach the boss or that a loved one is having a lousy day. These skills are so essential that those without them are considered disabled. Yet until recently, our machines could not identify even seemingly simple emotions, like anger or frustration. The GPS device chirps happily even when the driver is ready to hurl it out the window.

Neil Heffernan was listening to his fiancée, Cristina Lindquist, tutor one of her students in mathematics when he had an idea. Heffernan was a graduate student in computer science, and by this point -- the summer of 1997 -- he had been working for two years with researchers at Carnegie Mellon University on developing computer software to help students improve their skills. But he had come to believe that the programs did little to assist their users. They were built on elaborate theories of the student mind -- attempts to simulate the learning brain. Then it dawned on him: what was missing from the programs was the interventions teachers made to promote and accelerate learning.

A free online course at Stanford University on artificial intelligence, to be taught this fall by two leading experts from Silicon Valley, has attracted more than 58,000 students around the globe -- a class nearly four times the size of Stanford's entire student body. The course is one of three being offered experimentally by the Stanford computer science department to extend technology knowledge and skills beyond this elite campus to the entire world, the university is announcing on Tuesday. The online students will not get Stanford grades or credit, but they will be ranked in comparison to the work of other online students and will receive a "statement of accomplishment." For the artificial intelligence course, students may need some higher math, like linear algebra and probability theory, but there are no restrictions to online participation. So far, the age range is from high school to retirees, and the course has attracted interest from more than 175 countries.

Robotics expert Robin Murphy, an associate professor of computer science at the University of South Florida, was called immediately and arrived on site the morning after the collapse. Murphy's research on experimental mixed-initiative robots for urban rescue operations was originally funded by the National Science Foundation (NSF). Her response team included three graduate students -- Jenn Casper, Mark Micire and Brian Minten - who helped her develop the robots and their software-guided "marsupial" systems. These intelligent anonymous "marsupial" robots are especially useful in rubble because the "mother" robot releases smaller robots to explore tight spaces unreachable by other means. The mother carries the little ones in its "pouch" into the site as far as she can maneuver, releasing and providing "power" as the "babies" descend from her to perform their search negotiating smaller crevices and hidden spaces.

The Division of Information and Intelligent Systems (IIS) studies the inter-related roles of people, computers, and information. IIS supports research and education activities that 1) develop new knowledge about the role of people in the design and use of information technology; 2) increase our capability to create, manage, and understand data and information in circumstances ranging from personal computers to globally-distributed systems; and 3) advance our understanding of how computational systems can exhibit the hallmarks of intelligence. Cyber-Human Systems (CHS) - In a world in which computers and networks are increasingly ubiquitous, computing, information, and computation play a central role in how humans work, learn, live, discover, and communicate. Technology is increasingly embedded throughout society, and is becoming commonplace in almost everything we do. The boundaries between humans and technology are shrinking to the point where socio-technical systems are becoming natural extensions to our human experience – second nature, helping us, caring for us, and enhancing us.

Last spring, I taught a literature seminar called "Before Wikipedia." The subject was the history of encyclopedic writing, from ancient times to the present day. We read excerpts of Isidore of Seville's "Etymologies" and Diderot's "Encyclopédie" alongside works by Calvino, Sebald, and Flaubert. The word "Wikipedia" in the course title seemed to attract an unusual preponderance of science majors for a seminar in comparative literature. There were physicists and mathematicians, a cluster of coders, an engineer, a neuroscience major.

Three years ago, researchers at the secretive Google X lab in Mountain View, California, extracted some 10 million still images from YouTube videos and fed them into Google Brain -- a network of 1,000 computers programmed to soak up the world much as a human toddler does. After three days looking for recurring patterns, Google Brain decided, all on its own, that there were certain repeating categories it could identify: human faces, human bodies and … cats1. Google Brain's discovery that the Internet is full of cat videos provoked a flurry of jokes from journalists. But it was also a landmark in the resurgence of deep learning: a three-decade-old technique in which massive amounts of data and processing power help computers to crack messy problems that humans solve almost intuitively, from recognizing faces to understanding language. Deep learning itself is a revival of an even older idea for computing: neural networks.

Alan Turing proposed a fascinating test for "machine intelligence" in 1950 which remains at the center of controversy. Offered here is a flash animation showing one way the test might be conducted and the types of questions that might be asked. "Is it possible to build a person?" Various criteria of personhood are considered and students are challenged to develop their own criteria and to judge whether or not those properties could be “mechanized†and built into a machine. This is an Introduction to Linguistics textbook that pays particular attention to the contributions that cognitive science makes to our understanding of language.

For nearly 40 years, Ronald Kaplan has been working on better ways to talk with machines. A former researcher at Xerox PARC and Microsoft, he's now a consulting professor in computational linguistics at Stanford and a senior director at the Silicon Valley research division of Nuance Communications, a Massachusetts company that makes voice-enabled software for cars, smartphones, corporate call centers and medical transcription services. While its software has been used by Apple, Samsung, Ford and Subaru, among others, Nuance also competes with major tech firms, including Google and Microsoft, that are working on their own voice systems. We recently spoke with Kaplan at the Nuance offices in Sunnyvale; the following was edited for length and clarity. Q: Can you give me a high-level overview of where we are with voice technology?